9781492561347

Check Out the Online Video!
You will notice a reference throughout this version of Advanced Fitness Assessment and Exercise Prescription, Eighth Edition, to
online video. This resource is available to supplement your ebook. The online video includes more than 70 video clips
demonstrating key concepts from the book. If your ebook platform supports video clips, the video will appear directly within
this ebook. If video is not supported in your ebook platform, you can follow these steps to purchase access to the web
resource and supplement your ebook:
Follow these steps to purchase access to the online video:
1. Visit https://tinyurl.com/AdvFitnessAssessment.
2. Click the Add to Cart button and complete the purchase process.
3. After you have successfully completed your purchase, visit the book’s website at
www.HumanKinetics.com/AdvancedFitnessAssessmentAndExercisePrescription.
4. Click the eighth edition link next to the corresponding eighth edition book cover.
5. Click the Sign In link on the left or top of the page and enter the email address and password that you used
during the purchase process. Once you sign in, your online product will appear in the Ancillary Items box.
Click on the title of the online video to access it.
6. Once purchased, a link to your product will permanently appear in the menu on the left. All you need to do to
access your online video on subsequent visits is sign in to
www.HumanKinetics.com/AdvancedFitnessAssessmentAndExercisePrescription and follow the link!
Click the Need Help? button on the book’s website if you need assistance along the way.
2
Eighth Edition
Advanced Fitness Assessment

and
Exercise Prescription
Ann L. Gibson, PhD

University of New Mexico
Dale R. Wagner, PhD

Utah State University
Vivian H. Heyward, PhD

University of New Mexico, Professor Emerita
3
Library of Congress Cataloging-in-Publication Data
Names: Gibson, Ann L., [date]- author. | Wagner, Dale R., 1966- author. | Heyward,
Vivian H., author.
Title: Advanced fitness assessment and exercise prescription / Ann L. Gibson, PhD,
University of New Mexico, Dale R. Wagner, PhD, Utah State University, Vivian H.
Heyward, PhD, University of New Mexico.
Description: Eighth Edition. | Champaign, Illinois : Human Kinetics, [2019] | Previous
edition: 2014. | Includes bibliographical references and index. |
Identifiers: LCCN 2018008261 (print) | LCCN 2018009017 (ebook) | ISBN
9781492563549 (enhanced ebook) | ISBN 9781492561347 (print)
Subjects: LCSH: Physical fitness--Testing. | Exercise tests. | Health.
Classification: LCC GV436 (ebook) | LCC GV436 .H48 2019 (print) | DDC 613.7--
dc23
LC record available at https://lccn.loc.gov/2018008261
ISBN: 978-1-4925-6134-7 (print)
Copyright © 2019 by Ann L. Gibson, Dale R. Wagner, and Vivian H. Heyward
Copyright © 2014 by Vivian H. Heyward and Ann L. Gibson
Copyright © 2010, 2006, 2002, 1998, 1991 by Vivian H. Heyward
Copyright © 1984 by Burgess Publishing Company
All rights reserved. Except for use in a review, the reproduction or utilization of this work in
any form or by any electronic, mechanical, or other means, now known or hereafter invented,
including xerography, photocopying, and recording, and in any information storage and
retrieval system, is forbidden without the written permission of the publisher.
Notice: Permission to reproduce the following material is granted to instructors and agencies
who have purchased Advanced Fitness Assessment and Exercise Prescription, Eighth Edition: pp.
234, 380-381, 382-383, 386-387, 389-390, 391, 394-395, 415-420, 424-427, 428, 429, 430,
431, 434-435, 436, 437-439, 442-450, 451-455, 456-460. The reproduction of other parts of
this book is expressly forbidden by the above copyright notice. Persons or agencies who have
not purchased Advanced Fitness Assessment and Exercise Prescription, Eighth Edition, may not
4
reproduce any material.
The web addresses cited in this text were current as of July 2018, unless otherwise noted.
Senior Acquisitions Editor: Michelle Maloney; Senior Developmental Editor: Cynthia

McEntire; Senior Managing Editor: Amy Stahl; Copyeditor: Patricia L. MacDonald;
Indexer: Rebecca McCorkle; Permissions Manager: Martha Gullo; Graphic Designers: Sean
Roosevelt and Whitney Milburn; Cover Designer: Keri Evans; Cover Design Associate:
Susan Rothermel Allen; Photographs (interior): © Human Kinetics, unless otherwise noted;
Photo Asset Manager: Laura Fitch; Photo Production Manager: Jason Allen; Senior Art
Manager: Kelly Hendren; Illustrations: © Human Kinetics; Printer: Walsworth
We thank the Exercise Physiology Laboratory at the University of New Mexico,

Albuquerque, New Mexico, for assistance in providing the location for the photo shoot for
this book.
Human Kinetics books are available at special discounts for bulk purchase. Special editions or
book excerpts can also be created to specification. For details, contact the Special Sales
Manager at Human Kinetics.
The video contents of this product are licensed for educational public performance for
viewing by a traditional (live) audience, via closed circuit television, or via computerized local
area networks within a single building or geographically unified campus. To request a license
to broadcast these contents to a wider audience—for example, throughout a school district or
state, or on a television station—please contact your sales representative (www
.HumanKinetics.com/SalesRepresentatives).
Printed in the United States of America
10 9 8 7 6 5 4 3 2 1
The paper in this book is certified under a sustainable forestry program.
Human Kinetics
P.O. Box 5076
Champaign, IL 61825-5076
Website: www.HumanKinetics.com
In the United States, email info@hkusa.com or call 800-747-4457.
In Canada, email info@hkcanada.com.
5
In the United Kingdom/Europe, email hk@hkeurope.com.
For information about Human Kinetics’ coverage in other areas of the world, please visit our
website: www.HumanKinetics.com
E7227
6
Contents
Video Contents
Preface
Acknowledgments
Chapter 1 Physical Activity, Health, and Chronic Disease
Physical Activity, Health, and Disease: An Overview

Cardiovascular Disease
Hypertension
Hypercholesterolemia and Dyslipidemia
Tobacco
Diabetes Mellitus
Obesity and Overweight
Metabolic Syndrome
Cancer
Musculoskeletal Diseases and Disorders
Aging
Cognitive Performance
Exercise as Medicine
Review Material
Chapter 2 Preliminary Health Screening and Risk Classification
Preliminary Health Evaluation

Testing Procedures for Blood Pressure, Heart Rate, and Electrocardiogram
Review Material
Chapter 3 Principles of Assessment, Prescription, and Exercise Program

Adherence
Physical Fitness Testing

Basic Principles for Exercise Program Design
Exercise Program Adherence
Using Technology to Promote Physical Activity
7
Review Material
Chapter 4 Assessing Cardiorespiratory Fitness
Definition of Terms
Graded Exercise Testing: Guidelines and Procedures
Maximal Exercise Test Protocols
Submaximal Exercise Test Protocols
Field Tests for Assessing Aerobic Fitness
Exercise Testing for Children and Older Adults
Review Material
Chapter 5 Designing Cardiorespiratory Exercise Programs
The Exercise Prescription

Aerobic Training Methods and Modes
Personalized Exercise Programs
Review Material
Chapter 6 Assessing Muscular Fitness
Definition of Terms
Strength and Muscular Endurance Assessment
Muscular Power Assessment
Sources of Measurement Error in Muscular Fitness Testing
Additional Considerations for Muscular Fitness Testing
Muscular Fitness Testing of Older Adults
Muscular Fitness Testing of Children
Review Material
Chapter 7 Designing Resistance Training Programs
Types of Resistance Training

Developing Resistance Training Programs
Common Questions About Resistance Training
Effects of Resistance Training Programs
Muscular Soreness
Review Material
Chapter 8 Assessing Body Composition
Classification and Uses of Body Composition Measures

Body Composition Models
8
Reference Methods for Assessing Body Composition
Field Methods for Assessing Body Composition
Review Material
Chapter 9 Designing Weight Management and Body Composition Programs
Obesity, Overweight, and Underweight: Definitions and Trends

Obesity: Types and Causes
Weight Management Principles and Practices
Designing Weight Management Programs: Preliminary Steps
Designing Weight Loss Programs
Designing Weight Gain Programs
Designing Programs to Improve Body Composition
Review Material
Chapter 10 Assessing Flexibility
Basics of Flexibility
Assessment of Flexibility
Flexibility Testing of Older Adults
Review Material
Chapter 11 Designing Programs for Flexibility and Low Back Care
Training Principles
Stretching Methods
Designing Flexibility Programs: Exercise Prescription
Designing Low Back Care Exercise Programs
Review Material
Chapter 12 Assessing Balance and Designing Balance Programs
Definitions and Nature of Balance

Factors Affecting Balance and Risk of Falling
Assessment of Balance
Designing Balance Training Programs
Review Material
Appendix A Health and Fitness Appraisal
A.1 Physical Activity Readiness Questionnaire for Everyone (PAR-Q+)

A.2 Medical History Questionnaire
A.3 Risk Factors, Signs, and Symptoms of Disease
9
A.4 Electronic Physical Activity Readiness Medical Examination (ePARmed-X+)
A.5 Lifestyle Evaluation
A.6 Fantastic Lifestyle Checklist
A.7 Informed Consent
A.8 Websites for Selected Professional Organizations and Institutes
Appendix B Cardiorespiratory Assessments
B.1 Summary of Graded Exercise Test and Cardiorespiratory Field Test Protocols
B.2 Rockport Fitness Charts
B.3 Step Test Protocols
B.4 OMNI Rating of Perceived Exertion Scales
B.5 Analysis of Sample Case Study in Chapter 5
Appendix C Muscular Fitness Exercises and Norms
C.1 Standardized Testing Protocols for Digital Handheld Dynamometry

C.2 1-RM Squat and Bench Press Norms for Adults
C.3 Isometric Exercises
C.4 Dynamic Resistance Training Exercises
Appendix D Body Composition Assessments
D.1 Prediction Equations for Residual Volume

D.2 Standardized Sites for Skinfold Measurements
D.3 Skinfold Sites for Jackson’s Generalized Skinfold Equations
D.4 Standardized Sites for Circumference Measurements
D.5 Standardized Sites for Bony Breadth Measurements
D.6 Ashwell Body Shape Chart
Appendix E Energy Intake and Expenditure
E.1 Food Record and RDA Profile

E.2 Physical Activity Log
E.3 Gross Energy Expenditure for Conditioning Exercises, Sports, and Recreational Activities
Appendix F Flexibility and Low Back Care Exercises
F.1 Selected Flexibility Exercises

F.2 Exercise Dos and Don’ts
F.3 Exercises for Low Back Care
List of Abbreviations
10
Glossary
References
Index
About the Authors
11
Video Contents
We continue to offer online streaming video in this eighth edition, including over 70 videos
of content demonstrating key concepts from the book, such as assessments, procedures, tips,
stretches, and exercises. You can access the online video by visiting
www.HumanKinetics.com/AdvancedFitnessAssessmentAndExercisePrescription. If you
purchased a new print book, follow the instructions on the orange-framed page at the front of
your book. That page includes access steps and the unique key code that you’ll need the first
time you visit the Advanced Fitness Assessment and Exercise Prescription website. If you
purchased an e-book from HumanKinetics.com, follow the access instructions that were
emailed to you after your purchase. If you have purchased a used book, you can purchase
access to the online video separately by following the links at www.HumanKinetics.com
/AdvancedFitnessAssessmentAndExercisePrescription.
Once at the Advanced Fitness Assessment and Exercise Prescription website, select Online
Video in the ancillary items box in the upper-left corner of the screen. You’ll then see an
Online Video page with information about the video. Select the link to open the online video
web page. From the online video page, you can select the chapter and then the desired video,
numbered as they are in the text.
Following is a list of the clips in the online video.
Video 2.1 Resting BP measurement

Video 2.2 Sources of BP measurement error
Video 2.3 Measuring exercise BP
Video 2.4 Measuring resting HR by auscultation
Video 2.5 Measuring resting HR by palpation—radial artery
Video 2.6 Measuring HR using a heart rate monitor
Video 2.7 ECG electrode placement—limb leads
Video 2.8 ECG electrode placement—chest leads
Video 2.9 Exercise ECG
Video 3.1 Pedometer placement
12
Video 4.1 Measuring oxygen consumption (VO ) 2
Video 4.2 Safely mounting a treadmill

Video 4.3 Treadmill maximal exercise test
Video 4.4 Setting workloads on a cycle ergometer
Video 4.5 Cycle ergometer maximal exercise test
Video 4.6 Treadmill submaximal exercise test
Video 4.7 Cycle ergometer submaximal exercise test
Video 4.8 20 m shuttle run/PACER test
Video 4.9 6 min walk test
Video 6.1 Static muscle action
Video 6.2 Concentric muscle action
Video 6.3 Eccentric muscle action
Video 6.4 Grip strength testing procedures
Video 6.5 Grip endurance testing procedures
Video 6.6 Side bridge test
Video 6.7 Spotting exercises
Video 6.8 1-RM testing
Video 6.9 Biodex (isokinetic) testing
Video 6.10 Pull-up tests
Video 6.11 Push-up tests
Video 6.12 Vertical jump with VerTec
Video 7.1 Incline bench press
Video 7.2 Flat bench press
Video 7.3 Decline bench press
Video 7.4 Pronated arm curl
Video 7.5 Supinated arm curl
Video 7.6 Hammer curl
Video 7.7 Common errors in performing the squat
Video 7.8 Common errors in performing the bench press
Video 7.9 Eccentron training
Video 8.1 Guidelines for hydrostatic weighing
Video 8.2 Testing procedures for the Bod Pod
Video 8.3 A-mode ultrasound
Video 8.4 Whole-body BIA measures
13
Video 8.5 Upper body BIA measures
Video 8.6 Lower body BIA measures
Video 10.1 Shoulder flexion
Video 10.2 Knee flexion
Video 10.3 Ankle flexion
Video 10.4 Inclinometer test procedures
Video 10.5 Modified sit-and-reach test
Video 10.6 Modified back-saver sit-and-reach test
Video 11.1 PNF stretching techniques
Video 12.1 Unipedal stance test
Video 12.2 BESS test
Video 12.3 Functional reach test
Video 12.4 Timed up-and-go test
Video 12.5 Y-balance test
Video C3.1 Chest push
Video C3.2 Shoulder pull
Video C3.3 Triceps extension
Video D2.1 Measurement of the chest skinfold
Video D2.2 Measurement of the subscapular skinfold
Video D2.3 Measurement of the abdominal skinfold
Video D2.4 Measurement of the thigh skinfold
Video D2.5 Measurement of the calf skinfold
Video D4.1 Circumference measurement of the waist
Video D4.2 Circumference measurement of the hips
Video D5.1 Bony breadth measurement of the hips
Video D5.2 Bony breadth measurement of the elbow
Video F1.1 Hamstring stretch
Video F1.2 Chest stretch
14
Preface
Exercise professionals need to have extensive knowledge and technical skills in order to work
safely and effectively. Historically, individuals working in exercise settings, such as health and
fitness clubs, were not necessarily required to have specialized education and training in
exercise science. However, survey research indicates that a bachelor’s degree in exercise
science and certification from the American College of Sports Medicine (ACSM) or
National Strength and Conditioning Association (NSCA) are strong predictors of a personal
trainer’s knowledge (Malek et al. 2002). To carry the U.S. Bureau of Labor and Statistics’ job
title of “exercise physiologist,” one must have earned the minimum of a bachelor’s degree
(Simpson 2015). There is also a growing trend within health care facilities to require their
exercise physiologists to hold a master’s degree (Collora 2017); this corroborates Wagner’s
(2014) finding that a master’s degree is commonly held by exercise physiologists working in
clinical settings (69% of 140 survey respondents).
A global survey of fitness trends for 2018 revealed that “educated, certified, and
experienced fitness professionals” is ranked number 6 in importance, and this has been a top
10 concern since the annual survey began more than a decade ago (Thompson 2017). These
findings suggest that formal education and certification by professional organizations should
be required for personal fitness trainers and exercise science professionals. Their knowledge
and skills are instrumental in preparticipation screening, cardiorespiratory fitness testing,
muscular fitness testing, flexibility assessment, results interpretation, and scientifically sound
exercise prescription design. To promote exercise science as a profession, issues surrounding
accreditation, certification, national boards, and licensure need to be understood and
addressed.
ACCREDITATION
Organizations and programs are awarded accreditation by meeting or exceeding standards
established by an independent third-party accrediting agency. Although no single accrediting
agency exists for health and fitness and clinical exercise science programs, exercise science
professionals seem to agree that some form of regulation is needed.
15
Independent third-party accrediting agencies such as the Commission on Accreditation of
Allied Health Education Programs (CAAHEP) and the National Commission for Certifying
Agencies (NCCA) may serve this purpose. The CAAHEP accredits academic programs—
graduate programs in exercise physiology, baccalaureate programs in exercise science, and
certificate and associate degree programs for personal fitness trainers. Also, the American
Society of Exercise Physiologists (ASEP) has developed standards for the profession of
exercise physiology as well as accreditation standards for universities and colleges offering
academic degrees in exercise science (ASEP 2018). The NCCA accredits certification
programs; many organizations that provide professional credentialing or licensing exams in
the allied health professions are accredited through the NCCA (ACSM 2004).
CERTIFICATION
Fitness and exercise science professionals obtain certification by passing examinations
developed by professional organizations. These organizations typically offer education and
training programs, administer their own examinations (written and practical), and issue
certifications to individuals passing the examinations. These certifications are generally issued
for a 2 to 3 yr period; certification is maintained by taking continuing education courses and
earning continuing education credits. Some certification programs are accredited by third-
party agencies like the NCCA.
More than 75 organizations offer over 250 certifications for exercise science and fitness
professionals (Cohen 2004; Pierce and Herman 2004). Given that there is no governing
entity to oversee the development of certification examinations and eligibility requirements,
inequalities exist among the certifications available to exercise science professionals. Some
certification programs are more rigorous than others, having stringent eligibility
requirements; others may or may not be accredited by a third-party accrediting agency like
the NCCA. To address the inequality among certification programs, the NCCA formally
reviews applications for the accreditation of certification programs. In 2004, the International
Health, Racquet, and Sportsclub Association (IHRSA) recommended that all health clubs
belonging to their organization hire only personal fitness trainers certified by an NCCA-
accredited organization or agency. Wagner (2014) reported results from a survey of 589
exercise physiologists and indicated that 69% of the respondents held one certification while
28% held two or more. Nevertheless, not all exercise science and fitness certifications are
equal. This leads to confusion for the consumer in terms of knowing who is and who is not
highly trained and qualified as an exercise professional. It also complicates selecting the most
16
appropriate certification for yourself. Some agencies sponsor certification programs primarily
for financial gain, while others certify professionals in order to promote exercise science as a
profession.
Table 1 lists some of the organizations that offer certifications accredited by the NCCA.
Additionally, the Coalition for the Registration of Exercise Professionals (CREP), a not-for-
profit corporation composed of organizations that offer NCCA-accredited exercise
certifications, established a registry of professionals in the United States certified by any of six
organizations (www.usreps.org). This website is a convenient means for locating professionals
by location, certification, or name. Registries are also available for the United Kingdom
(www.exerciseregister.org), Europe (www.europeactive.eu/why-ereps), and New Zealand
(www.reps.org.nz).
Table 1 Selected Organizations Associated With National Commission for Certifying Agencies (NCCA) and National
Board of Fitness Examiners (NBFE)
NCCA affiliates NBFE affiliates

American Council on Exercise (ACE) Aerobics and Fitness Association of America (AFAA)
American College of Sports Medicine (ACSM) American Aerobic Association International/International Sports Medicine Association (AAAI/ISMA)
Cooper Institute for Aerobics Research International Sports Sciences Association (ISSA)
National Exercise and Sports Trainers Association (NESTA) National Association for Fitness Certification (NAFC)
National Exercise Trainers Association (NETA) National Council for Certified Personal Trainers (NCCPT)
National Federation of Professional Trainers (NFPT) National Exercise and Sports Trainers Association (NESTA)
National Strength and Conditioning Association (NSCA) National Gym Association (NGA)
International Fitness Professionals Association (IFPA) National Personal Training Institute (NPTI)
National Council on Strength and Fitness (NCSF) National Strength Professionals Association (NSPA)
National Academy of Sports Medicine (NASM)
NATIONAL BOARDS
Some professional organizations in the fitness industry believe there should be alternatives to
accreditation of certification programs by the NCCA or other third-party agencies. In the
United States, one such alternative was the establishment of National Board examinations for
fitness professionals. Unlike the multitude of certification examinations developed by
individual organizations and agencies, National Boards are standardized tests to assess the
knowledge, skill, and competence of professionals. Most medical and allied health professions
utilize National Boards.
In 2003, the National Board of Fitness Examiners (NBFE) was founded as a nonprofit
organization with the twin purposes of defining scopes of practice for all fitness professionals
and determining standards of practice for various fitness professionals, including floor
instructors, group exercise instructors, personal fitness trainers, specialists in youth and senior
fitness, and medical exercise specialists. The NBFE established national standards of
17
excellence that certifying organizations and colleges or universities may adopt. The written
portion of the National Boards for personal fitness trainers is now offered through the NBFE
(for additional information, visit www.NBFE.org). The practical portion of this exam is still
being developed and validated under the supervision of the National Board of Medical
Examiners (NBME). The NBME and the NBFE are engaged in preliminary discussions and
planning that will allow certification organizations to assist in the delivery of practical exams
for personal trainers.
To be eligible to sit for the National Boards, personal fitness trainers must successfully
complete a personal training certification program from an approved NBFE affiliate. Affiliate
status is available to qualified groups from the areas of medicine, certification organizations,
fitness professionals, health clubs, and higher education. In the future, the NBFE’s National
Boards may be used by certifying organizations, colleges and universities, and U.S. state
licensing programs to test the knowledge, skill, and competence of fitness professionals
(American Fitness Professionals and Associates 2004). Table 1 lists some of the organizations
offering personal training certifications affiliated with the NBFE.
LICENSURE
Although many practitioners in the fitness and exercise science fields agree that certification
ensures professional competency, other professionals believe that licensure is better suited for
protecting consumers and for enhancing the credibility and professionalism of exercise science
and fitness professionals (Eickhoff-Shemek and Herbert 2007). For the first time in the 12 yr
history of the worldwide survey of fitness trends, licensure for fitness professionals broke into
the top 20 trends (number 16 for 2018) (Thompson 2017). In the United States, licensure is
decided at the state level; therefore, requirements may vary from state to state. Louisiana was
the first state to pass a law requiring licensure of all clinical exercise physiologists (Herbert
1995). Licensure of clinical exercise physiologists has also been considered in Maryland,
Massachusetts, Michigan, North Carolina, Texas, and Utah (Clinical Exercise Physiology
Association, 2013). Several states including Georgia, Maryland, Massachusetts, New Jersey,
Nevada, Oregon, and the District of Columbia have considered licensure for personal trainers
(Eickhoff-Shemek and Herbert 2008b; Herbert 2004; Thompson 2017).
To promote exercise science and exercise physiology as a profession, the ASEP is working
with exercise professionals throughout the United States to develop uniform state licensure
requirements for exercise physiologists. Licensure would place exercise physiologists and
personal trainers on a par with other allied health professionals (e.g., nurses, nutritionists,
18
physical therapists, and occupational therapists) who are required to have licenses to practice.
Licensed fitness professionals may be more likely to obtain referrals from health care
professionals and to receive reimbursement for services from third parties (e.g., insurance
companies).
Along with advantages, added responsibilities and disadvantages are associated with state
licensure. Licensure may limit the scope of practice and services that exercise professionals are
currently able to provide to the public. For example, Louisiana licensure law requires clinical
exercise physiologists to work under the direction of a licensed physician. Also, the costs of
licensure, continuing education for licensure, and professional liability insurance may be more
expensive compared with the cost of certifications. Professionals moving from state to state
may be required to obtain another license because each state could require different
credentials for licensure (Eickhoff-Shemek and Herbert 2008a, 2008b).
STATUTORY CERTIFICATION
Instead of licensure, some American states use statutory certification for allied health
professionals. Statutory certification regulates what titles professionals can use and the
qualifications needed to obtain these titles. Only certified professionals with the required
credentials are allowed to use the specific title (e.g., certified nutritionist). Other professionals
without the necessary credentials can still practice in the state but must use a different title.
This approach could be promoted by the fitness and exercise professions to prevent the use of
titles, such as personal trainer or exercise physiologist, by individuals having no formal
education or professional certifications.
All these approaches demonstrate the pressing need to get a handle on certifications for
exercise professionals so we can gain control of who is practicing in our field. This will ensure
the safety of exercise program participants and enable individuals working in the fitness field
to be recognized as exercise science professionals. Until these issues are resolved and a list of
accredited certification agencies and organizations is finalized, you should select a
professional certification that matches your level of education and career goals. For more
information about certification programs, visit the websites of those professional certifying
organizations.
Many advantages are associated with obtaining either state licensure or certification with
professional organizations. You will have a better chance of finding a job in the health and
fitness field because many employers are now hiring only professionally certified health and
fitness instructors. Certification by reputable professional organizations upgrades the quality
19
of the typical person working in the field and assures employers and their clientele that
employees have mastered the knowledge and skills needed to be competent exercise science
professionals. Hence, the likelihood of lawsuits resulting from negligence or incompetence
may be lessened. Also, certification and licensure help validate exercise specialists as health
professionals who are equally deserving of the respect afforded to professionals in other allied
health professions. Individuals holding a Registered Clinical Exercise Physiologist (RCEP) or
Certified Clinical Exercise Physiologist (CEP) certification now have a National Provider
Identifier code that may be used for service reimbursement from insurance companies. For
more information on this development, visit the website of the Clinical Exercise Physiology
Association (www.acsm-cepa.org).
20
Acknowledgments
The first edition of this textbook was titled Designs for Fitness and was published by Burgess
Publishing in 1984. It was a softcover book of about 200 pages. Dr. Swede Schoeller took the
photos for that edition. Eileen Fletcher, our department secretary, typed the manuscript on
her Smith-Corona.
The second edition was published by Human Kinetics in 1991. This edition was a
hardcover book consisting of 350 pages. For this edition, Linda K. Gilkey took the photos.
For the first time, the manuscript was typed using a DOS word processing system, by
department secretary Sandi Travis.
In 1998, the third edition was published by Human Kinetics. The book grew in size from a
7" × 9" format to an 8.5" × 11" format. Once again, Linda K. Gilkey took the photos, and the
computer graphics were done by Dr. Robert Robergs, Dr. Brent Ruby, and Dr. Peter Egan.
The fourth edition, published by Human Kinetics in 2002, was 370 pages. Our colleagues
Dr. Christine Mermier, Dr. Virginia Wilmerding, Dr. Len Kravitz, and Dr. Donna Lockner
shared their excellent ideas and expertise. The developmental editors, Elaine Mustain and
Maggie Schwarzentraub, meticulously edited this edition.
In 2006, the fifth edition was released. For this edition, the total number of pages
increased to 425, and Human Kinetics updated all the photos. Sarah Ritz did an excellent job
organizing and taking these photos. Dr. Dale Wagner contributed the test question bank that
accompanied this edition.
The sixth edition was released in May 2010. For the first time, this book was also
published as an ebook. The book expanded to 465 pages. Dr. Dale Wagner updated the test
question bank, and Dr. Ann Gibson prepared the slides for the presentation package.
The seventh edition, published in 2014 by Human Kinetics, was coauthored with Dr. Ann
Gibson. In addition to being published as an ebook, the 537-page seventh edition was
supplemented with instructional videos.
The eighth edition is coauthored with Dr. Ann Gibson and Dr. Dale Wagner. Dr.
Wagner’s extensive background as a researcher and professor of exercise science has been
invaluable in updating and revising this edition. We also acknowledge Cynthia McEntire, our
21
Human Kinetics developmental editor, Martha Gullo, who obtained the publication
permissions for this edition, and Amy Stahl, the senior managing editor assigned to this
edition.
Many individuals have contributed to the continued success of Advanced Fitness Assessment
and Exercise Prescription. We are indebted to each person who played a role in the
metamorphosis of this book.
22
CHAPTER 1
Physical Activity, Health, and Chronic Disease

KEY QUESTIONS
Are adults in the United States and other countries getting enough physical activity?
How does physical inactivity differ from sedentarism?
What diseases are associated with a sedentary lifestyle, and what are the major risk factors for these diseases?
What are the benefits of regular physical activity in terms of disease prevention and healthy aging?
How does physical activity improve health?
How much physical activity is needed for improved health benefits?
What kinds of physical activities are suitable for typical people, and how often should they exercise?
Although physical activity plays an important role in preventing chronic diseases and
reducing the hazardous effects of extended periods of sitting time, an alarming percentage of
adults in the United States report no physical activity during leisure time. One of the national
health objectives for the year 2020 is to increase to 47.9% the proportion of people aged 18 yr
and older who regularly (preferably daily) engage in moderate physical activity at least 30 min
per day (U.S. Department of Health and Human Services 2010). According to a U.S.
national survey, in 2014 only a small percentage (21.5%) of adults over the age of 18 met the
2008 federal physical activity guidelines for adults in terms of both aerobic and muscle
strengthening activities. Slightly more than half (53.2%) met either the aerobic activity or the
muscle-strengthening guidelines, but not both (Centers for Disease Control and Prevention
2015a). Generally, women (50%) are less likely to meet the full aerobic and muscle-
strengthening recommendations than men (43.4%), and older (≥65 yr) adults are less likely
(58.7%) to meet them than younger (18-24 yr) adults (40.8%) (Centers for Disease Control
and Prevention 2015a).
Physical inactivity, the failure to meet the recommended physical activity guidelines, is not
just a problem in the United States; it is a global issue and the fourth leading cause of global
mortality (World Health Organization 2010). Cardiovascular diseases, diabetes, obesity,
chronic respiratory disorders, and cancers as a group of noncommunicable diseases (NCDs)
23
are the leading causes of death worldwide. These chronic conditions are heavily influenced by
poor lifestyle factors including physical inactivity and unhealthy diet (World Medical
Association 2017). NCDs accounted for approximately 52% of worldwide deaths occurring
before age 70 in 2012 (World Health Organization 2016d). Physical inactivity became a
targeted priority of the World Health Organization’s Global Action Plan for 2013-2020
(World Health Organization 2013); a global goal was set to reduce physical inactivity levels
by 10% by the year 2025 (Sallis et al. 2016).
Results from survey data collected from 146 countries representing all income levels
estimated that 23% of the global adult (≥15 yr) population was physically inactive in 2016.
However, an 8% decrease in physical inactivity between 2012 and 2016 may be less reflective
of changes in activity levels than in updated physical activity recommendations (150 min of
moderate-intensity activity or 75 min of vigorous-intensity activity per week, or combination
thereof). The current recommendations changed the frequency of exercise bouts from 5 days
per week (moderate-intensity) or 3 days per week (vigorous-intensity) to weekly totals of
minutes. The prevalence of physical inactivity ranges from approximately 38% in the eastern
Mediterranean countries to a low of 14.8% in southeast Asia; by World Bank income
classification, the low- and lower-middle-income countries were more physically active than
their upper-middle- and high-income counterparts (Sallis et al. 2016). In England and
Scotland, more than 65% of men and at least 50% of women met the government’s physical
activity guidelines in 2012 (British Heart Foundation 2015a). However, only 18% of
Canadian adults responding to the 2014-2015 Canadian Health Measures Survey met the
recommendation of 150 minutes of moderate-to-vigorous intensity activity in bouts lasting at
least 10 minutes (Statistics Canada 2017). Thus, as an exercise specialist, you face the
challenge of educating and motivating your clients to incorporate physical activity as a regular
part of their lifestyles and to reduce the amount of time spent being seated (Benatti and Ried-
Larsen 2015; Bergouignan et al. 2016; Levine 2015; Same et al. 2016).
USING TECHNOLOGY TO INCREASE PHYSICAL ACTIVITY AT WORK
Active workstations (e.g., treadmill desks or pedal desks) and adjustable-height work surfaces that allow employees to
stand (sit-stand desks) are becoming more commonplace. They provide a means to reduce prolonged periods of sitting.
Some employees have their own active workstations, while others have access to one located in a common area. A recent
review of studies about active workstations (Cao et al. 2016) indicates that the calories burned may increase two- to
fourfold for employees who change from sitting in a chair (~70-90 kcal·h−1) to active workstations. Additionally, daily
step counts and physical activity (min/day) increase dramatically for those using active workstations during the workday.
Crandall and colleagues (2016) found that using sit-stand workstations reduces sitting time by approximately 85
24
min/day. They also reported that employees using a shared treadmill desk accumulate slightly fewer than 9,000 steps·day
−1 while at work. Ongoing longitudinal research in this area may identify long-term effects of using active workstations
on employee health. Currently, these effects are not well documented.
This chapter deals with the physical activity trends, risk factors associated with chronic
noncommunicable diseases, the role of regular exercise and physical activity in disease
prevention and health, physical activity guidelines and recommendations for improved health,
and the importance of including exercise and physical activity as one of the vital signs (i.e.
heart rate, blood pressure, etc.) monitored during annual visits to the doctor. For definitions
of terminology used in this chapter, see the glossary.
PHYSICAL ACTIVITY, HEALTH, AND DISEASE: AN OVERVIEW

Technological advances affecting nearly every facet of life have substantially lessened work-
related physical activity as well as the energy expenditure required for performing activities of
daily living like cleaning the house, washing clothes and dishes, mowing the lawn, and
traveling to work. What would have once required an hour of physical work now can be
accomplished in just a few seconds by pushing a button or setting a dial. Survey results from
23 low-income and 25 upper-middle-income countries suggest that access to modern
technological conveniences underlies an inverse relationship between both education level and
financial assets with the prevalence of physical inactivity (Allen et al. 2017). The unfortunate
fact is, however, that many individuals do not engage in physical activity during their leisure
time and sit too much at work and after hours.
Although the human body is designed for movement and strenuous physical activity,
exercise is not part of the average person’s lifestyle. Industrialization and urbanization have
led to increased sedentarism and sedentary behaviors (performing activities of ≤1.5 METs
while in a sitting or reclining posture) (Benatti and Ried-Larsen 2015; Sedentary Behaviour
Research Network 2012). One cannot expect the human body to function optimally and to
remain healthy for extended periods if it is abused or is not used as intended.
Physical inactivity is recognized as a major contributor to the physical and economic
burden of disease nationally and globally. The identification of physical inactivity as the
fourth leading risk factor for mortality supports what experts noted nearly a decade ago—
physical inactivity may well be the most important public health problem in the 21st century
(Blair 2009). To highlight this, a global action plan was developed to increase the number of
people meeting the recommended weekly amount of physical activity by 10% (World Health
Organization 2013). The World Health Organization (2014) reported that physical inactivity
25
causes an estimated 3.2 million deaths annually. Data from large cohort studies conducted
around the world were pooled and analyzed; resulting estimations revealed that between 6%
and 10% of coronary heart disease, type 2 diabetes, and breast and colon cancers are due to
physical inactivity (Lee et al. 2012). As a risk factor, physical inactivity is basically equivalent
to the combined risk of smoking and obesity. Sedentarism has repeatedly been identified as
an independent risk factor associated with an increased risk for all-cause mortality and
metabolic and heart disorders (Benatti and Ried-Larsen 2015). Individuals who do not
exercise regularly and sit too much are at greater risk for developing chronic
noncommunicable diseases such as those in figure 1.1.
FIGURE 1.1 Role of physical activity and exercise in disease prevention and rehabilitation.
For years, exercise scientists as well as health and fitness professionals have maintained that
regular physical activity is the best defense against the development of many diseases,
disorders, and illnesses. The importance of regular physical activity in maintaining a high
quality of life and in preventing disease and premature death received recognition as a
national health objective in the first U.S. surgeon general’s report on physical activity and
health (U.S. Department of Health and Human Services 1996). This report identified
physical inactivity as a serious nationwide health problem, provided clear-cut scientific
evidence linking physical activity to numerous health benefits, presented demographic data
describing physical activity patterns and trends in the U.S. population, and made physical
activity recommendations for improved health. In 1995, the CDC and the American College
of Sports Medicine (ACSM) recommended that every U.S. adult should accumulate 30 min
or more of moderate-intensity physical activity on most, preferably all, days of the week (Pate
et al. 1995). This recommendation has since been adopted by many international
26
organizations.
Since 1995, new scientific evidence increased our understanding of the benefits of physical
activity for improved health and quality of life. In light of these findings, the American Heart
Association (AHA) and the ACSM updated physical activity recommendations for healthy
adults and older adults (Haskell et al. 2007; Nelson et al. 2007). These recommendations
address how much and what type of physical activity are needed to promote health and
reduce the risk of chronic disease in adults. Table 1.1 summarizes the ACSM and AHA
physical activity recommendations for adults.
The recommended amounts of physical activity are in addition to routine activities of daily
living (ADLs) such as housework, cooking, shopping, and walking around the home or from
the parking lot. The intensity of exercise is expressed as a metabolic equivalent of task
(MET). An MET is the ratio of the person’s working (exercising) metabolic rate to the
resting metabolic rate, with 1 MET defined as the energy cost of sitting quietly. Moderate-
intensity aerobic activity (3.0-6.0 METs or 5 or 6 on a 10-point perceived exertion scale) is
operationally defined as activity that noticeably increases heart rate and lasts more than 10
min (e.g., brisk walking at 3.0-4.0 mph [4.8-6.4 km·hr ]). Vigorous-intensity activity (>6.0
−1
METs or 7 or 8 on a 10-point perceived exertion scale) causes rapid breathing and increases
heart rate substantially (e.g., jogging or running at 4.5 mph [7.2 km·hr ] or higher). For
−1
27
adults (18-65 yr) and older adults (>65 yr), the ACSM recommends a minimum of 150 min
of moderate-intensity aerobic activity per week or 75 min of vigorous-intensity aerobic
exercise per week. It is also recommended that these totals be spread over the course of a
week to avoid injury). They also recommend moderate- to high-intensity (8- to 12-repetition
maximum [RM] for adults and 10-RM to 15-RM for older adults) resistance training for a
minimum of 2 nonconsecutive days per week. Balance and flexibility exercises are also
suggested for older adults.
Table 1.2 summarizes the physical activity guidelines (U.S. Department of Health and
Human Services 2008) for children and adolescents (6-17 yr), adults (18-64 yr), and older
adults (≥65 yr). The key message in these guidelines is that for substantial health benefits,
adults should engage in aerobic exercise at least 150 min/wk at a moderate intensity or 75
min/wk at a vigorous intensity or an equivalent combination thereof. In addition, adults of all
ages should do muscle-strengthening activities at least 2 days/wk. In addition to stretching to
support physical activity and activities of daily living, those who are at risk for falling should
also perform balance exercises. Children should do at least 60 min of physical activity every
day. Most of the 60 min per day should be either moderate or vigorous aerobic activity and
should include vigorous aerobic activities at least 3 days/wk. Part of the 60 min or more of
daily physical activity should be muscle-strengthening activities (at least 3 days/wk) and
bone-strengthening activities (at least 3 days/wk).
28
HEALTH BENEFITS OF PHYSICAL ACTIVITY
Lower risk of
dying prematurely;
coronary artery disease;
stroke;
type 2 diabetes and metabolic syndrome;
high blood pressure;
adverse blood lipid profile;
colon, breast, lung, and endometrial cancers; and
hip fractures.
Reduction of
abdominal obesity and
feelings of depression and anxiety.
Helps in
weight loss, weight maintenance, and prevention of weight gain;
prevention of falls and improved functional health for older adults;
improved cognitive function;
increased bone density; and
improved quality of sleep.
Data from U.S. Department of Health and Human Services 2008.
The term exercise deficit disorder (EDD) has been used to identify children who do not
attain at least 60 min of moderate- to vigorous-intensity physical activity (MVPA) on a daily
basis (Faigenbaum and Myer 2011). Children with EDD are at an increased risk for
developing harmful health effects in their adolescent and adult years due to a physically
inactive lifestyle (Stracciolini, Myer, and Faigenbaum 2013). For example, results from a
study that monitored children for 14 yr revealed that those who maintained their active
childhood MVPA levels through adolescence were less likely to become obese as young adults
(Kwon et al. 2015).
Exercising 150 min/wk equates to expending approximately 1,000 kcal·wk . Results from a −1
29
meta-analysis (Sattelmair et al. 2011) indicated that individuals meeting the 2008 physical
activity guidelines decrease their risk for coronary heart disease by 14% compared with those
reporting no leisure-time physical activity (LTPA). Participating in regular physical activity
and exercise on a daily basis provides numerous preventative benefits for no fewer than 25
chronic medical conditions (Warburton and Breden 2016) such as cardiovascular disease,
hypertension, diabetes, stroke, dementia, and several types of cancer. Disease risk is further
reduced when moderate-intensity physical activity (150-180 min/wk) is performed
throughout the week (i.e., 30 min/day on 5 days/wk) and in bouts lasting at least 10 min as
opposed to in one single session (Kesäniemi et al. 2010).
Sattelmair and colleagues (2011) reported that 300 min/wk of moderate-intensity physical
activity results in a 20% reduction in the risk for coronary heart disease (CHD). Furthermore,
a review of studies on asymptomatic adults (19-65 yr) revealed that 90 min of vigorous-
intensity physical activity accumulated throughout the week (90 min/wk) in increments of no
fewer than 10 min reduces the risk of all-cause mortality by 30%, as well as the risk for
cardiovascular disease (CVD), hypertension, stroke, type 2 diabetes, and breast and colon
cancer (Kesäniemi et al. 2010).
In 2009, an international consensus conference was convened to review Canada’s Physical
Activity Guide to Healthy Active Living (Health Canada 2003). The consensus panel
recommended that asymptomatic Canadian adults (19-65 yr) accumulate 150 min/wk of
moderate-intensity physical activity or 90 min/wk of vigorous-intensity activity as a primary
prevention against cardiovascular disease, stroke, hypertension, colon cancer, breast cancer,
type 2 diabetes, and osteoporosis. They also recommended multiple exercise sessions in a
week, with each session lasting a minimum of 10 min (Kesäniemi et al. 2010). In addition to
the aerobic exercise, they recommended strength activities (2-4 days/wk) and flexibility
activities (4-7 days/wk). The duration of the activity depends on the intensity or effort:
Perform light activities (e.g., walking, video gaming that promotes light effort, gardening,
carrying small children, or hairstyling) for 60 min, moderate activities (e.g., brisk walking,
swimming, vacuuming, moving furniture, or chopping wood) for 30 to 60 min, and vigorous
activities (e.g., jogging, hockey, wheelchair basketball, felling large trees, or rollerblading) for
20 to 30 min.
Improvements in health benefits depend on the volume (i.e., combination of frequency,
intensity, and duration) of physical activity. This is known as the dose-response relationship
(Loprinzi 2015). Because of the dose-response relationship between physical activity and
health, even a low level of MVPA each week is better than none; doses less than one-half of
30
the recommended guidelines may lead to notable health benefits for those with elevated risks
for chronic conditions and premature mortality (Warburton and Breden 2016). Exceeding
the minimum recommended MVPA dose by a factor of 5 (i.e., 750 min/wk or ≥10,000
MVPA MET-min/mo) may confer the greatest reduction in all-cause mortality risk; no
additional mortality-related benefit is associated with a dose 10 times higher than
recommended (Arem et al. 2015; Loprinzi 2015). MVPA MET-min/mo is easily computed
by multiplying the respective MET level for the specific activities (see appendix E.3) by the
number of minutes one engages in those MVPA activities within a month.
Figure 1.2 illustrates the general dose-response relationship between the volume of physical
activity participation and selected health benefits (e.g., muscular strength and aerobic fitness)
that do not require a minimal threshold intensity for improvement. The volume of physical
activity participation needed for the same degree of relative improvement (%) varies among
health benefit indicators. For example, to improve triglycerides from 0% to 40% requires 250
kcal·wk of physical activity compared with 1,800 kcal·wk for the same relative improvement
−1 −1
(0%-40%) in high-density lipoprotein (HDL; see figure 1.2). It appears that aerobic-style
activities that can be maintained for longer periods (e.g., bicycling, dancing, jogging) are
positively related to beneficial changes in HDL (Loprinzi 2015). Jogging at a slow or average
pace ≤3 days/wk for a total of 60 to 150 min/wk confers a favorable increase in heart function
and a similar decrease in mortality, whereas decades-long strenuous endurance training
routines (≥12 METs) in preparation for extreme endurance competitions may actually
damage the cardiovascular system (Schnohr et al. 2015). Therefore, too much physical
activity, defined as engaging in 5 hr of structured high-intensity activity per week, may be
associated with negative health consequences or overuse injuries.
FIGURE 1.2 Dose-response relationship for health benefits and volume of physical activity.
Courtesy of N. Gledhill and V. Jamnik of York University School of Kinesiology and Health Science.
Although no specific dose of sedentary behavior has been found, a direct linear relationship
31
between total daily time in sedentary behavior and negative health indicators associated with
metabolic syndrome (high triglycerides, high fasting blood glucose, and low HDL-C) has
been reported (Gennuso et al. 2015). Each 60 min increase in daily time spent being
sedentary is associated with a 9% increase in the odds of satisfying the criteria for metabolic
syndrome (Gennuso et al. 2015).
Although the physical activity guideline—a minimum of 150 min of moderate- to
vigorous-intensity aerobic activity weekly, preferably performed on a daily basis—reduces
disease risk, additional physical activity is needed to mitigate weight gain over time (Moholdt
et al. 2014). Levine (2015) describes how standing and walking double the energy expended
as compared with sitting; he also illustrates how office workers can expend approximately
1,000 kcal·day and increase time spent being active by incorporating walking meetings and
−1
short activity breaks in the typical business day. In 2002, the Institute of Medicine (IOM)
recommended 60 min of daily moderate-intensity physical activity. In the IOM report, the
expert panel stated that 30 min of daily physical activity is insufficient to maintain a healthy
body weight and to fully reap its associated health benefits. The IOM recommendation
addresses the amount of physical activity necessary to maintain a healthy body weight and to
prevent unhealthful weight gain (Brooks et al. 2004). The IOM recommendation of 60 min
of daily physical activity is consistent with recommendations for preventing weight gain made
by other organizations (i.e., Health Canada, International Association for the Study of
Obesity, and World Health Organization) (Brooks et al. 2004).
EXAMPLES OF MODERATE-INTENSITY AND VIGOROUS-INTENSITY

AEROBIC ACTIVITIES
This list provides several examples of moderate- and vigorous-intensity aerobic activities. Some activities can be
performed at varied intensities. This list is not all-inclusive; examples are provided to help people make choices. For a
detailed list of energy expenditures (METs) for conditioning exercises, sports, and recreational activities, see appendix
E.3 and http://links.lww.com/MSS/A82. Generally, light activity is defined as <3.0 METs, moderate activity as 3.0 to
6.0 METs, and vigorous activity as >6.0 METs.
Moderate Intensity
Walking briskly (3.0 mph [4.8 km·hr−1] or faster, but not race walking)
Skateboarding (noncompetitive)
Water aerobics and water calisthenics
Bicycling slower than 10 mph (16 km·hr−1)
32
Tennis (doubles)
Ethnic and cultural dancing (e.g., Middle Eastern, salsa, merengue, swing)
General gardening
Yoga (e.g., hatha, power)
Vigorous Intensity
Race walking, jogging, running, or vigorous lap swimming
Tennis (singles)
Dancing (e.g., folk, line, competitive ballroom)
Bicycling 10 mph (16 km·hr−1) or faster
Jumping rope
Backpacking
Circuit training (resistance based with some aerobics and minimal rest intervals)
Data from http://links.lww.com/MSS/A82 (accessed June 28, 2018).
The bottom line is that 150 min/wk of moderate-intensity physical activity provides
substantial health benefits but may be insufficient to prevent weight gain for many
individuals. It is a good initial goal and a sufficient amount of activity to move individuals
from a sedentary to low physical activity level (Brooks et al. 2004). As individuals adopt
regular physical activity and improve their lifestyle and fitness, they should increase the
duration of daily physical activity to a level (60 min) that prevents short-term weight gain and
provides additional health benefits. Progression to daily engagement in physical activity,
inclusive of resistance training, for 60 to 90 min is important for long-term weight
maintenance after weight loss (Bray et al. 2016; Ryan and Heaner 2014). Although there
appears to be little overall effect on long-term weight loss based on exercise type (aerobic vs.
resistance) or intensity (lower vs. higher), the reduced time requirement for equivalent energy
expenditure of high-intensity exercise as compared with low-intensity exercise may increase
exercise adherence and, hence, weight maintenance (Bray et al. 2016).
The Exercise and Physical Activity Pyramid illustrates a balanced plan of physical activity
and exercise to promote health and to improve physical fitness (see figure 1.3). Encourage
your clients to engage in physical activities around the home and workplace on a daily basis to
establish a foundation (base of pyramid) for an active lifestyle. Strategies for increasing energy
expenditure in the workplace are built on encouraging active breaks from sitting in order to
33
move around (e.g., step in place, walk laps around the office, perform light calisthenics, walk
down the hall to a colleague’s office instead of calling or e-mailing to deliver a message, climb
a flight of stairs to get a drink of water or use the restroom). Your clients should perform
aerobic activities a minimum of 3 days/wk; they should do weight-resistance exercises and
flexibility or balance exercises at least 2 days/wk. Recreational sport activities (middle levels of
pyramid) are recommended to add variety to the exercise plan. High-intensity training and
competitive sport (top of pyramid) require a solid fitness base and proper preparation to
prevent injury; most adults should engage in these activities sparingly.
FIGURE 1.3 The Exercise and Physical Activity Pyramid.

Adapted by permission from “Exercise and Activity Pyramid,” Metropolitan Life Insurance Company, 1995.
CARDIOVASCULAR DISEASE
Cardiovascular disease (CVD) is projected to cause more than 26 million deaths by 2030
(World Health Organization 2011b). CVD caused 17.9 million deaths (46% of the deaths
attributed to all noncommunicable diseases) worldwide in 2015. Of the deaths due to CVD
in 2015, the combination of stroke and ischemic heart disease accounted for the great
majority (85%) (GBD 2015 Mortality and Causes of Death Collaborators 2016). More than
75% of cardiovascular deaths occurred in low- and middle-income countries (World Health
Organization 2016a). CVD is the principal cause of premature death in Europe, accounting
for a nearly equal percentage of all deaths before age 75 in women (36%) and men (35%).
Interestingly, however, CVD was surpassed by cancer as the leading cause of death in several
34
Western European countries (Townsend et al. 2016). CVD is also a leading cause of disease
burden in developing low- and middle-income countries; deaths due to CVD range from a
low of 10% in sub-Saharan Africa to 58% in Eastern Europe (Wagner and Brath 2012).
In a 2015 report by the CDC identifying the underlying causes of death in the United
States between 1999 and 2003, diseases of the heart and blood vessels claimed the lives of
about 610,000 people (Centers for Disease Control and Prevention 2015a). CVD accounted
for 25% of all deaths (one out of every four) in the United States. Extrapolating to 2014
levels, the CDC estimated that more than 92 million Americans have some form of CVD
such as hypertension (~86 million), CHD (27.6 million), or stroke (7.2 million) (American
Heart Association 2017). Among American adults 20 yr of age or older, the estimated age-
adjusted prevalence of coronary heart disease is higher for black men and women compared
with Hispanic and white men and women (American Heart Association 2017).
One myth about CVD is that it is much more prevalent in men than in women. Between
2011 and 2014, the prevalence of CVD in adult women (35.9%) and men (37.7%) in the
United States was similar (American Heart Association 2017). Nearly 399,000 females died
from CVD in 2014 in the United States. Another misconception about CVD is that it afflicts
only the older population. Although it is true that older people are at greater risk, more than
50% of the people in the United States with CVD are younger than 60 yr (American Heart
Association 2017), and CVD ranks as the second-leading cause of death for children under
age 15 (American Heart Association 2012).
The prevalence of American adults with CHD was 45.1% in 2014 (American Heart
Association 2017). In Europe, CHD accounts for more than 1.7 million deaths, with nearly
19% of those occurring in adults below the age of 65 (Townsend et al. 2016). Coronary heart
disease (CHD) is caused by a lack of blood supply to the heart muscle (myocardial ischemia)
resulting from a progressive degenerative disorder known as atherosclerosis. Atherosclerosis
is an inflammatory process involving a buildup of low-density lipoprotein (LDL) cholesterol,
scavenger cells (monocytes), necrotic debris, smooth muscle cells, and fibrous tissue. This is
how plaques form in the intima, or inner lining, of the medium- and large-sized arteries
throughout the cardiovascular system. As more lipids and cells gather in the plaques, they
bulge into the arterial lumen (Barquera et al. 2015). In the heart, these bulging plaques
restrict blood flow to the myocardium and may produce angina pectoris, which is a temporary
sensation of tightening and heavy pressure in the chest and shoulder region. A myocardial
infarction, or heart attack, can occur if a blood clot (thrombus) or ruptured plaque obstructs
the coronary blood flow. In this case, blood flow through the coronary arteries is usually
35
reduced by more than 80%. The portion of the myocardium supplied by the obstructed artery
may die and eventually be replaced with scar tissue.
CARDIOVASCULAR DISEASE RISK FACTORS

Epidemiological research indicates that many factors are associated with the risk of CVD.
The greater the number and severity of risk factors, the greater the probability of CVD. The
positive risk factors for CVD are
age,
family history,
hypercholesterolemia,
hypertension,
tobacco use,
diabetes mellitus or prediabetes,
overweight and obesity, and
physical inactivity.
An increased level (≥60 mg·dl ) of high-density lipoprotein cholesterol, or HDL-

−1
cholesterol (HDL-C), in the blood decreases CVD risk. If the HDL-C is high, you should
subtract one risk factor from the sum of the positive factors when assessing your client’s CVD
risk.
PHYSICAL ACTIVITY AND CORONARY HEART DISEASE

Approximately 12% of CHD deaths in the United States can be attributed to a lack of
physical activity (American Heart Association 2017). As cited in American Heart Association
(2017), the percentage of physically inactive people worldwide in 2012 (35%) surpassed the
percentage of those who smoked (26%); however, Sallis and colleagues (2016), reported the
global percentage of physically inactive adults to be closer to 23%. As an exercise scientist,
you must educate your clients about the benefits of physical activity and regular exercise for
preventing CHD. Physically active people have lower incidences of myocardial infarction and
mortality from CHD and tend to develop CHD at a later age compared with their sedentary
or less active counterparts (American Heart Association 2017). Leading a physically active
lifestyle and sitting less than 4 hr a day may reduce cardiovascular disease mortality rates by
23% to 74% (Ekelund et al. 2016). Alternatively, in their analysis of multiple studies
36
investigating sedentary behavior and incidence of CVD, Biswas and associates (2015)
reported an increase in odds ranging from 6% to more than doubled.
Physical activity, just like sedentary behavior and cardiorespiratory fitness levels, exerts its
effect independently of other risk factors related to premature death from CHD and all
causes (Bouchard, Blair, and Katzmarzyk 2015). Another conclusion about the independent
effect of sedentary behavior (Carter et al. 2017) is that evidence increasingly points to the
likely link between sedentarism and its ability to further exacerbate the traditional, modifiable
CV risk factors (Benatti and Ried-Larsen 2015; Bergouignan et al. 2016; Same et al. 2016).
Also, in a meta-analysis of studies dealing with the dose-response effects of physical activity
and cardiorespiratory fitness on CVD and CHD risk, Williams (2001) reported that
cardiorespiratory fitness and physical activity have significantly different relationships to
CVD and CHD risk. Although physical fitness and physical activity each lower the risk of
developing CVD and CHD, the reduction in relative risk was almost twice as great for
cardiorespiratory fitness as for physical activity. These findings suggest that in addition to
physical activity level, low cardiorespiratory fitness level should be considered a potential risk
factor for CHD (U.S. Department of Health and Human Services 2008).
HYPERTENSION
Hypertension, or high blood pressure, is a chronic, persistent elevation of blood pressure.
Individuals with this diagnosis are often prescribed antihypertensive medicine. Elevated
blood pressure is the term used to identify systolic blood pressure (SBP) values between 120
and 129 mmHg, even if diastolic blood pressure (DBP) is lower than 80 mmHg. Stage 1
hypertension describes a value of 130 to 139 mmHg for SBP or a DBP value of 80 to 89
mmHg; stage 2 hypertension denotes SBP values ≥140 mmHg or DBP values ≥ 90 mmHg
(Whelton et al. 2017). An expanded link exists between hypertension and several forms of
CVD (Rapsomaniki et al. 2014). The World Health Organization (2011b) identified
hypertension as the leading cardiovascular risk factor, attributing 13% of deaths worldwide to
high blood pressure. If not kept in check, hypertension becomes a primary risk factor for
stroke, heart attacks, heart and kidney failure, dementia, and blindness (World Health
Organization 2014). In the United States, hypertension attributes to about 40% of all adult
deaths from CVD (Yang et al. 2012).
In 2014, about 22% of the global adult population (≥18 yr of age) had hypertension (World
Health Organization 2014). As of 2015, hypertension is more prevalent in low-income
countries in sub-Saharan Africa and south Asia than in high-income countries; however,
37
elevated blood pressure continues to be problematic in Eastern and Central Europe (NCD
Risk Factor Collaboration 2017). With an estimated 1.4 billion adult diagnoses worldwide,
hypertension is touted as being the leading preventable cause of death before age 70. Its
prevalence is lower in high-income countries (28.5%) as compared with low- and middle-
income countries (31.5%), which reflects differences in awareness levels as well as treatment
and control of the condition (Mills et al. 2016). Nearly one out of every three adults has
blood pressure values in the elevated rage (Centers for Disease Control and Prevention 2016).
In the United Kingdom, approximately 14% of adults are hypertensive, with Northern
Ireland having a lower prevalence compared with England and Scotland (British Heart
Foundation 2015b). In comparison, the prevalence of hypertension is estimated to be higher
for adults in Latin America and the Caribbean (~39%) than for the Pacific and East Asian
region (~36%), Europe and Central Asia (~32%), South Asia (~29%), and Africa (~27%)
(Sarki et al. 2015).
In the United States, more men than women are hypertensive prior to age 65; after that the
percentage of hypertensive women surpasses that of their male counterparts (American Heart
Association 2017). Up to age 45 yr, the percentage of American men with hypertension
(11%-23%) is slightly higher than that of women (8%-23%). Between ages 45 and 54 yr, the
prevalence of hypertension is similar for men (36.1%) and women (33.2%). Likewise, for
those between 55 and 64 yr, men have a slightly higher (57.6%) prevalence of hypertension
than do women (~55.5%). After age 65, the percentage of women (65.8%) with high blood
pressure is somewhat higher than that of men (63.6%). Women with hypertension have a 3.5
times greater risk of developing CHD than do women who have normal blood pressure
(normotensive). Also, the prevalence of high blood pressure for blacks in the United States
(45.5%) is among the highest in the world and is substantially greater than that of American
Indians or Alaskan Natives, Asians or Pacific Islanders, Hispanics, and whites in the United
States (American Heart Association 2017). Table 1.3 summarizes the risk factors associated
with developing hypertension.
38
For individuals with elevated blood pressure values, healthy lifestyle changes and periodic
BP reassessments are recommended as part of the treatment plan. For people whose blood
pressure is in the stage 1 range, their risk for stroke and CVD within the next 10 yr should be
assessed using the atherosclerotic cardiovascular disease risk calculator
(http://static.heart.org/riskcalc/app/index.html#!/baseline-risk) (Whelton et al. 2017).
Sharman, La Gerche, and Coombes (2015) combined data from studies investigating the
effect of exercise on blood pressure values in people diagnosed with hypertension. They
indicate that while both aerobic and resistance training can reduce blood pressure, aerobic
training is the preferred method. Their study also reports on the combination of exercise and
antihypertensive medications, with a cautionary note about monitoring postexercise blood
pressure responses. Regular physical activity prevents hypertension and lowers blood pressure
in younger and older adults who have normal, elevated, stage 1, or stage 2 values. Compared
with normotensive individuals, training-induced changes in resting systolic and diastolic
blood pressures (5-7 mmHg) are greater for hypertensive individuals who participate in
endurance exercise. However, even modest reductions in blood pressure (2-3 mmHg) by
endurance or resistance exercise training decrease CHD risk by 5% to 9%, stroke risk by 8%
39
to 14%, and all-cause mortality by 4% in the general population (Pescatello et al. 2004). See
Exercise Prescription for Individuals with Hypertension for an exercise prescription that the
ACSM endorses to lower blood pressure in adults with hypertension.
EXERCISE PRESCRIPTION FOR INDIVIDUALS WITH HYPERTENSION
Mode: Primarily endurance activities supplemented by resistance exercises
Intensity: Moderate-intensity endurance (40%-60% V̇O2R),* rate of perceived exertion of 12-13, and resistance
training (60%-80% 1-RM)
Duration: 30 min or more of continuous or accumulated aerobic physical activity per day, and a minimum of two
sets (8-12 reps) of resistance training exercises for each major muscle group
Frequency: Most, preferably all, days of the week for aerobic exercise; 2 or 3 days/wk for resistance training
2 2 Reserve (MET) Method section in chapter 5 for more information.

*V̇O R is the difference between the maximum and the resting rate of oxygen consumption. See the V̇O
Based on American College of Sports Medicine 2018.
HYPERCHOLESTEROL-EMIA AND DYSLIPIDEMIA

Hypercholesterolemia, an elevation of total cholesterol (TC) in the blood, is associated with
increased risk for CVD. Hypercholesterolemia is also referred to as hyperlipidemia, which is
an increase in blood lipid levels; dyslipidemia refers to an abnormal blood lipid profile.
Approximately 18% of strokes and 56% of heart attacks are caused by high blood cholesterol
(World Health Organization 2002a). Between 2011 and 2014, the number of adults (≥20 yr
of age) having a TC value ≥240 mg∙dl fell for all racial and ethnic subgroups; however, this
–1
decrease may be due to an increase in medication prescriptions instead of exercise or diet

(American Heart Association 2017). Results from the longitudinal, biracial CARDIA study
(Schneider et al. 2016) indicate that although TC dropped initially, values stabilized and
appear to be reversing toward the end of the 25 yr observation period.
More than 94.6 million Americans age 20 yr and older have total blood cholesterol levels
of 200 mg·dl or higher. According to data gathered between 2011 and 2014, 28.5 million
–1
American adults (≥20 yr) have TC levels classified as high risk (>240 mg·dl ); more women –1
(16.4 million) than men (10.6 million) have TC levels equaling or exceeding 240 mg·dl –1
(American Heart Association 2017). Of note, the prevalence of TC, when adjusted for age,
decreased in the 2013-2014 period as compared with the 2011-2012 period for both men and
women across the four major racial and ethnic groups; the one exception is a 2.6% increased
prevalence for non-Hispanic Asian males. Compared with Western countries, the average
TC levels for adults in China, Japan, and Indonesia are uniformly lower (190-207 mg·dl ) –1
(American Heart Association 2001). Risk factors for hypercholesterolemia are identified in
40
table 1.3.
LDLS, HDLS, AND TC

Cholesterol is a waxy, fatlike substance found in all animal products (meats, dairy products,
and eggs). The body can make cholesterol in the liver and absorb it from the diet. Cholesterol
is essential to the body, and it is used to build cell membranes, produce sex hormones, and
form bile acids necessary for fat digestion. Lipoproteins are an essential part of the complex
transport system that exchanges lipids among the liver, intestine, and peripheral tissues.
Lipoproteins are classified by the thickness of the protein shell that surrounds the cholesterol.
The four main classes of lipoproteins are chylomicron, derived from the intestinal absorption
of triglycerides (TG); very low-density lipoprotein (VLDL), made in the liver for the
transport of triglycerides; low-density lipoprotein (LDL), a product of VLDL metabolism
that serves as the primary transporter of cholesterol; and high-density lipoprotein (HDL),
involved in the reverse transport of cholesterol to the liver. The molecules of LDL are larger
than those of HDL and therefore precipitate in the plasma and are actively transported into
the vascular walls. Excess LDL-cholesterol (LDL-C) stimulates the formation of plaque in
the intima of the coronary arteries. Plaque formation reduces the cross-sectional area and
obstructs blood flow in these arteries, eventually producing a myocardial infarction.
Therefore, LDL-C values less than 100 mg·dl are considered optimal for reducing CVD
−1
and CHD risk (National Cholesterol Education Program 2001). The prevalence of
borderline high levels (≥130 mg·dl to <160 mg·dl ) of LDL-C is nearly identical for adult
−1 −1
women (31%) and adult men (32.5%) in the United States (Roger et al. 2012).
The smaller HDL molecules are suspended in the plasma and protect the body by picking
up excess cholesterol from the arterial walls and delivering it to the liver, where it is
metabolized. HDL-cholesterol (HDL-C) values less than 40 mg·dl are associated with a
−1
higher risk of CHD. Based on data collected between 2011 and 2014, 19% of men and
women in the United States who are older than 20 yr have low (<40 mg·dl ) HDL-C levels
−1
(Zwald et al. 2017).

Individuals with low HDL-C or high TC levels (dyslipidemia) have a greater risk of heart
attack. Those with lower HDL-C (<37 mg·dl ) are at higher risk regardless of their TC level.
−1
This emphasizes the importance of screening for both TC and HDL-C in adults.
PHYSICAL ACTIVITY AND LIPID PROFILES

Regular physical activity, especially habitual MVPA aerobic exercise, positively affects lipid
41
metabolism and lipid profiles (Lin, Zhang, et al. 2015). Cross-sectional comparisons of lipid
profiles in physically active and sedentary women and men suggest that physical fitness is
inversely related to TC and the TC/HDL-C ratio (Despres and Lamarche 1994; Shoenhair
and Wells 1995).
Data from 160 randomized controlled trials were pooled to examine the effects of aerobic
exercise on cardiometabolic biomarkers such as lipids and lipoproteins in a large number of
adults. Results show that compared with control groups, adults in moderate-intensity and
vigorous-intensity aerobic exercise interventions, respectively, reduce TC (4.3 and 3.87 mg·dl
), LDL-C (3.09 and 4.64 mg·dl ), VLDL-C (1.93 and 7.35 mg·dl ), and TG (5.31 and
−1 −1 −1
5.31 mg·dl ) and increase HDL-C (1.16 and 2.71 mg·dl ) (Lin, Zhang, et al. 2015).
−1 −1
However, Lin and colleagues found no differences across exercise-intensity subgroups, which
lends support to the premise that moderate- and vigorous-intensity exercise training confer
similar favorable results for cardiometabolic health. A 1% reduction in TC has been shown to
reduce the risk for CHD by 2%; likewise, a 1% reduction in HDL-C increases CHD risk by
2% to 3% (Gordon et al. 1989). However, for individuals with hyperlipidemia, lifestyle
changes (e.g., healthy diet) or pharmacologic interventions (e.g., statins), in addition to
aerobic exercise, may be necessary for optimizing lipid and lipoprotein profiles (Kelley and
Kelley 2006).
Increases in HDL-C in response to aerobic exercise appear to be related to the training
dose (interaction of the intensity, frequency, and duration of each exercise session and the
length of the training period), and they are less dramatic in women than in men. Across adult
age ranges, those who met (17.7%) the physical activity guidelines (≥150 min of MVPA per
week) had higher HDL-C levels than did those American adults (21.0%) who did not meet
the meet the guidelines. Interestingly, the prevalence of low HDL-C values decreased with
increasing age for adults meeting the physical activity guidelines; for those ≥60 yr old, only
12.6% of the active seniors had low HDL-C values compared with approximately 19% for the
younger age groups (Zwald et al. 2017). Based on results from a longitudinal study of biracial
adults, a high level of aerobic fitness as a young adult in combination with a continued
physically active lifestyle confers favorable results for blood lipid levels in the middle-age
adult years (Sarzynski et al. 2015).
The research on the effect of resistance training on cholesterol levels continues to remain
inconclusive. Ribeiro and associates (2016) reported improvements in HDL-C for the older,
physically independent women (67.6 ± 5.1 yr) randomly assigned to 8 wk of traditional (three
sets of 8-RM to 12-RM) or 8 wk of pyramid (12-RM/10-RM/8-RM) styles of resistance
42
training. After a 12 wk washout period, the women switched training styles. There were
numerous favorable responses, including increases in HDL-C, by the end of each 8 wk
period; however, there were no differences between training styles. Similarly, 12 wk of a
nonlinear resistance training program designed to increase strength significantly improved
HDL-C and other variables compared with the normally active controls in a sample of adults
(18-60 yr) living with HIV and taking prescribed highly active antiretroviral medications
(Zanetti et al. 2016). Conversely, 16 wk of combined aerobic (30 min) and resistance (27
min) training produced no significant improvements in HDL-C in postmenopausal women
as compared with those in the aerobic training (52 min) group (Rossi et al. 2016). It is
possible that the resistance training portion of their combined group (three or four sets of 12-
RM to 15-RM) may not have provided the exercise intensity needed to invoke significant
changes in HDL-C in their postmenopausal sample.
TOBACCO
Although tobacco usage (e.g., cigarettes and cigars) is declining in the United States and
other countries, there continues to be a steep increase worldwide (American Heart
Association 2017). Ng and colleagues (2014) attribute the increase in the number of smokers
to the world’s population growth. The World Health Organization (2011) estimates there are
approximately 1 billion smokers in the global population. According to age-standardized
results for smoking prevalence (Ng et al. 2014), between 16.5% and 19.7% of men in the
United States, Canada, Brazil, and Australia smoke, while 34.7% to 61.1% of men in Russia,
China, Eastern Europe, Egypt, and Turkey smoke. The lowest prevalence (0.5%-2.6%) of
female smokers is found in Africa, China, and the Persian Gulf, whereas the prevalence
exceeds 25% in Austria, Chile, France, and Hungary. Of the 187 countries included in the
study, the age-adjusted prevalence of men who smoke daily exceeds that of their female
counterparts in all but one country: Sweden. Although the prevalence of tobacco usage is
lower for women than men across the majority of the predominant race and ethnic groups in
the United States, the prevalence is slightly higher for Native American and Alaskan Indian
women and nearly equal for non-Hispanic white women compared with their respective male
counterparts (American Heart Association 2017).
Approximately 13.7% of American women and 16.7% of American men currently smoke
(American Heart Association 2017). Smoking cessation strategies in Canada, Iceland,
Mexico, and Norway have cut smoking rates in half since 1980 (Ng et al. 2014) and may
provide invaluable assistance for curbing tobacco use in other countries. In a study of school-
43
aged adolescents (average age 15 yr) representing 50 schools in six European cities (Lorant et
al. 2015), 17.4% of the 11,000 participants self-reported being a smoker. Even if people
abstain from smoking tobacco, the risk of death from CHD increases by 30% in those
exposed to environmental tobacco smoke at home or at work (American Heart Association
2004).
Smoking is one of the largest preventable causes of disease and premature death. Nearly
33% of CHD deaths are due to first- and secondhand exposure to smoke (American Heart
Association 2017). Cigarette smoking is linked to CHD, stroke, and chronic obstructive
pulmonary disease. It causes cancer of the lungs, larynx, esophagus, mouth, and bladder and
is also associated with no fewer than eleven cancers (Carter et al. 2015). Compared with
nonsmokers, smokers have more than twice the risk of heart attack and die, on average, at
least 10 yr earlier (American Heart Association 2017). As mentioned previously, cigarette
smoking is a major cause of stroke. It also multiplies the effect of CHD risk factors such as
elevated blood lipid levels, diabetes mellitus, and untreated hypertension. Some researchers
who study adults ≥55 yr of age are encouraging further investigations of the possible
associations between smoking and deaths resulting from infections, respiratory diseases,
prostate and breast cancer, intestinal ischemia, kidney failure, and hypertensive heart disease.
The relative risk of dying from these conditions drops with each year subsequent to quitting
(Carter et al. 2015). Additionally, although not well studied at this time, the inhaled vapors
from electronic cigarettes deliver nicotine and other substances for which the health risks are
not yet known.
When individuals stop smoking, their risk of CHD declines rapidly, regardless of how long
or how much they have smoked. Although health benefits associated with smoking cessation
happen within weeks or months, the relative risk of a former smoker dying from CHD
approximates that of a nonsmoker within 10 yr of quitting (American Heart Association
2017).
DIABETES MELLITUS
Diabetes is a global epidemic with rising prevalence rates, especially in the low- and middle-
income countries. Consequently, there is a commitment by world leaders to reduce, by one-
third, the rates of premature mortality from diabetes and the other priority NCDs by 2030
(World Health Organization 2016b). As of 2014, an estimated 422 million adults (8.5%)
worldwide have the disease (World Health Organization 2016b). Factors linked to this
epidemic include urbanization, aging, physical inactivity, unhealthy diet, and obesity (Wagner
44
and Brath 2012). At least 43% of the deaths attributable to elevated blood glucose levels
occur in people younger than 70 yr of age (World Health Organization 2016b). Diabetes is a
major contributor toward the development of CHD, stroke, specific cancers, kidney failure,
and cognitive disability (World Health Organization 2016b). This increased risk of CHD
and stroke is higher for women than men with diabetes for a variety of reasons: higher-level
CVD risk factors and obesity at time of diagnosis, longer exposure to an elevated risk profile
when in the prediabetic stage, and relative undertreatment following diagnosis (Peters,
Huxley, and Woodward 2014). In the United States, diabetes was the seventh leading cause
of death in 2010 (American Diabetes Association 2017).
In 2012, 29 million adults in the United States had type 2 diabetes, while 86 million ≥20 yr
of age were identified as having prediabetes (American Diabetes Association 2017). In China
and India, there are 138 million people with diabetes (Danaei et al. 2011). Danaei and
colleagues (2011) also estimated that approximately 42 million people with diabetes are from
Brazil, Indonesia, Japan, Mexico, and Pakistan. Furthermore, in 2008, they reported the
highest prevalence of diabetes was found in countries located in Oceania, northern Africa, the
Middle East, and the Caribbean. Conversely, the lowest prevalence of diabetes was in
southeast Asia, east Africa, and Andean Latin America (Danaei et al. 2011).
The prevalence of diabetes for adults (≥20 yr) in the United States was 12.3%; 1.7 million
people in this age group were diagnosed with diabetes for the first time in 2012 (Centers for
Disease Control and Prevention 2014). Compared with white adults in the United States, the
prevalence of diabetes and impaired blood glucose levels for blacks (13.2%), Hispanics
(12.8%), and American Indians/Alaska Natives (15.9%) is higher (Centers for Disease
Control and Prevention 2014). The age-adjusted prevalence of diabetes for American Indians
and Alaska Native adults is region dependent; American Indians in southern Arizona have a
prevalence of diabetes (24.1%) that is four times that of Alaska Natives (Centers for Disease
Control and Prevention 2014).
Prediabetes, in addition to being a positive risk factor for CVD, is a medical condition
identified by fasting blood glucose or glycated hemoglobin (HbA1c) levels that are above
normal values but lower than the threshold for a diagnosis of diabetes. HbA1c is an indicator
of the average blood glucose over the past 2 to 3 mo (Centers for Disease Control and
Prevention 2014). Fortunately for the 86 million American adults (Centers for Disease
Control and Prevention 2014) and others worldwide, prediabetes appears to respond
favorably to weight loss, dietary changes, and increases in physical activity. The age-adjusted
percentage of prediabetes in U.S. adults during the period 2009 to 2012 was nearly identical
45
for non-Hispanic whites, non-Hispanic blacks, and Hispanics (35%, 39%, and 38%,
respectively) (Centers for Disease Control and Prevention 2014).
Type 1 diabetes, formerly referred to as insulin-dependent diabetes mellitus (IDDM),
usually occurs in children and adolescents but can develop at any age. Type 2 diabetes,
previously known as non-insulin-dependent diabetes mellitus (NIDDM), is more common
and no longer occurs primarily in middle-aged and elderly adults; 90% to 95% of individuals
diagnosed with diabetes mellitus have type 2 diabetes (Centers for Disease Control and
Prevention 2014). Risk factors for developing diabetes are presented in table 1.3. Type 1
diabetes may be caused by autoimmune, genetic, or environmental factors, but the specific
cause is unknown. Unfortunately, although clinical trials are under way, there is currently no
known way to prevent type 1 diabetes (World Health Organization 2016b). Healthy
nutrition and increased physical activity, however, can reduce the risk of type 2 diabetes by as
much as 67% in high-risk individuals (Sanz, Gautier, and Hanaire 2010). Regular physical
activity, as part of a modest weight loss intervention, has reduced the risk of developing type
2 diabetes by a maximum of 58% for those in the high-risk category (Colberg et al. 2010).
Too much body fat is recognized as the dominant risk factor for type 2 diabetes. Elevated
waist circumferences and BMI values also increase the risk, but the associated risk varies by
geographic region (World Health Organization 2016b).
The effect of exercise alone as an intervention for people with type 2 diabetes is not well
known beyond its ability to improve glucose control (Handelsman et al. 2015). However, a
minimum of 150 min/wk of MVPA is recommended and should include flexibility and
strength training (Handelsman et al. 2015). Of note, though, for continued benefits, the
exercise program needs to be performed regularly and include both strength and aerobic
training to help those with type 2 diabetes achieve optimal health. Decreasing the time spent
being sedentary, in addition to increasing daily physical activity, is a viable means of
decreasing the risk for developing type 2 diabetes. As reported in a review of five studies, the
pooled hazard of developing type 2 diabetes is nearly double for those reporting high amounts
of sedentary time (Biswas et al. 2015). Although few adverse effects or diabetic complications
resulting from exercise have been reported, being watchful for acute postexercise
hypoglycemia and transient hyperglycemia is prudent (Colberg et al. 2010).
Research that associates physical activity with weight loss, fat loss, and glycemic control
suggests that regular physical activity in accordance with the recommended guidelines reduces
one’s risk of developing type 2 diabetes (Colberg et al. 2010). In a small sample of overweight
and obese participants, an intensive 6 mo nonrandomized lifestyle intervention consisting of
46
exercise and behavioral weight loss counseling reduced baseline HbA1C values (6.8 ± 0.2% to
6.2 ± 0.3%), consequently precluding the need for medications to reduce blood glucose levels.
Numerous other aspects (e.g., insulin levels, insulin resistance, blood pressure, body mass,
body composition) were also favorably affected (Ades et al. 2015). The frequency of exercise
is crucial for those with diabetes. If daily exercise is not possible, it should not be skipped 2
days in a row. Specific guidelines for prescribing exercise programs for people who have type
1 and type 2 diabetes are available elsewhere (American College of Sports Medicine 2018).
OBESITY AND OVERWEIGHT

Adult overweight and obesity are classified using the body mass index (BMI) (BMI = weight
[kg] / height squared [m ]). According to traditional BMI values, individuals ≥20 yr of age
2
with a BMI between 25 and 29.9 kg/m are classified as overweight; those with a BMI of 30
2
kg/m or more are classified as obese (Smith and Smith 2016). As the result of research on
2
people from various population subgroups, more conservative BMI cut-points for identifying
overweight (23-24.9 kg/m ) and obesity (≥25 kg/m ) have been identified for Asians and
2 2
South Asians (Seidell and Halberstadt 2015). Consequently, as noted by Seidell and
Halberstadt (2015), the prevalence of obesity in the world may be understated because many
Asians would be erroneously classified based on BMI. Although BMI has utility as a simple
index of obesity, it cannot account for relative fatness, and including some additional
determination or estimation of abdominal fat distribution is recommended for understanding
actual health risk (Seidell and Halberstadt 2015). The World Health Organization (2012b)
defines overweight and obesity as having abnormal or excessive fat accumulation that may impair
health. Regardless, overweight and obesity ranks as the fifth leading risk factor for death
worldwide.
More than 2.1 billion people worldwide are classified as being overweight or obese (Smith
and Smith 2016). Globally, more than 1 in 3 adults (≥18 yr) is overweight, and more than 1
in every 10 adults is obese (World Health Organization 2016b). The countries in the World
Health Organization’s Region of the Americas have the highest prevalence of obesity, while
those countries categorized into the South-East Asian Region have the lowest (World Health
Organization 2016b). In England, fairly equal percentages of men (24%) and women (27%)
were categorized as obese based on BMI in 2014 (NHS Digital 2014). Self-reported heights
and weights for the 48,000 Canadian adults responding to the Canadian Community Health
Survey in 2012 were used to calculate BMI for the younger (age 30-59 yr) and older (age 60-
80+ yr) age groups. Nearly 55% of the younger and 60% of the older group were overweight
47
or obese (Cohen, Baker, and Ardern 2016). In 2014, China surpassed all other countries for
adult obesity, with their obese men and women representing 16.3% and 12.4% of the world’s
sex-specific obesity prevalence; the United States ranked second for both sexes (men: 15.7%;
women: 12.3%) (NCD Risk Factor Collaboration 2017). For a detailed report of changes in
global BMI levels between 1975 and 2014 based on data from about 99% of the world’s
population, see the work of the NCD Risk Factor Collaboration group (2017).
In the United States, approximately 35% of adults are classified as obese, and one of every
three children and adolescents falls into the overweight or obese categories (Smith and Smith
2016). The age-adjusted prevalence of obesity for American men is approximately 35% for
whites and 12.6% for Asians, respectively; the obesity prevalence is approximately 38% for
non-Hispanic black and Hispanic men. For American women, the age-adjusted prevalence of
obesity based on BMI is 40.4%, 46.9%, 57.2%, and 12.4%, respectively, for white, Hispanic,
black, and Asian women. For those having a BMI in the class 3 obesity range (≥40 kg/m ), 2
the prevalence for both men and women across the four racial and ethnic groups ranged
between 5.5% and 9.9%, with the exception being 16.8% for black women (Flegal et al.
2016). Asian adults in the United States continue to have a much lower prevalence of obesity
compared with whites, blacks, and Hispanics (Flegal et al. 2016).
Childhood obesity (≥95th percentile for sex and age) is also a global problem (see chapter
9). Overweight adolescents have a 70% chance of becoming overweight adults; this increases
to 80% if one or both parents are overweight or obese (American Heart Association 2012). In
England, 33% of boys and 35% of girls, ages 2 to 15 yr, were either overweight or obese
(British Heart Foundation 2006). Similarly, in the United States, the prevalence of
overweight and obesity in children and adolescents, ages 2 to 19 yr, was approximately 33% in
2014, with 17.2% being classified as obese (American Heart Association 2017). That year’s
prevalence of obesity in children increased with each age group and ranged from 9.4%
(preschool children 2-5 yr) to 20.6% for adolescents (12-19 yr); the prevalence was 17.4% for
grade school–aged children (American Heart Association 2017). The World Health
Organization (2018b) reported that approximately 41 million children (0 to 5 yr) globally are
either overweight or obese, and nearly 340 million children (5 – 19 yr) are overweight or
obese). Table 1.3 summarizes factors associated with increased risk of obesity.
Excess body weight and fatness pose a threat to both the quality and duration of one’s life.
A rare longitudinal study spanning 40 yr tracked over 900 men to document changes in BMI
and cardiometabolic outcomes (Xian et al. 2017). BMI trajectories were modeled based on
assessments at ages 20, 40, 56, and 62 yr. Compared with the men who were normal weight
48
in their 20s but attained an overweight BMI at age 62, those having normal-weight BMIs at
baseline and ending with BMIs in the obese range (normal-obese) had significantly greater
risks of hypertension, diabetes, dyslipidemia, and inflammation; the same is true for the men
having baseline BMI values in the overweight range and entering the obesity level by age 40
and attaining the highest level of obesity (≥40 kg/m ) at age 62 (overweight-obese level 3).
2
However, the overweight-obese level 3 group had more than three times the risk of
hypertension, double the risk of inflammation, and a 60% higher risk of diabetes compared
with the normal-obese group. Interestingly, there were no differences in the three groups for
ischemic heart disease.
Although obesity is strongly associated with CHD risk factors such as hypertension,
glucose intolerance, and hyperlipidemia, the contribution of obesity to CHD appears to be
independent of the influence of obesity on these risk factors. Interestingly, an obesity paradox
has been identified; paradoxically and counterintuitively, when investigating the short- and
long-term prognosis for cardiovascular diseases, such as hypertension, atrial fibrillation, and
heart failure, prognosis is improved for those who are overweight or mildly obese as compared
with leaner clients (Lavie et al. 2014). For a comprehensive review of the effects of obesity on
cardiac performance, cardiac remodeling, aerobic fitness level, and the obesity paradox, see
the work of Lavie and colleagues (2014).
Obesity, the fifth leading cause of death, may be caused by genetic and environmental
factors as well as gut biome. Although studies suggest that genetic factors contribute to some
of the variation in body fatness, there has been no substantial change in the genotype of the
American population since the 1960s (Hill and Melanson 1999). Nevertheless, in terms of
prevalence, obesity varies across ethnic groups. Obesity clusters within families have been
reported, as have hereditability estimates. Genome-wide association studies (GWASs) are
now under way, and upwards of 90 possible areas of genetic variation associated with obesity
and BMI have been identified (Chen et al. 2017). Without any doubt, our environment and
culture are additional key contributors to the increases being seen in the rates of obesity. In
addition to the countless calorically dense food options we have and technological
advancements that reduce energy expenditure through physical activity and manual labor, we
are exposed daily to innumerable chemical compounds (e.g., pesticides, personal and home
care products, food additives, industrial waste) that promote obesity through their
interference with the endocrine system and metabolic pathway functions (Regnier and Sargis
2014).
As an exercise specialist, you play an important role in combating the obesity-related
49
health epidemic by encouraging a physically active lifestyle, planning scientifically sound
exercise programs, and consulting with your clients and trained nutrition professionals to
formulate appropriate diets. Restricting caloric intake and increasing caloric expenditure
through physical activity and exercise are effective ways of reducing body weight and fatness
while normalizing blood pressure and blood lipid profiles.
METABOLIC SYNDROME
Metabolic syndrome (MetS) refers to a combination of CVD risk factors associated with
hypertension, dyslipidemia, insulin resistance, and abdominal obesity. According to clinical
criteria adopted by the National Cholesterol Education Program (2001), individuals with
three or more CVD risk factors are classified as having metabolic syndrome (see table 1.4).
Although there is some overlap, these criteria vary among different organizations such as the
International Diabetes Federation (IDF), World Health Organization (WHO), European
Group for the Study of Insulin Resistance (EGIR), American Association of Clinical
Endocrinology (AACE), and American Heart Association/National Heart, Lung, and Blood
Institute (AHA/NHLBI). A side-by-side comparison of similarities and differences in
criteria is available in the article by O’Neill and O’Driscoll (2015). Body mass index is an
acceptable criterion according to the World Health Organization; however, all of the other
organizations use waist circumference as the reference for abdominal obesity. Sex- and
ethnic-specific references for the waist circumference criteria are also now defined (O’Neill
and O’Driscoll 2015). Alberti and colleagues (2009) present extensive information regarding
the history of metabolic syndrome and the ongoing efforts of major organizations to reach a
consensus on a single set of criteria. Likewise, Steinberger and associates (2009) highlight
similar issues for determining metabolic syndrome in children and adolescents.
Table 1.4 Risk Factors for Metabolic Syndrome
Risk factor Risk criteria

>102 cm (>40 in.) for men
Waist circumference
>88 cm (>35 in.) for women
≥130 mmHg (systolic BP) or

Blood pressure (BP)
≥85 mmHg (diastolic BP) or both
Fasting blood glucose ≥110 mg·dl −1 or ≥6.1 mmol·L−1

Triglycerides
≥150 mg·dl −1 or ≥1.6 mmol·L−1
<40 mg·dl −1 or <1.04 mmol·L−1 for men

HDL-C
<50 mg·dl −1 or <1.29 mmol·L−1 for women

Note: Metabolic syndrome is defined as three or more risk factors.
Data from National Cholesterol Education Program 2001.
50
Data reviewed by O’Neill and O’Driscoll (2015) indicate that approximately 34% of the
men and 35% of the women (≥20 yr) in the United States met the National Cholesterol
Education Program’s Adult Treatment Panel III (NCEP-ATPIII) criteria for metabolic
syndrome, as did 17% of the men and 19% of the women of similar age living in India.
O’Neill and O’Driscoll also present results from numerous studies of adults from Australia,
China, Denmark, Ireland, and South Korea. By far, the prevalence of metabolic syndrome in
adults is higher in adults from the United States, but disparate age ranges and sample sizes
across the groups may interfere with further comparisons. Of interest, for the listed countries
having prevalences identified by both NCEP-ATPIII and IDF criteria, the latter’s prevalence
is consistently higher for both sexes. Therefore, until a uniform definition of metabolic
syndrome is agreed upon, it is possible that its global prevalence may be misestimated.
Other common study findings include that the prevalence of MetS increases with age and
that physical inactivity plus unhealthy diet are key underlying factors. Metabolic syndrome
increases the risk of stroke (by two- to fourfold), CVD (by threefold), myocardial infarction
(by three- to fourfold), and type 2 diabetes (by fivefold) (Kaur 2014). Older adults (≥60 yr)
with MetS spend a larger percentage of time being sedentary and have longer bouts of being
sedentary compared with those without a MetS diagnosis (Bankoski et al. 2011). Publications
from genome-wide association studies focusing on MetS are growing in number and indicate
that a combination of genes may underlie the linkages of independent factors (e.g., visceral
adipose tissue, insulin resistance, hypertension) and the MetS condition (O’Neill and
O’Driscoll 2015).
Age and BMI directly relate to metabolic syndrome (National Cholesterol Education
Program 2001). The prevalence of MetS is higher (>40%) for older (>60 yr) adults than for
younger (20-29 yr) adults (7%). Also, the prevalence of MetS is much higher for obese (BMI
>30 kg/m ) individuals (~50%) than for normal weight (BMI ≤25 kg/m ) individuals (6.2%).
2 2
Lifestyle must be modified in order to manage metabolic syndrome. The combination of

healthy nutrition and increased physical activity is an effective way to increase HDL-C and to
reduce blood pressure, body weight, waist circumference, triglycerides, and blood glucose
levels.
CANCER
Cancer is a leading cause of death worldwide; it is also the second leading cause of death in
the United States behind heart disease for adults and accidents for children in the age range
51
of 1 to 14 yr (Siegel, Miller, and Jemal 2016). A systematic analysis of global cancer incidence
indicates that cancer accounted for 8.7 million deaths in 2015 (Fitzmaurice et al. 2017).
There is a sex-specific difference in the odds of developing cancer in a lifetime: one in three
for men and one in four for women (Fitzmaurice et al. 2017). Siegel and colleagues (2016)
estimated the number of cancer deaths in the United States; three of their top four estimates
—for lung and bronchus, colorectal, and pancreatic cancer—are identical for men and
women. The type of cancer second on the list is prostate for men and breast for women.
Fortunately, the death rates for lung, breast, prostate, and colorectal cancers have decreased
over the past 20 yr, with the decreases related to early detection and treatment as well as
public awareness campaigns (Siegel, Miller, and Jemal 2016). The most common types of
cancer vary by geographical region (Fitzmaurice et al. 2017; Siegel, Miller, and Jemal 2016)
and for developing as opposed to developed countries (Fitzmaurice et al. 2017). The main
risk factors for cancer are tobacco and alcohol use, unhealthy diet, physical inactivity, and
infection-related risk factors, such as hepatitis and human papilloma virus (World Health
Organization 2018a).
Globally, physical inactivity is one of the primary prevention targets for many chronic
conditions; physical inactivity is a modifiable risk factor associated with increased risk of
breast, colon, and endometrial cancers (Leitzmann et al. 2015). Specifically, physical
inactivity is believed to cause 10% of colon and 9% of breast cancer cases in Europe; on the
other hand, the risk of colon cancer decreases with increasing levels of physical activity
(Leitzmann et al. 2015). Moore and colleagues (2016) investigated the association of leisure-
time physical activity (LTPA) and multiple types of cancer in over 1.4 million people. They
found that the highest levels of LTPA (90th percentile) were associated with a lower risk for
13 of the 26 cancers of interest. For 7 of those cancers, LTPA reduced the risk by at least
20% (Moore et al. 2016). Strong evidence also shows that physically active people with a
cancer diagnosis may have higher survival rates than do inactive cancer patients.
Although the mechanisms through which physical activity reduces the risk of several
cancers are not fully elucidated at this time, attention is being focused on steroid hormones,
insulin resistance, growth factors, immune system function, and adipokines as well as body
composition (Leitzmann et al. 2015). The American Cancer Society’s 2017 guidelines for
reducing the risk of cancer through diet and physically active lifestyles recommend that adults
engage in 150 min/wk of moderate-intensity physical activity, 75 min/wk of vigorous-
intensity physical activity, or an equivalent combination thereof beyond the normal activities
of daily living. Children and adolescents are encouraged to engage in moderate or vigorous
52
physical activity at least 60 min each day (American Cancer Society 2017). Additionally,
maintaining a healthy body weight may be important for reducing cancer risk; 4.5 million
cancer deaths worldwide in 2013 were attributed to being overweight or obese (Lauby-
Secretan et al. 2016). Lauby-Secretan and associates (2016) concluded that lower body fat
levels reduce the risk for the majority of cancers, although research on intentional weight loss
to reduce cancer risk is lacking in humans. Studying associations between physical activity
levels and 26 types of cancer, Moore and colleagues (2016) reported that the associations for
most of the cancers they studied are independent of BMI.
MUSCULOSKELETAL DISEASES AND DISORDERS

Diseases and disorders of the musculoskeletal system, such as osteoporosis, osteoarthritis,
bone fractures, connective tissue tears, and low back syndrome, are also related to physical
inactivity and a sedentary lifestyle. Osteoporosis is a disease characterized by the loss of bone
mineral content and bone mineral density due to factors such as aging, amenorrhea,
malnutrition, menopause, and physical inactivity (see table 1.3 for osteoporosis risk factors).
It is reported to affect approximately 200 million people worldwide (Pisani et al. 2016), 54
million of whom live in the United States (National Osteoporosis Foundation 2017).
Osteoporosis usually remains silent until a fracture is sustained; consequently, it is becoming a
major health issue for both men and women over the age of 50 (Willson et al. 2015)—with
an osteoporotic fracture occurring every 3 sec worldwide—and has an associated high
socioeconomic impact.
Although osteoporotic fractures may occur in any bone, a hip fracture is recognized as a
surrogate measure of the health care burden and expense due to osteoporosis, especially for
men and women ≥50 yr. Wrist fractures precede the most common osteoporotic fracture,
vertebral fractures. For women, the risk of subsequent fractures in the hips and the
extremities is also associated with a prior wrist fracture; however, this has been studied more
internationally than in the United States (Crandall et al. 2015). Nevertheless, hip fractures
are the most devastating (International Osteoporosis Foundation 2015). In a study of hip
fractures in 63 countries around the world, Kanis and colleagues (2012) noted the highest
incidence rates per 100,000 women in Denmark, Norway, and Sweden. The lowest are in
Nigeria and South Africa. Age-standardized incidence rates of hip fractures in the men are
highest in Denmark but lowest in Ecuador (Kanis et al. 2012).
Osteoporosis affects an estimated 75 million people in Europe, the United States, and
Japan. Close to 12 million U.S. adults over the age of 50 have osteoporosis (Pisani et al.
53
2016). An estimated 1.5 million osteoporotic fractures occur each year in the United States,
with the majority being sustained by postmenopausal women (Black and Rosen 2016). Nine
percent of American men and women over the age of 50 have osteoporosis at either the hip
(femur) or spine, but the prevalence of osteoporosis varies with age, sex, and ethnicity.
Women continue to have a higher prevalence compared with men, as do older adults
compared with younger adults. Compared with non-Hispanic whites, Mexican-Americans
have a higher risk of osteoporosis or low bone mass, and non-Hispanic blacks have a lower
risk (Looker et al. 2012).
Individuals with osteoporosis have values for bone mineral density (BMD) that are more
than 2.5 standard deviations below the mean value for young adults. Osteopenia, or low bone
mineral mass, is a precursor to osteoporosis. More than one of every two adults aged 50 or
older has either osteoporosis or osteopenia (National Osteoporosis Foundation 2004). A
racial difference in the prevalence of osteoporosis is evident, with African-Americans having
the lowest and Asian-Americans the highest. Regardless, women have a higher prevalence of
osteoporosis than do men, and this difference is especially evident with increasing age
(Wright et al. 2017).
Kanis and colleagues (2005) developed a free online tool, called FRAX, to identify an
individual’s 10 yr risk of developing osteoporosis and experiencing a hip fracture. FRAX can
be accessed at www.shef.ac.uk/FRAX. To use this tool, the client answers 12 questions about
age, height, weight, prior fracture history, parental history of hip fracture, smoking,
rheumatoid arthritis, alcohol consumption, and long-term use of glucocorticoids. If available,
the bone mineral density of the femoral neck may be included to better refine the accuracy of
these estimations, especially for women. However, the World Health Organization based its
BMD T-score criterion at the femoral neck on a reference group of young women so that
criterion is not directly applicable for men. The combination of FRAX score and qualifying
fracture history is better at identifying osteoporosis in men over the age of 50 (Wright et al.
2017). The FRAX methodology is widely used around the world and has become integral to
the formation of intervention guidelines. The United States created new thresholds for the
purpose of guiding treatment plans for adults ≥40 yr with low bone mass.
A public-private partnership (National Bone Health Alliance, or NBHA) in the United
States spawned a working group tasked with expanding the clinical criteria for diagnosing
osteoporosis. Data from the 2005-2008 NHANES project formed the basis of the working
group’s efforts. Information from the predominantly Caucasian subjects in the 50+ yr age
group resulted in a prevalence of osteoporosis that was lower than for those in the 80+ yr
54
group (respectively, men: 16% and 46.3%; women: 29.9% and 88.1%) (Wright et al. 2017).
The more conservative NBHA definition is based on a man ≥50 yr or a postmenopausal
woman satisfying one of three criteria: traditional definition of low BMD at the hip or
lumbar spine, a past site-specific low trauma fracture, or a FRAX score ≥ cut-point values for
traditional intervention for low BMD. Compared with the corresponding prevalence of
osteoporosis diagnoses based solely on low BMD or FRAX scores in the study by Wright and
colleagues (2017), the prevalence of osteoporosis by NBHA criteria was higher.
Adequate calcium intake, vitamin D intake, and regular physical activity (especially of the
weight-bearing modalities) help counteract age-related bone loss; however, there may be sex-
specific dose-response differences, especially in terms of dietary supplementation (Willson et
al. 2015). Epidemiological studies show that the incidence of bone fracture is lower in those
with higher levels of physical activity. According to a recent update (Daly 2017), walking, by
itself, confers little if any effect on preventing muscle or bone loss. Likewise, Watson and
colleagues (2015) comment on the continued paucity of evidence that moderate-intensity
exercise is beneficial as an osteoporosis treatment targeting the hip and low spine. For
example, in a group of middle-aged Chinese adults, 12 wk of moderate-intensity exercise (tai
chi or self-paced walking) produced no significant change in BMD as compared to the
control group (Hui et al. 2015).
Vigorous-intensity activity, on the other hand, is associated with changes at the femoral
neck of 70 yr olds, whereas light- to moderate-intensity activity strengthens the tibia
(Johansson, Nordström, and Nordström 2015). According to preliminary results from a
sample of postmenopausal women with low to very low bone mass, a supervised 8 mo (30
min/day, 2 days/wk) high-intensity progressive resistance training program targeting the
skeletal system results in statistically significant and injury-free improvements in posture,
BMD, and functional performance as compared with the group doing home-based exercises
(Watson et al. 2015). Similarly, individualized high-velocity progressive resistance training
routines appear to best optimize a spectrum of musculoskeletal and functional outcomes; for
full benefit, the routines should include variable moderate and load-bearing exercises as well
as challenging activities for mobility and balance (Daly 2017). The ACSM (2018) highlights
the frequency, intensity, types (styles), sets, repetitions, and progression recommendations as
deemed appropriate given an individual’s age, medical history, resistance training history, and
goals (see chapter 7).
Peak bone mass is developed during childhood and adolescence, with 50% of the skeleton
laid down during the teenage years (Gordon et al. 2017). Consequently, peak bone mass is a
55
major factor associated with the risk of osteoporosis in the adult years (Mitchell et al. 2016).
Bone mass is higher in physically active children than in less active children. Given that
exercise-induced gains in bone mass during childhood and adolescence are maintained into
adulthood, the ACSM supports the application of adult resistance training guidelines for
children and adolescents who receive proper instruction and supervision (ACSM 2018). The
resistance training that children and adolescents receive is to be counted toward the
recommended target of 60+ min/day of MVPA.
Low back pain afflicts millions of people each year and is considered a major health issue
in many countries. It is one of the top reasons people miss work and restrict their activities
(Patrick, Emanski, and Knaub 2014). From 60 to 80% of adults are expected to experience
low back pain at some point (Gordon and Bloxham 2016). Low back pain is also reported by
adolescent athletes (Schmidt et al. 2014). Some low back problems are produced by muscular
weakness or imbalance caused by a lack of physical activity (see table 1.3). For physically
active people such as adolescent athletes, low back pain may be brought on by frequent
bending at the waist, twisting, accommodating loads carried high on the body, heavy and
repeated lifting, and holding awkward postures (Schmidt et al. 2014).
If the muscles are not strong enough to support the vertebral column in proper alignment,
poor posture results and low back pain develops. Excessive weight, poor flexibility, and
improper lifting habits also contribute to low back problems. By themselves, aerobic, muscle-
strengthening, and flexibility programs have been shown to improve nonspecific chronic low
back pain. However, research on combinations of the three exercise modalities is lacking
(Gordon and Bloxham 2016).
Because the origin of low back problems is often functional rather than structural, the
problem can be corrected through an exercise program that develops strength and flexibility
in the appropriate muscle groups. Also, people who remain physically active throughout life
retain more bone, ligament, and tendon strength, making them less prone to bone fractures
and connective tissue tears (McGill 2016). Physically active people also have thicker discs
between their lumbar vertebrae. This may prevent or delay the onset of degenerative disc
disorders, which can also result in low back pain (Teichtahl et al. 2015).
AGING
A sedentary lifestyle and lack of adequate physical activity reduce life expectancy by
predisposing the individual to aging-related diseases and by influencing the aging process
itself. With aging, a progressive loss of physiological and metabolic functions occurs;
56
however, biological aging may differ considerably among individuals because of variability in
genetic and environmental factors that affect oxidative stress and inflammation. Telomeres
are repeated DNA sequences that determine the structure and function of chromosomes.
With aging and diseases associated with increased oxidative stress (e.g., CHD, diabetes
mellitus, osteoporosis, and heart failure), telomere length decreases. Mechanisms underlying
the relationship between physical activity and telomere length in humans are yet to be firmly
established, but related work with rodent models is under way.
A study of normal, healthy twins reported that the telomere length of leukocytes is
positively associated with physical activity levels during leisure time. The longer telomere
length observed in more physically active individuals could not be explained by age, gender,
body mass index, smoking, socioeconomic status, and physical activity at work (Cherkas et al.
2008). Similarly, postmenopausal women who engaged in a combination of aerobic and
resistance exercise (≥60 min/day, 3 days/wk for >1 yr) had longer telomeres than their
sedentary counterparts (Kim et al. 2012). A direct dose-response relationship between the
number of movement-based behaviors engaged in and telomere length has been observed in a
national sample of adults in the United States (Loprinzi, Loenneke, and Blackburn 2015).
Participating in ≥300 min/wk of physical activity was positively related to longer telomere
length and may protect the telomeres against shortening. Adding 1 hr/wk of vigorous LTPA
seems to increase telomere length (Ogawa et al. 2017). The observations by the Loprinzi and
Ogawa research groups support earlier work by Richards and associates (2008), who noted
that the telomere length of leukocytes is inversely related to plasma homocysteine and C-
reactive protein levels; both are known markers of inflammation and cardiovascular risk.
Although additional long-term prospective studies are needed to fully understand the
antiaging effects of regular exercise, these findings suggest that exercise scientists should
promote the potential benefits of leisure-time physical activity in retarding the aging process
and diminishing the risk of aging-related diseases. Early indications about the relationship
between telomere length and movement-based behavior counts highlight that 40 to 64 yr olds
may be a very important target for physical activity promotion strategies encouraging regular
participation in multiple active behaviors as they transition from middle age to older adults
(Loprinzi, Loenneke, and Blackburn 2015).
Regular physical activity is believed to further promote healthy aging and longevity
through autophagy. This process occurs within the cytosol of cells and provides a means
through which damaged proteins and organelles are sequestered, reduced to usable
components, and recycled (Zampieri et al. 2015). As a result, cellular components are
57
maintained in good working order, and the cytosol is relatively free of deleterious debris (i.e.,
misfolded proteins, damaged mitochondria). This decreases the effects of aging on skeletal
muscle (Barbieri et al. 2015) and the brain (Garatachea et al. 2015). As noted in the review
by Garatachea and associates (2015), autophagy is upregulated by aerobic exercise in rodent
models. Although similar research with human subjects is limited, evidence points to
improved autophagy-related processes for lifelong exercisers, which, in turn, promote healthy
aging.
COGNITIVE PERFORMANCE
As longevity increases, aging adults face the distinct possibility of eventually experiencing a
notable decline in cognitive function. Some of the impairments affect immediate and delayed
recall, visual attention, psychomotor speed, problem solving, and reasoning. Brain-training
games and pastimes are growing in number and are marketed toward senior citizens, but they
are typically performed in a seated and, hence, sedentary manner. Exercise and time spent
being physically active may protect against cognitive decline and the onset of dementia.
Likewise, higher levels of aerobic fitness help preserve or slow the rate of decline in brain and
gray matter volume, which decreases age-related deterioration of key regions in the brain.
After controlling for vascular risk factors, relationships have been found between LTPA
and cognitive function. Little to no LTPA is associated with diminished cognitive function
and worsening scores over time (Willey et al. 2016). Northey and colleagues (2017) analyzed
data from exercise-related studies conducted with adults older than 50 yr. They concluded
that the cognitive function of older adults, regardless of their initial cognitive status,
improved as a result of the exercise interventions. Summarily, their recommendation is that
older adults engage in moderate- to vigorous-intensity exercise for no less than 45 min/day
on as many days of the week as possible (Northey et al. 2017). Higher cognitive function test
scores have been recorded for seniors who engage in higher levels of moderate- to vigorous-
intensity physical activity (Steinberg et al. 2015). Increased sedentary time, with the exception
of computer time, contributes to lower cognitive scores. From their sample of 125 adults
ranging in age from 65 to 95 yr, Steinberg’s team found the average sedentary time and time
in MVPA were 48 hr/wk and 5 hr/wk, respectively. The latter satisfied the ACSM (2018)
recommendation for MVPA per week. Results from a study investigating the postexercise
effect of exercise volume on the aspect of cognitive function known as executive function
(deals with problem solving, reasoning, working memory, task flexibility) also support the
ACSM recommendation (Tsukamoto et al. 2017). For their sample of predominantly young
58
male adults, the higher exercise volume based on moderate-intensity cycling improved
postexercise executive function for a longer duration than did a similar exercise volume based
on low-intensity cycling.
Exercise increases blood flow to the working muscles and brain in accordance with the
exercise intensity. Parallels in brain blood flow, cardiac output, and oxygen consumption are
known to occur. Blood flow to specific regions of the brain is believed to be associated with
the areas that monitor and adjust the neural networks required to produce the appropriate
level of physical work. Therefore, the mechanisms by which exercise and regular physical
activity protect the cardiovascular system may be similar to those protecting the brain. For
more in-depth information on this, see the review by Barnes in Advances in Physiology
Education (2015).
There may be no one best exercise modality for preserving cognitive function as we age.
Both aerobic and resistance training promote beneficial cardiorespiratory changes; intuitively,
they would likewise promote beneficial changes in the brain. As reported by Northey and
colleagues (2017), exercise modalities having similar efficacy in improving the cognitive
function of adults ≥50 yr are tai chi, aerobic, resistance, and multicomponent training. A
multimodal routine promoting variety, numerous muscle groups, challenges to balance and
coordination, and intellectual stimulation may prove optimal; however, that research is sparse.
EXERCISE AS MEDICINE
As is evident in the scientific and lay literature, a physically active lifestyle that meets or
exceeds the minimum recommendations established in 2008 confers numerous health-related
benefits (Eijsvogels and Thompson 2015; Lundqvist et al. 2017). Even initial engagement at
less than recommended levels induces health benefits (Wen et al. 2011), as does reducing the
amount of time spent sitting regardless of aerobic capacity (Benatti and Ried-Larsen 2015;
Bergouignan et al. 2016; Levine 2015; Same et al. 2016). Although it may not be possible to
completely prevent all known chronic and noncommunicable diseases, it is certainly possible
to ameliorate and delay their onset by reducing sedentary time and engaging in regular
exercise and physical activity. Physical inactivity is a better predictor of mortality from all
causes than are the former stalwarts of hypertension, diabetes, blood lipid levels, and
smoking. Improving cardiorespiratory fitness or exercise capacity fosters biological
mechanisms that favorably influence blood glucose and lipids, insulin sensitivity, body
composition, inflammation, and cognitive function (Coombes et al. 2015). Likewise,
sedentary behavior, defined as having an energy expenditure ≤1.5 METs, is believed to be a
59
CVD risk factor that is independent of physical activity levels (Same et al. 2016).
A recent prospective observational investigation (Lundqvist et al. 2017) of Swedish
primary care providers prescribing individualized physical activity treatment plans for inactive
adult patients (27-85 yr) with at least one metabolic risk factor gives testament to the benefits
of regular, individualized physical activity and exercise plans. At the 6 mo follow-up and in
comparison with baseline, physical activity increased to recommended levels (30-44 min of
moderate-intensity walking, 2-5 days/wk); six metabolic risk factors and health-related
quality-of-life factors improved significantly, as did vitality, mental health, and social
function (Lundqvist et al. 2017). The participating health care professionals received
standardized training on the effects of physical activity in advance of the study, and
individualized patient follow-ups (i.e., office visit, phone call) were offered. These follow-ups
occurred, on average, once or twice over the course of the 6 mo period; this low frequency of
provider support is indicative of the minimal impact on the primary care provider’s schedule.
Sadly, most medical school programs in the United States do not include training about the
benefits of physical activity, basics of sound individualized exercise program prescriptions,
and use of exercise as a primary prevention of many NCDs (Cardinal et al. 2015).
The ACSM created the Exercise is Medicine (EIM) initiative with a focus on getting
primary care physicians and similar health care providers to include physical activity in
treatment plans prescribed for their patients. Coombes and colleagues (2015) describe a six-
prong approach. Generally speaking, this approach revolves around increasing awareness of
the importance of a physically active lifestyle, inquiring about physical activity levels at each
visit to the doctor’s office, making proper referrals of patients to qualified exercise and fitness
professionals, changing public and private policy to comprehensively support physically active
lifestyles, alerting patients that their physical activity patterns will be discussed during office
visits, and becoming role models for physically active lifestyles.
The EIM initiative (see http://exerciseismedicine.org) began in the United States and is
growing as a global initiative. For information on related initiatives in participating global
regions and countries, see www.exerciseismedicine.org/support_page.php/regional-updates.
Opportunities abound for students and professionals in exercise science to participate in
initiatives to expand public awareness and influence local public and private policy. One such
opportunity is through the EIM Ambassador program
(http://www.exerciseismedicine.org/support_page.php/eim-ambassadors/).
Key Points
60
Fewer than half (49.5%) of all American adults meet the recommended amount of either the aerobic or
muscle-strengthening activity needed for health benefits.
The majority (79.3%) of American adults fail to meet the full set of physical activity guidelines for health.
Exercise deficit disorder is a term describing children who do not participate in 60 min of moderate- to
vigorous-intensity physical activity every day.
Major chronic noncommunicable diseases associated with a lack of physical activity are CVDs, diabetes,
obesity, musculoskeletal disorders, and cognitive disorders.
Cardiovascular diseases are responsible for 25% of all deaths in the United States and nearly equal sex-
specific percentages (36% for men, 35% for women) of all deaths before age 75 yr in Europe.
The positive risk factors for CHD are age, family history, dyslipidemia, hypertension, tobacco use,
prediabetes or glucose intolerance, obesity, and physical inactivity.
Sedentary behavior and cardiorespiratory fitness are independent risk factors for cardiovascular and
cardiometabolic disorders.
The prevalence of obesity is on the rise, especially in developed countries; in the United States, 35% of
adults and 30% of adolescents and children are overweight or obese.
BMI is used to identify and classify individuals as overweight or obese. Cutoff values for obesity, however,
vary depending on ethnicity.
Individuals with three or more cardiovascular disease risk factors are said to have metabolic syndrome.
Osteoporosis and low back syndrome are musculoskeletal disorders afflicting millions of people each year.
FRAX is an online tool that can be used to assess your client’s 10 yr risk of developing osteoporosis and
experiencing a hip fracture.
Moderate-intensity walking is beneficial for aerobic fitness benefits, but it may not help prevent fractures of
the hip and lumbar spine.
To benefit health and prevent disease, every adult should accumulate a minimum of 150 min/wk of
moderate-intensity physical activity or 75 min/wk of vigorous-intensity physical activity. For additional
health benefits, increase physical activity to 300 min/wk and 150 min/wk, respectively, for moderate- and
vigorous-intensity exercise.
Key Terms
Learn the definition for each of the following key terms. Definitions of key terms can be found in the glossary.
angina pectoris
atherosclerosis
autophagy
body mass index (BMI)
cardiovascular disease (CVD)
61
cholesterol
chylomicron
coronary heart disease (CHD)
dose-response relationship
dyslipidemia
elevated blood pressure
exercise deficit disorder (EDD)
genome-wide association studies (GWAS)
HbA1c
HDL-cholesterol (HDL-C)
high-density lipoprotein (HDL)
hypercholesterolemia
hyperlipidemia
hypertension
LDL-cholesterol (LDL-C)
lipoprotein
low back pain
low-density lipoprotein (LDL)
metabolic syndrome (MetS)
myocardial infarction
myocardial ischemia
noncommunicable diseases (NCDs)
normotensive
obesity
obesity paradox
osteopenia
osteoporosis
overweight
prediabetes
sedentarism
stage 1 hypertension
telomeres
total cholesterol (TC)
type 1 diabetes
type 2 diabetes
very low-density lipoprotein (VLDL)
Review Questions
In addition to being able to define each of the key terms just listed, test your knowledge and understanding of the
62
material by answering the following review questions.
1. What percentage of the American population does not get the recommended amount of physical activity for
health benefits?
2. What is the recommended minimum amount of daily physical activity for health?
3. Give examples of moderate-intensity physical activity.
4. What NCDs are related to physical inactivity?
5. What percentage of Americans have some form of CVD?
6. Name four types of CVD. Which is most prevalent?
7. Explain the etiology of CHD.
8. Identify the positive and negative risk factors for CHD.
9. In terms of SBP and DBP, differentiate the elevated, stage 1 hypertension, and stage 2 hypertension
categories.
10. Explain how regular physical activity affects each of the CHD risk factors as well as overall CHD risk.
11. Explain how physical inactivity differs from sedentary behavior.
12. Define obesity and overweight relative to BMI.
13. Explain the obesity paradox.
14. Define metabolic syndrome and identify its relationship to CVD.
15. Explain how regular physical activity may prevent or delay select forms of cancer and cognitive decline.
16. What types of exercise are effective for counteracting bone loss due to aging?
17. Explain how chronic adherence to moderate- to vigorous-intensity physical activity affects the aging
process.
18. Explain the relationship between physical inactivity and low back pain.
63
CHAPTER 2
Preliminary Health Screening and Risk Classification

KEY QUESTIONS
What are the major components of the health evaluation, and how is this information used to screen clients
for exercise testing and participation?
What factors do I need to focus on when evaluating the client’s medical history and lifestyle characteristics?
How will I know if my client is at risk for an adverse cardiovascular event during exercise or while being
physically active?
Should cardiovascular disease risk factors be identified as part of the preparticipation screening process?
Do all clients need a physical examination and medical clearance before beginning or progressing the
intensity of an exercise program?
What are the standards for classifying blood cholesterol levels?
How is blood pressure measured and evaluated? Are automated blood pressure devices accurate?
How is heart rate measured? Are heart rate monitors accurate?
What is an ECG, and does every client need to have one before or while taking an exercise test?
Is it safe to give a graded exercise test to all clients? When does a physician need to be present?
What are the major components of the lifestyle evaluation, and how can this information be used?
What are the purposes of informed consent?
Before assessing your client’s physical fitness profile, it is important to classify the person’s
health status and lifestyle. You will use information from the initial health and lifestyle
evaluations to screen clients for possible adverse cardiovascular events related to exercise or
physical activity. You also will use this information to identify individuals with medical
contraindications to exercise, with disease symptoms and risk factors, and with special needs.
This chapter discusses the components of a comprehensive health evaluation, including a
coronary risk factor profile, medical history questionnaire, lifestyle evaluation, and informed
consent. It also presents guidelines and standards for classifying blood cholesterol levels,
blood pressures, and disease risk, along with techniques and procedures for measuring heart
64
rate and blood pressure at rest and during exercise and for conducting a resting 12-lead
electrocardiogram (ECG).
PRELIMINARY HEALTH EVALUATION

The purposes of the health evaluation are to detect the presence or suggestion of disease and
to assess the likelihood that your client will have an unanticipated cardiovascular situation
during a bout of exercise or physical activity. The components of a comprehensive health
evaluation are listed in table 2.1. To evaluate the client’s health status, information from
questionnaires and data from clinical tests are analyzed. Minimally, for pretest health
screening of clients for exercise testing and exercise program participation, you should
administer the Physical Activity Readiness Questionnaire for Everyone (PAR-Q+),

evaluate client participation in regular exercise in the past 3 mo,
identify signs and symptoms of diseases,
analyze the coronary risk factors, and
determine if medical clearance is needed.
Step-by-step procedures for conducting a comprehensive health evaluation are listed in

Procedures for Comprehensive Pretest Health Screening.
Table 2.1 Components of a Comprehensive Health Evaluation
Component Purpose
QUESTIONNAIRES OR SCREENING FORMS
2018 PAR-Q+ To determine client’s readiness for physical activity
Signs and symptoms of disease and medical clearance To identify individuals in need of medical referral and to obtain evidence of physician approval for exercise testing and participation
Coronary risk factor analysis To determine the number of coronary heart disease risk factors for client
Medical history To review client’s past and present personal and family health history, focusing on conditions requiring medical referral and clearance
Lifestyle evaluation To obtain information about the client’s living habits
Informed consent To explain the purpose, risks, and benefits of physical fitness tests and to obtain client’s consent for participation in these tests
CLINICAL TESTS
Physical examination To detect signs and symptoms of disease
Blood chemistry profile To determine if client has normal values for selected blood values; values of blood cholesterol are used in the coronary risk factor analysis
Blood pressure assessment To determine if client is hypertensive; these values are also used in the coronary risk factor analysis
12-lead electrocardiogram To evaluate cardiac function and detect cardiac abnormalities that are contraindications to exercise
Graded exercise test To assess functional aerobic capacity and to detect cardiac abnormalities due to exercise stress
Additional laboratory tests (e.g., angiograms, echocardiograms, pulmonary tests) To provide a more in-depth assessment of client’s health status, particularly if there is known disease
QUESTIONNAIRES AND SCREENING FORMS

Appendix A provides questionnaires and forms that may be used to obtain information for
the preliminary health screening and evaluation of your clients. The client should complete
65
the PAR-Q+, medical history questionnaire, lifestyle evaluation, and informed consent form.
You will interview your client to gather information about signs and symptoms of disease,
analyze your client’s coronary heart disease (CHD) risk factors, and determine if it is
necessary for your client to obtain a medical clearance before beginning an exercise program
or progressing the existing exercise program (table 2.2).
Physical Activity Readiness Questionnaire for Everyone

The PAR-Q+ (http://eparmedx.com/; see Appendix A.1) has seven general health questions
that identify individuals who need medical clearance before starting an exercise program,
becoming more physically active, or undergoing a fitness appraisal (see appendix A.1). If
clients answer yes to any of these questions, they need to answer the follow-up questions
about their medical conditions. If clients answer yes to one or more of the follow-up
questions, recommend they seek additional information from a qualified professional before
66
changing their current activity level. Also, they should complete the ePARmed-X+
(www.eparmedx.com; see appendix A.4) on their own or with the assistance of an exercise
professional. Depending on the answers to the ePARmed-X+ survey, the client will either
receive clearance to participate or be offered suggestions on how to safely proceed until such
clearance is attained. These suggestions include getting more information by meeting with
his personal physician or a qualified exercise professional who has advanced university
training. While waiting for the meeting, the client is encouraged to engage in low-intensity
activities. Following the meeting, the client may receive clearance to proceed with his activity
aspirations while under direct supervision of a qualified exercise professional (PAR-Q+
Collaboration 2017).
PROCEDURES FOR COMPREHENSIVE PRETEST HEALTH SCREENING
Here are step-by-step procedures you should follow when conducting a comprehensive health evaluation:
Greet the client.
Explain the purpose of the health evaluation and lifestyle evaluation.
Obtain the client’s informed consent for health screening.
Administer and evaluate the PAR-Q+; refer client for medical clearance if needed.
Administer and evaluate client’s medical history, focusing on signs, symptoms, and diseases; refer client for
medical clearance if needed.
Evaluate client’s lifestyle profile.
Evaluate and classify the client’s cholesterol and lipoprotein levels if test results are available.
Measure and classify the client’s resting blood pressure and heart rate.
Assess the client’s coronary risk factors and current exercise participation history.
Evaluate the client’s blood chemistry profile if test results are available.
If so requested by the client’s physician, you may do the following:
Explain the purpose of and answer any questions about the 12-lead resting ECG and graded exercise test
(GXT).
Obtain the client’s informed consent for these tests.
Prepare the client and administer the 12-lead resting ECG.
Have a physician interpret the results of the 12-lead resting ECG.
Use the client’s disease risk classification to determine whether a maximal or submaximal GXT should be
67
administered and whether a physician needs to be present or in the immediate vicinity during this test.
Assess the client’s resting blood pressure and heart rate.
Administer the GXT.
Assess and classify the client’s functional aerobic capacity.
Medical History Questionnaire

You should require your clients to complete a comprehensive medical history questionnaire
that includes questions concerning personal and family health history (see appendix A.2). Use
the questionnaire to
examine the client’s record of personal illnesses, surgeries, and hospitalizations

(section A),
assess previous medical diagnoses and signs and symptoms of disease that have
occurred within the past year or are currently present (section B), and
analyze your client’s family history of diabetes, heart disease, stroke, and
hypertension (section C).
Also, when reviewing the medical history, you should carefully focus on conditions that
require medical referral (see Absolute and Relative Contraindications to Exercise Testing). If
any of these conditions are noted, refer your client to a physician for a physical examination
and medical clearance prior to exercise testing or starting an exercise program. Some
individuals have medical conditions and risk factors that outweigh the potential benefits of
exercise testing. You should not administer an exercise test to individuals with absolute
contraindications unless their physician orders an exercise test. Individuals with relative
contraindications may be tested if the potential benefit from exercise testing outweighs the
relative risk of testing. In some cases, individuals who are asymptomatic at rest can be tested
using low-level endpoints. It is also important to note the types of medication being used by
the client. Drugs such as digitalis, beta-blockers, bronchodilators, vasodilators, diuretics, and
insulin may alter the individual’s heart rate, blood pressure, ECG, and exercise capacity. If
your client reports a medical condition or drug that is unfamiliar to you, be certain to consult
medical references or a physician to obtain more information before conducting any exercise
tests or allowing the client to participate in an exercise program.
68
ABSOLUTE AND RELATIVE CONTRAINDICATIONS TO EXERCISE
TESTING
Absolute Contraindications
1. Acute myocardial infarction (within 2 days)
2. Ongoing unstable angina
3. Uncontrolled cardiac arrhythmias causing symptoms or hemodynamic compromise
4. Active endocarditis
5. Symptomatic severe aortic stenosis
6. Decompensated heart failure
7. Acute pulmonary embolism, pulmonary infarction, or deep vein thrombosis
8. Acute myocarditis or pericarditis
9. Acute aortic dissection
10. Physical disability that precludes safe and adequate testing
Relative Contraindications
1. Known obstructive left main coronary artery stenosis
2. Moderate to severe aortic stenosis with uncertain relation to symptoms
3. Tachydysrhythmias with uncontrolled ventricular rates
4. Acquired advanced or complete heart attack
5. Hypertrophic obstructive cardiomyopathy with severe resting gradient
6. Recent stroke or transient ischemic attack
7. Mental impairment with limited ability to cooperate
8. Resting hypertension with systolic or diastolic blood pressure >200/110 mmHg, respectively
9. Uncorrected medical conditions, such as significant anemia, important electrolyte imbalance, and
hyperthyroidism
Note: For definitions of specific medical terms, refer to the glossary.

Reprinted by permission from G.F. Fletcher, et al., “Exercise standards for testing and training: A scientific statement from the American Heart Association,” Circulation 128 (2013): 873-934.
Signs and Symptoms of Disease and Medical Clearance

As part of the pretest health screening, you should ask your clients if they have any of the
conditions or symptoms listed in appendix A.3, Risk Factors, Signs, and Symptoms of
Disease. Feel free to reproduce and use this checklist.
69
Clients with any of the signs or symptoms on the checklist should be referred to their
physician, as a signed medical clearance prior to any exercise testing or participation may be
warranted. The electronic Physical Activity Readiness Medical Examination (ePARmed-X+)
was designed for this purpose. The ePARmed-X+ is a physical activity–specific checklist (see
appendix A.4) that is used to assess and convey medical clearance for physical activity
participation or to make a referral to a medically supervised exercise program for individuals
who answered yes to one of the questions in the Physical Activity Readiness Questionnaire
for Everyone (PAR-Q+). For definitions of specific medical terms, refer to the glossary. The
ePARmed-X+ is available electronically at www.eparmedx.com.
Coronary Risk Factor Analysis

Even though this analysis has been removed from the ACSM’s (2018) health screening
process for exercise preparticipation assessment, it is still important to assess your client’s
coronary risk profile; evaluate each item in table 2.3 carefully. If your client will not or cannot
provide the information you need to in order to include or exclude a positive risk factor, then
you should include it in your total risk factor count. Guidelines for classification of blood
pressure and blood cholesterol levels in adults are presented in tables 2.4 and 2.5, respectively.
If your client’s high-density lipoprotein cholesterol (HDL-C) equals or exceeds 60 mg·dl , −1
subtract 1 from the total number of positive risk factors. This information is especially helpful
in identifying factors related to disease prevention and management (ACSM 2018).
Table 2.3 Coronary Heart Disease Risk Factors
Positive risk
Criteria
factorsa
Age Men: ≥45 yr, women: ≥55 yr
Myocardial infarction, coronary revascularization, or sudden death before 55 yr of age in father or other first-degree male relative (brother or son) or before 65 yr of age in mother or other first-
Family history
degree female relative (sister or daughter)
Cigarette smoking Current cigarette smoking, exposure to environmental smoke, or smoking cessation within previous 6 mo
Hypertension Systolic BP ≥ 130 mmHg or diastolic BP ≥ 80 mmHg, measured on two separate occasions; individual is taking antihypertensive medication.
Dyslipidemia
HDL-C < 40 mg·dl −1 or LDL-C ≥ 130 mg·dl−1; on lipid-lowering medication; use TC ≥200 mg·dl−1 if no cholesterol subfractions are available.
Diabetes
Fasting plasma glucose ≥ 126 mg·dl −1 or 2 hr oral glucose tolerance test values ≥ 200 mg·dl−1, measured on two separate occasions, or HbA1C ≥ 6.5%.
Obesity
Body mass index (BMI) ≥ 30 kg/m 2 or waist circumference > 102 cm (40 in.) for men and > 88 cm (35 in.) for women.
Physical inactivity Not participating in ≥30 min moderate-intensity physical activity on at least 3 days/wk for at least 3 mo
Negative risk
factorb
High HDL-C
Serum HDL-C ≥ 60 mg·dl −1.
aIf client cannot or will not provide a risk factor value, count it as a positive risk factor except for diabetes. For diabetes, only count a missing or unknown IFG or IGT value for adults ≥45 yr with a BMI ≥25 kg/m2 or for adults
<45 yr with a BMI ≥25 kg/m 2 and additional CVD risk factors for prediabetes.
70
bIf HDL-C ≥ 60 mg·dl−1, subtract 1 from the sum of positive risk factors.
Data from National Cholesterol Education Program 2001; Roger et al. 2012; Whelton et al. 2017.
Table 2.4 Classification of Blood Pressure for Adults 18 yr or Older
Systolic BP (mmHg) Category Diastolic BP (mmHg)

<120 Normal <80
120-129 Elevated <80
130-139 Stage 1 hypertension 80-89
≥140 Stage 2 hypertension ≥90
Note: For individuals not taking antihypertensive medication and not acutely ill. Based on average of two or more readings on two or more occasions. When systolic and diastolic pressures fall into different categories, use the
higher category for classification.
Data from Whelton et al. 2017.
Disease Risk Classification

The coronary risk factor analysis is no longer used to classify an individual’s risk of CHD.
Instead, it provides an assessment of an individual’s blood chemistry values and other factors
known to be associated with atherosclerotic CVD. Additionally, all but the risk factors for
age and family history are considered modifiable as they are usually responsive to committed
lifestyle modifications to exercise and dietary patterns. Along with your client’s exercise
participation habits, any signs or symptoms of cardiovascular, renal, or metabolic disease or a
diagnosis of cardiovascular, renal, or metabolic disease (see appendix A.3) guides decisions
regarding the need for medical clearance. Furthermore, the new ACSM (2018) screening
algorithm is instrumental in determining the recommended exercise intensity.
Another tool used to estimate the 10 yr risk of a first fatal cardiovascular event due to
atherosclerosis is the SCORE system (Conroy et al. 2003). The SCORE low-risk charts are
71
used to estimate the 10 yr risk of mortality from CVD for adults from European countries
with recent and substantial reductions in CV mortality risk. The high-risk chart was used to
calculate the 10 yr risk of individuals from European countries not having reported such
reductions. To use the chart, you must know your client’s total cholesterol (mmol·L or mg·dl
−1
), systolic blood pressure (SBP; mmHg), age (yr), smoking status, and sex.
−1
Since the initial publication of the SCORE system, most if not all European countries
have undertaken their own investigations to determine the predictive accuracy of the original
SCORE system or their own country-specific SCORE-related algorithm. By evaluating their
more recent population-specific data, countries have been able to recalibrate or validate their
specific SCORE-related algorithms given public health initiatives undertaken to reduce the
traditional modifiable risk factors (e.g., smoking, hypertension, dyslipidemia) in the
preceding decade. Among these recalibration studies, some predicted the 10 yr risk for
instances of nonfatal CVD requiring hospitalization (Jørstad et al. 2017; Panagiotakos et al.
2015) as well as fatal CVD events (Jdanov et al. 2014; Rücker et al. 2016). Others (Graversen
et al. 2016) evaluated the effect of adding additional variables (e.g., ECG abnormalities,
high-sensitivity C-reactive protein, waist-hip ratio) to their revised algorithms. Therefore,
you may want to refer to revised SCORE algorithms for the country you are interested in.
Sawano and colleagues (2016) assessed the 2003 European SCORE system for Japanese
adults to determine how well it applied to a population subgroup not represented in the
Conroy (2003) study. Although they did not use the SCORE system, Edwards, Addoh, and
Loprinzi (2016) investigated existing equations to determine their ability to predict the 10 yr
risk of a first atherosclerotic CVD event for a large sample of American adults (40 to 79 yr)
free of CVD at baseline. Another tool, the Relative Risk Chart (Perk et al. 2012) may be
used to educate younger people how, relative to their age group peers, their risk for an
atherosclerotic event is affected by lifestyle choices (smoking) and modifiable risk factors
(hypercholesterolemia, hypertension).
Lifestyle Evaluation
Planning a well-rounded physical fitness program for an individual requires that you obtain
information concerning the client’s living habits. The lifestyle assessment provides useful
information regarding the individual’s risk factor profile. Factors such as smoking, lack of
physical activity, and diets high in saturated fats or cholesterol increase the risk of CHD,
atherosclerosis, and hypertension. These factors can be used to pinpoint patterns and habits
that need modification and to assess the likelihood of the client’s adherence to the exercise
72
program. You can obtain a lifestyle profile for your clients by using either the Lifestyle
Evaluation form (appendix A.5) or the Fantastic Lifestyle Checklist (appendix A.6). The
Fantastic Lifestyle Checklist is a self-administered tool for assessing a client’s present health-
related behaviors.
Informed Consent
Before conducting any physical fitness tests or exercise programs, you should see that each
participant signs the informed consent (see appendix A.7). This form explains the purpose
and nature of each physical fitness test, any inherent risks in the testing, and the expected
benefits of these tests. The informed consent also assures your clients that test results will
remain confidential and that their participation is strictly voluntary. If your client is underage
(<18 yr), a parent or guardian must also sign the informed consent. All consent forms should
be approved by your institutional review board or legal counsel.
CLINICAL TESTS
For a comprehensive health screening, you will need to evaluate information and data
obtained from the physician’s medical examination and clinical tests. Clinical tests provide
data about your client’s blood chemistry, blood pressure, cardiopulmonary function, and
aerobic capacity.
Physical Examination
Your prospective exercise program participants may need to obtain a physical examination
and a signed medical clearance from a physician (ePARmed-X+: www.eparmedx.com or
appendix A.4) if they
have high blood pressure or a heart condition;

experience noticeable chest discomfort during physical activity or activities of daily
living;
have lost consciousness in the past 12 mo or were so dizzy they lost their balance;
have been diagnosed with a chronic medical condition besides high blood pressure
or heart disease;
are taking prescribed medications for any chronic medical condition;
have now or within the past 12 mo a bone, joint, muscle, ligament, or tendon issue
that could get worse as a result of exercise or physical activity; or
have been told by a doctor that they should perform only medically supervised
73
activity.
The physical examination should focus on signs and symptoms of CHD and should
include an evaluation of body weight, orthopedic problems, edema, acute illness, pulse rate,
cardiac regularity, blood pressure (supine, sitting, and standing), and auscultation of the
heart, lungs, and major arteries. The physical examination and medical history may reveal
signs or symptoms of CHD, particularly if accompanied by shortness of breath, chest
discomfort, leg cramps, or high blood pressure. It is recommended that clients with these
symptoms obtain a signed medical clearance (see appendix A.4) prior to exercise testing or
exercise participation.
Blood Chemistry Profile

Information obtained from a complete blood analysis is used to assess your client’s overall
health status and readiness for exercise. Table 2.6 provides normal values for selected blood
variables. If any of these values fall outside of the normal range, refer your clients to their
physician. Pay special attention to your client’s fasting blood glucose and blood lipid values.
Table 2.6 Normal Values for Selected Blood Variables
Variable Ideal or typical values

Triglycerides
<150 mg·dl −1
Total cholesterol
<200 mg·dl −1
LDL-cholesterol
<100 mg·dl −1
HDL-cholesterol
≥40 mg·dl −1
TC/HDL-cholesterol <3.5
Blood glucose 60-99 mg·dl −1
13.5-17.5 g·dl −1 (men)

Hemoglobin
11.5-15.5 g·dl −1 (women)

Hemoglobin A1c ≤6%
40%-52% (men)
Hematocrit
36%-48% (women)
Potassium
3.5-5.5 meq·dl −1
Blood urea nitrogen
4-24 mg·dl −1
Creatinine
0.3-1.4 mg·dl −1
40-190 μg·dl −1 (men)

Iron
35-180 μg·dl −1 (women)

Calcium
8.5-10.5 mg·dl −1
The National Cholesterol Education Program (NCEP) established guidelines for
74
classifying lipoprotein levels and major risk factors that modify low-density lipoprotein
cholesterol (LDL-C) treatment goals (2001). For adults 20 yr or older, the NCEP (2001)
recommends that a fasting lipoprotein profile (i.e., total cholesterol, LDL-C, HDL-C, and
triglycerides) be obtained every 5 yr. To classify your client’s lipoprotein values, use the
NCEP (2001) guidelines (see table 2.5). For nonfasting lipoprotein tests, only the total
cholesterol (TC) and HDL-C values can be evaluated. If your client’s TC is borderline high
(200-239 mg·dl ) or high (≥240 mg·dl ), and the HDL-C level is less than 40 mg·dl , a
−1 −1 −1
follow-up fasting lipoprotein test will be needed to assess LDL-C. Refer clients to their
physicians for an extensive clinical evaluation and dietary therapy if they have high (160-189
mg·dl ) or very high (≥190 mg·dl ) LDL-C values. Treatment goals for lowering LDL-C
−1 −1
depend on the number of major risk factors (exclusive of LDL-C) the client has. To
determine your client’s risk factors, focus on the following in table 2.3: cigarette smoking,
hypertension, low HDL-C, family history of premature CHD, and age (men ≥45 yr; women
≥55 yr). Table 2.7 is the NCEP’s listing of three risk categories that modify LDL-C
treatment goals.
Table 2.7 Three Risk Categories That Modify LDL-C Goals
Risk category LDL-C goal (mg·dl−1)
CHD and CHD risk equivalents a <100
Multiple (2+) risk factorsb <130
0-1 risk factor <160
aCHD risk equivalents include diabetes and atherosclerotic disease (i.e., peripheral arterial disease, abdominal aortic aneurysm, and symptomatic carotid artery disease).
bRisk factors include cigarette smoking, hypertension, low high-density lipoprotein cholesterol, family history of premature CHD, and age.
NCEP 2001.
In addition to TC and lipoproteins, you can evaluate your client’s triglyceride value and the
ratio of TC to HDL-C. Clients with triglyceride levels of ≥150 mg·dl or TC/HDL-C ratios −1
>5.0 are at higher risk for CHD.
Resting Blood Pressure

Blood pressure (BP) is a measure of the force or pressure exerted by the blood on the arteries.
The highest pressure, systolic blood pressure (SBP) reflects the pressure in the arteries during
systole of the heart when myocardial contraction forces a large volume of blood into the
arteries. Following systole, the arteries recoil and the pressure drops during diastole, or the
filling phase of the heart. Diastolic blood pressure (DBP) is the lowest pressure in the artery
during the cardiac cycle and reflects organ perfusion. The difference between the systolic and
75
diastolic BPs is known as the pulse pressure. The pulse pressure creates a pulse wave that can
be palpated at various sites in the body to determine pulse rate and to estimate BP.
Values used for classification of resting BP are presented in table 2.4. Normal BP
(normotensive) is defined as values less than 120/80 mmHg. The elevated blood pressure
category (systolic BP = 120-129 mmHg, with diastolic BP ≤80mmHg) is added to identify
individuals at risk of developing hypertension. Stage 1 hypertension is defined as a resting
SBP value between 130 and 139 mmHg and DBP between 80 and 89 mmHg; stage 2
hypertension blood pressure values are those equaling or exceeding 140/90 mmHg on two or
more occasions (Whelton et al. 2017).
Although a blood pressure value in the elevated range does not connote a disease, it
significantly increases the risk for CHD (Huang et al. 2015). Individuals with elevated blood
pressures are encouraged to modify their lifestyle in order to reduce their risk of developing
hypertension by
losing body weight if overweight;

adopting a healthy eating plan that includes a diet rich in fruits, vegetables, and
low-fat dairy products but reduced in cholesterol, saturated fat, and total fat;
restricting dietary sodium intake to no more than 2.4 g (100 mmol) per day;
engaging in aerobic physical activities at least 150 min/wk; and
limiting alcohol consumption to no more than 1 oz (29.6 ml) per day for men and
0.5 oz (14.8 ml) per day for women.
When lifestyle modifications are ineffective, pharmacological therapy may be required to

lower BP. There are numerous drugs available to treat hypertension (see James et al. 2014),
such as
diuretics to rid the body of excess salt and fluids,

beta-blockers to reduce heart rate and cardiac output,
calcium channel blockers to reduce heart contractility and dilate arteries,
direct renin inhibitors to block conversion of angiotensinogen into angiotensin-1,
potassium channel openers to hyperpolarize vascular smooth muscles and
endothelial cells,
sympathetic nerve inhibitors to prevent constriction of arterioles,
76
vasodilators to induce relaxation in smooth muscles of arterial walls, and
angiotensin-converting enzyme inhibitors to disrupt the body’s production of
angiotensin, which constricts arterioles.
Additional Clinical Tests

For individuals with known or suspected CHD, additional tests may be indicated. These may
include a resting 12-lead ECG, an angiogram, an echocardiogram, and a physician-
monitored graded exercise test. A chest X-ray, comprehensive blood chemistry, and complete
blood count may also be pertinent (ACSM 2018). For clients with known pulmonary disease,
the ACSM (2018) recommends a chest X-ray, pulmonary function tests, and specialized
pulmonary tests (e.g., blood gas analysis and oxygen saturation).
Graded Exercise Test

Ischemic heart disease (IHD) is often not detectable from the resting ECG, and
abnormalities may not appear until the individual engages in relatively strenuous exercise.
Most clinical graded exercise tests incorporate ECG monitoring and are performed in
relation to the diagnosis and evaluation of IHD. It is common for clinical GXTs to be
terminated at the onset of symptoms of IHD. Another reason for administering a GXT as
part of the health evaluation is to assess functional aerobic capacity. A low level of functional
aerobic capacity (low cardiorespiratory fitness) is now considered a strong predictor of CVD,
all-cause mortality, and mortality from certain cancers. Low functional aerobic capacity may
be a better predictor than are the other well-known modifiable risk factors such as type 2
diabetes, high blood pressure, smoking, and aberrant cholesterol levels (Ross et al. 2016).
Healthy individuals interested in learning their cardiorespiratory fitness (CRF) levels may
find graded exercise testing services available to the public through colleges and universities
that have degree-granting exercise science programs; these GXTs most often are not ordered
by a physician. Regardless, graded exercise tests should be administered only by trained,
professionally certified personnel such as exercise scientists, physicians, and nurses.
You need to be familiar with medical conditions that are absolute and relative
contraindications to exercise testing in an out-of-hospital setting (see Absolute and Relative
Contraindications to Exercise Testing). Individuals with absolute contraindications should
not be given a graded exercise test unless their condition has been stabilized or medically
treated. In cases in which the benefits outweigh the risks, individuals with relative
contraindications may perform exercise tests. These tests, however, should use low-level
77
endpoints and be administered with caution (ACSM 2018).
As noted in statements by the American Heart Association (Myers et al. 2014), maximal
exertion stress testing may be safely conducted by certified exercise specialists and allied
health professionals who are well trained and experienced in screening clients prior to testing,
monitoring exercise tests, and handling emergencies (ACSM 2018). To ascertain if direct
supervision by a physician is needed or if the supervising physician can be nearby and
immediately available while a nonphysician clinical exercise testing professional supervises the
test, consult the policies of your facility and locale. The results from graded exercise tests
provide a basis for prescription of exercise for healthy and coronary-prone individuals, as well
as for cardiopulmonary patients.
TESTING PROCEDURES FOR BLOOD PRESSURE, HEART RATE,

AND ELECTROCARDIOGRAM
One of your major responsibilities as an exercise scientist is to become proficient at measuring
and monitoring BP, heart rate, and ECGs during rest and exercise. During a graded exercise
test, you will be expected to obtain accurate and precise measurements of BP and heart rate
while the client is exercising. Because of their importance and complexity, this section is
devoted to a thorough discussion of these procedures.
MEASURING BLOOD PRESSURE

Blood pressure can be measured directly or indirectly. The gold standard is the direct
measurement of intra-arterial BP. This method is invasive and requires catheterization.
Therefore, in clinical or field settings, BP is typically measured indirectly by auscultation or
oscillometry. With advancements in technology and varying degrees of attention paid to
strictly following the standard procedures for assessing blood pressure via cuff and
stethoscope, automated blood pressure assessment is surpassing auscultation for resting blood
pressure assessment in clinical settings. To date, unfortunately, there is no gold standard
automated oscillometric device. Regardless, manual blood pressure assessment continues to
be an invaluable skill; it remains the preferred method for monitoring changes in blood
pressure during and after exercise (Sharman and LaGerche 2015).
00:00 / 00:00
78
Video 2.1
For auscultation, a stethoscope and a sphygmomanometer consisting of a BP cuff (cloth

cover and bladder) and an aneroid manometer are used. Step-by-step instructions for the
auscultatory method are presented in Resting Blood Pressure Measurement Via Manual
Auscultation. Oscillometry uses an automated electronic manometer to measure oscillations
in pressure (i.e., waveforms) when the cuff is deflated. Systolic and diastolic BPs are
calculated with the use of proprietary algorithms provided by each manufacturer.
BLOOD PRESSURE–RELATED SMARTPHONE APPS
There are no fewer than 100 hypertension-related apps available for download (Kumar et al. 2015). Most of them allow
users to track their BP, BMI, and body weight by entering the data into their smartphones. Some of these apps provide
access to information about hypertension, medication adherence, and diet. Reports indicate that these tracking and
educational apps include design features that make them beneficial for monitoring BP and factors related to it (e.g., body
weight, stress level).
Some Android apps assess blood pressure and heart rate by using the phone’s camera and microphone. These apps
may provide opportunities to track blood pressure for general knowledge. It is important to understand that none of
them have undergone the rigorous testing required of traditional blood pressure devices. The apps have also not been
approved by the U.S. Food and Drug Administration. Therefore, health care providers should be cautious with
information from these apps as provided to them by their clients.
Blood Pressure Measurement Techniques

Measure resting BP in the supine and exercise (sitting or standing) positions prior to testing.
The client should be wearing a short-sleeved or sleeveless garment and should be seated in a
quiet room. Take BP measurements rapidly, and completely deflate the cuff for at least 30 sec
between consecutive readings. For more accurate results, obtain two or three determinations
of pressure from each arm.
It takes a great deal of practice to become proficient at measuring BPs. When you are first
learning this method, it is highly recommended that you practice with a trained BP
technician, using a dual- or multiple-head stethoscope so you can listen simultaneously and
compare BP readings for the same trial.
Follow the manufacturer’s instructions when measuring blood pressure using an automated
device.
Sources of Measurement Error
79
Sources of error in measuring BP are numerous (adapted from Kallioinen et al. 2017,
Ogedegbe and Pickering 2010; Tolonen et al. 2015) and may be related to equipment, the
technician, or the client. You need to be aware of the following sources of error and do as
much as possible to control them:
00:00 / 00:00
Video 2.2
Inaccurate sphygmomanometer
Improper cuff width or length
Cuff not centered, too loose, or over clothing
Back, feet, or arm unsupported or elbow not at heart level
Poor auditory acuity or reaction time of technician
Improper rate of inflation or deflation of the cuff pressure
Improper stethoscope placement or pressure
Expectation bias and inexperience of the technician
Conversation between technician and client
Parallax error in reading the manometer
Background noise
Client holding onto something (e.g., cane, chair arms, treadmill handrails, or cycle
ergometer handlebars)
Client having full bladder
Client having exercised, smoked, eaten, or consumed alcohol prior to appointment
Resting Blood Pressure Measurement Via Manual Auscultation
To measure resting BP (seated position) with a manually operated aneroid or mercury device,
80
use the following recommended procedures (adapted from Kallioinen et al. 2017; Ogedegbe
and Pickering 2010; Tolonen et al. 2015):
1. Seat the client in a quiet area for at least 5 min, longer if the client is hypertensive. The
client’s bare arm, palm up, should be resting on a solid surface (table or desk) so that the
middle of the arm is at the level of the heart. The client’s back must be supported and both
feet flat on the floor or other solid surface such as a step stool.
2. Select the appropriate cuff size by estimating the client’s arm circumference or
measuring it at the midpoint between the acromion process of the shoulder and the olecranon
process of the elbow (see appendix D.4 for a description of measuring arm circumference)
using an anthropometric tape measure. The bladder of the cuff should encircle 80% of an
adult’s arm and 100% of a child’s arm.
3. Locate the brachial artery by palpating the brachial artery pulse on the anteromedial
aspect of the arm below the belly of the biceps brachii and 2 to 3 cm (1 in.) above the
antecubital fossa.
4. Wrap the deflated cuff snuggly around the upper arm so that the midline of the cuff or
artery marker is over the brachial artery pulse; if the cuff is loose, BP will be overestimated.
The lower edge of the cuff should be approximately 2.5 cm (1 in.) above the antecubital fossa.
Avoid placing the cuff over clothing; if the shirtsleeve is rolled up, make certain it is not
occluding the circulation.
5. Position the pressure gauge so it is at your eye level and the cuff’s tubing so it is not
overlapping or obstructed.
6. Estimate blood pressures by locating and palpating the radial pulse (see Palpation
section later in chapter for anatomical description of this site), completely closing the
inflation bulb valve of the BP unit, and rapidly inflating the cuff to 70 mmHg. Slowly
increase the pressure in 10 mmHg increments while palpating the radial pulse, noting when
the pulse disappears (estimate of systolic BP). Partially open the valve to slowly release the
pressure at a rate of 2 to 3 mmHg·sec , noting when the radial pulse reappears (estimate of
−1
diastolic BP). Fully open the valve to completely deflate the cuff. The estimate of systolic BP
from this method is used to determine how much the cuff needs to be inflated for measuring
BP using the auscultatory technique. In this way, you can avoid over- or underinflating the
cuff for clients with low or high BPs, respectively.
7. Position the earpieces of the stethoscope so they are aligned with the auditory canals
(i.e., angled anteriorly).
81
8. Place the entire head of the stethoscope on the skin and over the brachial pulse (about 1
cm superior and medial to the antecubital fossa). To avoid extraneous noise, do not place any
part of the head of the stethoscope underneath the cuff.
9. Close the inflation bulb valve; quickly and steadily inflate the cuff pressure to about 20
to 30 mmHg above the estimated systolic pressure previously determined by palpation.
10. Partially open the valve to slowly release the pressure at a rate of 2 to 3 mmHg·sec . −1
Note when you hear the first sharp thud caused by the sudden rush of blood as the artery
opens. This is known as the first Korotkoff sound and corresponds to the systolic pressure
(phase I).
11. Continue reducing the pressure slowly (no faster than 2 mmHg·sec ), noting when the
−1
metallic tapping sound becomes muffled (phase IV diastolic pressure) and when the sound
disappears (phase V diastolic pressure). Typically, the phase V value is used as the index of
diastolic pressure. However, both phase IV and V diastolic pressures should be noted. During
rhythmic exercise, the phase V pressure tends to decrease because of reduction in peripheral
resistance. In some cases, it may even drop to zero.
12. After noting the phase V pressure, continue deflating the cuff for at least 10 mmHg,
making certain that no additional sounds are heard. Then rapidly and completely deflate the
cuff.
13. Record all three BP values (phases I, IV, and V) to the nearest 2 mmHg. Wait at least
30 sec and repeat the measurement. Use the average of the two measurements for each of the
three values.
The following section addresses questions about measuring BP and provides tips for taking
more accurate BP measurements during rest or exercise.
Which type of sphygmomanometer provides more valid and reliable measures of resting
blood pressure?
For over a century, the mercury column manometer has been considered the gold standard for
indirect measurement of BP. Calibrated aneroid manometers may yield less error than
automated devices when compared with mercury column manometers. As part of their
systematic review of sources of inaccuracies in resting blood pressure assessment, Kallioinen
and colleagues (2017) reported on the comparison of manual and automated blood pressure
devices with a mercury column manometer. Of the 13 comparisons of aneroid manometers
with a mercury column criterion, only one reported a significant difference, and that was for
82
systolic blood pressure. Thirty-nine studies compared automated devices with a mercury
column criterion, and significant differences were reported for systolic pressure (n = 5),
diastolic pressure (n = 1), and both systolic and diastolic pressures (n = 11).
Although mercury column and aneroid manometers are similarly susceptible to technician
error, mercury column manometers are preferred for a number of reasons. They are based on
gravity, leaving little room for mechanical errors. In contrast, the aneroid manometer is a
spring-based device that can fatigue with use and thereby lose its calibration more easily. It
can become inaccurate without the technician’s awareness, as was the case in nearly 23% of
the devices checked in six accredited doctoral physical therapy programs (Arena, Simon, and
Peterson 2016). Therefore, aneroid manometers must be calibrated frequently (at least every
6 mo). Often when the aneroid manometer fails the calibration test, it must be returned to
the manufacturer for repair. For a complete list of recommended aneroid
sphygmomanometers, see www.dableducational.org.
Unlike aneroid and mercury manometers, oscillometric devices require less calibration and
maintenance; additionally, they require little technician training. Oscillometric devices are
frequently used in the home setting, ambulatory blood pressure monitoring markets, and
community service blood pressure screenings; they are also gaining favorability in clinical
office settings. Although the common belief is that oscillometric devices may misestimate the
BP of clients with irregular heart rhythms, Lakhal and associates (2015) reported similar BP
values for an oscillometric device compared with intra-arterial BP measurements in a sample
of arrhythmic patients (n = 135) and those with regular heart rhythm (n = 136) in three
intensive care units. For a comprehensive report on the strengths and weaknesses of various
oscillometric algorithms incorporated into automated blood pressure devices, see the 2015
article by Forouzanfar and colleagues.
How can I check the accuracy of an aneroid manometer?
To check the accuracy of an aneroid manometer against a mercury unit, follow the procedure
adapted from Arena, Simon, and Peterson (2016) and Emmanuel (2013):
Disconnect the bulbs of both cuffs and reconnect the bulb of the aneroid unit to the
cuff of the mercury unit.
Use a T or Y connector to reconnect the aneroid gauge being tested to the mercury
unit. (Note: This step may be skipped if you are testing a single-tube aneroid device
because the gauge is integrated into the inflation bulb handle.)
83
Completely deflate the cuff being tested, and check that the aneroid gauge
indicator is at zero mmHg.
Wrap the deflated cuff around a rigid metal or plastic cylinder approximately 10 cm
in diameter.
Hold the pressure gauge of the aneroid manometer close to the mercury column
and compare the two readings.
Inflate the cuff to 250 mmHg and compare readings. Slowly deflate the cuff and
compare readings at regular intervals (e.g., 200, 150, 100, and 50 mmHg).
If the aneroid and mercury manometer pressures differ by more than 3 mmHg at
any point, send the aneroid manometer to the manufacturer for adjustment.
What criteria are used to judge the accuracy of devices that measure blood pressure?
The Association for the Advancement of Medical Instrumentation (AAMI), the British
Hypertension Society (BHS), the European Society of Hypertension International Protocol
(ESH-IP), and the German Hypertension League (DHL) established separate criteria for
judging the accuracy of BP devices. Most validation studies use one or more of these sets of
criteria. For each set, measured values from the device are compared with those obtained
from a mercury sphygmomanometer. To meet AAMI criteria, the measured average BP
(systolic and diastolic) should not differ from the mercury standard by more than 5 mmHg,
and the standard deviation should not exceed 8 mmHg. For the BHS criteria, differences in
both systolic and diastolic BPs are graded as A, B, C, or D depending on the cumulative
percentage of absolute individual difference scores falling within three categories: 5, 10, and
15 mmHg (see table 2.8). To be recommended, a device must achieve at least a B; A and D
denote the greatest and least degree of agreement with the mercury standard.
84
The European Society of Hypertension (ESH) protocol, also known as the International
Protocol (ESH-IP), is more complex than that of the BHS. Basically, it categorizes mean
differences in BP as follows: 0 to 5 mmHg = very accurate, 6 to 10 mmHg = slightly
inaccurate, 11 to 15 mmHg = moderately inaccurate, and >15 mmHg = very inaccurate. The
number of comparisons cumulatively falling within 5, 10, and 15 mmHg is counted (i.e., the
5 mmHg zone represents all values falling within 0-5 mmHg; the 10 mmHg zone represents
all values falling within 0-5 mmHg and 6-10 mmHg; the 15 mmHg zone represents all
values falling within 0-5 mmHg, 6-10 mmHg, and 10-15 mmHg). These values are then
compared against standards set for each of two phases of the validation process. Devices
recommended for clinical use must pass both phases of the validation process. The 2002
ESH-IP criteria (ESH-IP1) were revised and clarified in 2010 (ESH-IP2) and made more
stringent. In a retrospective investigation of validation studies, Stergiou and colleagues (2011)
determined that about 33% of the BP monitors previously validated under the ESH-IP 2002
criteria failed to meet the ESH-IP 2010 requirements. For a detailed description of the
ESH-IP2, see the work of O’Brien and colleagues (2010). For a detailed analysis of BP
monitoring devices that passed the ESH-IP 2002 criteria yet failed the 2010 revised criteria,
see the 2011 study by Stergiou and associates.
The German Hypertension League established its Quality Seal Protocol to allow
evaluation of blood pressure measuring devices targeting the German market. This clinical
validation protocol requires ≥96 patients in specific blood pressure and age ranges. For each
patient, six standard and device-specific measurements are taken in a predefined sequence,
with a 30 to 60 sec inter-assessment rest interval. Reference measurements cannot differ by
more than 4 mmHg, and their averages cannot differ by more than 16 mmHg from the first
to the sixth paired assessment, all completed within 40 min. Of the six paired readings, at
least three qualifying readings per patient are required (288 paired readings) for subsequent
analysis. Similar to the AAMI standards, the DHL Quality Seal Protocol requires that the
differences between means for SBP and DBP paired readings are within 5 mmHg and
standard deviations are ≤8 mmHg. Point scores are assigned based on the proximity of mean
differences for both SBP and DBP pairs. A minimum 55% of the total possible score is
required for earning the quality seal. For an in-depth comparison of the AAMI, BHS, ESH-
IP, and DHL protocols, see the article by Beime et al. (2016).
While endorsing the need for automated BP device testing standards, Wan and colleagues
(2010) highlighted the importance of reassessing device accuracy in community settings,
where misestimating BP has the highest consequences. In addition to identifying eight
85
discussion points for future consideration, Beime and colleagues (2016) highlight the typical
2-yr life cycle of a blood pressure assessment device before upgrades hit the market as a
reason for short device validation protocols.
The dabl Educational Trust has a website that provides up-to-date, evidence-based
information about BP measurement techniques and devices (www.dableducational.org). Here
you will find tables evaluating the validity of various types of BP devices according to AAMI,
BHS, and International Protocol criteria. You may also find lists of blood pressure devices
validated and approved for home and clinical use on the website of the British Hypertension
Society (http://bhsoc.org/bp-monitors/bp-monitors); the criteria on which they were
approved are also identified.
In the future, will the mercury column manometer be banned? If so, what types of devices
will replace it?
Because of the growing environmental concerns regarding the toxic effects of mercury, a
global treaty was signed at the October 2013 Minamata Convention in Japan. This treaty
establishes the year 2020 as the phase-out date for mercury-containing products like mercury
column manometers and thermometers. As soon as 50 countries sign the treaty, it will go into
effect (U.S. Environmental Protection Agency 2017). After the phase-out date, mercury-
containing products cannot be manufactured, imported, or exported. Consequently, clinics
and doctors’ offices in many countries have been phasing out mercury manometers and
thermometers. Use of automated oscillometric manometers and other devices utilizing
semiconductor pressure sensors is accelerating (Asayama et al. 2016) as practitioners seek a
viable replacement of the mercury column manometer.
The Scientific Committee on Emerging and Newly Identified Health Risks (SCENIHR)
is on record as supporting limited use of mercury manometers but finds hybrid devices
validated using ESH-IP standards to be a suitable alternative for BP assessment in a clinical
setting. SCENIHR also supports the use of such hybrid devices as a criterion reference
against which to validate new BP devices (Parati and Ochoa 2012). Although no health care
agencies in the country forbid mercury manometers, the United States has already signed the
Minamata Treaty. The National Institutes of Health (NIH) launched an initiative in 2001 to
become mercury-free by replacing mercury-containing devices in its labs and facilities
(National Institutes of Health 2012). None of the aneroid or nonmercury manual blood
pressure sphygmomanometers for clinical use that passed AAMI, BSH, or ESH-IP1
validation criteria have received a passing score under the more conservative ESH-IP2 (dabl
86
Educational Trust 2017); however, that may change as the website
(www.dableducational.org) is updated. Alternatively, more than 40 of the automated devices
on that web page have received ESH-IP2 passing scores. In assessing the accuracy of aneroid
and digital manometers against the standard mercury column for the assessment of
hypertensive status, Shahbabu and associates (2016) reported that the aneroid manometer
was more consistently (>89%) within 5 ± 8 mmHg of the criterion than was the digital
manometer (<44%).
When replacing a mercury column manometer with a mercury-free device, be sure to verify
that the device has been validated using rigorous standards. It is also suggested that you
conduct your own determination of equivalence between the replacement device you have
chosen and your mercury column. For example, the hypertension cut-point blood pressure
reading of 140/90 mmHg on the mercury column was found to be equivalent to 143/79
mmHg and 149.5/84.5 mmHg for the aneroid and digital devices, respectively, as tested by
Shahbabu and colleagues (2016).
The AHA made the following recommendations for health care and fitness settings that
exclusively use aneroid or automated devices (Jones et al. 2001):
Select only devices that satisfy the validation criteria of the AAMI, BHS, or similar
organizations.
Schedule regular maintenance and calibration.
Insist on the use of mercury manometers for calibration.
Ensure regular training of personnel who measure BP.
Hybrid sphygmomanometers are mercury-free and combine features of both electronic and
auscultatory devices. Several types of hybrid sphygmomanometers have successfully
undergone clinical validation against a mercury column manometer (Stergiou et al. 2012a;
Stergiou et al. 2012b). With the hybrid sphygmomanometer, the mercury column is replaced
with an electronic pressure gauge. The technician uses a stethoscope to listen for the
Korotkoff sounds. For older models, the technician presses a button next to the deflation
knob once systolic and diastolic pressures are heard; this freezes the display showing the
systolic and diastolic pressures. Newer models display both pressures after the diastolic
pressure has been determined. The pressure is displayed digitally or as a simulated mercury
column or aneroid display. The hybrid sphygmomanometer combines some of the best
features of mercury and electronic devices and may be a good candidate to replace the
87
mercury sphygmomanometer as the gold standard in clinical settings (Stergiou et al. 2012a).
How accurate are automated blood pressure devices?
There are many automated devices available for clinical and home use. These automated
devices inflate and deflate a cuff that is placed over the brachial artery (upper arm device),
radial artery (wrist device), or digital artery (finger device). The automated electronic
manometer assesses oscillations in pressure while the cuff is gradually deflated. The
maximum oscillation corresponds to mean arterial pressure; algorithms, which vary among
manufacturers, are used to calculate systolic and diastolic pressures. An advantage of
automated BP devices is that they eliminate terminal digit bias, the tendency of the
technician to round BP values to the nearest 0 or 5 mmHg instead of reporting values
rounded to the nearest even number. Factors that can affect the accuracy of automated
devices include the age of mechanical components and sensors, the environment, and the
provider’s failure to adhere to manufacturer guidelines regarding the proper assessment
procedures (Forouzanfar et al. 2015).
For a complete list of recommended, not recommended, and questionable automated
upper arm, wrist, and finger blood pressure measurement devices as evaluated by AAMI,
BHS, ESH-IP1, and ESH-IP2 criteria, see www.dableducational.org. Tholl and associates
(2016) present the results of validations undertaken using DHL Quality Seal Protocol criteria
on upper arm and wrist devices (N = 105) between 1999 and 2014.
Neuhauser and colleagues (2015) investigated the agreement in blood pressure values
obtained via mercury sphygmomanometry and an oscillometric device for a sample of 65
women and 40 men. Meticulous blood pressure measurement procedures and simultaneous
auscultation by two observers, blinded to each other’s scores, were used in accordance with
device-specific arm circumference cuff size determination guidelines. Significantly higher
systolic and diastolic pressures were reported using the oscillometric device for all blood
pressure categories (optimal, elevated, hypertensive). Likewise, systolic and diastolic pressures
differed significantly for comparisons based on arm circumferences <28 cm and between 28
and 35 cm. Nobody in their sample had an arm circumference exceeding 36 cm. Interestingly,
cuff dimensions for the oscillometric device were both wider and longer than for the mercury
sphygmomanometer. Commenting on possible reasons as to why their results differed from
those of other studies using the oscillometric device, Neuhauser and coauthors highlighted
the changes in cuff-selection rules and cuff sizes over time, as well as their reporting of results
based on stratified blood pressure and arm circumference categories. Ultimately, they have
88
alerted clinicians to potential unintended consequences associated with the unquestioned
replacement of devices and associated cuffs, as doing so may result in dramatic or attenuated
differences in blood pressure values, treatment plans, and effect on comorbidities.
Generally, automated upper arm devices are more accurate than automated wrist devices
for measuring resting BP. Wrist devices become inaccurate if the arm is not kept at heart
level during measurement, and the position of the wrist during measurement may also
influence accuracy. Tholl and colleagues (2016) reported that 11 automated wrist models in
their study passed the DHL Quality Seal Protocol validation criteria. Likewise, numerous
automated wrist models for home or clinical use are listed on the www.dableducational.org
site and identified as passing according to at least one of the four identified protocols.
Finger devices generally are not recommended for measuring BP. Although the Finometer
is listed as having satisfied the criteria of the AAMI and BHS for measuring the resting BP
of black women in a clinical setting, the sample size is in question (see
www.dableducational.org). As a result, finger devices should not be used for clinical
measurement of BP.
Are aneroid and automated devices accurate at high altitudes?
There continue to be limited data about how nonmercury BP devices perform at higher
elevations. If you consider that more than 170 million people worldwide are reported to live
at or visit altitudes higher than 2,500 m (Li et al. 2012), having a valid and reliable method
for assessing BP is important for monitoring the health of these people. The first study,
although limited by its small sample size (N = 10), compared mercury column and aneroid BP
measurements at 4,370 m (Kametas et al. 2006). Since the aneroid device fulfilled the AAMI
recommendations at altitude, the authors concluded that it is a suitable alternative to mercury
column manometers for adults at the altitudes similar to the Peruvian highlands. Li and
colleagues (2012) reported significant differences for SBP but not DBP when comparing
simultaneous BP measurements obtained with an oscillometric manometer (Omron HEM-
759P) and a mercury column manometer for a sample of high-altitude (4,300 m) residents in
Tibet. Another automated upper-arm oscillometric blood pressure cuff was successfully
validated against a mercury column device in accordance with ESH-IP2 standards in a
sample of high-altitude (3,650 m) residents (≥25 yr of age) of Tibet (Cho et al. 2013).
Consequently, the Omron HEM-7201 device is suitable for use at similar altitudes.
However, in their review of these two studies from the Tibetan region of China, Mingji and
colleagues (2016) indicate that the extent of agreement between the two upper-arm
89
oscillometric devices and mercury column reference measures was high for DBP whereas the
agreement for SBP was inconsistent.
Can automated devices be used to measure blood pressure during exercise?
A review of the dabl Educational Trust (2017) website reveals no validation of clinical or
home-use blood pressure assessment devices under exercise conditions. Therefore the lack of
validity and accuracy of automated devices for measuring exercise BP as reported by Griffin,
Robergs, and Heyward (1997) continues to hold true. To date, no criteria have been
established to evaluate the accuracy of devices for measuring BP under stress (e.g., exercise).
To assess the repeatability of automated BP measurements during exercise, a small sample of
young men performed two identical maximal exertion treadmill tests; BP measurements were
obtained every 4 min using a manometer that combined oscillometric and auscultatory
methods (Instebo, Helgheim, and Greve 2012). Of the possible 70 pairs of SBP and DBP
measurements over the two testing days, less than half of each measurement from the first
day were reproducible on the second day.
The accuracy of some finger devices (i.e., Finapres and Portapres Model 2) designed for
continuous and noninvasive ambulatory BP monitoring has been assessed during incremental
cycle ergometer exercise (Blum et al. 1997; Eckert and Horstkotte 2002; Idema, van den
Meiracker, and Imholz 1989). In these studies, the mean differences between the automated
(Finapres and Portapres Model 2) and the intra-arterial measures of BP during low-intensity
(~100 W) exercise ranged from 12 to 22 mmHg for systolic pressure and from −5 to −9.8
mmHg for diastolic pressure. The arm-cuff component for brachial pressure calibration with
the Finapres improves its accuracy over the Portapres. Additional stabilization of the hand in
the neutral position and splinting of the monitored finger to prevent grasping of the
handlebars during cycling improves the peripheral pressure wave crucial for blood pressure
calculation (Critoph et al. 2013). During exercise, these automated finger devices
systematically underestimated and overestimated systolic and diastolic BPs, respectively, and
average differences increased as exercise intensity increased. Therefore, these devices should
not be used to measure BP during exercise.
Incorporating photoplethysmography, pulse wave velocity, and pulse transit time
processing, alone or in combination, may improve the accuracy of these noninvasive means of
blood pressure assessment in the resting state; however, published research using these
devices during exercise is limited. Integrating ECG and finger plethysmography provides the
means to include pulse transit time and pulse wave velocity for blood pressure determination
90
during stationary cycling. Although the correlation between the auscultated aneroid cuff
pressure and calculated systolic blood pressure was strong for the group of participants, there
was a 10 to 20 mmHg difference between systolic blood pressure values (Gesche et al. 2012;
Wibner et al. 2014). The differences become more pronounced as the individual mean blood
pressure increases above 140 mmHg (Gesche et al. 2012).
How do body position and arm position affect blood pressure measurements?
Posture affects BP; generally, BP increases from lying (supine) to sitting to standing. Usually,
resting BP is measured in the sitting position. Regardless of body position, the upper arm
must be held or supported horizontally at the level of the heart (right atrium); the midsternal
level most closely approximates the level of the right atrium. Raising the arm above heart level
underestimates BP, and positioning the arm below heart level tends to overestimate BP.
When the cuff is below the heart, there is a compounding influence of the hydrostatic
pressure within the limb’s vascular system, which predominantly affects SBP (Casiglia et al.
2016). Typically, the arm is supported by resting it on a table or by having the technician
hold it at the elbow. Even when supine BP is measured, a pillow should be placed under the
upper arm to support it at heart level.
The accuracy of automated wrist devices is greatly affected if the wrist is not held at heart
level. An observational study investigated BP measurements obtained under supervision using
a certified device worn at the wrist compared with measurements obtained at home without
supervision after instruction at the physician’s office; all participants underwent BP
assessments and received training on and subsequently demonstrated the proper use of the
devices and anatomical positioning (Casiglia et al. 2016). Casiglia and colleagues (2016) used
a BHS-certified automated upper-arm device as their criterion, and there was no position
sensor in the ESH-IP2 certified wrist cuff. In the office setting, systolic pressure was
significantly lower at the wrist; at home, significantly elevated wrist blood pressures were
recorded for both SBP and DBP. Higher pressures at the wrist are associated with the wrist
being below the level of the heart, and higher limb-specific hydraulic pressure is especially
manifested in SBP values for those having long forearms. These differences by geographic
location (office vs. home) occurred regardless of age and years of schooling (Casiglia et al.
2016); the differences highlight the importance of patient training quality in addition to the
patient’s ability to mimic proper procedures.
What is white coat hypertension?
In the condition known as white coat hypertension, individuals who have a normal BP
91
outside of the clinical environment and are not taking any prescribed antihypertensive
medications develop higher than normal values when their BP is measured by a health
professional (Franklin et al. 2016; Sivén et al. 2016). To confirm this condition, BP should be
measured outside of the clinical environment via self-measurement at home, 24 hr
ambulatory BP monitoring, automated BP assessment in a community pharmacy, or an
automated blood pressure measuring device while the client is alone in the examination room.
Given the latter’s more standardized and reproducible assessment of blood pressure compared
with auscultation, an automated BP measurement with the client resting alone in an
examination room is recommended as part of the new Canadian algorithm for determining
hypertension (Cloutier et al. 2015).
Minimizing the patient-observer interaction reduces the white coat effect, which describes
the acute elevation in blood pressure at the doctor’s office, regardless of one’s blood pressure
at home or antihypertensive medication prescription status (Franklin et al. 2016). The
likelihood of white coat hypertension is at least five times higher when a physician measures
BP with the traditional manual method compared with automated BP readings taken with
the client quietly resting alone in an examination room (Myers et al. 2009). In a study
investigating the incidence of white coat hypertension in three different settings (home,
community pharmacy, or physician’s office), the same model of automated BP testing device
was used in all three settings. The home and community pharmacy BP readings were similar
for both SBP and DBP. The systolic and diastolic blood pressure values from both the
community pharmacy and home settings were significantly lower than from the physician’s
office for a sample of hypertensive adults (Sendra-Lillo et al. 2011).
Studies suggest that white coat hypertension is not benign (Franklin et al. 2016; Martin
and McGrath 2014; Sivén et al. 2016). A 10 yr follow-up study of 420 patients with stage 1
or 2 hypertension (of whom 18% had white coat hypertension) showed that individuals with
white coat hypertension have an increased risk of CVDs (Gustavsen et al. 2003) and of
developing sustained hypertension (Sivén et al. 2016) and diabetes (Martin and McGrath
2014) compared with normotensive individuals. However, awake ambulatory blood pressure
is reported to more accurately predict cardiovascular events compared with BP measured in
an office. Regardless of gender, DBP is higher when assessed in the doctor’s office than
during awake ambulatory measures after age 45 yr; SBP is also higher in the office setting,
but after the age of 50 yr. Conversely, ambulatory blood pressure values are higher than those
from an office setting for younger adults. Office blood pressure measurements are higher than
those obtained in the home setting, but BP measurements from these two settings are more
92
similar for elderly adults than for younger adults (Ishikawa et al. 2011). This finding suggests
that health care professionals should consider white coat hypertension when evaluating
cardiovascular risk factors.
What is masked hypertension?

Masked hypertension is a term coined in 2002 to describe a condition in which individuals
exhibit higher than normal BP readings outside of the physician’s office yet have normal BP
values in the office (Sivén et al. 2016). Although white coat hypertension is more prevalent in
older adults, masked hypertension occurs more frequently in younger adults (Ohkubo et al.
2005). Both, however, are harbingers for future cardiovascular events and target organ
damage in comparison to normotensive individuals, even after accounting for the traditional
cardiovascular risk factors (Tientcheu et al. 2015).
In their review of studies concerning the phenomenon of masked hypertension, Ogedegbe
and colleagues (2010) highlighted underlying factors related to lifestyle choices and daily
activities and their effect on everyday BP values. Some of the factors they cited include job
stress, tobacco smoking, alcohol consumption, conflict in relationships, physical activity
levels, and poor adherence with taking blood pressure medications as prescribed. They also
highlighted that the risk for masked hypertension may be influenced by gender (higher for
men than women) and BMI (higher for overweight and obese individuals compared with
those classified as normotensive or having white coat hypertension) (Sivén et al. 2016;
Ogedegbe, Agyemang, and Ravenell 2010). Regardless, if the condition is left untreated, then
those with masked hypertension have a similar risk for target organ and cardiovascular
damage as do those with sustained hypertension. Adults with masked hypertension have a
fourfold increase in risk for developing sustained hypertension than do those with blood
pressures in the normal range (Sivén et al. 2016).
What are miscuffing and cuff hypertension?
Miscuffing (i.e., undercuffing or overcuffing) is a serious source of measurement error caused

by using a BP cuff with a bladder that is not appropriately scaled for the client’s arm
circumference. This is a potential problem regardless of whether you are using a manual or
automated means of blood pressure assessment. Experts recommend using a cuff with a
bladder width that is 40% of the measured upper arm circumference and a length that
encircles at least 80% of the arm circumference; however, this recommendation does not
guard against the risk of miscuffing. Undercuffing occurs when the bladder is too small for
the arm circumference, leading to the overestimation of BP, known as cuff hypertension.
93
Conversely, overcuffing underestimates BP because the bladder is too large for the arm
circumference (Ringrose et al. 2015). To avoid these problems, the correct cuff and bladder
size must be selected for each client. Furthermore, clients who monitor their own blood
pressure with an oscillometric device at home must be made aware of the importance of
purchasing a cuff that is appropriate for their own arm circumference. Many manufacturers
offer devices for home and clinical use that have not undergone rigorous validation during
development (Campbell et al. 2016) and ship them without offering recommendations
regarding cuff size selection and its importance (Ringrose et al. 2015).
How can I determine the appropriate cuff size for my client?

To ensure accurate BP readings, you need to select a cuff size appropriate for your client’s
arm circumference. You should not assume that the cuff sent by the manufacturer of the in-
home blood pressure measuring device is appropriate for all arms. Generally, four cuff sizes
are commercially available: children, standard adult, large adult, and obese (i.e., thigh). To
select the proper cuff size, measure your client’s arm circumference (see appendix D.4 for a
description of measuring arm circumference). For adults with larger proximal upper arm (e.g.,
just below the deltoid) than distal upper arm (e.g., just above the elbow) circumferences, a
conical cuff may be more appropriate for BP measurements. In adults with proximal arm
circumferences exceeding 32 cm, BP was significantly overestimated using a traditional
rectangular-shaped cuff compared with a conical cuff. The extent of BP overestimation
increased with increasing arm circumference (Palatini et al. 2012).
Similarly, you should not assume that a child’s cuff is appropriate for all children. Using
National Health and Nutrition Examination (NHANES) data from 2007 through 2010,
Ostchega and colleagues (2014) reported that average midarm circumference for children and
adolescents has increased such that approximately 53% of boys and 48% of girls aged 12 to 15
yr required a standard adult cuff; nothing smaller than a standard adult cuff was needed for
89% of boys and 57% of girls between 16 and 19 yr. A large adult cuff was required for proper
cuff fit for approximately 30% and 24% of obese boys and girls, respectively. If arm
circumference cannot be measured directly, you can estimate it using gender-specific
prediction equations (see Ostchega et al. 2004). Table 2.9 presents recommended cuff and
bladder sizes for measured or estimated arm circumferences.
Table 2.9 Recommended Cuff and Bladder Sizes for Arm Circumferences
Client or patient Arm circumference (cm) Bladder width × length (cm)

Older child NR 9 × 18
94
Small adult 22-26 12 × 22
Adult 27-34 16 × 30
Larger adult 35-44 16 × 36
Obese adult or high upper body muscularity (thigh cuff) 45-52 16 × 42
NR = not reported.
Data from Pickering et al. 2005.
How can I measure exercise blood pressure more accurately?

Measuring BP during exercise is much more difficult than doing so during rest. You should
not attempt to measure exercise BP until you have demonstrated competency and have
confidence in your ability to measure resting BP. It is particularly difficult to accurately
measure BP when the client is running on the treadmill because of extraneous noise and arm
movement during running. Sometimes you will not be able to determine diastolic BP during
exercise because of the noise and vibration. Novice technicians should first practice taking
BPs during cycle ergometer exercise and then try measuring BP during treadmill exercise. An
expected BP response to exercise is for SBP to increase with increasing workload; usually,
DBP fluctuates very little or decreases somewhat (Fletcher et al. 2013). See Tips for
Measuring Exercise Blood Pressure for pointers on improving your BP measurements during
exercise.
TIPS FOR MEASURING EXERCISE BLOOD PRESSURE
When measuring exercise BP, take extra precautions to ensure accurate readings, and record BP values in even numbers
to the closest 2 mmHg (Sharman and LaGerche 2015).
Instruct the client to refrain from grasping the handlebars or handrails of the exercise apparatus or your
shoulder during the BP measurement.
Position the cuff on the arm so that the tubing protruding from its bladder is superior instead of inferior.
This position lessens extraneous noise caused by the tubing contacting the stethoscope during exercise.
Stand where the client can keep the arm in the sagittal plane as much as possible. This position will distract
the client less during exercise than will having the arm abducted 90°.
Limit arm movement during the BP measurement; stabilize the client’s arm with the cuff at heart level by
placing and holding it firmly between your arm and trunk.
Inflate the cuff well above the anticipated value or reading obtained during the previous stage of the graded
exercise test, keeping in mind that systolic BP increases with exercise intensity.
Position the manometer so it is no more than 3 ft (92 cm) away and is at eye level so you can read the scale
easily. Errors will occur if you do not keep your eyes close to the level of the meniscus of the mercury column
or perpendicular to the aneroid scale. For mercury column sphygmomanometry, use a model that is
mounted on a stand with wheels so the manometer can be properly positioned during incremental stages of
the exercise test. Positioning is particularly important when the client is performing graded treadmill tests
95
that progressively increase the incline of the treadmill.
00:00 / 00:00
Video 2.3
MEASURING HEART RATE

The average resting heart rate for adults is 60 to 80 beats per minute (bpm), with the average
resting heart rate of women typically 7 to 10 bpm higher than that of men. Heart rates as low
as 28 bpm have been reported for highly conditioned endurance athletes, whereas poorly
trained sedentary individuals may have heart rates that exceed 100 bpm.
Do not use resting heart rate as a measure of cardiorespiratory fitness. There is wide
variability in resting heart rate within the population, and a low resting heart rate is not
always indicative of cardiorespiratory fitness level. In some cases, a low resting heart rate
indicates a diseased heart or a medication that reduces heart rate. The following general
guidelines may be used to classify resting heart rate:
1. <60 bpm = bradycardia (slow rate)

2. 60 to 100 bpm = normal rate
3. >100 bpm = tachycardia (fast rate)
Before you measure resting heart rate, your client should rest for 5 to 10 min in either a
supine or a seated position. It is important that you measure resting heart rate carefully
because this value is sometimes used in the calculation of target exercise heart rates for
submaximal exercise tests, as well as for exercise prescriptions. You can measure heart rate
using auscultation, palpation, heart rate monitors, or ECG recordings.
Auscultation
When measuring resting heart rate by auscultation, place the bell of the stethoscope over the
96
third intercostal space to the left of the sternum. The sounds arising from the heart are
counted for 30 or 60 sec. The 30 sec count is multiplied by 2 to convert it to beats per
minute.
00:00 / 00:00
Video 2.4
Palpation
With use of the palpation technique for determining heart rate, the pulse is palpated at one
of the following sites:
Brachial artery—on the anteromedial aspect of the arm below the belly of the
biceps brachii, approximately 2 to 3 cm (1 in.) above the antecubital fossa
Carotid artery—in the neck just lateral to the larynx
Radial artery—on the anterolateral aspect of the wrist directly in line with the base
of the thumb
Temporal artery—along the hairline of the head at the temple
00:00 / 00:00
Video 2.5
For precautions necessary for ensuring your measurement is accurate, refer to Heart Rate
Determination by Palpation.
97
HEART RATE DETERMINATION BY PALPATION
Follow these procedures when determining heart rate by palpation:
Use the tips of the middle and index fingers. Do not use your thumb; it has a pulse of its own and may
produce an inaccurate count.
When palpating the carotid site, do not apply heavy pressure to the area. Baroreceptors in the carotid
arteries detect this pressure and cause a reflex slowing of the heart rate.
If you start the stopwatch simultaneously with the pulse beat, count the first beat as zero. If the stopwatch is
running, count the first beat as 1. Continue counting either for a set period of time (6, 10, 15, 30, or 60 sec)
or for a set number of beats. When the heart rate is counted for less than 1 min, use the following multipliers
to convert the count to beats per minute: 6 sec count × 10; 10 sec count × 6; 15 sec count × 4; 30 sec count × 2.
Typically, shorter time intervals (i.e., 6 or 10 sec counts) are used to measure exercise and postexercise heart
rates during and immediately following exercise. Because there is a rapid and immediate decline in heart
rate when a person stops exercising, the 6 or 10 sec count reflects the individual’s actual exercise heart rate
more accurately than the longer counts do.
Heart Rate Monitors and Electrocardiogram Recordings

Heart rate can also be measured using heart rate monitors or an ECG monitoring system.
Generally, heart rate monitors detect either the pulse or the ECG electrical signal from the
heart and provide a digital display of the heart rate. Pulse monitors use infrared sensors
attached to the client’s fingertip, earlobe, or wrist (i.e., heart rate watch) to detect pulsations
in blood flow during the cardiac cycle. Chest-strap wire and wireless ECG-type monitors
tend to be more accurate and reliable than pulse monitors, especially during vigorous exercise.
However, the accuracy of wireless chest-strap monitors may be affected by electrical
equipment (such as some treadmills, stair climbers, rowing machines, and video screens)
generating radio or magnetic interference. Generally, heart rate monitors provide an accurate
measure of ECG heart rate during rest and exercise (Vehrs et al. 2002).
00:00 / 00:00
Video 2.6
98
Most ECG monitoring systems provide a continuous digital display of the heart rate. This
value is usually recorded at the top of the ECG strip recording. If your equipment does not
provide a digital readout, you can use a heart rate ruler that converts the distance of two
cardiac cycles to beats per minute.
No matter which technique is used to measure heart rate, you should be aware that heart
rate fluctuates easily due to temperature, anxiety, exercise, stress, eating, smoking, drinking a
caffeinated beverage, time of day, body position, and some over-the-counter medications. In
a supine position, the resting heart rate is lower than in either a sitting or a standing position.
TWELVE-LEAD ELECTROCARDIOGRAM
The electrocardiogram (ECG) is a composite record of the electrical events in the heart
during the cardiac cycle. As the heart depolarizes and repolarizes during contraction, an
electrical impulse spreads to the tissues surrounding the heart. Electrodes placed on opposite
sides of the heart transmit the electrical potential to an ECG recorder.
In addition to providing baseline data, the resting ECG is used to detect such
contraindications to exercise testing as evidence of previous myocardial infarction, ischemic
ST-segment changes, conduction defects, and left ventricular hypertrophy. The reading and
interpretation of ECGs require a high degree of skill and practice. As an exercise technician,
you can administer the resting 12-lead ECG, but a qualified physician should interpret the
results. This chapter includes only basic information about administering an ECG. You
should consult other references for more detailed information concerning the reading and
interpretation of ECG abnormalities (Dubin 2000; Garcia 2015; Martindale and Brown
2017; Thaler 2015).
Electrocardiogram Basics
A typical normal ECG (figure 2.1) is composed of a P wave that represents depolarization of
the atria. The PR interval indicates the delay in the impulse at the atrioventricular node.
Electrical currents generated during ventricular depolarization and contraction produce the
QRS complex. The T wave and ST segment correspond to ventricular repolarization.
99
FIGURE 2.1 Typical normal electrocardiogram.
A lead is a pair of electrodes placed on the body and connected to an ECG recorder. An
axis is an imaginary line connecting the two electrodes. A standard 12-lead ECG consists of
three limb leads, three augmented unipolar leads, and six chest leads. Each of the 12 ECG
leads records a different view of the heart’s electrical activity. Thus, the tracings from the
various leads differ from one another.
Resting 12-Lead Electrocardiogram Procedures

To create the 12 leads, 10 electrodes are used. The electrodes for the three limb leads (I, II,
and III) are placed on the right arm, left arm, and left leg. A ground electrode is placed on
the right leg. This is electronically equivalent to placing the electrodes at the shoulders and
the symphysis pubis. Limb lead I measures the voltage differential between the left and right
arm electrodes. Limb leads II and III measure the voltage between the left leg and right (lead
II) and left (lead III) arms. Figure 2.2 shows the three limb leads and three augmented
unipolar leads.
FIGURE 2.2 Three limb leads and three augmented unipolar leads.
100
The three augmented unipolar leads are aVF (feet), aVL (left), and aVR (right). The
augmented unipolar lead compares the voltage across one of the limb electrodes with the
average voltage across the two opposite electrodes. Lead aVL, for example, records the
voltage across an electrode placed on the left arm and the average voltage across the other two
limb electrodes (see figure 2.2).
00:00 / 00:00
Video 2.7
The six chest leads (V to V ) measure the voltage across a specific area of the chest, with
1 6
the average voltage across the other three limb leads. Figure 2.3 illustrates electrode
placement for the chest leads, V through V .
1 6
FIGURE 2.3 Electrode placement for chest leads, V1 to V6.
During the resting ECG, the client should lie quietly in a supine position on a table. The
electrode sites should be shaved if hair is present and should be cleaned with alcohol. Remove
the superficial layer of skin at each site by rubbing it with fine-grain emery paper or a gauze
101
pad. Disposable electrodes contain electrode gel and adhesive discs. After applying the
electrode, tap it firmly to test for noisy leads. You should always calibrate the ECG recorder
prior to use by recording the standard 1 mV deflection per centimeter. Also, to standardize
the time base for the ECG, set the paper speed to 25 mm·sec . −1
ANATOMICAL LOCATIONS FOR CHEST ELECTRODES
V1—fourth intercostal space to the right of the sternal border
V2—fourth intercostal space to the left of the sternal border
V3—at the midpoint of a straight line between V2 and V4
V4—fifth intercostal space along the midclavicular line
V5—horizontal to V4 on the anterior axillary line
V6—horizontal to V4 and V5 on the midaxillary line
The 12-Lead Exercise ECG

To avoid poor ECG tracings caused by moving limbs during exercise, the electrode
configuration is modified slightly for an exercise 12-lead ECG (see figure 2.4). The right and
left arm electrodes are placed below the right and left clavicles, respectively. The right and left
leg electrodes are attached to the right and left sides of the trunk, below the rib cage on the
anterior axillary line. The six chest electrodes are positioned as previously described.
00:00 / 00:00
Video 2.8
00:00 / 00:00
102
Video 2.9
FIGURE 2.4 Electrode placement for 12-lead exercise electrocardiogram.
Key Points
The purpose of the health evaluation is to detect disease and to assess disease risk.
Important components of the health evaluation are a medical history, CHD risk factor analysis, physical
examination, clinical tests, and medical clearance.
The lifestyle evaluation includes information about the diet, tobacco and alcohol use, physical activity, and
psychological stress levels of the individual.
All clients are required to sign an informed consent prior to taking any physical fitness tests or participating
in an exercise program.
The resting evaluation of cardiorespiratory function includes heart rate, BP, and a 12-lead ECG that is
interpreted by a qualified physician.
Resting BP can be assessed using auscultation or automated BP devices.
103
Appropriate blood pressure cuff size for the client’s arm is critical for accurate BP assessment.
Heart rate may be taken using auscultation, palpation, heart rate monitors, or ECG recordings.
The 12-lead ECG includes three limb leads (I, II, III), three augmented unipolar leads (aVF, aVR, aVL),
and six chest leads (V1 through V6).
A graded maximal exercise test is the best way to assess functional aerobic capacity, but submaximal exercise
tests can be used to estimate a client’s maximal functional aerobic capacity.
Key Terms
augmented unipolar leads

auscultation
bradycardia
chest leads
cuff hypertension
diastolic blood pressure (DBP)
electrocardiogram (ECG)
elevated blood pressure
limb leads
masked hypertension
miscuffing
normotensive
oscillometry
overcuffing
palpation
PR interval
pulse pressure
P wave
QRS complex
sphygmomanometer
ST segment
systolic blood pressure (SBP)
tachycardia
terminal digit bias
T wave
undercuffing
white coat effect
104
white coat hypertension
Review Questions
In addition to being able to define each of the key terms, test your knowledge and understanding of the material by
answering the following review questions.
1. Identify the purpose of each component of the comprehensive health evaluation.
2. At minimum, a pretest health screening should include four items. Name these.
3. Identify cardiovascular, pulmonary, metabolic, and musculoskeletal diseases or disorders that may limit
exercise performance (name three signs or symptoms for each category).
4. Identify the positive and negative risk factors for CHD. Specify the criteria for each of these risk factors.
5. Identify the cutoff values for classifying resting BPs.
6. Identify the cutoff values for classifying TC, LDL-C, HDL-C, and triglycerides.
7. Explain the importance of BP cuff size selection.
8. Name three methods for measuring BP. Which one is considered the gold standard? Is each method
accurate at high altitude?
9. Describe the proper positioning of the wrist for assessing resting blood pressure with a wrist cuff.
10. Name three sources of error in measurement of BP.
11. Identify two BP devices that are best suited for assessing BP during exercise.
12. Describe three things you should do to ensure accurate BP readings during exercise.
13. Describe the effects of miscuffing on BP readings.
14. What effect do arm position and body posture have on BP readings?
15. Name three methods for measuring heart rate.
16. Identify the component parts of a typical normal ECG tracing. What does each component represent
relative to the cardiac cycle?
17. Describe the anatomical locations for placement of the 10 electrodes used to obtain a 12-lead ECG
recording.
18. Name three absolute and three relative contraindications to exercise testing.
105
106
CHAPTER 3
Principles of Assessment, Prescription, and Exercise

Program Adherence
KEY QUESTIONS
What are the essential components of a physical fitness profile?
What are the purposes of physical fitness tests and how can I use the results?
Several physical fitness tests are available; how do I select the best test for my client?
Are field tests as good as laboratory tests for measuring physical fitness?
What is the best way to interpret test results for my client?
What are the essential elements of an exercise prescription?
Is one type of exercise better than others for improving each component of physical fitness?
Does high-intensity exercise improve physical fitness faster than low-intensity exercise?
Is it safe to exercise every day?
When should I increase the frequency, intensity, and duration in an exercise prescription? Can these
elements be increased simultaneously?
Do older people benefit as much from exercise as younger people?
How can I get my clients to stick with their exercise programs?
How can technology be used to promote physical activity?
Health and fitness professionals need to master the basic principles of physical fitness
assessment and exercise prescription. You must know how to use the results of physical
fitness tests to plan scientifically sound exercise programs that are individualized to meet your
clients’ needs, interests, and abilities. With your knowledge, leadership, and guidance, your
clients can reduce their risk of disease and improve their health and physical fitness levels
safely and effectively.
As an exercise specialist, you will have diverse responsibilities, such as
educating clients about the positive benefits of regular physical activity;
107
conducting pretest health evaluations to screen clients for exercise participation (see
chapter 2);
selecting, administering, and interpreting tests designed to assess each component
of physical fitness;
designing individualized exercise programs;
leading exercise classes;
analyzing your clients’ exercise performance and correcting performance errors;
educating your clients about the dos and don’ts of exercise; and
motivating your clients to improve their adherence to exercise.
Exercise specialists play many roles: educator, leader, technician, and artist. To be effective
in these roles, you must integrate knowledge from many disciplines such as anatomy,
physiology, chemistry, nutrition, education, and psychology, as well as refine your exercise
testing, prescription, and leadership skills.
This chapter presents principles of exercise testing and prescription, along with
information about exercise program adherence and the use of technology to promote physical
activity.
PHYSICAL FITNESS TESTING

In order to plan and administer physical fitness tests, you must understand the following:
The components of physical fitness to be tested

Purposes of physical fitness testing
Testing order and the testing environment
Test validity, reliability, and objectivity
Prediction equation evaluation
Test administration and interpretation
COMPONENTS OF PHYSICAL FITNESS

Physical fitness is the ability to perform occupational, recreational, and daily activities
without becoming unduly fatigued. As an exercise specialist, one of your primary
responsibilities is to assess each of the following physical fitness components:
108
1. Cardiorespiratory endurance. Cardiorespiratory endurance is the ability of the heart,
lungs, and circulatory system to supply oxygen and nutrients efficiently to working muscles.
Exercise physiologists measure the maximum oxygen consumption (V̇O max), or the rate of
2
oxygen utilization of the muscles during aerobic exercise, in order to assess cardiorespiratory
endurance and maximal functional aerobic capacity. Physical fitness evaluations should
include a test of cardiorespiratory function during rest and exercise. Graded exercise tests
(GXTs) are used for this purpose. Improved cardiorespiratory endurance is one of the most
important benefits of aerobic exercise training programs. Chapters 4 and 5 present detailed
information about graded exercise testing and aerobic exercise programs.
2. Musculoskeletal fitness. Musculoskeletal fitness refers to the ability of the skeletal and
muscular systems to perform work. This requires muscular strength, muscular endurance,
muscular power, and bone strength. Muscular strength is the maximal force or tension level
that can be produced by a muscle group, muscular endurance is the ability of a muscle to
maintain submaximal force levels for extended periods, muscular power refers to the rate of
force development, and bone strength is related to the risk of bone fracture and is a function
of the mineral content and density of the bone tissue. Resistance training is one of the most
effective ways to improve the strength of muscles and bones and to develop muscular
endurance. Plyometrics and explosive free weight lifts are effective means of developing
muscular power. Chapters 6 and 7 provide detailed information about assessing
musculoskeletal fitness and designing resistance training programs.
3. Body weight and body composition. Body weight (BW) refers to the size or mass of the
individual. Body composition refers to body weight in terms of the absolute and relative
amounts of muscle, bone, and fat tissues. Aerobic exercise and resistance training are effective
in altering body weight and composition. Chapters 8 and 9 discuss body composition
assessment techniques and exercise programs for weight management.
4. Flexibility. Flexibility is the ability to move a joint or series of joints fluidly through the
complete range of motion. Flexibility is limited by factors such as bony structure of the joint
and the size and strength of muscles, ligaments, and other connective tissues. Daily stretching
can greatly improve flexibility. Chapters 10 and 11 give more information about assessing
flexibility and designing stretching programs.
5. Balance. Balance is the ability to keep the body’s center of gravity within the base of
support when maintaining a static position, performing voluntary movements, or reacting to
external disturbances. Functional balance refers to the ability to perform daily movement
109
tasks requiring balance such as picking up an object from the floor, dressing, and turning to
look at something behind you. Tai chi and yoga are two examples of activities that can be
used to improve balance. Chapter 12 addresses the assessment of balance and design of
programs for improving balance.
PURPOSES OF PHYSICAL FITNESS TESTING

As mentioned in chapter 2, it is imperative that you carefully screen your clients prior to
exercise testing, identify any contraindications to exercise testing, determine if medical
clearance is recommended prior to initiating an exercise program, and obtain each client’s
informed consent before conducting any physical fitness tests. You can use laboratory and
field tests to assess each component of physical fitness and to develop physical fitness profiles
for your clients. Results from these tests enable you to identify strengths and weaknesses and
to set realistic and attainable goals for your clients. Data from specific tests (e.g., heart rates
from a GXT) will help you make accurate and precise exercise prescriptions for each client.
Also, you can use baseline and follow-up data to evaluate the progress of exercise program
participants.
TESTING ORDER AND THE TESTING ENVIRONMENT

When you administer a complete battery of physical fitness tests in a single session, use the
following test sequence to minimize the effects of previous tests on subsequent test
performance:
Resting blood pressure and heart rate

Body composition and balance
Cardiorespiratory endurance
Muscular fitness
Flexibility
Often, clients are apprehensive about taking physical fitness tests. Test anxiety may affect
the validity and reliability of test results. Therefore, you should put your clients at ease by
establishing good rapport, projecting a sense of relaxed confidence, and creating a testing
environment that is friendly, quiet, private, safe, and comfortable. Room temperature should
be maintained at 70 to 74 °F (21-23 °C), and the relative humidity should be controlled
whenever possible. For pretest health screening and interpretation of the client’s test results,
110
the room should have comfortable chairs and a table for completing questionnaires and
paperwork, as well as an examination table or bed for the resting evaluation of heart rate,
blood pressure, and the 12-lead electrocardiogram. All equipment used for physical testing
should be carefully calibrated and prepared before your clients arrive for testing. This will
ensure valid test data and efficient use of time.
TEST VALIDITY, RELIABILITY, AND OBJECTIVITY

To accurately assess your clients’ physical fitness status, you must select tests that are valid,
reliable, and objective. It is necessary to understand these basic concepts fully in order to
evaluate the relative worth of specific physical fitness tests and prediction equations.
Test Validity
With regard to physical fitness testing, test validity is the ability of a test to measure accurately,
with minimal error, a specific physical fitness component. Reference (or criterion) methods
are used to obtain direct measures of physical fitness components. However, some physical
fitness components cannot always be measured directly, requiring the use of indirect measures
for estimation of the value of the reference measure. For example, exercise physiologists
consider the direct measurement of V̇O max (i.e., collection and analysis of expired gas
2
samples) during maximal exercise to be the criterion measure of cardiorespiratory fitness.

Direct measurement of V̇O max, however, requires expensive equipment, considerable
2
technical expertise, and very high levels of client motivation. Therefore in the laboratory
setting, V̇O max is usually estimated using formulas to convert the amount of work output
2
during a GXT to oxygen consumption (see chapter 4). In field settings, prediction equations
are used to estimate V̇O max from a combination of physiological, demographic, and
2
performance predictor variables.

One way in which researchers quantify the validity of physical fitness tests is by calculating
the relationship between predicted scores (y') and the criterion scores (y) using correlation
coefficients (r ). This value, r , is known as the validity coefficient. The magnitude of the
y,y' y,y'
validity coefficient cannot exceed 1.0. The closer the value is to 1.0, the stronger the validity
of the test. Valid physical fitness field tests and prediction equations typically have validity
coefficients in excess of r = .80.
y,y'
Because field tests indirectly estimate a physical fitness component, there will be a
difference between the measured (reference) and predicted values for that component. This
difference (y − y') is called the residual score. The standard error of estimate (SEE) is a
measure of prediction error and is used to quantify the accuracy of the prediction equation
111
and the validity of the field test. The magnitude of the SEE depends on the size of the
residual scores and reflects the average degree of deviation of individual data points around
the line of best fit (or regression line) depicting the linear relationship between the measured
and the predicted scores. When individual data points fall close to the regression line, the
SEE is small (see figure 3.1). A valid field test has a high validity coefficient and a small
prediction error.
FIGURE 3.1 Line of best fit and SEE (prediction error).
In addition to test validity, test sensitivity and specificity are often reported. Sensitivity
refers to the probability of correctly identifying individuals who have risk factors for a specific
disease or syndrome. An example is the probability of correctly identifying individuals with
risk factors for CVD (cardiovascular disease) using body mass index (BMI) and waist
circumference cutoff values. Specificity is a measure of the ability to correctly identify
individuals with no risk factors. Given that the sensitivity and specificity of tests are typically
less than 1.00 (i.e., <100% correct), some individuals will be identified as having risk factors
even though they have none (false positive), and some will be identified as having no risk
factors when they do have some (false negative).
Test Reliability
Reliability is the ability of a test to yield consistent and stable scores across trials and over time.
112
For example, the skinfold test is considered to be reliable because a trained skinfold
technician obtains similar skinfold values when taking duplicate measurements on the same
person. Researchers quantify reliability by calculating the relationship between trial 1 and trial
2 test scores or day 1 and day 2 test scores. This value, r , is known as the reliability
x1,x2
coefficient. The magnitude of the reliability coefficient cannot exceed 1.0. In general,
physical fitness tests have high reliability coefficients, typically exceeding r x1,x2 = .90.
It is important to know that test reliability affects test validity. Tests with poor reliability
also have poor validity because unreliable tests fail to produce consistent test scores. It is
possible, however, for a test to have excellent reliability (r x1,x2 > .90) but poor validity. Even
when a test yields stable and precise values across trials or between days, it may not validly
measure a specific physical fitness component. For example, researchers reported high test-
retest reliability (r
x1,x2 = .99) for the sit-and-reach test but also noted that this test has poor
validity (r ' = .12) as a measure of low back flexibility in women (Jackson and Langford
y,y
1989).
Test Objectivity
Objectivity is also known as intertester reliability. Objective tests yield similar test scores for a
given individual when the same test is administered by different technicians. Objectivity is
quantified by calculating the correlation between pairs of test scores measured on the same
individuals by two different technicians. This value, r , is known as the objectivity
1,2
coefficient. As with validity and reliability coefficients, the magnitude of the objectivity
coefficient cannot exceed 1.0. Most physical fitness tests have high objectivity coefficients (r 1,2
> .90), especially when highly trained technicians practice together and carefully follow
standardized testing procedures.
PREDICTION EQUATION EVALUATION

Although reference measures obtained in the laboratory setting provide the most valid
assessment of each physical fitness component, these tests are expensive and time consuming
and require considerable technical expertise. In field and clinical settings, you can obtain
estimates of these reference measures by selecting valid field tests and prediction equations
that have good predictive accuracy. Table 3.1 provides an overview of the types of tests used
in laboratory and field settings to assess each physical fitness component.
113
To select the most appropriate tests for measuring your clients’ physical fitness, it is
important to evaluate the relative worth of the fitness tests and their prediction equations. To
do this, you should ask the following questions:
What reference measure was used to develop the prediction equation?
As mentioned earlier, the reference or criterion measure of a specific physical fitness

component is obtained by directly measuring the component. Reference measures are used as
a gold standard for validating field tests and developing prediction equations that accurately
estimate the reference measure. For example, skinfold prediction equations are developed and
cross-validated through comparison of the estimated body density (calculated from the
skinfold equation) against the reference measure of body density typically obtained from
hydrodensitometry (underwater weighing). Similarly, the validity of the sit-and-reach test for
measuring low back flexibility was tested by comparing sit-and-reach scores with range of
motion values (reference measure) directly measured by X-ray or goniometric methods. Table
3.1 describes reference measures that experts commonly use to assess each physical fitness
component. Field tests and prediction equations developed using indirect methods instead of
reference methods as the criterion have questionable validity.
114
How large was the sample used to develop the prediction equation?
Large randomly selected samples (N = 100-400 subjects) are generally needed to ensure the
data are representative of the population for whom the prediction equation was developed.
Also, equations based on large samples tend to have more stable regression weights for each
predictor variable in the equation.
What is the ratio of sample size to the number of predictor variables in the equation?
In multiple regression, the correlation between the reference measure of the physical fitness
component and the predictors in the equation is represented by the multiple correlation
coefficient (R ). The larger the R
mc mc (up to maximum value of 1.00), the stronger the
relationship. The size of R will be artificially inflated if there are too many predictors in the
mc
equation compared with the total number of subjects. Statisticians recommend a minimum of
20 subjects per predictor variable. For example, if a skinfold (SKF) prediction equation has
three predictors (e.g., triceps SKF, calf SKF, and age), then the minimum sample size needs
to be 60 subjects. Prediction equations that are based on small samples or that have a poor
subject-to-predictor ratio are suspect and should not be used.
What were the sizes of the Rmc and the standard error of estimate for the prediction
equation?
In general, the R for equations predicting physical fitness components exceeds .80. This
mc
means that at least 64% [R = .80 × 100] of variance in the reference measure can be
2 2
accounted for by the predictors in the equation. As you can easily see, the larger the R , the
mc
greater the amount of shared variance between the reference measure and predictor variables.
When you evaluate the relative worth of a prediction equation, it is more important to focus
on the size of the prediction error (SEE) than on the R because the magnitude of R is
mc mc
greatly affected by sample size and variability of the data. Keep in mind that SEE reflects the
degree of deviation of individual data points (participants’ scores) around the line of best fit
through the entire sample’s data points. In multiple regression, the line of best fit is the
regression line that depicts the linear relationship between the reference measure and all the
predictor variables in the equation. Table 3.1 presents standard values for evaluating
prediction errors of physical fitness prediction equations.
To whom is the prediction equation applicable?
To answer this question, you need to pay close attention to the physical characteristics of the
sample used to derive the equation. Factors such as age, gender, race, fitness level, and body
115
fatness need to be examined carefully. Prediction equations are either population specific or
generalized. Population-specific equations are intended only for individuals from a specific
homogeneous group. For example, separate skinfold equations have been developed for boys
and girls 6 to 17 years of age (see table 8.3). Population-specific equations are likely to
systematically over- or underestimate the physical fitness component if they are applied to
individuals who do not belong to that population subgroup. On the other hand, there are
generalized prediction equations that can be applied to individuals who differ greatly in
physical characteristics. Generalized equations are developed using diverse, heterogeneous
samples, and they account for differences in physical characteristics by including these
variables as predictors in the equation. For example, the prediction equation for the Rockport
walking test (see chapter 4) is generalized because gender and age are predictors in this
equation.
How were the variables measured by the researchers who developed the prediction
equation?
It is important to know not only which variables are included in a prediction equation but
also how each one of these predictors was measured by the researchers developing the
equation. Although it is highly recommended that standardized procedures be used for all
physical fitness testing, this is not always done. For example, the suprailiac skinfold used in
the skinfold equations developed by Jackson, Pollock, and Ward (1980) is measured above
the iliac crest at the anterior axillary line. In contrast, the Anthropometric Standardization
Reference Manual (Lohman, Roche, and Martorell 1988) recommends that the suprailiac
skinfold be measured above the iliac crest at the midaxillary line. For most individuals, there
will be a difference between skinfold thicknesses measured at these two sites. Thus, larger-
than-expected prediction errors may result if physical fitness variables are not measured
according to the descriptions provided by the researchers who developed the equation.
Was the prediction equation cross-validated on another sample from the population?
An equation must be tested on other samples from the population before its validity or
predictive accuracy can be determined. For example, the Rockport 1.0 mi (1.6 km) walking
test was originally developed to assess the cardiorespiratory fitness of women and men aged
20 to 69 (Kline et al. 1987). Other researchers cross-validated this equation to establish its
predictive accuracy for women 65 yr of age or older (Fenstermaker, Plowman, and Looney
1992) and for male officers and enlisted men (19 to 44 yr) of the U.S. Air Force (Weiglein et
al. 2011). In general, prediction equations that have not been cross-validated on the original
116
study sample or on additional samples in other studies should not be used.
What were the sizes of the validity coefficient (ry,y") and the prediction errors when this
equation was applied to the cross-validation sample (i.e., what is the group predictive
accuracy of the equation)?
An equation with good predictive accuracy has a moderately high validity coefficient (r > y,y"
.80) and an acceptable prediction error (see the group prediction error column in table 3.1).
In cross-validation studies, the accuracy of an equation for estimating the reference values of a
group is assessed by analyzing two types of prediction error: the SEE and the total error (TE).
As mentioned, the SEE reflects the average deviation of individual data points from the
regression line, or line of best fit (see figure 3.1). The total error (TE) is the average degree of
deviation of individual data points from the line of identity (see figure 3.2). The line of
identity has a slope of 1.0 and a y-intercept equal to 0. When an equation closely predicts the
actual or measured scores of the cross-validation sample, individual data points fall close to
the line of identity (i.e., TE is small). Acceptable values for evaluating group prediction errors
(SEE and TE) are presented in table 3.1.
FIGURE 3.2 Line of identity and total error (prediction error).
Was the average predicted score similar to the average reference score for the cross-
validation sample?
The prediction equation should yield similar mean values for the actual (measured or
reference) and predicted scores of the cross-validation sample. The constant error (CE) is the
difference between the actual and predicted means. The means are compared using a paired
t-test, and they should not differ significantly from each other. A large significant difference
117
indicates a bias or systematic difference (i.e., over- or underestimation) between the original
validation sample and the cross-validation sample. This difference is caused by technical error
or biological variability between the samples.
How good is the prediction equation for estimating reference values of individual clients
(i.e., what is the individual predictive accuracy of the equation)?
Although a prediction equation may accurately estimate the average reference score for a
specific group, it may not necessarily give accurate estimates for all individuals comprising
that group. To evaluate how well a prediction equation works for individuals, researchers use
the Bland and Altman method (1986), which sets limits of agreement around the average
difference (d) between the actual and predicted scores for the sample. With this method,
difference scores (actual − predicted values) and average scores [(actual + predicted values) / 2]
are calculated for each individual in the sample and are plotted on a graph (see figure 3.3).
When the difference scores are normally distributed, 95% lie within ±2 standard deviations
from the overall mean difference (d) for the group. In this case, the standard deviation of the
difference scores (S ) is used to set the upper (+2S ) and lower (−2S ) limits of agreement.
d d d
Smaller 95% limits of agreement indicate that the equation has a better individual predictive
accuracy. The limits of agreement estimate how well you will be able to predict your clients’
actual value when using the equation. In the example in figure 3.3, the predictive accuracy of
the equation for estimating the actual relative body fat (%BF) of individual clients is
approximately ±6% BF (note the upper and lower limits of agreement on the y-axis of the
graph).
FIGURE 3.3 Bland-Altman plot with 95% limits of agreement.
In summary, you should apply all of the following evaluation criteria when selecting field
118
tests and prediction equations that indirectly assess the physical fitness of your clients:
An acceptable method is used to derive reference measures of the physical fitness

component.
A large sample (N = 100-400) and 20 to 40 subjects per predictor variable are used
to develop the equation.
The sizes of the multiple correlation and validity coefficients exceed .80.
The group prediction errors (SEE and TE) are acceptable (see table 3.1).
Demographic characteristics (e.g., age, gender, race, fitness status) of the validation
and cross-validation samples are described.
The prediction equation is cross-validated in the original study or on independent
samples from other studies.
The constant error (bias), or difference between the measured and predicted means
for the cross-validation sample, is not statistically significant.
The 95% limits of agreement are acceptable (see table 3.1).
PHYSICAL FITNESS TESTS: ADMINISTRATION AND

INTERPRETATION
To obtain good test results, it is important to prepare your clients for physical fitness testing
by giving them appropriate instructions at least 1 day before the scheduled exercise tests.
Pretest Instructions
Give the client directions to the testing facility and make special arrangements if the facility
requires a parking pass. Make sure the client has the following instructions in preparation for
the test:
Wear comfortable clothing, socks, and athletic shoes, if available.

Drink plenty of fluids during the 24 hr period before the test.
Refrain from eating, smoking, and drinking alcohol or caffeine for 3 hr prior to the
test.
Do not engage in strenuous physical activity the day of the test.
Get adequate sleep (6-8 hr) the night before the test.
119
Test Administration
Later chapters give detailed procedures for administering laboratory and field tests for each
physical fitness component. Your technical skills and expertise in administering these tests are
directly related to your mastery of standardized testing procedures and the amount of time
you spend practicing testing techniques. For example, to become a proficient skinfold
technician, you should practice on at least 50 people (Jackson and Pollock 1985). You also
need a great deal of practice in order to measure exercise blood pressures and heart rates
accurately and to coordinate the timing of these measurements during a GXT on the
treadmill or cycle ergometer. Remember that you cannot obtain valid test scores if you do not
follow the standardized testing procedures.
Test Interpretation
After collecting the test data, you must analyze and interpret the results for each client.
Computer software programs are available that display and compare the client’s test results
against normative data. Some graphs display the individual’s physical fitness profile so you
and your client can easily pinpoint strengths as well as physical fitness components in need of
improvement.
To classify your clients’ physical fitness status, you should compare test scores against
established norms. For this purpose, age-gender norms are provided for many of the
cardiorespiratory fitness, muscular fitness, body composition, flexibility, and balance tests
included in this book. For some tests, percentile rankings are used to classify a client’s
performance. To illustrate the interpretation of a percentile ranking, let’s use the example of a
35 yr old male client whose sit-and-reach score ranks in the 60th percentile. This ranking
means his score is better than 60% of the scores of all males the same age taking this test.
When interpreting results for clients, use lay language, rather than highly technical terms
and jargon, to explain their test scores. Whenever possible, try to phrase poor results in
positive terms. For example, if a female client’s body fat level is classified as obese, do not
embarrass and alarm her by saying something like this: “Your underwater weighing test
indicates you are obese and need to lose at least 20 pounds to achieve a healthy body fat level
in order to reduce your risk of diseases linked to obesity. You need to decrease your caloric
intake and increase your caloric expenditure by dieting and exercising. The sooner you start a
weight management program, the better.”
Instead, you should use a more positive and less intimidating approach when interpreting
this result. The following approach is more appropriate, especially for clients with low self-
120
efficacy or motivation to initiate and adhere to an exercise program: “Women with more than
35% body fat are at risk for disease. If you wish, I will evaluate your daily calorie intake and
suggest healthy foods you like to eat that are low in fat. Also, we can discuss ways to increase
your physical activity level. I think we can find some activities you will enjoy and have time
for, so that you’ll burn more calories each day. With these changes, you should be able to
lower your body fat to a healthy level in a reasonable amount of time.”
BASIC PRINCIPLES FOR EXERCISE PROGRAM DESIGN

A number of basic training principles apply to all types of exercise programs, whether they
improve cardiorespiratory fitness, musculoskeletal fitness, body composition, or flexibility.
• Specificity-of-training principle. The specificity principle states that the body’s
physiological and metabolic responses and adaptations to exercise training are specific to the
type of exercise and the muscle groups involved. For example, physical activities requiring
continuous, dynamic, and rhythmic contractions of large muscle groups are best suited for
stimulating improvements in cardiorespiratory endurance; stretching exercises develop range
of joint motion and flexibility; and resistance exercises are effective for improving muscular
strength and muscular endurance. Furthermore, the gains in muscular fitness are specific to
the exercised muscle groups, type and speed of contraction, and training intensity.
• Overload training principle. To promote improvements in physical fitness components, the
physiological systems of the body must be taxed using loads that are greater (overload
principle) than those to which the individual is accustomed. Overload can be achieved
through increases in the frequency, intensity, and duration of aerobic exercise. Muscle groups
can be effectively overloaded through increases in the number of repetitions, sets, or exercises
in programs that improve muscular fitness and flexibility.
• Principle of progression. Throughout the training program, you must progressively increase
the training volume, or overload, to stimulate further improvements (i.e., progression
principle). The progression needs to be gradual because doing too much, too soon may cause
musculoskeletal injuries. This is a major reason why some individuals drop out of exercise
programs.
• Principle of initial values. Individuals with low initial physical fitness levels will show
greater relative (%) gains and a faster rate of improvement in response to exercise training
than individuals with average or high fitness levels (initial values principle). For example,
during the first month of an aerobic exercise program, the V̇O max of a client with poor
2
cardiorespiratory endurance capacity may improve 12% or more, whereas a highly trained
121
endurance athlete may improve only 1% or less.
• Principle of interindividual variability. Individual responses to a training stimulus are quite
variable and depend on a number of factors such as age, initial fitness level, and health status
(i.e., interindividual variability principle). You therefore must design exercise programs with
the specific needs, interests, and abilities of each client in mind and develop personalized
exercise prescriptions that take into account individual differences and preferences.
• Principle of diminishing returns. Each person has a genetic ceiling that limits the extent of
improvement that is possible from exercise training. As individuals approach their genetic
ceiling, the rate of improvement in physical fitness slows and eventually levels off (i.e.,
diminishing returns principle).
• Principle of reversibility. The positive physiological effects and health benefits of regular
physical activity and exercise are reversible. When individuals discontinue their exercise
programs (detraining), exercise capacity diminishes quickly. Within a few months, most of
the training improvements are lost (i.e., reversibility principle).
THE ART AND SCIENCE OF EXERCISE PRESCRIPTION

Traditionally, some exercise specialists have focused on rigidly applying scientific principles of
exercise prescription and have devoted little or no attention to the art of exercise prescription.
As an exercise programming artist, you need to be creative and flexible, able to modify the
exercise prescription based on your clients’ goals, behaviors, and responses to the exercise.
Using both a scientific and an artistic approach will enable you to personalize the exercise
prescription, increasing the probability your clients will make long-term commitments to
include physical activity and exercise as an indispensable part of their lifestyles.
BASIC ELEMENTS OF THE EXERCISE PRESCRIPTION

Although prescriptions are individualized for each client, basic elements are common to all
exercise prescriptions. These include mode, intensity, duration, frequency, and progression.
Mode
As mentioned earlier, the specificity-of-training principle implies that certain types of
exercise training are better suited than others for developing specific components of physical
fitness. Table 3.2 presents types of training and examples of exercise modes that optimize
improvements for each physical fitness component.
Table 3.2 Types of Training and Exercise Modes for Improving Physical Fitness Components
122
Physical fitness
Type of training Exercise modes
component
Walking, jogging, cycling, rowing, swimming, stair climbing, simulated cross-country skiing, aerobic dance, step aerobics, elliptical
Cardiorespiratory endurance Aerobic exercise
activity
Muscular strength and muscular

Resistance exercise Free weights, exercise machines, body weight exercises, exercise bands
endurance
Weight-bearing high-impact aerobic exercise and Load-bearing calisthenics, high-intensity cardio, plyometrics, free weights, body weight exercises, exercise machines, whole-body
Bone strength
resistance exercise vibration, walking for femoral neck strength
Body composition Aerobic exercise and resistance exercise Same modes as listed for cardiorespiratory endurance and muscular strength
Flexibility Stretching exercise Static stretches, PNF stretches, yoga, tai chi, Pilates
Balance Balance training Tai chi, yoga, Pilates, balance exercises
PNF = proprioceptive neuromuscular facilitation.
To promote changes in body composition and bone strength, many experts recommend
using more than one type of exercise training. For body composition changes, you should
prescribe a combination of aerobic exercise to reduce body fat and resistance exercise to build
muscle and bone. Similarly, high-intensity weight-bearing activities, plyometrics, and
resistance training are all effective for building bone mass for improved bone health.
Intensity
Exercise intensity dictates the specific physiological and metabolic changes in the body during
exercise training. As mentioned previously, the initial exercise intensity in the exercise
prescription depends on the client’s program goals, age, capabilities, preferences, and fitness
level. This intensity should stress, but not overtax, the cardiopulmonary and musculoskeletal
systems. Later chapters provide detailed information and guidelines on selecting exercise
intensities for the development of each physical fitness component as well as for the
progression of exercise intensity.
Duration
Duration and intensity of exercise are inversely related: The higher the intensity, the shorter
the duration of the exercise. Exercise duration depends not only on the intensity of exercise
but also on the client’s health status, initial fitness level, functional capability, and program
goals. For improved health benefits, the American College of Sports Medicine (ACSM) and
the Centers for Disease Control and Prevention (CDC) recommend that every individual
should accumulate at least 150 min/wk of moderate-intensity or 75 min/wk of vigorous-
intensity aerobic exercise or a weekly combination of moderate- and vigorous-intensity
aerobic exercise. This amount of physical activity can be achieved in either daily continuous
bouts (e.g., 30 min moderate-intensity exertion 5 days/wk) or multiple bouts of shorter
duration throughout the day (e.g., multiple bouts of 10 min or more in a day), depending on
the client’s functional capacity and time constraints.
123
As the client adapts to the exercise training, the duration of exercise may be slowly
increased (e.g., by 5-10 min per session) about every 1 to 2 wk for at least the first month.
For older and less fit individuals, the ACSM (2018) recommends increasing exercise
duration, rather than intensity, in the initial stages of the exercise program; however,
gradually moving the client toward the minimum threshold requirement of both duration and
intensity is important in terms of maximizing the benefits of the program. For most clients,
the duration of aerobic, resistance, and flexibility exercise workouts should not exceed 60 min.
This will lessen the chance of overuse injuries and exercise burnout.
Frequency
Frequency typically refers to the total number of weekly exercise sessions. Research shows
that exercising 3 days/wk on alternate days is sufficient to improve various components of
physical fitness. However, frequency is related to the duration and intensity of exercise and
varies depending on the client’s program goals and preferences, time constraints, and
functional capacity. Sedentary clients with poor initial fitness levels may exercise more than
once a day, and clients with diabetes should exercise daily or miss no more than 2 consecutive
days in a week. When improved health is the primary goal of the exercise program, the
ACSM and CDC recommend either 3 days/wk of vigorous-intensity exercise or 5 days/wk of
moderate-intensity exercise or 3 to 5 days/wk of a combination of moderate- and vigorous-
intensity exercise. If you prescribe daily physical activity for an apparently healthy client, it is
important to vary the type of exercise (i.e., aerobic, resistance, flexibility, and balance
exercises) or exercise mode (e.g., walking, cycling, and weightlifting) to lessen the risk of
overuse injuries to the bones, joints, and muscles.
Progression of Exercise
Throughout the exercise program, physiological and metabolic changes enable the individual
to perform more work. For continued improvements, the cardiopulmonary and
musculoskeletal systems must be progressively overloaded through periodic increases in the
frequency, intensity, and duration of the exercise.
When applying the principle of progression to an exercise prescription, increase the
frequency, intensity, and duration gradually, and do so one element at a time. A simultaneous
increase in frequency, intensity, and duration, or in any combination of these elements, may
overtax the individual’s physiological systems, thereby increasing the risk of exercise-related
injuries and exercise burnout. Generally, for older and less fit clients, it is better to increase
exercise duration, instead of exercise intensity, especially during the initial stage of their
124
exercise prescriptions. Using subjective ratings of perceived exertion is more beneficial than
metabolic equivalent (MET) levels when progressing the exercise prescription of an older
adult. On a 10-point scale, a 5 or 6 is representative of moderate intensity (noticeably
increased heart rate, or HR, and breathing rate) while a 7 or 8 represents vigorous intensity
(substantial increases in HR and breathing rate) (ACSM 2018).
STAGES OF PROGRESSION IN THE EXERCISE PROGRAM

Most individualized exercise programs include initial conditioning, improvement, and
maintenance stages. The initial conditioning stage typically lasts 1 to 6 wk and serves as a
primer to familiarize the client with exercise training. During this stage, you should prescribe
stretching exercises, light calisthenics, and low-intensity aerobic or resistance exercises. Have
your clients progress slowly by increasing exercise duration first, followed by small increases in
exercise intensity. The initial stage of the exercise program may be skipped for some
physically active individuals provided their initial fitness level is good to excellent and they are
accustomed to the exercise modes prescribed for their programs.
The improvement stage of the exercise program typically lasts 4 to 8 mo, and the rate of
progression is more rapid than in the initial conditioning stage. During this stage, the
frequency, intensity, and duration are systematically and slowly advanced, one element at a
time, until the client’s fitness goal is reached.
The maintenance stage of the exercise program helps clients preserve the level of fitness
they achieved by the end of the improvement stage. This stage should continue on a regular,
long-term basis. The amount of exercise required to maintain the client’s physical fitness level
is less than that needed to improve specific fitness components. Thus, the frequency of a
specific mode of exercise used to develop any given fitness component can be decreased and
that mode replaced with other types of physical activities. By the end of the improvement
stage, for example, a client may be jogging 5 days/wk. For maintenance, jogging may be
reduced to 2 or 3 days/wk, and different aerobic activities (e.g., swimming, rowing, cycling,
stair climbing) or other types of exercise and sport activities (e.g., weightlifting, hiking,
tennis) may be substituted the other 3 days. Including a variety of enjoyable physical activities
during this stage helps clients counteract boredom and maintain their interest level.
EXERCISE PROGRAM ADHERENCE

Exercise professionals face the challenge of convincing individuals to start exercising and to
make a lifelong commitment to a physically active lifestyle. Approximately 8 out of every 10
125
adults (78.5%) in the United States do not get the recommended amount of physical activity
(Centers for Disease Control and Prevention 2015b). Exercise specialists play an important
role in educating the public about why regular physical activity is absolutely essential for good
health and on how to exercise safely and effectively.
Of those individuals starting an exercise program, almost 50% will drop out within 1 yr
(Dishman, Sallis, and Orenstein 1985). A newer survey indicates there is a 3.7% probability
(i.e., very small) that a new member will maintain active membership beyond 1 yr. It is
anticipated that 37% will quit by the third month (Sperandei, Vieira, and Reis 2016). As an
exercise specialist, you must help your clients develop a positive attitude toward physical
activity and make a firm commitment to the exercise program. To increase adherence, you
need to be aware of factors related to exercise attrition.
Many factors influence regular participation in physical activity and adherence to an
exercise program (see table 3.3). Although every individual is unique, terminating a fitness
center membership may not mean the client stopped participating in regular physical activity.
Statistical predictors of continued fitness center membership are associated with initial
physical activity level, body mass index, and age at enrollment. The key motivating factors are
health, aesthetics, hypertrophy, and weight loss (Sperandei, Vieira, and Reis 2016). Knowing
the factors associated with continued participation in physical activity will direct your
approach and the steps you take to facilitate your clients’ adherence to their exercise
programs. Focus on factors that are potentially modifiable, such as exercise facilities, program
variables (e.g., exercise intensity and perceived exertion), enjoyable scenery while exercising,
and support from their spouse, family, friends, and peers.
Table 3.3 Select Factors Related to Physical Activity Participation and Exercise Program Adherence
Category Positive factors Negative factors

Education Age
Demographic and biological

Gender a Race b
Socioeconomic status Overweight or obesity
Enjoyment of exercise
Expected benefits of exercise
Barriers to exercise
Psychological, cognitive, emotional Perceived health and fitness
Mood disturbance
Self-efficacy
Self-motivation
Activity history during adulthood

Behavioral Smoking
Healthy dietary habits
Physician influence
Social-cultural Social isolation
Support from spouse, family, friends, or peers
Access to exercise facilities

Satisfaction with exercise facility
Exercise equipment at home Climate or season
Environmental
Enjoyable scenery Urban location
Observing others exercising
Neighborhood safety
Exercise leadership and supervision Initial exercise intensity
126
Variety of exercise modes and activities Perceived effort
aMales are more likely to be physically active than females.
bWhites are more physically active than non-whites.

Data from Sallis and Owen 1999; Trost et al. 2002.
As an exercise specialist, you also need to understand and implement psychological models
related to successful behavior change. For an excellent overview of behavior change theories
and discussion of strategies you can use to help your clients adopt and maintain a physically
active lifestyle, see Napolitano and colleagues (2010). Models that may be useful for
encouraging exercise and improving adherence to exercise include the following:
Behavior modification model

Health belief model
Social cognitive model
Transtheoretical model of health behavior change (stages of motivational readiness
for change)
Decision-making theory
Model of reasoned action and theory of planned behavior
Self-determination theory
With the behavior modification model, clients become actively involved in the change
process by setting realistic short- and long-term goals, developing a plan to achieve these
goals, and signing a contract that describes each goal and how it may be achieved.
Throughout the exercise program, you should provide your clients with feedback and revise
the plan as needed. You can help your clients adopt physical activity into their lifestyle,
develop a social support system, and implement behavior counseling strategies such as
keeping a diary of their physical activity. Sometimes it can be effective to give rewards such as
T-shirts, certificates, emblems, and pins to recognize the attainment of specific goals, such as
walking a total of 50 mi (80.5 km) in 1 mo. Help your clients set both short-term and long-
term goals that are attainable. For this purpose, you can periodically reevaluate your clients’
fitness levels to assess improvement. You can state goals in performance or physiological
terms. An example of a short-term performance goal is to complete a 3 mi (4.8 km) fun run
in less than 33 min. A long-term physiological goal might be to increase maximum oxygen
uptake (V̇O max) by 15% in 4 mo. As the exercise specialist, you must help each individual
2
set realistic goals.
127
set realistic goals.
The health belief model is based on the assumption that individuals will engage in exercise
on a regular basis because they perceive the threat of disease and believe this threat is severe
and they are susceptible to disease. When the benefits outweigh the barriers, individuals will
take action and adopt exercise into their lifestyle. Self-efficacy and cues to action are
important components of this model (ACSM 2018).
The social cognitive model, developed by Bandura (1982), is based on the concepts of self-
efficacy and outcome expectation. The likelihood that people will engage in a specific
behavior, like exercising regularly, depends on their self-efficacy or perception of their ability
to perform the task, as well as their confidence in making the behavioral change
(Grembowski et al. 1993). To assess self-efficacy, have your clients rate, on a scale of 0% to
100%, their confidence in making the specific behavior change. Individuals with high self-
efficacy ratings (≥70%) believe they have the knowledge and skill to exercise successfully. As a
result, they are more likely to succeed in making a long-term behavior change. To increase
self-efficacy, educate your clients so they fully understand their beliefs, and help them identify
specific barriers to engaging in physical activity. Techniques for improving your clients’
exercise self-efficacy include performance mastery (e.g., teach your clients scientifically sound
and safe exercise principles and techniques, and allow them to practice these techniques);
modeling (e.g., give clients an opportunity to observe role models who are performing the
exercise successfully); positive reinforcement (e.g., compliment clients when they perform
activities correctly or improve a specific physical fitness component); and emotional arousal
(e.g., educate clients about the health benefits of physical activity and exercise). Ashford,
Edmunds, and French (2010) provide a detailed review and analysis of studies designed to
change self-efficacy for the promotion of lifestyle and recreational physical activity.
The transtheoretical model describes the process clients go through when adopting a
change in health behavior (e.g., exercising). The basic concepts of this model are as follows:
Clients progress through five stages of changes at different rates.

In this process, clients may move back and forth through the stages of change.
Clients use different cognitive and behavioral strategies in this process.
Clients weigh the costs and benefits of the health behavior change.
To effectively apply this model, the exercise specialist needs to be aware of the client’s stage
of readiness for participating in exercise. The stages of motivational readiness for change
128
and maintain a new habit (Prochaska and DiClemente 1982). This model has been used to
facilitate long-term changes in health behaviors such as smoking (Gökbayrak et al. 2015),
weight management (da Silva et al. 2015), dietary modification (Knight et al. 2015), and
stress management (Jones et al. 2017) as well as in physical activity behaviors (Dishman,
Jackson, and Bray 2014). A client’s ability to make a long-term commitment to an exercise
program or to daily physical activity is based on the individual’s motivational readiness for
change. The following example illustrates the five stages of motivational readiness in terms of
changing exercise behavior:
1. Precontemplation: Client does not exercise and does not intend to start exercising.
2. Contemplation: Client is not exercising but intends to start.
3. Preparation: Client is exercising but is not meeting the recommended amount of
physical activity.
4. Action: Client has been performing the recommended amount of exercise regularly
for less than 6 mo.
5. Maintenance: Client has been exercising regularly at the recommended amount for
6 mo or longer.
Individuals are at different stages of readiness for change; therefore, you need to match
intervention strategies to the client’s stage and tailor your approach to meet the individual’s
needs, interests, and concerns. Detailed descriptions of how to plan and deliver physical
activity intervention strategies specific to the stages of change are available (see ACSM 2018;
de Vries et al. 2016; Hebden et al. 2013; Partridge et al. 2015; Pekmezi, Barbera, and Marcus
2010).
The decision-making theory proposes that individuals decide whether or not to engage in
a behavior by weighing the perceived benefits and costs of that behavior. Clients are more
likely to exercise when they perceive that the benefits outweigh the costs (e.g., “I feel better
about myself when I exercise even though it takes time from my busy schedule”). Clients in
early stages of motivational change (e.g., precontemplation stage) tend to perceive more
disadvantages compared with clients in later stages (e.g., action stage) of change (Pekmezi,
Barbera, and Marcus 2010). To assess your clients’ motivational readiness and decisional
balance for exercise, you may use a 16-item self-report tool (see Marcus, Rakowski, and Rossi
1992).
The theory of reasoned action proposes a way to understand and predict an individual’s
129
The theory of reasoned action proposes a way to understand and predict an individual’s
behavior. According to this theory, intention is the most important determinant of behavior;
intention is highly influenced by the individual’s attitudes and subjective behavioral norms.
For example, believing that exercise results in positive outcomes leads to a favorable attitude
about engaging in physical activity and the intention to do so. Subjective behavioral norms, or
perceptions about what others think or believe about exercise, may also influence a client’s
intention (Downs 2006). The theory of planned behavior extends the theory of reasoned
action by taking into consideration the client’s perception of behavioral control (i.e., perceived
power and control). This theory proposes that individuals intend to perform a specific
behavior (e.g., exercising) if they evaluate it positively (e.g., attitude), believe others think it is
important (subjective norms), and perceive the behavior to be under their control (e.g.,
power). Although this theory provides useful information behind the formulation of intention
for adopting exercise behavior, intention alone is insufficient for predicting whether or not
your clients will adopt a physically active lifestyle (Napolitano et al. 2010). A client’s
perception of control influences his intention and behavior toward becoming physically
active. You can bolster your clients’ belief in control through strategies to reduce barriers,
increase opportunities, and increase access to resources for participation (Motalebi et al
2014).
In helping your clients adopt and maintain a physically active lifestyle, it is also important
to understand their motivation or degree of determination for changing or avoiding this
behavior. Motivation is a complex construct; it may be described as falling along a
continuum, ranging from no motivation (i.e., amotivation) to intrinsic motivation. The
schematic (Teixeira et al. 2012) of the self-determination theory (Deci and Ryan 2000)
depicts mediating mechanisms that may influence specific psychological needs (i.e.,
autonomy, competence, and relatedness). These needs ultimately lead through a continuum
of motivation and on toward adoption and maintenance of exercise behavior. The self-
determination theory identifies four levels of motivation (Mears and Kilpatrick 2008):
1. Amotivation: The individual has no intention or desire to engage in exercise.

2. Other-determined motivation: The individual is motivated to exercise by outside
factors such as rewards, guilt, fear, or pressure; long-term adherence is unlikely.
Possible motives for exercising may be “I exercise to lose weight” or “My partner
thinks I should exercise more.”
3. Self-determined extrinsic motivation: The individual values exercise, is motivated
130
autonomy) to exercise without a sense of outside pressure. A possible motive for
exercising may be “I exercise because it is an important part of my healthy lifestyle.”
4. Intrinsic motivation: The individual engages in exercise for the sheer enjoyment
and satisfaction it brings to sense of well-being; enjoying exercise for its own sake
leads to adherence. The probable motive for exercising is “I am a physically active
person, and I exercise because I like doing it.”
The ultimate goal of this approach is to get clients to value physical activity and to think of
themselves as exercisers rather than to use exercise to attain an external goal like weight loss.
Some individuals may never reach the point of exercising for sheer enjoyment of the activity;
however, valuing exercise may be enough to get clients to adhere to their exercise regimens
(Rodgers and Loitz 2009). Much more research, including longitudinal research, is needed to
better understand why people do or do not adopt a physically active lifestyle. Analyzing
results by gender may reveal information that is lost when looking at the data of men and
women together (Teixeira et al. 2012).
Questionnaires have been developed to assess your clients’ exercise motivation. The
Behavioral Regulation in Exercise Questionnaire measures your clients’ level of motivation on
a continuum, ranging from amotivation to intrinsic motivation (Markland and Tobin 2004).
The Exercise Motivation Inventory (Markland and Ingledew 1997) measures specific motives
(i.e., guilt, enjoyment, fitness) for engaging in exercise; Egli and colleagues (2011) identified
differences in exercise motivation by age, race, and sex with this questionnaire. You can use
questionnaire results to help your clients understand their level of motivation and to develop
ways to improve their exercise motivation. Rodgers and Loitz (2009) offer suggestions and
steps you can take to understand and improve your clients’ motivation to exercise (see Tips to
Enhance Exercise Motivation).
As an exercise specialist, you need to integrate principles from each of these models and
implement strategies to improve your clients’ exercise program adherence. The ACSM
(2018) recommends program modifications and motivational strategies to increase long-term
adherence to an exercise program (see Strategies to Increase Exercise Program Adherence).
The key to increasing exercise program adherence lies in the leadership, education, and
motivation that you provide. First, you must be a positive role model. You also must be
knowledgeable and able to educate clients about exercise and fitness, provide motivation, and
encourage social support. The mediators most likely to help clients become more physically
active include self-regulation, self-efficacy, and autonomous motivation (Teixeira et al. 2015).
131
active include self-regulation, self-efficacy, and autonomous motivation (Teixeira et al. 2015).
TIPS TO ENHANCE EXERCISE MOTIVATION
Try to understand why the client is there:
Is the motive external? Try to move the client’s focus to a value motive.
Focus on integrating the exercise with the client’s sense of self.
Create opportunities to experience competence:
Put clients in a position where they can easily see and hear you and receive direction from you.
Celebrate meaningful successes; don’t overemphasize trivial accomplishments.
Use clear, appropriate communication strategies; avoid jargon.
Be respectful of your client’s efforts.
Create opportunities for autonomy:
Give choices and options.
Relate the exercises to your client’s goals.
Avoid coercive and controlling encouragement.
Create opportunities for relatedness:
Introduce the client to other participants.
Give tips and instructions on the expected behavior, including proper etiquette.
Communicate understanding of your client’s perspective.
Bottom line:
Pay attention to factors that create opportunities for your clients to feel competent, related, and
autonomous.
Encourage value motives for exercising; downplay external reasons for exercising.
Based on Rodgers and Loitz 2009.
STRATEGIES TO INCREASE EXERCISE PROGRAM ADHERENCE
Recruit physician support of the exercise program.
132
Advocate exercising with others.
Offer a variety of enjoyable exercise and fitness activities.
Provide positive reinforcement through periodic testing.
Recruit support for the program from clients’ families and friends. Add optional recreational games to the
conditioning program.
Use progress charts to record exercise achievements.
Establish a reward system to recognize participant accomplishments.
Provide qualified exercise professionals who are well trained, innovative, and enthusiastic.
USING TECHNOLOGY TO PROMOTE PHYSICAL ACTIVITY

Technology is a double-edged sword. Computers, for example, contribute to sedentary
leisure-time behaviors (e.g., playing seated computer games, surfing the web). On the other
hand, technology is being used to promote physical activity and change exercise behavior. For
years, wearable technology such as pedometers, accelerometers, and heart rate monitors have
been used as motivational tools. Integration of these older tools with newer technologies (e.g.,
smartphones, apps) has expanded the potential for technology to provide nearly instantaneous
feedback, monitoring, and promotion of physical activity by the individual and by one’s social
network. Clinicians, trainers, and health promotion experts are using physical activity
interventions that incorporate technology to encourage and change exercise behavior. Such
interventions are more effective if they incorporate behavior change theory, especially planned
behavior theory, include multiple behavior change techniques, and utilize multiple methods
(e.g., text messaging, social media posts) for interacting with clients (Webb et al. 2010).
Efforts are under way to identify the most synergistic combination of behavior change
techniques and tools with the greatest likelihood of increasing physical activity behaviors,
regardless of how the behavioral change message is delivered (van Genugten et al. 2016). In
this section, questions addressing the use of various technologies for promoting exercise
program adherence are addressed.
How are pedometers used to promote physical activity?
Pedometers count and track the number of steps taken over the course of a day. Most
pedometers provide a fairly accurate count of steps taken while walking, jogging, and
running. Clients can track and monitor their progress in meeting exercise program goals.
Pedometers are fairly simple, low-cost devices. They are sometimes given away as incentives
133
Pedometers are fairly simple, low-cost devices. They are sometimes given away as incentives
for participating in an activity-related initiative such as health fairs, community-based
programs, or employee wellness programs. Pedometer-based walking programs are associated
with significant decreases in BMI, body weight, waist circumference, systolic blood pressure,
and CVD risk factors. Improvements in quality of life and HDL-C values have also been
reported (Cayir, Menekse, and Akturk 2015; Guglani, Shenoy, and Singh 2014; Miyazaki et
al. 2015).
00:00 / 00:00
Video 3.1
Do pedometers have to be positioned at the waist to ensure accuracy?
To provide accurate step counts, older, simpler pedometer models need to be attached in an
upright position to a firm waistband or the shank of the lower leg. However, newer
pedometers with piezoelectric sensors are position-independent and can be worn anywhere
on the body (Lee et al. 2015; Liu et al. 2015). Studies revealed that the accuracy of pedometer
step counts is related to the combination of walking speed and where the pedometer is worn
(Ehrler, Weber, and Lovis 2016; Femina et al. 2016; Lee et al. 2015). For people who walk at
a slow pace, have difficulty walking, or shuffle their feet, a wrist-worn pedometer is more
accurate than one at the hip. For those capable of moving at faster speeds, a waist-worn
pedometer is more accurate than pedometers worn elsewhere (Ehrler, Weber, and Lovis
2016; Lee et al. 2015).
What is the recommended number of steps per day for health benefits?
Tracking progress toward an age-appropriate goal for steps taken in a day is key as a person
moves from being irregularly active to being more consistently active. For adults,
accumulating 10,000 steps per day (see table 3.4) is a recommended goal. Accumulating
8,000 to 9,000 steps per day at a rate of 100 steps∙min or more is equivalent to 30 min of
−1
adult moderate-intensity physical activity, the health benefit threshold. For adult weight loss,
134
recommend a higher step count goal: 12,000 steps∙day for girls and 15,000 steps∙day for
−1 −1
boys (Katanista et al. 2015; Tudor-Locke et al. 2004).
What additional information can be tracked using a pedometer?
Some newer pedometers can estimate how far the wearer has traveled, the total time spent
walking or jogging at a moderate intensity, and how many 10-min bouts of moderate-
intensity activity were performed. Although some pedometers provide an estimate of the
calories expended during physical activity, these results generally underestimate the reference
values. Additional information about the validity and accuracy of pedometers is available
(Femina et al. 2016; Lee et al. 2015; Tudor-Locke et al. 2011).
Are accelerometers better than pedometers for monitoring activity?

Accelerometers record acceleration of the body or body segments moment by moment.
Consequently, they may provide more detailed information about the frequency, duration,
intensity, and patterns of movement than do traditional pedometers. Some accelerometers
can identify body position (e.g., seated, standing) and estimate daily energy expenditure. Like
pedometers, however, accelerometers do not provide accurate estimates of energy expenditure
and should not be worn at the hip during slow walking. Accelerometers provide an objective
measure of the wearer’s compliance with an aerobic exercise prescription or the recommended
physical activity guidelines for cardiorespiratory health (see chapter 1). Since the data storage
capacity of accelerometers is larger than that of pedometers, your clients can store weeks’
worth of their activity data.
Accelerometer technology is now integrated into some of the newer types of pedometers.
As a result, some pedometers may be capable of providing several of the same features and
functions as an accelerometer.
Do accelerometers need to be placed at a specific location on the body to ensure accuracy?
135
Do accelerometers need to be placed at a specific location on the body to ensure accuracy?
The internal workings of accelerometers (piezoelectric sensors plus miniaturized gyroscopes,
inclinometers, magnetometers, and so on) make them location-independent. This means they
can be worn anywhere on the body. Keep in mind that accelerometers require body segment
motion in order to track activities; so, for example, the counts will be inaccurate if an
accelerometer is worn at the wrist during cycling.
What type of device should my clients use to track and monitor HR during exercise?
Heart rate monitors can be used to measure HR during rest and exercise and to monitor
exercise intensity. Typically, these monitors use a simple strap worn next to the skin just
below the pectoral muscles. The strap continuously transmits the HR data to a nearby device
(e.g., a watch-style receiver or your smartphone) and displays the HR in bpm. Because HR is
linearly related to oxygen uptake, it can be used to estimate exercise energy expenditure.
However, remember that HR may be affected by factors such as temperature, humidity,
hydration, and emotional stress as well as some medications and dietary supplements. Any
one of these factors may affect the accuracy of the estimated energy expenditure.
There are numerous watch-style heart rate monitors that do not require a chest strap.
These monitors vary in price and features, so tell your clients to do their research before
investing in one. For example, if a client wants to monitor HR while swimming or playing
water polo, the monitor needs to be waterproof. Some wrist-worn monitors track heart rate in
a continuous fashion. Others need to be touched in order to display the HR. The latter could
present some challenges in tracking HR while cycling or skiing. Not all watch-style HR
monitors have been properly validated; clients should determine whether the device has been
tested by unbiased, reputable, independent sources.
Do smartphone cameras accurately measure HR?
Smartphone cameras measure HR by sending a light beam through the skin and monitoring
how the light is reflected as the blood flows rhythmically through the capillaries. This
technology is known as photoplethysmography (PPG). It takes a few moments for the
proprietary algorithm in the smartphone app to measure the pulsatile fluctuations before
converting them into bpm. The quality of contact between the user’s finger and the camera
lens can greatly influence the results. Thus, exercise HR may actually be a few beats higher
than what is displayed. The algorithms differ among devices and apps depending on the
manufacturers. Consequently, these devices may yield inaccurate or different results.
Smartphone apps that have not been approved by the U.S. Food and Drug Administration
136
My client likes to hike and run in the mountains. Is there some type of technology that
would help him track these activities?
Global positioning system (GPS) technology may meet your client’s needs. GPS technology
uses a combination of satellites and ground stations to calculate geographic locations. This
means there is a line-of-sight requirement between the user and the satellites; without it (e.g.,
when spelunking or when indoors), the user cannot count on GPS being able to compute
location at that moment (Cho, Rodriguez, and Evenson 2011). GPS units that can be worn
on the wrist, hip, or upper arm are commercially available and provide information about
altitude, distance, travel time, and average velocity while hiking. When used in conjunction
with accelerometry, GPS-enabled devices can assess and monitor the intensity of physical
activity and estimate calories expended (Rodriguez, Brown, and Troped 2005; Schutz and
Herren 2000; Troped et al. 2008). The ability of the GPS to accurately calculate location
depends on the GPS unit and the environment in which it is used. The projected location
may be single to double digits (in meters) away from the actual location. As a result,
calculations of the distance between two geographic locations, the elapsed time to travel that
distance, and the average velocity of travel may also be affected (Jankowska, Schipperijn, and
Kerr 2015).
My client’s smartphone has a GPS and accelerometer. Will this work to track her outdoor
activities?
This is an ongoing area for research. One study reported that the combination of
smartphones, Bluetooth, GPS, and accelerometry is a cost-effective method of monitoring
the details (time and space) of day-to-day movements both indoors and out (Schenk et al.
2011). As the technology develops, GPS in combination with the global telecommunications
networks may become more widely used to assess and promote physical activity worldwide.
However, intra- and interunit validity and reliability need to be established as part of any
future research project incorporating GPS technology (Abraham et al. 2012).
Should I encourage children to play some of the active video games?
Active video games (AVGs) provide an enjoyable way to be physically active (Bailey and
McInnis 2011; Maddison et al. 2011; Zhu 2008). Therefore, this style of activity may be a
good way to show children that exercise can be fun. These games can be played alone or with
others and provide an alternative to exercising outdoors during inclement weather. AVGs
may also serve as a transition to participation in sports and physical activities (Chamberlin
and Gallagher 2008).
137
may also serve as a transition to participation in sports and physical activities (Chamberlin
and Gallagher 2008).
When the AVG requires the players to actively move, then this style of game play is known
as exergaming. Although there is little research about the effects of exergaming, reductions in
waist circumference, blood pressure, and weight gain have been noted for overweight children
who participate (Maddison et al. 2011; Murphy et al. 2009). One study reported that MET
levels for exergaming range from moderate to vigorous intensity; Wii boxing is at the low end
of moderate intensity, but Sportwall and Xavix are vigorous-intensity exergames (Bailey and
McInnis 2011).
What types of active video games are available?

Games that can be played at multiple skill levels are good choices since they provide a form of
progression. Dance Dance Revolution (DDR) in its various renditions is a fun way to
promote physical activity and weight loss in obese children and adults (Epstein et al. 2007;
Zhu 2008). It is available for home use on Wii and Xbox. The number of calories burned
while playing appears to be directly related to experience level; inexperienced players burn
fewer calories than do experienced players (Sell, Lillie, and Taylor 2008). Overall, DDR
games can provide a moderate-intensity workout (Bailey and McInnis 2011). Bronner,
Pinkser, and Noah (2015) reported that the freeware version of DDR, StepMania Endless,
requires a level of energy expenditure that equates to vigorous intensity (9.2 ± 2.0 METs).
Wii Sports is a home video game that uses body motions to mimic sport activities such as
tennis, golf, bowling, baseball, and boxing. Although playing Wii Sports will not burn as
many calories as actually playing the sport, children playing Wii bowling, tennis, golf, and
boxing games burn 2% more calories than do children playing sedentary computer games
(Graves et al. 2007). Wii boxing, preferred more by boys than girls, can be played at varying
levels of difficulty—all of which provide moderate-intensity MET level exertion. Miyachi
and colleagues (2010) quantified the MET levels for Wii Sports games for adults. They
reported that the average MET level of all Wii Sports games for adults is only 3.0, which
places this activity at the low end of moderate-intensity exercise.
What are the potential health benefits gained from playing active video games?
There are several documented benefits associated with playing AVGs. These include
improved cognitive processes in children (Best 2011) and older adults (Bleakley et al. 2015).
Increased energy expenditure (Warburton et al. 2009) and improved range of motion (Barry
et al. 2016; Parry et al. 2014; Staiano and Flynn 2014) were also reported. Balance, mental
138
Active video game play holds promise for promoting functional independence, improving
balance, preventing falls, reducing premature disability, and maintaining health by increasing
the physical activity levels of adults and seniors (deJong 2010). Adults at least 6 mo past their
stroke event successfully played Wii tennis and boxing at a moderate intensity level while
standing (Hurkmans et al. 2011). Consequently, playing AVGs can be beneficial in helping
children and adults of all ages reap the benefits of physical activity regardless of their ability to
stand and walk.
Do Wii Fit videos provide a good training workout?

Wii Fit Plus offers more than 60 training activities. These activities are categorized into four
areas: aerobics (e.g., hula hoops and running), strength training (e.g., lunges and leg
extensions), yoga, and balance training. Each category is made up of multiple types of
exercises and activities. This exercise modality uses the handheld Wii remote controller and a
balance board peripheral for some of the activities (e.g., running in place and yoga poses). At
the beginner level, only the resistance and aerobic training activities provide moderate-
intensity MET levels.
How may virtual reality technology be used to promote physical activity?
Virtual reality, or simulation technology, is an exciting advancement of the human-computer

interface. It certainly has the potential to gain ground in the interactive healthy behaviors
domain. There are two styles of virtual reality. Immersive virtual reality uses head-mounted
displays, body-motion sensors, real-time graphics, and very high-tech interface devices (e.g.,
specialized helmets) to provide an individualized experience in a simulated environment
(Rizzo et al. 2011). Nonimmersive virtual reality uses current flat-screen (e.g., television or
computer screens) and traditional interface devices, such as keyboards, game pads, and
joysticks. Although this technology is still evolving, the Xbox Kinect system uses a special
camera and reflective adhesive markers to capture full-body movement. Since the user’s body
is the interface device, the user can move more naturally. With further interdisciplinary
research and development, this style of interactive exergaming may offer full body–interaction
gaming that further promotes physically active lifestyles (Rizzo et al. 2011).
How can persuasive technology be used to promote exercise program adherence?
Persuasive technology is defined as a computer system, device, or application that

intentionally changes a person’s attitude or behavior (Fogg 2003). This technology uses tools
(e.g., pedometer or balance board), media (e.g., video and audio), and social interaction (e.g.,
139
intentionally changes a person’s attitude or behavior (Fogg 2003). This technology uses tools
(e.g., pedometer or balance board), media (e.g., video and audio), and social interaction (e.g.,
playing with another person or networking) to persuade individuals to adopt the behavior
without their conscious knowledge. Although DDR was not developed specifically to
promote physical activity, it has changed exercise attitudes and behavior among children and
youth by incorporating principles of persuasive technology. DDR uses video, music, and a
dance platform to capture interest and engage children in the activity without making them
fully aware that they are exercising. The emerging field of persuasive technology has
enormous potential for promoting physical activity and healthy behaviors (Fogg and Eckles
2007; Zhu 2008).
Are social networks useful for promoting exercise program adherence?
With the explosion of social networking (e.g., Facebook, Twitter, Instagram, and YouTube),
exercise professionals and fitness consumers have innumerable opportunities for nearly
instantaneous sharing and accessing of information. This information exchange is facilitated
through mobile technologies and the Internet. Unprecedented opportunities now exist for
directly accessing social networks to deliver health behavior change interventions. Although
social media and apps have a greater capacity to infiltrate social networks and create a
contagion, they are challenging to develop, and the user must formally consent (e.g.,
subscribe, download). Conversely, online pages and groups offer low barriers to social
networking (Cobb and Graham 2012). Facebook is a feasible platform for delivering social
support interventions for physical activity among young college-age women (Cavallo et al.
2012). Websites and mass-reach broadcast media are two of the delivery mechanisms
underlying social marketing strategies (Peterson, Chandlee, and Abraham 2008). These
media are now accessible to everyone with smartphone technology. To best leverage social
media technology to educate and interact with clients, health behavior and fitness
professionals are encouraged to consider the POST (people, objectives, strategies,
technology) approach as discussed by Torgan and Cousineau (2012).
Key Points
The essential components of physical fitness are cardiorespiratory endurance, musculoskeletal fitness, body
composition, and flexibility.
Valid, reliable, and objective laboratory and field tests have been developed to assess each fitness
component.
140
Objective tests give similar test scores when different technicians administer the test to the same client.
All physical fitness prediction equations need to be validated and cross-validated to determine their
applicability and suitability for use in the field.
The line of best fit is a regression line depicting a linear relationship between a reference measure and all the
predictor variables in the regression equation.
The SEE is a type of prediction error that reflects the degree of deviation of individual data points around
the line of best fit or regression line.
The TE is a type of prediction error that reflects the degree of deviation of individual data points around the
line of identity.
Sensitivity and specificity are measures of the ability of a test to correctly identify individuals with and
without risk factors for diseases.
Use standard evaluation criteria to judge the relative worth of newly developed physical fitness tests and
prediction equations.
The Bland and Altman method evaluates how well a prediction equation works for estimating a physical
fitness component of an individual within a group.
To obtain valid and reliable test results, clinicians must follow standardized testing procedures and have
technical skills.
Established norms for most tests are available and are used to classify physical fitness status based on the
client’s test scores.
When explaining test results to clients, clinicians need to be positive and to use simple, nontechnical terms.
To design an effective exercise program, it is necessary to understand and apply training principles. These
principles include specificity, overload, progression, initial values, interindividual variability, diminishing
returns, and reversibility.
The basic elements of an exercise prescription are mode, intensity, duration, frequency, and progression.
The exercise prescription should be individualized to meet the needs, interests, and abilities of the client.
The three stages of an exercise program are initial conditioning, improvement, and maintenance.
Throughout the improvement stage of an exercise program, the frequency, intensity, and duration of
exercise are increased, one at a time.
Physical activity participation and exercise adherence are related to demographic, biological, psychological,
cognitive, emotional, behavioral, social, cultural, and environmental factors.
When developing strategies for increasing exercise program adherence, it is important to integrate
principles and concepts from psychological models and theories related to successful behavior change.
To promote physical activity participation and adherence, pedometers, accelerometers, HR monitors, GPS
units, active video gaming, social networking, and smartphone apps can be used.
Persuasive technology uses tools, media, and social interaction to promote physical activity and healthy
behaviors.
141
units, active video gaming, social networking, and smartphone apps can be used.
Persuasive technology uses tools, media, and social interaction to promote physical activity and healthy
behaviors.
Key Terms
Learn the definition of each of the following key terms. Definitions of terms can be found in the glossary.
accelerometer
balance
behavior modification model
bias
Bland and Altman method
body composition
body weight (BW)
bone strength
cardiorespiratory endurance
constant error (CE)
criterion method
decision-making theory
diminishing returns principle
exergaming
false negative
false positive
flexibility
functional balance
generalized prediction equations
global positioning system (GPS)
health belief model
heart rate monitor
improvement stage
initial conditioning stage
initial values principle
interindividual variability principle
limits of agreement
line of best fit
line of identity
maintenance stage
maximum oxygen consumption (V̇O2max)
multiple correlation coefficient (Rmc)
muscular endurance
142
objectivity
objectivity coefficient
overload principle
pedometer
persuasive technology
photoplethysmography (PPG)
physical fitness
population-specific equations
progression principle
reference method
regression line
reliability
reliability coefficient
residual score
reversibility principle
self-determination theory
self-efficacy
sensitivity
social cognitive model
specificity
specificity principle
stages of motivational readiness for change model
standard error of estimate (SEE)
theory of planned behavior
theory of reasoned action
total error (TE)
transtheoretical model
validity
validity coefficient
wearable technology
Review Questions
1. Define physical fitness. Name and define the four components of physical fitness.
2. What is the recommended sequence of testing for administering a complete physical fitness test battery?
3. Identify the reference (criterion) method for each of the four components of physical fitness.
143
2. What is the recommended sequence of testing for administering a complete physical fitness test battery?
3. Identify the reference (criterion) method for each of the four components of physical fitness.
4. Which is more important: test validity or test reliability? Explain your choice.
5. Select one physical fitness component and explain how you can determine the relative worth or predictive
accuracy of a field test developed to assess this component.
6. Select one physical fitness component and give an example of how each of the seven training principles can
be applied to it.
7. Identify exercise modes suitable to develop each of the four components of fitness.
8. Identify the three elements of an exercise prescription. For older or less fit clients, which of the elements
should be increased first during the initial stage of their exercise programs?
9. Name the three stages of an exercise program. On average, how long should each stage last?
10. Identify three positively related and three negatively related factors associated with physical activity
participation.
11. Choose one of the psychological models related to successful behavior change and give specific examples of
how this model could be applied to a client undertaking a resistance training program to develop muscular
fitness.
12. Explain why pedometers and accelerometers worn at the hip occasionally give different counts than one
worn at the wrist.
13. Elaborate on factors relating to activity tracker accuracy during walking, running, and cycling.
14. How does GPS technology contribute to understanding environmental influences on exercise and physical
activity?
15. Explain how you would select an app for your client.
16. What is persuasive technology, and how can it be used to promote physical activity?
144
CHAPTER 4
Assessing Cardiorespiratory Fitness

KEY QUESTIONS
How is cardiorespiratory fitness (V̇O2max) assessed?
What is a graded exercise test?
How is V̇O2max estimated from a graded exercise test and field test data?
Should all clients be given a maximal graded exercise test? What factors should I consider in determining
whether to give my client a maximal or submaximal exercise test?
How accurate are submaximal exercise tests and field tests in assessing cardiorespiratory fitness?
What exercise modes are suitable for graded exercise testing?
What are the standardized testing procedures for graded exercise testing?
What are the criteria for terminating a graded exercise test?
Is it safe to give children and older adults a graded exercise test?
One of the most important components of physical fitness is cardiorespiratory endurance.

Cardiorespiratory endurance is the ability to perform dynamic exercise involving large muscle
groups at moderate to high intensity for prolonged periods (American College of Sports
Medicine 2018). Every physical fitness evaluation should include an assessment of
cardiorespiratory function during both rest and exercise.
This chapter presents guidelines for graded exercise testing as well as maximal and
submaximal exercise test protocols and procedures. Although many of the graded exercise test
(GXT) protocols presented in this chapter were developed years ago, these classic protocols
are still widely used in research and clinical settings. In addition, each of these protocols
meets the ACSM (2018) guidelines for graded exercise tests. The chapter also addresses
graded exercise testing for children and older adults and includes a discussion of
cardiorespiratory field tests. All of the test protocols included in this chapter are summarized
in appendix B.1, Summary of Graded Exercise Test and Cardiorespiratory Field Test
Protocols.
145
DEFINITION OF TERMS
Exercise physiologists consider directly measured maximum oxygen uptake (V̇O max) the
2
most valid measure of functional capacity of the cardiorespiratory system. The V̇O max, or
2
rate of oxygen uptake during maximal exercise, reflects the capacity of the heart, lungs, and
blood to deliver oxygen to the working muscles during dynamic exercise involving large
muscle mass. The V̇O max is widely accepted as the criterion measure of cardiorespiratory
2
fitness.
Traditionally, a plateau in oxygen consumption despite an increase in workload is the
criterion used to determine the attainment of a true V̇O max during a maximum exercise
2
tolerance test. Evidence suggests, however, that the incidence of a V̇O plateau during
2
incremental exercise testing is highly variable, ranging from 16% to 94% (Day et al. 2003;
Edvardsen, Hem, and Anderssen 2014; Magnan et al. 2013; Mier, Alexander, and Mageean
2012; Yoon, Kravitz, and Robergs 2007). In fact, studies have established that a plateau
phenomenon is not a prerequisite for identifying a true V̇O max in the majority of individuals
2
(Noakes 2008; Poole and Jones 2017). Some researchers now suggest that a verification bout
of constant load exertion (~10% higher than the highest workload achieved in a ramp trial) is
more appropriate for determining V̇O max (Poole and Jones 2017). According to Magnan
2
and colleagues (2013), the incidence of a plateau for inactive people is related to body mass
index (BMI), waist-to-hip ratio, sense of self-efficacy, gender, and method for determining
the plateau. However, when participants attain similar V̇O max values without consistently
2
attaining a plateau with four different maximal exertion treadmill protocols, then the impact
of protocol selection and day-to-day biologic variability cannot be overlooked (Beltz et al.
2016).
00:00 / 00:00
Video 4.1
V̇O peak is the highest rate of oxygen consumption measured during the exercise test,
2
regardless of whether or not a V̇O plateau is reached. V̇O peak may be higher than, lower
2 2
146
than, or equal to V̇O max. For many individuals who do not reach an actual V̇O plateau, the
2 2
V̇O peak attained during a maximum-effort incremental test to the limit of tolerance is a
2
valid index of V̇O max (Day et al. 2003; Hawkins et al. 2007; Howley 2007).
2
Maximal and submaximal V̇O are expressed in absolute or relative terms. Absolute V̇O is
2 2
measured in liters per minute (L·min ) or milliliters per minute (ml·min ) and provides a
−1 −1
measure of energy cost for non-weight-bearing activities such as leg or arm cycle ergometry.
Absolute V̇O is directly related to body size; thus, men typically have a larger absolute V̇O
2 2
max than women.

Because absolute V̇O depends on body size, V̇O is typically expressed relative to body
2 2
weight (i.e., in ml·kg ·min ). Relative V̇O max is used to classify an individual’s
−1 −1 2
cardiorespiratory (CR) fitness level or to compare fitness levels of individuals differing in

body size. Relative V̇O can also be used to estimate the energy cost of weight-bearing
2
activities such as walking, running, and stair climbing. However, although the relationship
between absolute V̇O max and body mass is strong (r = .86), it is not perfect (r = 1.00).
2
Therefore, when V̇O max is expressed simply as a linear function of body mass, CR fitness
2
levels of heavier (>75.4 kg) and lighter (<67.7 kg) individuals may be under- or overclassified,
respectively (Heil 1997). Some experts propose scaling exercise capacity (i.e., V̇O , 6 min 2
walk test distance) to an exponential function of body mass (Buresh and Berg 2002; Dourado
and McBurnie 2012; Heil 1997). A current limitation of doing so is that the norms used to
classify CR fitness levels were established for relative V̇O max values expressed as ml·min ·kg
2 −1
−1 and not as ml·min ·kg −1 0.67 or ml·min ·kg , where the exponents are suggested to correct
−1 0.75
relative oxygen consumption for body mass. Carrick-Ranson and colleagues (2012) suggested
that scaling relative to fat-free mass (FFM; see chapter 8), the most metabolically active
tissue, is more appropriate than is allometric scaling. They used scaling relative to FFM to
demonstrate that a decrease in maximal heart rate, not maximal stroke volume and total
blood volume, is the likely source of the age-related decline in V̇O for both men and women.
2
Expressing V̇O relative to the individual’s FFM (i.e., as ml·kgFFM ·min ) provides you
2 −1 −1
with an estimate of cardiorespiratory endurance that is independent of changes in body

weight. For example, your client’s improvement in relative V̇O max following a 16 wk 2
aerobic exercise program may reflect both improved capacity of the cardiorespiratory system
(increase in absolute V̇O max) and weight loss (increase in relative V̇O expressed as ml·kg
2 2
·min due to a decrease in body weight). Thus, expressing V̇O max relative to FFM, instead
−1 −1 2
of body weight, reflects the oxygen consumption of the tissues most active during exercise
and physical activity.
147
The rate of oxygen consumption can also be expressed as a gross V̇O or net V̇O . Gross
2 2
V̇O is the total rate of oxygen consumption and reflects the caloric costs of both rest and
2
exercise (gross V̇O = resting V̇O + exercise V̇O . On the other hand, net V̇O represents the
2 2 2) 2
rate of oxygen consumption in excess of the resting V̇O and is used to describe the caloric
2
cost of the exercise. Both gross and net V̇O can be expressed in either absolute (e.g., L·min )
2 −1
or relative (ml·kg ·min ) terms. Unless specified as a net V̇O , the V̇O values reported
−1 −1 2 2
throughout this book refer to gross V̇O .2
GRADED EXERCISE TESTING: GUIDELINES AND PROCEDURES

Exercise scientists and physicians use exercise tests to evaluate functional cardiorespiratory
capacity (V̇O max) objectively. The V̇O max, determined from graded maximal or
2 2
submaximal exercise tests, is used to classify the cardiorespiratory fitness levels of your clients
(see table 4.1). You can use baseline and follow-up data to evaluate the progress of exercise
program participants and to set realistic goals for your clients. You can use the heart rate
(HR) and oxygen uptake data obtained during the graded exercise test to make accurate,
precise exercise prescriptions.
As discussed in chapter 2, an individual’s exercise history, disease status (i.e., known versus
nonexistent cardiovascular, metabolic, or renal disease), and positive symptomology, or lack
thereof, for those same three diseases now drive the preparticipation screening process. As the
preparticipation screening algorithm indicates (see table 2.2), the recommended initial
148
exercise program intensity is dependent on the individual’s exercise history, disease status,
symptomology, and requirement for medical clearance. The ACSM (2018) no longer
recommends a graded maximal exercise test before engaging in an exercise program.
However, one may be included as part of the medical clearance process. For medical
conditions that constitute absolute and relative contraindications to exercise testing, see
chapter 2. For risk stratification of patients in medical fitness and cardiac rehabilitation
facilities, the American Association of Cardiovascular and Pulmonary Rehabilitation
(AACVPR) has a more thorough set of risk stratification procedures you need to follow
(Williams 2001).
GENERAL GUIDELINES FOR EXERCISE TESTING

You may use a maximal or submaximal graded exercise test (GXT) to assess the
cardiorespiratory fitness of the individual. The selection of a maximal or submaximal GXT
depends on
your client’s habitual exercise patterns, risk factors, and diagnosis or symptomology
of cardiovascular, metabolic, or renal disease;
your reasons for administering the test (physical fitness testing or clinical testing);
and
the availability of appropriate equipment and qualified personnel.
In clinical and research settings, V̇O max is typically measured directly, which requires
2
expensive equipment and experienced personnel. Although V̇O max can be predicted from
2
maximal exercise intensity with a fair degree of accuracy, submaximal exercise tests also
provide a reasonable estimate of cardiorespiratory fitness level. They are less costly, less time
consuming, and not as risky. Submaximal exercise testing, however, is considered less
sensitive as a diagnostic tool for coronary heart disease (CHD).
In either case, the exercise test should be a multistage graded test. This means the
individual exercises at gradually increasing submaximal workloads. Many commonly used
exercise test protocols require that each workload be performed for 3 min. The GXT protocol
is the means through which maximum functional capacity (V̇O max) is determined. When
2
direct measures are being used, a combination of specific criteria is applied to confirm that
the individual being tested gave a maximal effort. One of those criteria requires that oxygen
uptake plateaus and does not increase by more than 150 ml·min with a further increase in
−1
149
workload. Poole and Jones (2017) recommend confirming maximal effort by utilizing a
constant supramaximal load verification bout instead of relying on a plateau or secondary
criteria. Historically, however, combinations of the following secondary criteria have been
used to confirm that an individual who did not attain a V̇O plateau gave a maximal effort
2
and, hence, attained a true V̇O max:

2
Failure of the HR to increase with increases in exercise intensity

Venous lactate concentration exceeding 8 mmol·L −1
Respiratory exchange ratio (RER) greater than 1.15

Rating of perceived exertion (RPE) greater than 17 using the original Borg scale
(6-20)
It should be noted that one’s ability to satisfy one or more of these secondary criteria has
been shown to be related to age and sex (Edvardsen, Hem, and Anderssen 2014). If the test is
terminated before the person reaches a plateau in V̇O and an RER greater than 1.15, the
2
GXT is considered to be a measure of V̇O peak rather than V̇O max. Children, older adults,
2 2
sedentary individuals, and clients with known disease are more likely than other groups to
attain a V̇O peak rather than a V̇O max. For CHD screening and classification purposes,
2 2
bringing a person to at least 85% of the age-predicted maximal HR is desirable because some
electrocardiogram (ECG) abnormalities do not appear until the HR reaches this level of
intensity.
Evidence suggests that maximal exercise tests are no more dangerous than submaximal
tests provided you carefully follow guidelines for exercise tolerance testing and continuously
monitor the physiological responses of the exercise participant. Eight nonfatal and no fatal
events were identified in a retrospective study of 5,060 symptom-limited exercise tests
(adverse event rate of 0.16%) performed on clients with various underlying high-risk cardiac
diagnoses (Skalski, Allison, and Miller 2012). For clinical testing, the risk of an exercise test
being fatal is no greater than 0.4 to 0.5 per 10,000 tests (Atterhog, Jonsson, and Samuelsson
1979; Goodman, Thomas, and Burr 2011; Rochmis and Blackburn 1971; Skalski, Allison,
and Miller 2012), although the risk of myocardial infarction has been estimated to be 4 per
10,000 tests (Thompson 1993). Based on a review of studies including clients with and
without CVD, Goodman and colleagues (2011) identified the average risk of an adverse
event during exercise testing as being less than 2.9 nonfatal and 0.3 fatal events per 10,000
tests. The risk for apparently healthy individuals (without known disease or symptomology) is
150
very low, with no complications occurring in 380,000 exercise tests done on young individuals
(Levine, Zuckerman, and Cole 1998). Similarly, there were no complications reported in the
700,000-plus exercise tests performed on sports-persons and athletes in the studies reviewed
by Goodman and colleagues (2011). As a result, Goodman and colleagues concluded that the
risks of maximal exertion exercise testing reflect a fatal-event incidence of 0.2 to 0.8 per
10,000 tests and a nonfatal-event incidence of 1.4 per 100,000 tests.
Nonphysicians may safely supervise exercise tests as long as they have demonstrated the
competencies required to do so. Individual competencies, training, support, and certifications
recommended for the continued safe monitoring of exercise tests by nonphysicians are
identified in the 2014 scientific statement from the AHA (Myers et al. 2014). This statement
also lists medical conditions for which a physician should be present in the room during
exercise testing.
GENERAL PROCEDURES FOR CARDIORESPIRATORY FITNESS

TESTING
At least 1 day before the exercise test, you should give your client pretest instructions (see
chapter 3). Prior to graded exercise testing, the client should read and sign the informed
consent and complete the 2018 PAR-Q+; see appendix A.1, Physical Activity Readiness
Questionnaire for Everyone (PAR-Q+). Step-by-step instructions are listed in Procedures for
Administering a Graded Exercise Test.
Pretest, exercise, and recovery HRs can be measured using the palpation or auscultation
technique (see chapter 2) if a HR monitor or ECG recorder is unavailable. Because of
extraneous noise and vibration during exercise, it may be difficult to obtain accurate
measurements of BP, especially when your client is running on the treadmill. To become
proficient at taking exercise BP, you need to practice as much as possible.
For years, the Borg scales have been used to obtain ratings of perceived exertion (RPEs)
during exercise testing. The original scale (6-20) and the revised scale (0-10) allow clients to
rate their degree of exertion subjectively during exercise; the two scales are highly related to
exercise HRs and V̇O . Both RPE scales take into account the linear rise in HR and V̇O
2 2
during exercise. The revised scale also reflects nonlinear changes in blood lactate and
ventilation during exercise. Ratings of 6 on the original scale and 0 on the revised scale
correspond to no exertion at all; ratings of 10 on the revised scale and 19 on the original scale
usually correspond with the maximal level of exercise (Borg 1998). Moderate-intensity
exercise is rated between 12 and 14 on the original scale and rated 5 or 6 on the revised scale.
151
Ratings of perceived exertion are useful in determining the endpoints of the GXT,
particularly for patients who are taking beta-blockers or other medications that may alter the
HR response to exercise. You can teach your clients how to use the RPE scales to monitor
relative intensities during aerobic exercise programs.
Alternatively, you may use OMNI scales to obtain a client’s RPE for various modes of
exercise testing. The OMNI scales can be used to measure RPE for the overall body, the
limbs, and the chest. These scales were originally developed for children and adolescents and
used a picture system to illustrate intensity (0 = extremely easy to 10 = extremely hard) of
effort during exercise. Later the scales were modified for use with adults engaging in cycle
ergometer, treadmill, stepping, elliptical, and resistance exercises. As part of the validation
testing for the cycling, stepping, elliptical, and treadmill ergometry scales, the OMNI RPE
values have been correlated with HR and V̇O data. Concurrent validity coefficients ranged
2
from .82 to .95 for HR and OMNI RPE; likewise, the validity coefficients ranged between
.88 and .96 for V̇O and OMNI RPE (Guidetti et al. 2011; Krause et al. 2012; Mays et al.
2
2010; Robertson 2004). For resistance exercise, RPE values from the OMNI scale were
correlated with weight lifted, yielding validity coefficients ranging from .72 to .91 (Robertson
2004; Robertson et al. 2005). A derivation of the OMNI 0 to 10 RPE scale uses sketches of
simple facial expressions (e.g., smiling, frowning, neutral expression) and a numeric scale
(Chen, Chiou, et al. 2017). The correlations of RPE with stationary cycling workload and
with heart rate for young adults (r > .97) were nearly identical for the Borg 10-point and
facial expression scales. For children, the two scales were perceived similarly for RPE and
workload (r > .97). The facial expression scale (r > .90) was more closely related to heart rate
than was the Borg scale (r > .49).
Appendix B.4 contains sample instructions, procedures, and OMNI pictorial scales for
boys, girls, and adults engaging in cycling, treadmill walking or running, stepping, and
resistance exercise. Like the Borg scales, the OMNI scales can be used by your clients to
monitor the intensity of their workouts during aerobic and resistance exercise training. For a
detailed discussion of how to use these scales, refer to the work of Chen, Chiou, and
colleagues (2017); Guidetti and colleagues (2011); Robertson (2004); Krause and colleagues
(2012); and Mays and colleagues (2010). Table 4.2 summarizes the verbal cues corresponding
to the numerical values of the OMNI RPE scales.
Table 4.2 Verbal Cues for OMNI RPE Scales
Adults Children
152
Extremely easy = 0 Not tired at all = 0
Easy = 2 A little tired = 2
Somewhat easy = 4 Getting more tired = 4
Somewhat hard = 6 Tired = 6
Hard = 8 Really tired = 8
Extremely hard = 10 Very, very tired = 10
Procedures for Administering a Graded Exercise Test
Measure the client’s resting HR and blood pressure (BP) in the exercise posture
(see chapter 2 for these procedures).
Begin the GXT with a 2 to 3 min warm-up to familiarize the client with the
exercise equipment and prepare him for the first stage of the exercise test.
During the test, monitor HR, BP, and ratings of perceived exertion (RPEs) at
regular intervals. Measure HR at least two times during each stage, near the end of
the second and third minutes of each stage. A steady-state HR (two HR
measurements within ±5 bpm) should be reached for each stage of the test. Do not
increase the workload until a steady-state HR is reached.
Blood pressure should be measured during the last minute of each stage of the test
and repeated if a hypotensive or hypertensive response is observed.
Rating of perceived exertion should be assessed near the end of the last minute of
each exercise stage using either the Borg or OMNI scales.
Throughout the exercise test, continuously monitor the client’s physical appearance
and symptoms.
Discontinue the GXT when the test termination criteria are reached (e.g., 70%
HRR or 85% HRmax, if the client requests stopping the test, or if any of the
indications for stopping an exercise test are apparent [see General Indications for
Termination of a Graded Exercise Test]).
Have the client cool down by exercising at a low work rate that does not exceed the
intensity of the first stage of the exercise test (e.g., walking on the treadmill at 2
mph [53.6 m·min ] and 0% grade, or cycling on the cycle ergometer at 50 to 60
−1
revolutions per minute [rpm] and zero resistance). Active recovery reduces the risk
of hypotension from venous pooling in the extremities.
During recovery, continue measuring postexercise HR and BP for at least 5 min. If
an abnormal response occurs, extend the recovery period. The HR and BP during
active recovery should be stable but may be higher than preexercise levels. Continue
153
monitoring the client’s physical appearance during recovery.
If your client has signs of discomfort or if an emergency occurs during the test, use
a passive cool-down with the client in a sitting or supine position.
Based on ACSM 2018.
00:00 / 00:00
Video 4.2
TEST TERMINATION
In a maximal or submaximal GXT, the exercise usually continues until the client voluntarily
terminates the test or a predetermined endpoint is reached. As an exercise technician,
however, you must be acutely aware of all indicators for stopping a test. If you notice any of
the signs or symptoms listed in General Indications for Termination of a Graded Exercise
Test, you should stop the exercise test prior to the client’s reaching V̇O max (for a maximal
2
GXT) or the predetermined endpoint (for a submaximal GXT).
MAXIMAL EXERCISE TEST PROTOCOLS

Many maximal exercise test protocols have been devised to assess cardiorespiratory capacity.
As the exercise technician, you must be able to select an exercise mode and test protocol that
are suitable for your clients given their age, gender, and health and fitness status. Commonly
used modes of exercise are treadmill walking or running and stationary cycling. Arm
ergometry is useful for persons with paraplegia and clients who have limited use of the lower
extremities. Also, combined leg and arm ergometry and total body recumbent stepper exercise
tests may be suitable alternatives to treadmill testing for assessing the cardiorespiratory fitness
of older persons with balance deficits, gait impairments, and decreased coordination
(Billinger, Loudon, and Gajewski 2008; Loudon et al. 1998). Bench stepping is not highly
recommended but could be useful in field situations when large groups need to be tested.
Whichever mode of exercise you choose, be sure to adhere to the principles explained in
General Principles of Exercise Testing.
154
GENERAL INDICATIONS FOR TERMINATION OF A GRADED EXERCISE
TEST
1. Onset of angina or angina-like symptoms
2. Drop in systolic BP of >10 mmHg from baseline BP despite an increase in workload
3. Excessive rise in BP: systolic pressure >250 mmHg or diastolic pressure >115 mmHg
4. Shortness of breath, wheezing, leg cramps, or claudication
5. Signs of poor perfusion (e.g., ataxia, dizziness, pallor, cyanosis, cold or clammy skin, or nausea)
6. Failure of HR to rise with increased exercise intensity
7. Noticeable change in heart rhythm
8. Client’s request to stop
9. Physical or verbal manifestations of severe fatigue
10. Failure of the testing equipment
Note: For definitions of specific terms, refer to the glossary.

Adapted from American College of Sports Medicine, ACSM’s Guidelines for Exercise Testing and Prescription, 10th ed. (Philadelphia: Lippincott Williams & Wilkins, 2018), 84, with permission of Wolters Kluwer.
The exercise test may be continuous or discontinuous. A continuous exercise test is

performed with no rest between work increments. Continuous exercise tests can vary in the
duration of each exercise stage and the magnitude of the increment in exercise intensity
between stages. Researchers recommend a total test duration between 8 and 12 min to
increase the probability of moderately and highly trained individuals reaching V̇O max 2
(Fletcher et al. 2013; Gibbons et al. 2002). A slightly longer, hence less aggressive, test may
be better suited for less fit individuals (Beltz et al. 2016). Midgley and colleagues (2008)
challenged the 8 to 12 min recommendation (American College of Sports Medicine 2014;
Fletcher et al. 2013; Gibbons et al. 2002) based on an extensive review of studies dealing with
this topic. They concluded that the duration of cycle ergometer tests should be between 7 and
26 min and that treadmill tests should be between 5 and 26 min to yield a valid
determination of V̇O max. This recommendation assumes that an adequate warm-up
2
precedes the shorter-duration tests and that the treadmill grade does not exceed 15% during
the protocol. For most continuous exercise test protocols, the exercise intensity is increased
gradually (2 to 3 METs for low-risk individuals) throughout the test, and the duration of
each stage is usually 2 or 3 min, allowing most individuals to reach a steady-state V̇O during 2
each stage. Across the stages of this type of GXT, the workload may increase linearly or
nonlinearly. Each increment in workload is dictated by the specific protocol and does not vary
155
among individuals. Although this type of GXT is widely used in research and clinical
settings, it may not be optimal for assessing the functional capacity of all individuals,
especially those with low exercise tolerance.
Continuous ramp-type tests are widely used because they can be individualized for the
client’s estimated exercise tolerance. For example, increments in work rate during a ramp
protocol are much higher for endurance-trained athletes than for sedentary individuals (e.g.,
30 W·min vs. 10 W·min ). Also, each exercise stage for ramp protocols is much shorter
−1 −1
(e.g., 20 sec) than that of the traditional continuous GXT protocols (2-3 min). Ramp
protocols provide continuous and frequent increments in work rate throughout the test so
that the V̇O increases linearly; they are designed to bring individuals to their limit of exercise
2
tolerance in approximately 10 min. In a study comparing four ramp protocol durations (5, 8,
12, and 16 min) during incremental cycling exercise, Yoon and colleagues (2007) reported
that the optimal protocol duration to elicit V̇O max of healthy, moderately to highly trained
2
men and women is between 8 and 10 min.

Because of the frequent (e.g., every 10 or 20 sec) increases in work rate with ramp
protocols, V̇O plateaus are rarely observed. Regardless, when plateau is defined as a change in
2
V̇O ·min <0.5 L·min during the final 30 sec of a cycling protocol (Yoon, Kravitz, and
2 −1 −1
Robergs 2007), plateaus are observable for moderately and highly trained adults. However, as
previously mentioned, the V̇O peak from ramp-type protocols appears to be a valid index of
2
V̇O max even without a plateau in V̇O (Day et al. 2003). The ramp approach potentially
2 2
improves the prediction of V̇O max given that V̇O increases linearly across work rates.
2 2
Ramp protocols allow some individuals to reach a higher exercise tolerance compared with
traditional GXT protocols. However, there are disadvantages. To design an individualized
ramp protocol, the maximum work rate for each client must be predetermined or accurately
estimated from training records or questionnaires so you can select a work rate that allows the
client to reach peak exercise tolerance in approximately 10 min. Also, ramp protocols increase
work rate frequently (e.g., 25-30 stages in a 10 min test), requiring more expensive
electromagnetically braked cycle ergometers and programmable treadmills that make rapid
and smooth transitions between the stages of the exercise test. Finally, inexperienced
technicians may have difficulty measuring exercise BP during each minute of the ramp
protocol.
General Principles of Exercise Testing
156
1. Typically, you will use either a treadmill or stationary cycle ergometer for graded
exercise testing. All equipment should be calibrated before use.
2. Begin the GXT with a 2 to 3 min warm-up to orient the client to the equipment
and prepare her for the first stage of the GXT.
3. The initial exercise intensity should be considerably lower than the anticipated
maximal capacity.
4. Exercise intensity should be increased gradually throughout the stages of the test.
Work increments may be 2 METs or greater for apparently healthy individuals and
as small as 0.5 MET for patients with disease.
5. Closely observe contraindications for testing and indications for stopping the
exercise test. When in any doubt about the safety or benefits of testing, do not
perform the test at that time.
6. Monitor the HR at least two times, but preferably each minute, during each stage
of the GXT. Heart rate measurements should be taken near the end of each
minute. If the HR is higher than 110 bpm and does not reach steady state (two
HRs within ±5 or 6 bpm), extend the work stage an additional minute or until the
HR stabilizes.
7. Measure BP and RPE once during each stage of the GXT, in the later portion of
the stage. Repeat BP if the value indicates a hypertensive or hypotensive response.
8. Continuously monitor client appearance and symptoms.
9. For submaximal GXTs, terminate the test when the client’s HR reaches 70% HRR
(heart rate reserve) or 85% HRmax (maximal heart rate), unless the protocol
specifies a different termination criterion. Also, stop the test immediately if there is
an emergency situation, if the client fails to conform to the exercise protocol, or if
the client experiences signs of discomfort.
10. The test should include a cool-down period of at least 5 min, or longer if abnormal
HR and BP responses are observed. During recovery, HR and BP should be
monitored each minute. For active recovery, the workload should be no more than
that used during the first stage of the GXT. A passive recovery is used in
emergency situations and when clients experience signs of discomfort and cannot
perform an active cool-down.
11. Exercise tolerance in METs should be estimated for the treadmill or ergometer
protocol used, or directly assessed if oxygen uptake is measured during the GXT.
157
12. The testing area should be quiet and private. The room temperature should be 21
to 23 °C (70-72 °F) or less and the humidity 60% or less if possible.
Note: Medical clearance recommended for individuals having known cardiovascular, renal, and metabolic disease
or symptomology thereof.
For discontinuous exercise tests, the client rests 5 to 10 min between workloads. The
workload is progressively increased until the client reaches maximum exercise tolerance
(exhaustion). Typically, each stage of the discontinuous protocol lasts 5 or 6 min, allowing
V̇O to reach a steady state. On average, discontinuous tests take five times longer to
2
administer than do continuous tests. Similar V̇O max values are attained using discontinuous
2
and continuous (increasing workload every 2-3 min) protocols (Maksud and Coutts 1971);
therefore, continuous tests are preferable in most research and clinical settings.
McArdle, Katch, and Pechar (1973) compared the V̇O max scores as measured by six
2
commonly used continuous and discontinuous treadmill and cycle ergometer tests. They
noted that the V̇O max scores for the cycle ergometer tests were approximately 6% to 11%
2
lower than for the treadmill tests. Many subjects identified local discomfort and fatigue in the
thigh muscles as the major factors limiting further work on both the continuous and
discontinuous cycle ergometer tests. For the treadmill tests, subjects indicated windedness
and general fatigue as the limiting factors and complained of localized fatigue and discomfort
in the calf muscles and lower back. Lambrick and associates (2017) compared continuous and
discontinuous treadmill protocols for healthy children. Although the duration of the
discontinuous protocol was longer, this type of protocol elicited similar values for V̇O peak 2
and maximal HR. However, peak running speed was faster and RER lower for the
discontinuous protocol.
TREADMILL MAXIMAL EXERCISE TESTS

Generally, the treadmill is the preferred exercise test modality in the United States (Balady et
al. 2010). For treadmill maximal exercise tests, the exercise is performed on a motor-driven
treadmill with variable speed and incline (see figure 4.1). Speed varies up to 25 mph (40
km·hr ), and the incline is measured in units of elevation per 100 horizontal units and is
−1
expressed as a percentage. The workload on the treadmill is raised through increases in the
speed or incline or both. Workload is usually expressed in miles per hour and percent grade.
158
00:00 / 00:00
Video 4.3
FIGURE 4.1 Treadmill.
It is difficult and expensive to measure oxygen consumption during exercise. Therefore,

ACSM (2018) equations (table 4.3) to estimate the metabolic cost of exercise (V̇O may be
2)
used. These equations provide a valid estimate of V̇O for steady-state exercise only. When
2
used to estimate the maximum rate of energy expenditure (V̇O max), the measured V̇O will
2 2
be less than the estimated V̇O if steady state is not reached. Also, because maximal exercise
2
involves both aerobic and anaerobic components, the V̇O max will be overestimated since the
2
159
contribution of the anaerobic component is not known.
Table 4.3 Metabolic Equations for Estimating Gross V̇O2
Exercise mode gross V̇O2 (ml·kg Resting V̇O2 (ml·kg

Comments
−1·min−1) −1·min−1)
1. For speeds of 50-100 m·min −1 (1.9-3.7 mph)

Walking
+3.5 2. −1·m−1 = O2 cost of walking horizontally
2 = Sa × 0.1 + S × Gb × 1.8
0.1 ml·kg
V̇O
3. 1.8 ml·kg −1·m−1 = O2 cost of walking on incline (% grade of treadmill)
1. For speeds >134 m·min −1 (>5.0 mph)
2. −1 (3-5
If truly jogging (not walking), this equation can also be used for speeds of 80-134 m·min
Running
mph)
2 = Sa × 0.2 + S × Gb × 0.9
+3.5
V̇O
3. 0.2 ml·kg −1·m−1 = O2 cost of running horizontally
4. 0.9 ml·kg −1·m−1 = O2 cost of running on incline (% grade of treadmill)
1. For work rates between 50 and 200 W (300-1,200 kgm·min −1)
2. kgm·min −1 = kg × m·rev−1 × rev·min−1

Leg ergometry
+3.5 3. −1; Tunturi = 3 m·rev−1
2 = Wc / Md × 1.8 + 3.5
Monark and Bodyguard = 6 m·rev
V̇O
4. 1.8 ml·kg −1·min−1 = O2 cost of cycling against external load (resistance)
5. 3.5 ml·kg −1·min−1 = O2 cost of cycling with zero load
1. For work rates between 25 and 125 W (150-750 kgm·min −1)
Arm ergometry 2. kgm·min −1 = kg × m·rev−1 × rev·min−1; m·rev−1 for Monark arm ergometer = 2.4 m
2 = Wc / Md × 3.0 + none
+3.5
V̇O
3. 3.0 ml·kg −1·min−1 = O2 cost of cycling against external load (resistance)
4. None = due to small mass of arm musculature, no special term for unloaded (zero load) cycling needed
1. Appropriate for stepping rates between 12 and 30 steps·min −1 and step heights between 0.04 m (1.6
in.) and 0.40 m (15.7 in.)
Stepping 2. 0.2 ml·kg −1·m−1 = O2 cost of moving horizontally

+3.5
V̇O2 = Fe × 0.2 + F × htf × 1.8 × 1.33
3. 1.8 ml·kg −1·m−1 = O2 cost of stepping up (bench height)
160
4. 1.33 includes positive component of stepping up (1.0) + negative component of stepping down (0.33)
aS = speed of treadmill in m·min−1; 1 mph = 26.8 m·min−1.
bG = grade (% incline) of treadmill in decimal form (e.g., 10% = 0.10).
cW = work rate in kgm·min−1; 1 Watt = 6.12 kgm·min−1.
dM = body mass in kilograms; 1 kg = 2.2 lb.
eF = frequency of stepping in steps per minute.
fht = bench height in meters; 1 in. = 0.0254 m.

ACSM 2018.
Before using any of the ACSM metabolic equations to estimate V̇O , make certain that all 2
units of measure match those in the equation (see Converting Units of Measure).
The ACSM metabolic equations in table 4.3 are useful in clinical settings for estimating
the total rate of energy expenditure (gross V̇O ) during steady-state treadmill walking or
2
running. The total energy expenditure, in ml·kg ·min , is a function of three components:
−1 −1
speed, grade, and resting energy expenditures. For treadmill walking, the oxygen cost of
raising one’s body mass against gravity (vertical work) is approximately 1.8 ml·kg ·m , and −1 −1
0.1 ml·kg ·m of oxygen is needed to move the body horizontally. For treadmill running, the
−1 −1
oxygen cost for vertical work is one-half that for treadmill walking (0.9 ml·kg ·m ), whereas −1 −1
the energy expenditure for running on the treadmill (0.2 ml·kg ·m ) is twice that for walking. −1 −1
See ACSM Walking Equation for an example of how to take these three factors into account
when figuring V̇O . 2
The V̇O estimated from the ACSM walking equation (see table 4.3) is reasonably accurate
2
for walking speeds between 50 and 100 m·min (1.9-3.7 mph). However, since the equation
−1
is more accurate for walking up a grade than on the level, V̇O may be underestimated as 2
much as 20% during walking on the level. For the ACSM running or jogging equations, the
V̇O estimates are relatively accurate for speeds exceeding 134 m·min (5 mph) and speeds as
2 −1
low as 80 m·min (3 mph) provided that the client is jogging and not walking (American
−1
College of Sports Medicine 2014). When HRs fall between 110 bpm and 85% of age-
predicted maximum HR, the ACSM running equation provides a reasonably good (SEE
[standard error of estimate] = 4.2 to 4.35 ml·kg ·min ) estimation of maximal aerobic −1 −1
capacity (Marsh 2012).
Converting Units of Measure
161
Convert body mass (M) in pounds to kilograms (1 kg = 2.2 lb). For example, 170 lb
/ 2.2 = 77.3 kg.
Convert treadmill speed (S) in miles per hour to meters per minute (1 mph = 26.8
m·min ). For example, 5.0 mph × 26.8 = 134.0 m·min .
−1 −1
Convert treadmill grade (G) from percent to decimal form by dividing by 100. For
example, 12% / 100 = 0.12.
Convert METs to ml·kg ·min by multiplying (1 MET = 3.5 ml·kg ·min ). For
−1 −1 −1 −1
example, 6 METs × 3.5 = 21.0 ml·kg ·min . −1 −1
Convert kgm·min to watts (W) (1 W ≈ 6 kgm·min ) by dividing. For example,

−1 −1
900 kgm·min / 6 = 150 W.

−1
Convert step height in inches to meters (1 in. = 0.0254 m) by multiplying. For

example, 8 in. × 0.0254 = 0.2032 m.
ACSM Walking Equation
To calculate the gross V̇O for a 70 kg (154 lb) subject who is walking on the treadmill at a
2
speed of 3.5 mph and a grade of 10%, follow these steps:
V̇O = speed + (grade × speed) + resting V̇O (ml·kg ·min )

2 2 −1 −1
= [speed (m·min ) × 0.1] + [grade (decimal) × speed (m·min ) × 1.8] + 3.5

−1 −1
1. Convert the speed in mph to m·min ; 1 mph = 26.8 m·min .

−1 −1
3.5 mph × 26.8 = 93.8 m·min −1
2. Calculate the speed component (S).
S = speed (m·min ) × 0.1 = 93.8 m·min × 0.1 = 9.38 ml·kg ·min

−1 −1 −1 −1
3. Calculate the grade × speed component (G × S). Convert % grade into a decimal by
dividing by 100.
G × S = grade (decimal) × speed × 1.8 = 0.10 × (93.8 m·min ) × 1.8 = 16.88 ml·kg ·min −1 −1 −1
4. Calculate the total gross V̇O in ml·kg ·min by adding the speed, grade × speed, and
2 −1 −1
resting V̇O (R).

2
V̇O = S + (S × G) + R = (9.38 + 16.88 + 3.5) ml·kg ·min = 29.76 ml·kg ·min

2 −1 −1 −1 −1
162
Figure 4.2 illustrates commonly used treadmill exercise test protocols. These protocols
conform to the general guidelines for maximal exercise testing. Some of the protocols are
designed for a specific population, such as well-conditioned athletes or high-risk cardiac
patients. The exercise intensity for each stage of the various treadmill test protocols can be
expressed in METs. The MET estimations for each stage of some commonly used treadmill
protocols are listed in table 4.4.
FIGURE 4.2 Treadmill exercise test protocols.
163
FIGURE 4.2 (continued)
Population-specific and generalized equations have been developed to estimate V̇O max2
from exercise time for some treadmill protocols (see table 4.5). It is important for exercise
technicians to keep in mind that the initial workload in some of the protocols designed for
highly trained athletes is too intense (exceeding 2-3.5 METs) for the average individual. The
Balke and Bruce protocols are well suited for low-risk individuals, and the Bruce protocol is
easily adapted for high-risk individuals using an initial workload of 1.7 mph at 0% to 5%
grade.
164
Balke Treadmill Protocol
To administer the Balke and Ware (1959) exercise test protocol (see figure 4.2), set the
treadmill speed at 3.4 mph (91.1 m·min ) and the initial grade of the treadmill at 0% during
−1
the first minute of exercise. Maintain a constant speed on the treadmill throughout the entire
exercise test. At the start of the second minute of exercise, increase the grade to 2%.
Thereafter, at the beginning of every additional minute of exercise, increase the grade by only
1%.
Use the prediction equation for the Balke protocol in table 4.5 to estimate a client’s V̇O 2
max from exercise time. Alternatively, you can use a nomogram (see figure 4.3) developed for
the Balke treadmill protocol to calculate V̇O max. To use this nomogram, locate the time
2
corresponding to the last complete minute of exercise during the protocol along the vertical
axis labeled “Balke time,” and draw a horizontal line from the time axis to the oxygen uptake
axis. Be certain to plot the exercise time of women and men in the appropriate column when
using this nomogram.
165
FIGURE 4.3 Nomogram for Balke graded exercise test.
Reprinted by permission from N.K. Ng, METCALC Software: Metabolic Calculations in Exercise and Fitness (Champaign, IL: Human Kinetics, 1995), 30.
Bruce Treadmill Protocol

The Bruce, Kusumi, and Hosmer (1973) exercise test is a multistaged treadmill protocol (see
figure 4.3). The protocol increases the workload by changing both the treadmill speed and
percent grade. During the first stage (minutes 1-3) of the test, the normal individual walks at
a 1.7 mph pace at 10% grade. At the start of the second stage (minutes 4-6), increase the
grade by 2% and the speed to 2.5 mph (67 m·min ). In each subsequent stage of the test, −1
increase the grade by 2% and the speed by either 0.8 or 0.9 mph (21.4 or 24.1 m·min ) until −1
the client is exhausted. Prediction equations for this protocol have been developed to estimate
the V̇O max of active and sedentary women and men, cardiac patients, and people who are
2
elderly (see table 4.5). As an alternative, you may use the nomogram (see figure 4.4)
developed for the Bruce protocol. Plot the client’s exercise time for this protocol along the
vertical axis labeled “Bruce time,” and draw a horizontal line from the time axis to the oxygen
uptake. Again, be certain to use the appropriate column for men and women.
166
FIGURE 4.4 Nomogram for standard Bruce graded exercise test.
Reprinted by permission from N.K. Ng, METCALC Software: Metabolic Calculations in Exercise and Fitness (Champaign, IL: Human Kinetics, 1995), 32.
Modified Bruce Protocol

The modified Bruce protocol (see figure 4.2) is more suitable than the Bruce protocol for
high-risk and elderly individuals. However, with the exception of the first two stages, this
protocol is similar to the standard Bruce protocol. Stage 1 starts at 0% grade and a 1.7 mph
walking pace. For stage 2, the % grade is increased to 5%. McInnis and Balady (1994)
compared physiological responses to the standard and modified Bruce protocols in patients
with CHD and reported similar HR and BP responses at matched exercise stages despite the
additional 6 min of low-intensity exercise performed using the modified Bruce protocol.
Note that the prediction equations for the Bruce protocol (see table 4.5) can be used for
only the standard, not the modified, Bruce protocol. To estimate V̇O for the modified Bruce 2
protocol, use the ACSM metabolic equation for walking (see table 4.3).
Self-Paced Protocols
Self-paced protocols are designed to be somewhat free-form in that the individual adjusts the
treadmill speed and incline or cycling workload to his liking with the understanding that he
167
needs to reach the point of exhaustion within 8 to 12 min. In most situations, speed and
incline can only be adjusted upward. Periodic data collection is required, just as it is with
standard GXTs. Also, when metabolic gas analysis collection is used with this style of ramp
protocol, a specific time increment (e.g., every 30 sec) is identified; HR and RPE are
obtained and expired gas values during that increment are averaged for further analysis. In
this example, V̇O max may then be identified as the highest 30 sec average. Comparison of
2
these incremental averages from the last minute or two of the protocol may reveal the
attainment of a V̇O plateau (e.g., <2 ml·kg ·min increase; Nieman 2003).
2 −1 −1
Sperlich and colleagues (2015) compared self-paced, standard GXT, and ramped maximal
exertion treadmill protocols. Their results indicate that a self-paced protocol can be
completed, on average, within 10 min and produce RPE, blood lactate, HR, and RER values
that satisfy the secondary criteria for determining maximal exertion. The lowest V̇O max2
occurred with the standard GXT protocol.
Treadmill Ramp Protocols

Kaminsky and Whaley (1998) developed a standardized ramp protocol (i.e., BSU/Bruce ramp
protocol) for assessing the functional cardiorespiratory capacity of symptomatic, sedentary,
and apparently healthy individuals. For this protocol, the treadmill speed increases gradually
(in 0.1-0.4 mph, or 2.68-10.72 m·min , increments) every minute. The minimum speed is
−1
1.0 mph (26.8 m·min ); the maximum speed is 5.8 mph (155 m·min ). The treadmill grade
−1 −1
also increases gradually (by 0%-5%) every minute. The minimum grade is 0%; the maximum
grade is 20%. Every 3 min during this ramp protocol, the work rates (i.e., speed and grade)
equal those of the traditional Bruce protocol (see table 4.6). For example, during the sixth
minute of exercise, the treadmill speed (2.5 mph, or 53.6 m·min ) and grade (12%) are the
−1
same, allowing comparisons between the two types of protocols. The ramp approach has the
advantage of avoiding large, unequal increments in workload. Also, it results in uniform
increases in hemodynamic and physiological responses to incremental exercise and more
accurately estimates exercise capacity and ventilatory threshold.
168
Porszasz and colleagues (2003) devised a ramp protocol that increases work rate linearly so
that the individual walking on a treadmill reaches exhaustion in approximately 10 min. To
linearly increase work rate over time, it is necessary to couple linear increases in walking speed
with curvilinear increases in treadmill grade. Because this protocol starts with slow walking
(i.e., 0.5-1.0 mph, or 13.4-26.8 m·min ), it is suitable for individuals with low exercise
−1
tolerance as well as for sedentary individuals with a range of exercise tolerances. As with all
types of ramp protocols, this protocol is individualized. The peak work rate, a comfortable
range of walking speeds, and the increments in treadmill incline or grade are determined for
each client.
This protocol compares favorably to cycle ergometer ramp protocols that increase work rate
linearly so that maximum exercise tolerance is reached in ~10 min. The slope of the
169
relationship between V̇O and work rate, however, is consistently steeper on the treadmill
2
than on the cycle ergometer (Porszasz et al. 2003). This steeper slope reflects additional use
of the limbs (i.e., swinging the arms and legs) and frictional force as treadmill speed increases.
For each individual, the time course for the grade increments needed to elicit a linear increase
in work rate can be calculated with a prediction equation based on the client’s body weight,
desired initial and final walking speeds, initial grade, and estimated peak work rate (see
Porszasz et al. 2003). These individual variables, along with the prediction equation for
increasing grade, can be programmed into the computer of a contemporary treadmill. Thus,
each individualized ramp protocol is controlled by the computer so that the frequent increases
in speed and grade are smooth and rapid.
CYCLE ERGOMETER MAXIMAL EXERCISE TESTS

The cycle ergometer is a widely used instrument for assessing cardiorespiratory fitness and is
the preferred modality for exercise tests conducted on individuals with conditions affecting
their ability to safely walk or jog on a treadmill (Balady et al. 2010). On a friction-type cycle
ergometer (see figure 4.5), resistance is applied against the flywheel using a belt and weighted
pendulums. The hand wheel adjusts the workload by tightening or loosening the brake belt.
The workload on the cycle ergometer is raised through increases in the resistance on the
flywheel. The power output is usually expressed in kilogram-meters per minute (kgm·min ) −1
or watts (1 W ≈ 6 kgm·min ) and is easily measured using the equation

−1
00:00 / 00:00
Video 4.4
power = force × distance / time
where force equals the resistance or tension setting on the ergometer (kilograms) and distance
is the distance traveled by the flywheel rim for each revolution of the pedal multiplied by the
number of revolutions per minute. On the Monark and Bodyguard cycle ergometers, the
flywheel travels 6 m per pedal revolution. Therefore, if a resistance of 2 kg is applied and the
170
pedaling rate is 60 rpm, then
power = 2 kg × 6 m × 60 rpm = 720 kgm·min or 120 W.

−1
To calculate the distance traveled by the flywheel of cycle ergometers with varying-sized
flywheels, measure the circumference (in meters) of the resistance track on the flywheel and
multiply the circumference by the number of flywheel revolutions during one complete
revolution (360°) of the pedal (Gledhill and Jamnik 1995).
FIGURE 4.5 Cycle ergometer (mechanically braked).
When you are standardizing the work performed on a friction-type cycle ergometer, the
client should maintain a constant pedaling rate. Some cycle ergometers have a speedometer
that displays the individual’s pedaling rate. Check this dial frequently to make certain your
client is maintaining a constant pedaling frequency throughout the test. If a speedometer is
not available, use a metronome to establish your client’s pedaling cadence. Controlling the
pedaling rate on an electrically braked cycle ergometer (figure 4.6) is unnecessary. An
electromagnetic braking force adjusts the resistance for slower or faster pedaling rates, thereby
171
keeping the power output constant. This type of cycle ergometer, however, is difficult to
calibrate.
FIGURE 4.6 Cycle ergometer (electrically braked).
Most cycle ergometer test protocols for untrained cyclists use a pedaling rate of 50 or 60
rpm, and power outputs are increased by 150 to 300 kgm·min (25-50 W) in each stage of
−1
the test. However, you can use higher pedaling rates (≥80 rpm) for trained cyclists. A
pedaling rate of 60 rpm produces the highest V̇O max when compared with rates of 50, 70, or
2
80 rpm (Hermansen and Saltin 1969). Figure 4.7 illustrates some widely used discontinuous
and continuous maximal exercise test protocols for the cycle ergometer. Guidelines for use of
cycle ergometers are presented in Testing With Cycle Ergometers.
172
FIGURE 4.7 Cycle ergometer exercise test protocols.
To calculate the energy expenditure for cycle ergometer exercise, use the ACSM equations
provided in table 4.3. The total energy expenditure or gross V̇O , in ml·kg ·min , is a
2 −1 −1
function of the oxygen cost of pedaling against resistance (power output in watts), the oxygen
cost of unloaded cycling (approximately 3.5 ml·kg ·min at 50-60 rpm with zero resistance),
−1 −1
and the resting oxygen consumption. The cost of cycling against an external load or resistance
is approximately 1.8 ml·kg ·m . For a sample calculation, see ACSM Leg Ergometry
−1 −1
Equation.
Keep in mind that the leg and arm ergometry equations are accurate in estimating V̇O 2
only if the client attains a steady state during the maximal GXT. If, for example, the client is
able to complete only 1 min of exercise during the last stage of the maximal test protocol, the
power output from the previous stage (in which the client reached steady state) should be
used to estimate V̇O max rather than the power output corresponding to the last stage.
2
Testing With Cycle Ergometers
The following guidelines are suggested for the use of cycle ergometers:
1. Calibrate the cycle ergometer often by hanging known weights from the belt of the
flywheel and reading the dial on the hand wheel.
2. Always release the tension on the belt between tests.
3. Establish pedaling frequency before setting the workload.
173
4. Check the load setting frequently during the test because it may change as the belt
warms up.
5. Set the metronome so that one revolution is completed for every two beats (e.g., set
the metronome at 120 for a test requiring a pedaling frequency of 60 rpm).
6. Adjust the height of the seat so the knee is slightly flexed (about 25°) at maximal
leg extension with the ball of the foot on the pedal.
7. Have the client assume an upright seated posture, with hands properly positioned
on the handlebars.
ACSM Leg Ergometry Equation
To calculate the energy expenditure of a 62 kg (136 lb) woman cycling at a work rate or
power output of 360 kgm·min , follow these steps:
−1
1. Calculate the energy cost of cycling at the specified power output.
V̇O = work rate (W) / body mass (M) × 1.8 = 360 kgm·min / 62 kg × 1.8 = 10.45 ml·kg
2 a −1
−1·min −1
2. Add the estimated cost of cycling at zero load (i.e., 3.5 ml·kg ·min ). −1 −1
V̇O = 10.45 ml·kg ·min + 3.5 ml·kg ·min = 13.95 ml·kg ·min
2 −1 −1 −1 −1 −1 −1
3. Add the estimated resting energy expenditure (3.5 ml·kg ·min ). −1 −1
V̇O = 13.95 ml·kg ·min + 3.5 ml·kg ·min = 17.45 ml·kg ·min
2 −1 −1 −1 −1 −1 −1
aWork rate is in kgm·min .

−1
Åstrand Cycle Ergometer Maximal Test Protocol

For the Åstrand (1965) continuous test protocol (see figure 4.7), the initial power output is
300 kgm·min (50 W) for women and 600 kgm·min (100 W) for men. Because the pedaling
−1 −1
rate is 50 rpm, the resistance is 1 kg for women (1 kg × 6 m × 50 rpm = 300 kgm·min ) and 2 −1
kg for men (2 kg × 6 m × 50 rpm = 600 kgm·min ). Have your client exercise at this initial
−1
workload for 2 min. Then increase the power output every 2 to 3 min in increments of 150
kgm·min (25 W) and 300 kgm·min (50 W) for women and men, respectively. Continue the
−1 −1
test until the client is exhausted or can no longer maintain the pedaling rate of 50 rpm. Use
the ACSM metabolic equation for leg ergometry to estimate V̇O from your client’s power 2
output during the last steady-state stage of the GXT.
174
00:00 / 00:00
Video 4.5
Fox Cycle Ergometer Maximal Test Protocol

The Fox (1973) protocol is a discontinuous test consisting of a series of 5 min exercise bouts
with 10 min rest intervals. The starting workload is between 750 and 900 kgm·min (125- −1
150 W) for men and 450 and 600 kgm·min (75-100 W) for women. The progressive
−1
increments in work depend on the client’s HR response and are usually between 120 and 180
kgm·min (20-30 W). The client exercises until exhausted or until no longer able to pedal for
−1
at least 3 min at a power output that is 60 to 90 kgm·min (10-15 W) higher than the
−1
previous workload. You can use the metabolic equations to convert the power output from
the last steady-state stage of this protocol to V̇O max. 2
Self-Paced Cycle Ergometer Maximal Test Protocol

For self-paced cycle ergometer protocols, the client adjusts the pedaling cadence and flywheel
resistance throughout the test protocol. Mauger and Sculthorpe (2012) designed a self-paced,
maximal exertion 10-min protocol. This protocol requires the rider to select workloads for
five 2 min stages. For each stage, there is a target RPE, also known as a stage “clamp”,
culminating in an RPE of 20 on the Borg scale for the last stage. Compared with the
traditional cycling protocol, the self-paced RPE-clamped protocol results in a V̇O max value
2
that is approximately 8% higher and a peak power output that is 35 W higher, on average.
Conversely, other researchers report no difference in maximal values for self-paced RPE-
clamped protocols compared with more traditional ones using the same modality (Chidnok et
al. 2013; Evans, Parfitt, and Eston 2014). The self-paced protocol is perceived as being
preferred over the traditional ramp protocol (Evans, Parfitt, and Eston 2014). The clamping
technique produces similar maximal results in comparison to standardized protocols,
regardless of the modality; therefore, it is a viable alternative for maximal exertion testing.
BENCH STEPPING MAXIMAL EXERCISE TESTS
175
The least desirable mode of exercise for maximum exercise testing is bench stepping. During
bench stepping, the individual is performing both positive (up phase) and negative (down
phase) work. Approximately one-quarter to one-third less energy is expended during negative
work (Morehouse 1972). This factor, coupled with adjusting the step height and stepping
rate for differences in body weight, makes standardization of the work extremely difficult.
General Procedures
Most step test protocols increase the intensity of the work by gradually increasing the height
of the bench or stepping rate. The work (W) performed can be calculated using the equation
W = F × D, where F is body weight in kilograms and D is bench height multiplied by
number of steps per minute. For example, a 50 kg (110 lb) woman stepping at a rate of 22
steps·min on a 30 cm (0.30 m) bench is performing 330 kgm·min of work (50 kg × 0.30 m
−1 −1
× 22 steps·min ).−1
The following equations can be used to adjust the step height and stepping rate for
differences in body weight to achieve a given work rate (Morehouse 1972):
step height (cm) = work (kgcm·min ) / body weight (kg) × stepping rate
−1
stepping rate (steps·min ) = work (kgcm·min ) / body weight (kg) × step height (cm)
−1 −1
For example, if you devise a graded step test protocol that requires a client weighing 60 kg
(132 lb) to exercise at a work rate of 300 kgm·min , and the stepping rate is set at 18
−1
steps·min , you need to determine the step height that corresponds to the work rate:
−1
step height = 300 kgm·min / (60 kg × 18 steps·min )

−1 −1
= 0.28 m, or 28 cm
Alternatively, you may choose to keep the step height constant and vary the stepping
cadence for each stage of the GXT. For example, if the step height is set at 30 cm (0.30 m),
and the protocol requires that a client weighing 60 kg (132 lb) exercise at a work rate of 450
kgm·min , you need to calculate the corresponding stepping rate for this client:
−1
stepping rate = 450 kgm·min / (60 kg × 0.30 m) = 25 steps·min

−1 −1
You can calculate the energy expenditure in METs using the ACSM metabolic equation
for stepping exercise (see table 4.3). The total gross V̇O is a function of step frequency, step
2
height, and the resting energy expenditure. The oxygen cost of the horizontal movement is
approximately 0.2 ml·kg ·m for each four-count stepping cycle. The oxygen demand for
−1 −1
176
stepping up is 1.8 ml·kg ·m ; approximately one-third more must be added (i.e., constant of
−1 −1
1.33 in equation) to account for the oxygen cost of stepping down. For an example of such
calculations, see ACSM Stepping Equation.
Nagle, Balke, and Naughton Maximal Step Test Protocol

Nagle, Balke, and Naughton (1965) devised a graded step test for assessing work capacity.
Have your client step at a rate of 30 steps·min on an automatically adjustable bench (2-50
−1
cm). Set the initial bench height at 2 cm and increase the height 2 cm every minute of
exercise. Use a metronome to establish the stepping cadence (four beats per stepping cycle).
To establish a cadence of 30 steps·min , set the metronome at 120 (30 × 4). Terminate the
−1
test when the subject is fatigued or can no longer maintain the stepping cadence. Use the
ACSM metabolic equation for stepping exercise to calculate the energy expenditure (V̇O 2
max) corresponding to the step height and stepping cadence during the last work stage of this
protocol.
ACSM Stepping Equation
To calculate the energy expenditure for bench stepping using a 16 in. (about 40 cm) step
height at a cadence of 24 steps·min , use the following procedure:
−1
V̇O in ml·kg ·min = [frequency (F) in steps·min × 0.2] + (step height in m·step × F in
2 −1 −1 −1 −1
steps·min × 1.33 × 1.8) + resting V̇O

−1 2
1. Calculate the V̇O for the stepping frequency (F).

2
V̇O = stepping frequency (F) × 0.20 = 24 steps·min × 0.20 = 4.8 ml·kg ·min
2 −1 −1 −1
2. Convert the bench height to meters (1 in. = 2.54 cm or 0.0254 m).
ht = 16 in. × 0.0254 m = 0.4064 m
3. Calculate the V̇O for the vertical work performed during stepping.
2
V̇O = bench ht × stepping rate × 1.33 × 1.8 = 0.4064 m × 24 steps·min 1.33 × 1.8 = 23.35
2 −1 ×
ml·kg ·min−1 −1
4. Add resting V̇O to the calculated V̇O from steps 1 and 3.

2 2
V̇O = 4.8 ml·kg ·min + 23.35 ml·kg ·min + 3.5 ml·kg ·min = 31.65 ml·kg ·min
2 −1 −1 −1 −1 −1 −1 −1 −1
RECUMBENT STEPPER MAXIMAL EXERCISE TEST
177
Billinger and colleagues (2008) developed a maximum exercise test using a total body
recumbent stepper (NuStep TRS 4000). The protocol begins with a 2 min warm-up at load
setting 1 (50 W). Immediately after the warm-up, the initial workload is set to 4 (75 W). The
resistance is increased progressively until the participant reaches test termination criteria. A
constant cadence (115 steps·min ) is used throughout the exercise protocol. Compared with
−1
treadmill testing (Bruce protocol), the recumbent stepper test elicits a lower HRmax (181 vs.
188 bpm) and V̇O (3.13 vs. 3.67 L·min ) on average. These differences are expected given
2 −1
the seated posture during the recumbent stepper exercise test. The correlation coefficients for
V̇O max (r = .92) and HRmax (r = .96) indicate a strong relationship between the Bruce
2
protocol and the recumbent stepper protocol.

This test modality may be especially useful for assessing the cardiorespiratory fitness of
individuals with neuromuscular disorders that impair gait, coordination, and balance. Seated
steppers are now widely used as a training modality in rehabilitation centers, fitness centers,
and retirement communities.
SUBMAXIMAL EXERCISE TEST PROTOCOLS

It is desirable to directly determine the functional cardiorespiratory capacity of the individual
for classifying the aerobic fitness level and prescribing an aerobic exercise program. However,
this is not always practical to do. The actual measurement of V̇O max requires expensive
2
laboratory equipment, a considerable amount of time to administer, and a high level of

motivation on the part of the client.
Alternatively, you can use submaximal exercise tests to predict or estimate the V̇O max of
2
the individual. Many of these tests are similar to the maximal exercise tests described
previously but differ in that they are terminated at some predetermined HR intensity. You
will monitor the HR, BP, and RPE during the submaximal exercise test. The treadmill, cycle
ergometer, and bench stepping exercises are commonly used for submaximal exercise testing.
ASSUMPTIONS OF SUBMAXIMAL EXERCISE TESTS

Submaximal exercise tests assume that a steady-state HR is achieved and is consistent for each
exercise work rate. Steady-state HR is usually achieved in 3 to 4 min at a constant
submaximal work rate. Also, it is assumed that a linear relationship exists between V̇O and HR
2
within the range of 110 to 150 bpm. The HR and work rate from two submaximal work
outputs can be plotted (i.e., HR-V̇O relationship) and extrapolated to HRmax to estimate
2
V̇O max from submaximal data (see figure 4.10). Although the linear relationship between
2
178
HR and V̇O holds for light to moderate workloads, the relationship between oxygen uptake
2
and work rate becomes curvilinear at heavier workloads. If your clients are taking medications
that alter HR, you should not use submaximal HR data to estimate their V̇O max.
2
Another assumption of submaximal testing is that the mechanical efficiency during cycling or
treadmill exercise is constant for all individuals. However, a client with poor mechanical
efficiency while cycling has a higher submaximal HR at a given workload, and the actual V̇O 2
max is underestimated because of this inefficiency. As a result, V̇O max predicted by

2
submaximal exercise tests tends to be overestimated for highly trained individuals and
underestimated for untrained, sedentary individuals.
Submaximal tests also assume that the HRmax for clients of a given age is similar. However,
it has been shown to vary as much as ±11 bpm, even after controlling for variability due to age
and training status (Londeree and Moeschberger 1984). Also, for submaximal tests, the
HRmax is estimated from age. The equation HRmax = 220 − age is widely used. The
HRmax of approximately 5% to 7% of men and women is more than 15 bpm less than their
age-predicted values. On the other hand, 9% to 13% exceed their age-predicted HRmax by
more than 15 bpm (Whaley et al. 1992). Because of interindividual variability in HRmax and
the potential inaccuracy with use of age-predicted values, there may be considerable error
(±10%-15%) in estimating your clients’ V̇O max, especially when submaximal data are
2
extrapolated to an age-predicted HRmax.

In addition, Tanaka, Monahan, and Seals (2001) noted that the traditional age-predicted
equation (220 − age) overestimates the measured HRmax of younger individuals and
increasingly underestimates the actual HRmax of individuals older than 40 yr. After
reviewing numerous studies of healthy adults (18-81 yr), the authors reported that age singly
accounts for 80% of the variance in HRmax and is independent of gender and physical
activity status. They derived an equation to predict HRmax from age: HRmax = 208 − (0.7 ×
age). Estimates from this equation differ from those of the traditional one, particularly in
older (>40 yr) adults. For example, the age-predicted HRmax for a 60 yr old client is 166
bpm for the revised equation (208 – [0.7 × 60] = 166 bpm) and 160 bpm for the traditional
equation (220 − 60 = 160 bpm).
Gellish and colleagues (2007) modeled the relationship between HRmax and age as
individuals grow older. Their data yielded a prediction equation (HRmax = 207 – [0.7 × age])
that is similar to the equation derived by Tanaka and colleagues (2001). The confidence
interval (CI) for predicting HRmax of adults 30 to 75 yr was ±5 to 8 bpm. Additionally,
Gellish and colleagues developed a quadratic equation, HRmax = 192 − (0.007 × age ). 2
179
Although this quadratic equation improves the prediction error (95% CI = ±2-5 bpm), it is
not practical to use.
After determining there was no difference in HRmax compared to that obtained through
treadmill testing, Cleary and colleagues (2011) suggested that the highest HR from two 200
m maximal exertion sprints is a suitable alternative to the age-related HRmax prediction
equations for adults (18-33 yr). Of interest, they found that the Gellish quadratic equation
and the gender-specific equations of Fairbarn and colleagues (1994) (women: [HRmax = 201
− (0.63 × age)]; men: HRmax = [208 − (0.80 × age)]) produced estimations similar to that
from the 200 m sprint.
Because of interindividual variability in HRmax and the potential inaccuracy of age-
predicted equations, the actual HRmax should be measured directly (by ECG or HR
monitor) whenever possible. An accurate HRmax is particularly important in situations in
which
the exercise test is terminated at a predetermined percentage of either HRmax

(%HRmax method) or heart rate reserve (HRR = % [HRmax – HRrest] + HRrest),
the client’s V̇O max is estimated from submaximal exercise test data that are
2
extrapolated to an age-predicted HRmax,

or HRmax is used to determine target exercise HRs for aerobic exercise
prescriptions (see chapter 5).
TREADMILL SUBMAXIMAL EXERCISE TESTS

Treadmill submaximal tests provide an estimate of functional cardiorespiratory capacity (V̇O 2
max) and assume a linear increase in HR with successive increments in workload. Compared
to clients with low cardiorespiratory fitness levels, the well-conditioned individual presumably
is able to perform a greater quantity of work at a given submaximal HR.
00:00 / 00:00
Video 4.6
180
A perceptually regulated exercise test (PRET) offers an alternative to estimating V̇O peak
2
using HR. These tests are performed on cycle ergometers and treadmills. PRETs require that
the workload be adjusted so that the individual exercises in a graded format (3 min stages) at
intensities associated with four different RPE values (9, 11, 13, 15). Metabolic gas data
collected during the last 30 sec of each stage are averaged and plotted against the
corresponding RPE; the linear regression technique is used to extrapolate to an RPE of 19 or
20 for the estimation of V̇O peak.
2
Eston and colleagues (2012) validated a PRET for treadmill testing for a mixed group of
active and sedentary adults (18-72 yr). They concluded that extrapolating to an RPE of 19 is
valid for estimating V̇O peak for sedentary and active adults. Evans and colleagues (2015)
2
included PRET protocols in their review of submaximal treadmill tests for estimating V̇O 2
peak. They discussed the importance of familiarizing individuals with the Borg 6 to 20 RPE
scale before the test begins. Additionally, Evans and colleagues (2015) commented on the
benefit of using self-identified workloads at each of the RPEs instead of using an age-
predicted maximal HR.
You can also use treadmill maximal test protocols (figure 4.2) to identify the slope of an
individual’s HR response to exercise. The V̇O max can be predicted from either one (single-
2
stage model) or two (multistage model) submaximal HRs. The accuracy of the single-stage
model is similar to that of the multistage model.
Multistage Model
To estimate V̇O max with the multistage model, use the HR and workload data from two or
2
more submaximal stages of the treadmill test. Be sure your client reaches steady-state HRs
between 115 and 150 bpm (Golding 2000). Determine the slope (b) by calculating the ratio
of the difference between the two submaximal (SM) workloads (expressed as V̇O and the
2)
corresponding change in submaximal HRs:
b = (SM − SM ) / (HR − HR )
2 1 2 1
Calculate the V̇O for each workload using the ACSM metabolic equation (table 4.3), and
2
use the following equation to predict V̇O max:

2
V̇O max = SM + b(HRmax − HR )

2 2 2
If the actual maximal HR is not known, estimate it using one of the age-predicted HRmax
equations previously mentioned. See Multistage Model for Estimating V̇O max for an
2
181
example that illustrates how V̇O max is estimated from submaximal treadmill test data for a
2
38 yr old male. In this example, the Bruce protocol was administered to the client. Please
note that this model may be used for any multistage GXT test.
MULTISTAGE MODEL FOR ESTIMATING V̇O MAX 2
Submaximal Data From Bruce Protocol
Stage 2a
V̇O2b = 24.5 ml·kg−1·min−1 (SM2)
HR = 145 bpm (HR2)
Stage 1a
V̇O2 = 16.1 ml·kg−1·min−1 (SM1)
HR = 130 bpm (HR1)
Maximal HR: 220 − age = 182 bpm
Slope (b) = (SM2 − SM1) / (HR2 − HR1)
b = (24.5 − 16.1) / (145 − 130)
b = 8.4 / 15
b = 0.56
V̇O2max: = SM2 + b(HRmax − HR2)
= 24.5 + 0.56(182 − 145) = 24.5 + 20.72
V̇O2max = 45.22 ml·kg−1·min−1
aStages 1 and 2 refer to the last two stages of the GXT completed by the client, and not the first and second stage of the test protocol. For example, if the client completes three stages of the submaximal exercise test protocol,
2
data from stage 2 and stage 3 are used to estimate V̇O .
b V̇O2 is calculated using ACSM metabolic equations (see table 4.3). V̇O2 can be expressed in L·min−1, ml·kg−1·min−1, or METs.
Single-Stage Model
To estimate V̇O max with the single-stage model, use one submaximal HR and one
2
workload. The steady-state submaximal HR during a single-stage GXT should reach 130 to
150 bpm. Formulas for Men and Women shows formulas that have been developed
182
(Shephard 1972).
FORMULAS FOR MEN AND WOMEN
Men
V̇O2max = SMV̇O2 × [(HRmax − 61) / (HRSM − 61)]
Women
V̇O2max = SMV̇O2 × [(HRmax − 72) / (HRSM − 72)]
SM V̇O2 is calculated using the ACSM metabolic equations (see table 4.3). Estimate HRmax
(if not known) using one of the age-predicted HRmax formulas; HRsubmax is the
submaximal HR.
Single-Stage Model for Estimating V̇O max provides an example to illustrate how this
2
model is used to predict V̇O max from submaximal treadmill data for a 45 yr old female. In
2
this example, the Balke protocol was administered. Please note that this model may be used
for any GXT protocol.
Single-Stage Treadmill Walking Test

Ebbeling and colleagues (1991) developed a single-stage treadmill walking test suitable for
estimating V̇O max of healthy, low-risk adults 20 to 59 yr. The Ebbeling treadmill test also
2
produced high test-retest reliability and validity with V̇O max for a sample of middle-aged
2
(45-65 yr) women (Mitros et al. 2011). For this protocol, walking speed is individualized and
ranges from 2.0 to 4.5 mph (53.6-120.6 m·min ) depending on your client’s age, gender, and
−1
fitness level. Establish a walking pace during a 4 min warm-up at 0% grade. The warm-up
work bout should produce a HR within 50% to 70% of the individual’s age-predicted
HRmax. The test consists of brisk walking at the selected pace for an additional 4 min at 5%
grade. Record the steady-state HR at this workload, and use it in the following equation to
estimate V̇O max: 2
V̇O max = 15.1 + 21.8(speed in mph) (ml·kg ·min ) − 0.327(HR in bpm) − 0.263(speed ×
2 −1 −1
age in years) + 0.00504(HR × age) + 5.48(gender: female = 0; male = 1)
SINGLE-STAGE MODEL FOR ESTIMATING V̇O MAX 2
Submaximal Data From Balke Protocol: Stage 3
V̇O2 = 5.0 METs (SMV̇O2)
183
HR = 148 bpm (HRSM)
Maximal HR: 220 − age = 175 bpm
V̇O2max: = SMV̇O2 × [(HRmax − 72) / (HRsubmax − 72)]
= 5 × [(175 − 72) / (148 − 72)] = 5 × (103 / 76) = 6.8 METs
Single-Stage Treadmill Jogging Test

You can estimate the V̇O max of younger adults (18-28 yr) using a single-stage treadmill
2
jogging test (George et al. 1993). For this test, select a comfortable jogging pace ranging from
4.3 to 7.5 mph (115.2-201 m·min ), but not more than 6.5 mph (174.2 m·min ) for women
−1 −1
and 7.5 mph (201 m·min ) for men. Have the client jog at a constant speed for about 3 min.
−1
The steady-state exercise HR should not exceed 180 bpm. Estimate V̇O max using the 2
following equation:
V̇O max = 54.07 − 0.1938(body weight in kg (ml·kg ·min ) + 4.47(speed in mph) −

2 −1 −1
0.1453(HR in bpm) + 7.062(gender: female = 0; male = 1)
CYCLE ERGOMETER SUBMAXIMAL EXERCISE TESTS

Cycle ergometer multistage submaximal tests can be used to predict V̇O max. These tests are 2
either continuous or discontinuous and are based on the assumption that HR and oxygen
uptake are linear functions of work rate. The HR response to submaximal workloads is used
to predict V̇O max.
2
00:00 / 00:00
Video 4.7
Alternatively, PRETs may be used if metabolic gases are collected during the submaximal
cycling test. Instruct your client to pedal for 3 min each at an RPE of 9, 11, 13, and 15. Plot
the average V̇O from the last 30 sec of each stage against the respective RPE value.
2
184
Extrapolate the regression line to an RPE of 20 to estimate V̇O peak. PRETs remove errors
2
associated with using an equation to calculate an age-predicted maximal heart rate (Coquart
et al. 2016; Evans et al. 2015).
Åstrand-Ryhming Cycle Ergometer Submaximal Exercise Test Protocol

The Åstrand-Ryhming protocol (1954) is a single-stage test that uses a nomogram to predict
V̇O max from HR response to one 6 min submaximal workload. A power output is selected
2
that produces a HR between 125 and 170 bpm. The initial workload is usually 450 to 600
kgm·min (75-100 W) for trained, physically active women and 600 to 900 kgm·min (100-
−1 −1
150 W) for trained, physically active men. An initial workload of 300 kgm·min (50 W) may
−1
be used for unconditioned or older individuals.

During the test, measure the HR every minute and record the average HR during the fifth
and sixth minutes. If the difference between these two HRs exceeds 5 or 6 bpm, extend the
work bout until a steady-state HR is achieved. If the HR is less than 130 bpm at the end of
the exercise bout, increase the workload by 300 kgm·min (50 W) and have the client exercise
−1
an additional 6 min.
To estimate V̇O max for this protocol, use the modified Åstrand-Ryhming nomogram
2
(see figure 4.8). This nomogram estimates V̇O max (in L·min ) from submaximal treadmill,
2 −1
cycle ergometer, and step test data. For each test mode, the submaximal HR is plotted with
either oxygen cost for treadmill exercise (V̇O in L·min ), power output (kgm·min ) for cycle
2 −1 −1
ergometer exercise, or body weight (kg) for stepping exercise. For the cycle ergometer test,
plot the client’s power output (kgm·min ) and the steady-state exercise HR in the
−1
corresponding columns of the Åstrand-Ryhming nomogram (see figure 4.8). Connect these
points with a ruler, and read the estimated V̇O max at the point where the line intersects the
2
V̇O max column.

2
185
FIGURE 4.8 Modified Åstrand-Ryhming nomogram.
From “Aerobic Capacity in Men and Women with Special Reference to Age,” by I. Åstrand, 1960. Acta Physiologica Scandinavica 49 (Suppl. 169), p. 51. Copyright 1960 by Acta Physiologica Scandinavica. Reprinted by permission.
The correlation between measured V̇O max and the V̇O max estimated from this 2 2
nomogram is r = .74. The prediction error is ±10% and ±15%, respectively, for well-trained
and untrained individuals (Åstrand and Rodahl 1977). A cross-validation study of this
protocol and nomogram yielded a validity coefficient of .82 and a prediction error of 5.1
ml·kg ·min for estimating the V̇O max of adults 18 to 44 yr (Swain et al. 2004).
−1 −1 2
For clients younger or older than 25 yr, you must use the following age-correction factors
to adjust the V̇O max predicted from the nomogram for the effect of age. For example, if the
2
estimated V̇O max from the nomogram is 3.2 L·min for a 45 yr old client, the adjusted V̇O
2 −1 2
max is 2.5 L·min (3.2 × 0.78 = 2.5 L·min ).

−1 −1
YMCA Cycle Ergometer Submaximal Exercise Test Protocol

The YMCA protocol (Golding 2000) is a cycle ergometer submaximal test for women and
men. This protocol uses three or four consecutive 3 min workloads on the cycle ergometer
designed to raise the HR to between 110 bpm and 85% of the age-predicted HRmax for at
least two consecutive workloads. The pedal rate is 50 rpm, and the initial workload is 150
186
kgm·min (25 W). Using a friction-type cycle ergometer, set the resistance to 0.5 kg (0.5 kg ×
−1
50 rpm × 6 m = 150 kgm·min ). To achieve this work rate using a plate-loaded cycle
−1
ergometer, use one weight plate (1.0 kg) and reduce the pedaling frequency to 25 rpm (1.0 kg
× 25 rpm × 6 m = 150 kgm·min ). Use the HR during the last minute of the initial workload
−1
to determine subsequent workloads (see figure 4.9). If the HR is less than 86 bpm, set the
second workload at 600 kgm·min . If HR is 86 to 100, the workload is 450 kgm·min for the
−1 −1
second stage of the protocol. If the HR at the end of the first workload exceeds 100 bpm, set
the second workload at 300 kgm·min . −1
AGE-CORRECTION FACTORS FOR ÅSTRAND-RYHMING NOMOGRAM
Age Correction factor

15 1.10
25 1.00
35 0.87
40 0.83
45 0.78
50 0.75
55 0.71
60 0.68
65 0.65
Set the third and fourth workloads accordingly (see figure 4.9). Measure the HR during
the last 30 sec of minutes 2 and 3 at each workload. If these HRs differ by more than 5 or 6
bpm, extend the workload an additional minute until the HR stabilizes. If the client’s steady-
state HR reaches or exceeds 85% of the age-predicted HRmax during the third workload,
terminate the test.
FIGURE 4.9 YMCA cycle ergometer protocol.
187
Calculate the energy expenditure (V̇O for the last two workloads using the ACSM
2)
metabolic equations (see table 4.3). To estimate V̇O max from these data, use the equations
2
for the multistage model to calculate the slope of the line depicting the HR response to the
last two workloads. Alternatively, you can graph these data to estimate V̇O max (see figure 2
4.10). To do this, plot the V̇O for each workload and corresponding HRs. Connect these
2
two data points with a straight edge, extending the line so that it intersects the predicted
maximal HR line. To extrapolate V̇O max, drop a perpendicular line from the point of
2
intersection to the x-axis of the graph. If this is done carefully, the graphing method and
multistage method will yield similar estimates of V̇O max. 2
FIGURE 4.10 Plotting heart rate versus submaximal work rates to estimate maximal work capacity and V̇O2max.
PRELIMINARY PROCEDURES AND GENERAL INSTRUCTIONS FOR SWAIN

PROTOCOL
To select the protocol, follow these steps:
1. Measure the body weight and record your client’s age.
2. Classify your client’s activity level as either active (>90 min/wk of vigorous activity or >120 min/wk of
moderate-intensity exercise) or inactive (<90 min/wk of vigorous activity or <120 min/wk of moderate-
intensity exercise). Vigorous activities include running, vigorous cycling, or any equivalent; moderate-
intensity activities include brisk walking, moderate cycling, or any equivalent.
3. Estimate your client’s age-predicted HRmax (220 − age). Calculate the target exercise HRs corresponding to
45%, 55%, and 75% HRR (see figure 5.3 for an example). Target HR = %HRR × (HRmax − HRrest) +
188
HRrest.
4. Select a protocol based on your client’s body weight and activity level. Instruct your client to maintain a 60
rpm pedaling frequency throughout the test.
5. Measure exercise HRs during the last 15 sec of each minute of the test. Terminate the test immediately if
the target HR corresponding to 75% HRR is exceeded.
To estimate maximum workload and the corresponding V̇O2max from the final 6 min stage of this test, use the following
steps:
1. Calculate the power in watts (W) for the final 6 min workload. Power6 min (W) = resistance (kg) × 60 rpm ×
9.81 m·sec−2.
2. Average the fifth- and sixth-minute HRs from the final stage (HR6 min), and calculate the client’s age-
predicted HRmax using 220 − age.
3. Calculate the client’s %HRR for the final stage: %HRR = (HR6 min − HRrest) / (HRmax − HRrest).
4. Estimate the client’s maximum workload or power in watts (W) by dividing the power of the final stage,
calculated in step 1, by the %HRR calculated in step 3: powermax (W) = power6 min / %HRR.
5. Use the ACSM metabolic equation for cycle ergometry to convert maximum power to an estimated
V̇O2max: V̇O2max = 7 + [10.8 × powermax (W) / body mass in kg].
Based on Swain et al. 2004.
Swain Cycle Ergometer Submaximal Exercise Test Protocol

Swain and colleagues (2004) devised a submaximal cycle ergometry protocol for estimating
V̇O max based on the relationship between heart rate reserve (HRR) and V̇O reserve (V̇O R)
2 2 2
rather than on the HR-V̇O relationship. This protocol gradually approaches a target HR of
2
65% to 75% HRR in 1 min stages. This target HR zone is equivalent to 65% to 75% V̇O R. 2
When the client reaches her target HR, she continues to exercise at that workload for an
additional 5 min. The initial work rate and increments in work rate differ depending on the
client’s body mass and activity level (see figure 4.11). The predictive validity of this test was
good (r = .89; SEE = 4.0 ml·kg ·min ) for estimating the V̇O max of adults ages 18 to 44 yr.
−1 −1 2
However, more cross-validation studies are needed to determine this test’s applicability to
older or high-risk clients.
189
FIGURE 4.11 Swain cycle ergometer protocol for active clients and inactive clients.
Figure 4.11 illustrates the Swain test protocols for active and inactive clients who weigh
<90 kg or ≥90 kg (198 lb). To select the appropriate protocol and to calculate your client’s
estimated V̇O max, follow the instructions in Preliminary Procedures and General
2
Instructions for Swain Protocol.
Fox Single-Stage Cycle Ergometer Test Protocol

You can modify the maximal exercise test protocol (see figure 4.7) designed by Fox (1973) to
predict V̇O max (ml·min ). Have your client perform a single workload (i.e., 900 kgm·min
2 −1 −1
or 150 W) for 5 min. The standard error of estimate for this test is ±246 ml·min , and the−1
standard error of prediction is ±7.8%. The correlation between actual and predicted V̇O max 2
is r = .76. To estimate V̇O max, measure the HR at the end of the fifth minute of exercise
2
(HR ) and use the following equation:

5
V̇O max (ml·min ) = 6,300 − 19.26(HR )

2 −1 5
BENCH STEPPING SUBMAXIMAL EXERCISE TESTS

There are many step tests available to evaluate cardiorespiratory fitness; however, few provide
equations for predicting V̇O max. Only step test protocols with prediction equations are
2
included in this section. Although these step test protocols were designed to be submaximal,
the energy expenditure required of obese, short, or inactive individuals may exceed moderate-
190
intensity exertion and approach V̇O max levels (Hansen et al. 2011). 2
Åstrand-Ryhming Step Test Protocol

As mentioned previously, you can use the Åstrand-Ryhming nomogram (see figure 4.8) to
predict V̇O max from postexercise HR and body weight during bench stepping. For this
2
protocol, the client steps at a rate of 22.5 steps·min for 5 min. The bench height is 33 cm −1
(13 in.) for women and 40 cm (15.75 in.) for men. Measure the postexercise HR by counting
the number of beats between 15 and 30 sec immediately after exercise (convert this 15 sec
count to beats per minute by multiplying by 4). Correct the predicted V̇O max from the 2
nomogram if your client is older or younger than 25 yr (using the age-correction factors).
Hansen and colleagues (2011) modified the bench height requirement and used the 40 cm
platform for the middle-aged (45 ± 13 yr) participants at least 170 cm tall. A 33 cm platform
was used for those shorter than 170 cm. Not everyone was able to complete the 5 min
exercise period. Given the level of exertion required for stepping (from 75% to over 95%
V̇O max), Hansen and colleagues suggested that medical supervision of fixed-rate stepping
2
tests may be required.
Queens College Step Test Protocol

In a step test to predict V̇O max devised by McArdle and colleagues (1972), the client steps
2
at a rate of 22 steps·min (females) or 24 steps·min (males) for 3 min. The bench height is
−1 −1
16.25 in. (41.3 cm). Have your client remain standing after the exercise. Wait 5 sec and then
take a 15 sec HR count. Convert the count to beats per minute by multiplying by 4. If you are
administering this test simultaneously to more than one client, you should teach your clients
how to measure their own pulse rates (see How to Measure Your Pulse Rate). To estimate
V̇O max in ml·kg ·min , use the equations listed in table 4.7. The standard error of
2 −1 −1
prediction for these equations is ±16%.
Table 4.7 Prediction Equations for Cardiorespiratory Field Tests
Field test Equationa Source
DISTANCE RUN/WALK
1.0 mi steady-state jog

2
V̇O max = 100.5 − 0.1636(BW, kg) − 1.438(time, min) − 0.1928(HR, bpm) + 8.344(gender) b George et al. (1993)
1.0 mi run/walk (8-17 yr)

2
V̇O max = 108.94 − 8.41(time, min) + 0.34(time, min) 2 + 0.21(age × gender)b − 0.84(BMI)c Cureton et al. (1995)
2
1.0 mi run/walk (13-16 yr) V̇O peak = 7.34 × speed, m·sec −1 + 0.23(age, yr × gender b) + 17.75 Burns et al. (2016)
1.5 mi run/walk
2
V̇O max = 88.02 − 0.1656(BW, kg) − 2.76(time, min) + 3.716(gender) b George et al. (1993)
1.5 mi run/walk
2
V̇O max = 100.16 + 7.30(gender) b − 0.164(BW, kg) − 1.273(time, min) − 0.1563(HR, bpm) Larsen et al. (2002)
191
12 min run 2
V̇O max = 0.0268(distance, m) − 11.3 Cooper (1968)
15 min run 2
V̇O max = 0.0178(distance, m) + 9.6 Balke (1963)
1.0 mi walk 2
V̇O max = 132.853 − 0.0769(BW, lb) − 0.3877(age, years) + 6.315(gender) b − 3.2649(time, min) − 0.1565(HR, bpm) Kline et al. (1987)
STEP TESTS
2
Men: V̇O max (L·min −1) = 3.744 [(BW + 5) / (HR − 62)]
Åstrand Marley and Linnerud (1976)
2
Women: V̇O max (L·min −1) = 3.750 [(BW − 3) / (HR − 65)]
2
Men: V̇O max = 111.33 − (0.42 HR, bpm)
Queens College McArdle et al. (1972)
2
Women: V̇O max = 65.81 − (0.1847 HR, bpm)
STEP tool 2
V̇O max (ml·kg −1·min−1) = 3.9 + (1511 / time) × [(weight, kg / HR, bpm) × 0.124] − (age, yr × 0.032) − (genderb × 0.633) Knight, Stuckey, and Petrella (2014)
Individualized 2
V̇O max (ml·kg −1·min−1) = 45.938 + 0.253(genderb) − 0.140(weight, kg) + 0.670(PFA) + 0.429(FSR) − 0.149(45sRHR) Webb et al. (2014)
2
Men: V̇O max (ml·kg −1·min−1) = 129.6 − (3.82 O2 pulse) − (5.32 time to completion, s) − (0.22 age, yr) − (0.24 BMIc) − (0.12 HR, bpm)
Self-paced (≥65 yr) Petrella et al. (2001)
2
Women: V̇O max (ml·kg −1·min−1) = 116.4 − (5.10 O2 pulse) − (2.81 time to completion, s) − (0.24 age, yr) − (0.24 BMIc) − (0.14 HR, bpm)
HR = heart rate; m = meters; PFA = perceived functional ability score; FSR = final step rate, steps·min −1; 45sRHR = HR at 45 sec after test termination; O2 pulse = step test V̇O2 cost / HR.
aAll equations estimate V̇O2max in ml·kg−1·min−1 unless otherwise specified.
bFor gender, substitute 1 for males and 0 for females.
cBMI = body mass index, or body weight (BW, in kg) / ht2 (in meters).
STEP Tool Protocol

The Step Test and Exercise Prescription (STEP) tool was created for health care
professionals to quickly estimate the cardiorespiratory fitness levels of clients during an office
visit. For this test, your client completes 20 self-paced stepping cycles on a standardized set of
two steps (20 cm step height). Before the test begins, let your client practice for 10 stepping
cycles. Instruct your client to step onto the first step with one foot, step onto the second step
with the other foot, and then bring both feet together on the second step. Stepping down is
done similarly—first one foot down one step and then the other foot to the floor. When both
feet are on the floor again, that constitutes one stepping cycle. The stepping pattern is up, up,
together, down, down, together (Knight, Stuckey, and Petrella 2014). Timing begins when
the first foot touches the first step and stops when the 20th stepping cycle is completed.
Maximal aerobic capacity is estimated using body mass, age, sex, time to complete the 20
stepping cycles, and postexercise HR (see table 4.7). HR is palpated at the radial artery for 6
sec immediately after exercise and multiplied by 10 to get beats per minute. This equation
was independently validated using several adult populations, ranging in age from 18 to 85 yr,
but may not be suitable for people using medications to control their HR. Knight, Stuckey,
and Petrella (2014) assessed the predictive accuracy of the equation against the Bruce
treadmill protocol. There was a moderately strong relationship (r = .79) between the V̇O 2
192
estimated from the STEP and the average V̇O during the last 30 sec of the treadmill test.
2
However, the STEP tool systematically overpredicted V̇O max by 6.4 ml·kg ·min . The 95%
2 −1 −1
confidence interval was 4.1 to 8.7 ml·kg ·min .

−1 −1
Webb Step Test Protocol

The Webb protocol was developed for college-age adults and uses the client’s perceived
functional ability (PFA) as determined by questionnaire (George, Stone, and Burkett 1997).
Step height is calculated using your client’s height (step height = 0.19 × height in cm), and
termination HR is 75% of your client’s age-predicted HRmax (207 − [0.7 × age]). Instruct
your client to wear a heart rate monitor. The initial stepping cadence depends on your client’s
PFA (see Webb et al. 2014). Use a metronome to help your client maintain the proper
stepping cadence (steps·min ) in an up, up, down, down fashion. A brief familiarization with
−1
the stepping cadence is recommended. Your client steps at the initial cadence for 2 min.
Record the HR and RPE in the last 30 sec of each stage. At the end of each 2 min stage,
increase the stepping cadence by 5 steps·min , but do not change the step height. Continue
−1
this pattern until the termination HR is attained. Have your client finish this stage and
immediately sit down; record the HR immediately and again every 15 sec for 1 min. To
estimate V̇O max (see table 4.7), use the final stepping rate (cadence, FSR) and posttest HR
2
at the 45 sec interval.
ADDITIONAL MODES FOR SUBMAXIMAL EXERCISE TESTING

If you are working at a health or fitness club, you may have access to stair climbers,
recumbent steppers, and rowing ergometers. You can use some of these exercise machines for
submaximal exercise testing of your clients.
Stair Climbing Submaximal Test Protocols

In light of the popularity of and continued interest in step aerobic training, you may choose
to use a simulated stair climbing machine to estimate the aerobic fitness of some clients. The
StairMaster 4000 PT and 6000 PT are two step ergometers commonly used in health and
fitness settings. The StairMaster 4000 PT has step pedals that go up and down, whereas the
6000 PT model has a revolving staircase. Howley, Colacino, and Swensen (1992) reported
that the HR response to increasing submaximal workloads (4.7 and 10 METs) on the
StairMaster 4000 PT step ergometer was linear. Also, compared against values with treadmill
exercise, the HRs measured during stepping were systematically higher (7-11 bpm) at each
submaximal intensity. However, the MET values read from the step ergometer were about
193
20% higher than the measured MET values. To obtain more accurate MET values for each
submaximal intensity, use the following equation:
actual METs = 0.556 + 0.745 (StairMaster 4000 PT MET value)
The StairMaster 4000 PT test protocol, developed by the manufacturer, provides a

relatively more accurate estimate of V̇O max for young women (20-25 yr) who use this device
2
for aerobic training (r = .57; SEE = 5.3 ml·kg ·min ; CE = 1.0 ml·kg ·min ) as compared
−1 −1 −1 −1
with estimates for their untrained counterparts (r = .00; SEE = 6.7 ml·kg ·min ; CE = 6.9
−1 −1
ml·kg ·min ) (Roy et al. 2004). This finding illustrates that the exercise testing mode should
−1 −1
match the exercise training mode (i.e., application of the specificity principle).
How to Measure Your Pulse Rate
1. Use your middle and index fingers to locate the radial pulse on the outside of your
wrist just below the base of your thumb. Do not use your thumb to feel the pulse
because it has a pulse of its own and may produce an inaccurate count.
2. If you cannot feel the radial pulse, try locating the carotid pulse by placing your
fingers lightly on the front of your neck, just to the side of your voice box. Do not
apply heavy pressure because this will cause your HR to slow down.
3. Use a stopwatch or the second hand of your wristwatch and count the number of
pulse beats for a 6, 10, or 15 sec period.
4. Convert the pulse count to beats per minute using the following multipliers: 6 sec
count × 10; 10 sec count × 6; and 15 sec count × 4.
5. Remember this value and record it on your scorecard.
To estimate V̇O max, measure the steady-state HR and calculate the corrected MET value
2
for each of two submaximal exercise intensities (e.g., 4 and 7 METs). Each stage of the test
should last 3 to 6 min in order to produce steady state. Then use either the multistage model
formulas (see Multistage Model for Estimating V̇O max) or the graphing method (see figure
2
4.10) to predict V̇O max. 2
During the test, clients may hold the handrail lightly for balance but should not support
their body weight. If they support their body weight, V̇O max will be overestimated (Howley
2
et al. 1992). Also, compared against the value with treadmill testing, your clients’ estimated
V̇O max may be lower because stair climbing produces systematically higher HRs at any
2
194
given submaximal exercise intensity.
Recumbent Stepper Submaximal Test Protocol

The YMCA submaximal cycling protocol has been adapted for use with the NuStep T5xr
recumbent stepper and can be used to estimate the V̇O peak (r = .91; SEE = 4.09 ml·kg ·min
2 −1
; TE = 4.11 ml·kg ·min ) of your clients, 20-60 yr (Billinger et al. 2012). The Billinger
−1 −1 −1
equation may also be used for older adults, 60-80 yr (r = .87; SEE = 4.2 ml·kg ·min ) (Herda
−1 −1
et al. 2013).
The client must maintain a stepping rate between 90 and 100 steps·min throughout the
−1
protocol. Similar to the YMCA cycling protocol, the stage change is dependent on the
client’s having attained a steady-state HR. The initial workload is 30 W, and workload is
increased every 3 min in accordance with the HR-derived protocol track. Use the HR during
the last 10 sec of the second and third minutes of each stage to determine if steady-state HR
has been attained (within ± 5 bpm). If the HR at the end of the initial stage is less than 80
bpm, set the second workload at 125 W. If HR is 80 to 89 bpm, the workload is 100 W for
the second stage of the protocol. If the initial stage HR is 90 to 100 bpm, the second-stage
workload is 75 W. If the HR at the end of the first workload exceeds 100 bpm, set the second
workload at 50 W. Subsequent workloads increase 25 W every third minute thereafter,
assuming a steady-state HR was achieved in the previous stage. The protocol terminates
when the client reaches 85% of the age-predicted HRmax or volitional exhaustion. Estimate
V̇O peak (ml·kg ·min ) using the following equation:
2 −1 −1
V̇O peak = 125.707 − (0.476 × age, yr) + (7.686 × sex) − (0.451 × wt, kg) + (0.179 × W
2 )
end_submax
− (0.415 × HR end_submax )
Note: For sex, 0 = female and 1 = male.

W end_submax = watts equivalent to final workload.
HR end_submax = HR at test termination.
Rowing Ergometer Submaximal Test Protocols

Submaximal exercise protocols have been developed for the Concept II rowing ergometer and
can be used to estimate your clients’ V̇O max. The Hagerman (1993) protocol is designed for
2
noncompetitive or unskilled rowers. Before beginning the test, set the fan blades in the fully
closed position and select the small axle sprocket. For this test, select a submaximal exercise
intensity (the HR should not exceed 170 bpm) that the client can sustain for 5 to 10 min.
Measure the exercise HR at the end of each minute. Continue the rowing exercise until the
195
client achieves a steady-state HR. Use the Hagerman (1993) nomogram (see figure 4.12) to
estimate V̇O max from the submaximal power output (watts) and the steady-state HR during
2
the last minute of exercise.
FIGURE 4.12 Concept II nomogram for estimating V̇O2max in noncompetitive and unskilled male and female
rowers.
From Concept II Rowing Ergometer Nomogram for Prediction of Maximal Oxygen Consumption by Dr. Fritz Hagerman, Ohio University, Athens, OH. The nomogram is not appropriate for use with non-Concept II ergometers
and is designed to be used by noncompetitive or unskilled rowers participating in aerobic conditioning programs. Adapted by permission of Concept II, Inc., 105 Industrial Park Drive, Morrisville, VT 05661 (800) 245-5676.
ELLIPTICAL CROSS-TRAINER SUBMAXIMAL TEST PROTOCOL

Dalleck, Kravitz, and Robergs (2006) developed an equation to predict V̇O max from 2
submaximal testing using an elliptical cross-trainer. Their participants performed three 5 min
stages. Cadence and HR were recorded for the second stage. Compared with V̇O max from 2
an elliptical test to exhaustion, the prediction equation
V̇O max = 73.676 + 7.383(gender) − 0.317(weight, kg) + 0.003957(age × cadence) −

2
0.006452(age × heart rate at stage 2)
estimated V̇O max well (r = .86; SEE = 3.91 ml·kg ·min ). Furthermore, the actual and
2 −1 −1
196
predicted V̇O max values were similar for the sample of healthy young adults (29.5 ± 7.1 yr).
2
For details about the testing protocol, see Dalleck, Kravitz, and Robergs (2006).
Newer models of elliptical cross-trainers display a value for METs. Mays and associates
(2016) used both steady-state and non-steady-state HRs recorded over the course of
submaximal trials. These HR values were used to estimate V̇O peak. The V̇O peak predicted
2 2
from the MET levels as derived from the device’s proprietary equations was lower than that
derived from the two (steady state and combined steady state and non-steady state) HR-
based prediction equations. The displayed MET levels may be inaccurate across the spectrum
of exercise intensities. The authors cautioned trainers about designing exercise programs
based on V̇O peak values derived from these displayed MET values.
2
FIELD TESTS FOR ASSESSING AEROBIC FITNESS

The maximal and submaximal exercise tests using a treadmill or a cycle, rowing, arm-crank,
or elliptical ergometer are not well suited for measuring the cardiorespiratory fitness of large
groups in a field situation. Thus, a number of performance tests such as distance runs have
been devised to predict V̇O max (see table 4.7). These tests are practical, inexpensive, less
2
time consuming than the treadmill or cycle ergometer tests, easy to administer to large
groups, and suitable for personal training settings; they can be used to classify the
cardiorespiratory fitness levels of healthy men (≤45 yr) and women (≤55 yr). You cannot use
field tests to detect CHD because HR, ECG, and BP are usually not monitored during the
performance. Most field tests used to assess cardiorespiratory endurance involve walking,
running, swimming, cycling, or bench stepping; they require that clients be able to accurately
measure their postexercise HR. Pollock, Broida, and Kendrick (1972) found that with
practice, men could learn to measure their own pulse rates accurately. The correlation
between manual and electronic measurements of pulse rate ranged between r = .91 and .94.
Similar results (r = .95) were reported for college women for pulse rates measured manually
and electronically (Witten 1973). Prior to administering field tests that require the
measurement of HR, you should teach your clients how to measure their pulse rates using the
palpation technique described in How to Measure Your Pulse Rate.
DISTANCE RUN TESTS

The most commonly used distance runs involve distances of 1.0 or 1.5 mi (1,600 or 2,400 m)
to evaluate aerobic fitness. Distance run tests are based on the assumption that the fitter
individual will be able to run a given distance in less time or to run a greater distance in a
197
given period of time. Using factor analysis, Disch, Frankiewicz, and Jackson (1975) noted
that runs greater than 1.0 mi tended to load exclusively on the endurance factor rather than
the speed factor.
You should be aware that the relationship between distance runs and V̇O max has not been
2
firmly established. Although performance on a distance run can be accurately measured, it

may not be an accurate index of V̇O max or a substitute for the direct measurement of
2
V̇O max. Endurance running performance may be influenced by other factors such as
2
motivation; percent fat (Cureton et al. 1978; Katch et al. 1973); running efficiency (pacing
ability); lactate threshold (Costill and Fox 1969; Costill, Thomason, and Roberts 1973); and
storage and reuse of elastic energy, lower leg architecture, and body weight (Lacour and
Bourdin 2015).
The correlations between distance run tests and V̇O max tend to vary considerably (r =
2
.29-.97) depending on the client’s age, sex, and training status. Sample size, test distance, and
testing procedures may also affect this relationship (Mayorga-Vega et al. 2016). Generally,
the longer the run, the higher the correlation with V̇O max. On the basis of this observation,
2
it is recommended that you select a test with a distance of at least 1.0 mi (1,600 m) or
duration of at least 9 min.
The most widely used distance run tests are the 9 and 12 min runs and the 1.0 and 1.5 mi
runs. Some physical fitness test batteries for children and adolescents recommend using either
the 9 min or 1.0 mi run test.
9 or 12 Min Run Test

To administer the 9 or 12 min run test, use a 400 m track or flat course with measured
distances so the number of laps completed can be easily counted and multiplied by the course
distance. Place markers to divide the course into quarters or eighths of a mile so you can
quickly determine the exact distance covered in 9 or 12 min. Instruct your clients to run as far
as possible. Walking is allowed, but the objective of these tests is to cover as much distance as
possible in either 9 or 12 min. At the end of the test, calculate the total distance covered in
meters and use the appropriate equation in table 4.7 to estimate the client’s V̇O max.
2
1.5 Mile Run/Walk Test

The 1.5 mi (2.4 km) run/walk test is conducted on a 400 m track or flat measured area. To
measure the course, use an odometer or measuring wheel. For the 1.5 mi run, instruct your
clients to cover the specified distance in the fastest possible time. Walking is allowed, but the
objective is to cover the distance in the shortest possible time while maintaining a steady
198
exercise pace. Call out the elapsed time (in minutes and seconds) as each client crosses the
finish line. You can use a HR monitor to ensure that participants maintain a steady exercise
pace during this test. Instruct your clients to keep their target HR between 60% and 90%
HRmax. The exercise HR at the end of the test, along with gender, body mass, and elapsed
exercise time, can be substituted into the Larsen equation (see table 4.7) to estimate the V̇O 2
max of young (18-29 yr) adults (Larsen et al. 2002). Cross-validation of this equation yielded
a high validity coefficient (r = .89) and small prediction errors (SEE = 2.5 ml·kg ·min ; TE =
−1 −1
2.68 ml·kg ·min ) for a sample of young military personnel (Taylor et al. 2002).
−1 −1
To use the V̇O max prediction equations for the 1.5 mi run/walk test (see table 4.7),
2
convert the seconds to minutes by dividing the seconds by 60. For example, if a client’s time
for the test is 12:30, the exercise time is converted to 12.5 min (30 / 60 sec = 0.5 min).
1.0-Mile Jogging Test

One limitation of distance run tests is that individuals are encouraged to run as fast as
possible and give a maximal effort, thereby increasing the risk of cardiovascular and
orthopedic injuries. The potential risk is even greater for untrained individuals who do not
run or jog regularly and have difficulty selecting a proper jogging pace. To address this
problem, George and colleagues (1993) developed a submaximal 1 mi (1.6 km) track jogging
test for 18 to 29 yr old women and men that requires only moderate steady-state exertion.
For this test, instruct your clients to select a comfortable, moderate jogging pace and to
measure their postexercise HR immediately following the test. The elapsed time for 1 mi
should be at least 8 min for males and 9 min for females, and the postexercise HR (15 sec
count × 4) should not exceed 180 bpm. To help establish a suitable pace, precede the timed 1
mi test with a 2 to 3 min warm-up. Use either an indoor or outdoor track for this test. Record
the time, in minutes, required to jog 1 mi, and have your clients measure their postexercise
HRs using the palpation technique (radial or carotid sites). Estimate the client’s V̇O max 2
using the prediction equation for the 1.0 mi steady-state jog test (see table 4.7).
20 m Shuttle Run Test

A field test used to estimate V̇O max, the 20 m shuttle run is also known by other names
2
such as the beep test and the PACER. There are several derivations of the protocol and
equations used to predict V̇O max (see Mayorga-Vega, Aguilar-Soto, and Viciana 2015).
2
This test is performed by having individuals (adults and children) run back and forth between
markers (e.g., cones, lines taped or painted on the floor) that are 20 m apart. A prerecorded
soundtrack establishes the speed needed to cover the distance between markers. The test
199
begins with a sound, and the 20 m distance must be covered before the next sound is heard.
When that sound is heard, the runner turns around and returns to the original marker. This
pattern continues as the time between sounds decreases incrementally. The test ends when
the runner cannot cover the 20 m distance before the sound.
00:00 / 00:00
Video 4.8
Permutations of this test are based on starting speed (i.e., 7.5 to 8.5 km·h ) and increases
−1
in speed (i.e., 0.5 to 1.0 km·h ) each minute (Mayorga-Vega, Aguilar-Soto, and Viciana
−1
2015). A review of the studies validating the 20 m shuttle run for estimating V̇O max 2
indicates a moderate to high criterion-related validity (r = .66-.84) for the performance scores
alone. The validity improves (r = .78-.95) when participant-specific demographics (e.g., body
mass, sex, age) are included in the prediction equation (Mayorga-Vega, Aguilar-Soto, and
Viciana 2015). The authors concluded that the 20 m shuttle run is a viable option for
estimating cardiorespiratory fitness of adults (Mayorga-Vega, Aguilar-Soto, and Viciana
2015) and should be used instead of other traditional walk/run protocols (Mayorga-Vega et
al. 2016).
Walking Test
The Rockport Walking Institute (1986) has developed a walking test to assess
cardiorespiratory fitness for men and women ages 20 to 69 yr. Because this test requires only
fast walking, it is useful for testing older or sedentary individuals (Fenstermaker, Plowman,
and Looney 1992). The test was developed and validated for a large heterogeneous sample of
86 women and 83 men (Kline et al. 1987). The cross-validation analysis resulted in a high
validity coefficient and small standard error of estimate (SEE), indicating that the 1.0 mi
walking test yields a valid submaximal assessment of estimated V̇O max. Other researchers
2
have substantiated the predictive accuracy of this equation for women 65 yr of age and older
(Fenstermaker, Plowman, and Looney 1992), military men (18-44 yr) (Weiglein et al. 2011),
and adults (19-44 yr) (Seneli et al. 2013). Testing on a nonmotorized treadmill did not alter
200
the walking time, but the postexercise HR was increased. Consequently, V̇O max is 2
significantly underestimated when using a nonmotorized treadmill for this 1.0 mi walking
test (Seneli et al. 2013).
To administer this test as originally designed, instruct your clients to walk 1.0 mi as quickly
as possible and to take their HR immediately at the end of the test by counting the pulse for
15 sec. It is important that clients know how to take their pulse accurately. The walking
course should be a measured mile that is flat and uninterrupted, preferably a 400 m track.
Clients should warm up for 5 to 10 min before the test and wear good walking shoes and
loose-fitting clothes.
To estimate a client’s V̇O max, use the generalized equation for the 1.0 mi walking test
2
(see table 4.7). Alternatively, you can use the Rockport relative fitness charts (appendix B.2)
or the sex-specific equations (see Kline et al. 1987) to classify your client’s cardiorespiratory
fitness level. Locate the walking time and corresponding postexercise HR (bpm) on the
appropriate chart for the individual’s age and gender. These charts are based on body weights
of 125 lb for women and 170 lb for men. If the client weighs substantially more than this, the
cardiorespiratory fitness level will be overestimated.
STEP TESTS
The major advantage of using step tests to assess cardiorespiratory fitness is that they can be
administered to large groups in a field situation without requiring expensive equipment or
highly trained personnel. Most of these step tests use postexercise and recovery HRs to
evaluate aerobic fitness, but they do not provide an estimate of the individual’s V̇O max. Step 2
test protocols and scoring procedures are described in appendix B.3, Step Test Protocols.
The validity of step tests is highly dependent on the accurate measurement of pulse rate.
Step tests that use recovery HR tend to have lower validity than those using the time required
for the HR to reach a specified level during performance of a standardized workload
(Baumgartner and Jackson 1975). The correlation coefficients between step test performance
and V̇O max range between r = .32 and .77 (Cureton and Sterling 1964; deVries and Klafs
2
1965; McArdle et al. 1972).
MONITORING HRS DURING FIELD TESTS
If you monitor field test HRs with a technological device (i.e., heart rate monitor, accelerometer, pulse oximeter, smart
watch, or smartphone app) instead of palpating postexercise HR, you are advised to record the HR displayed at the
postexercise time interval specified by the protocol when it was created and validated (e.g., 15 sec postexercise for the 1
mi walk and 1 mi jogging tests). Using a HR captured for a different time interval may introduce additional error in the
201
estimation of V̇O2max.
Many smart devices detect HR noninvasively by using light to detect changes in microvascular blood volume or blood
flow at the wrist or in the nail bed. This light-based technique is known as photoplethysmography (PPG). Proprietary
algorithms within the device and app convert the fluctuations in blood flow and blood volume into HRs. Therefore, the
quality of the algorithms and the integrity of the blood-sensor interface are important for the accuracy of devices relying
on PPG. Increasing exercise intensities, alterations in wrist position, tattoos, and skin pigmentation patterns may
compromise the integrity of the blood-sensor interface (Spierer et al. 2015). If you plan on using a smart device to
measure HR, look for one that has been successfully validated against an ECG reference (i.e., no significant differences
between device and ECG means, small SEEs, correlations ≥.80). Errors in the calculation of HR lead to errors in the
estimation of V̇O2max. See the research of El-Amrawy, Pharm, and Nounou (2015) and LeBoeuf and colleagues (2014)
for their assessments of PPG technology for accurate monitoring of HR.
ADDITIONAL FIELD TESTS

In addition to running, walking, and step tests, cycling and swimming tests have been devised
for use in field situations (Cooper 1977). The 12 min cycling test, using a bike with no more
than three speeds, is conducted on a hard, flat surface when the wind velocity is less than 10
mph (268 m·min ). These conditions limit the effect of outside influences on the rider’s
−1
performance. Five- and 10-speed bikes are not employed unless use of the lower gears can be
restricted. Use an odometer to measure the distance traveled in 12 min. In the 12 min
swimming test, the client may use any stroke and rest as needed. Norms for the 12 min
cycling test and 12 min swimming test are available (Cooper 1977).
Of these two tests, the swimming test is the less preferred because the outcome is highly
skill dependent. For example, a skilled swimmer with an average cardiorespiratory fitness
level will probably be able to swim farther in 12 min than a poorly skilled swimmer with an
above-average cardiorespiratory fitness level. In fact, Conley and colleagues (1991, 1992)
reported that the 12 min swim has low validity (r = .34-.42) as a cardiorespiratory field test
for male and female recreational swimmers. Whenever possible, select an alternative field test
and avoid using the 12 min swim test.
EXERCISE TESTING FOR CHILDREN AND OLDER ADULTS

You may need to modify the generic guidelines for exercise testing (see General Principles of
Exercise Testing earlier in the chapter) of low-risk adults when you are assessing
cardiorespiratory fitness of children and older adults. You must take into account growth,
maturation, and aging when selecting exercise testing modes and protocols for these groups.
WORKING WITH GPS-CAPABLE DEVICES
202
Most smart devices that have integrated GPS capabilities require line of sight with the satellites used for triangulating
location (Hillsdon et al. 2015). This makes it challenging to rely on GPS for accurate calculation of distances if the
activities are performed indoors or in a high-density urban environment (Jankowska, Schipperijn, and Kerr 2015). Newer
receivers that have a SiRFstarIII GPS chip are reported to work indoors, which should reduce the amount of missing
data (McGrath, Hopkins, and Hinckson 2015). Two commercially available GPS units were compared against the time
it took research subjects to cover the reference distance of 2.4 km while jogging at two speeds and walking on an outdoor
400 m track (Benson, Bruce, and Gordon 2015). Significant differences in time to completion and distance covered were
found. Precise spatial accuracy is required when using GPS features to set distance. Otherwise, spatial inaccuracies will
influence the time to completion of field tests. Therefore, at this point, it is not recommended that you use the GPS
features of smart devices for distance and time to completion when conducting a field test to estimate V̇O2max.
ASSESSING CARDIORESPIRATORY FITNESS OF CHILDREN

In the laboratory setting, you can assess the cardiorespiratory fitness of children using either
the treadmill or cycle ergometer. Treadmill testing is usually preferable, especially for younger
children, because their shortened attention span may not allow them to maintain a constant
pedaling rate during a cycle ergometer test. Also, children younger than 8 yr or shorter than
50 in. (127 cm) may not be tall enough to use a standard cycle ergometer. To accommodate
children, modify the seat height, pedal crank length, and handlebar position.
For treadmill testing, you may choose to use the modified Balke protocol (see table 4.8)
because the speed is constant and the means of increasing intensity is to change the grade.
Either the modified Balke protocol or the modified Bruce protocol (i.e., 2 min instead of 3
min stages) may be used when assessing the cardiorespiratory fitness levels of children. Age
and gender endurance-time norms for children (4-18 yr) for the modified Bruce protocol are
available elsewhere (Wessel, Strasburger, and Mitchell 2001).
203
For cycle ergometer testing, you can use the McMaster protocol (see table 4.8). For this
protocol, the pedaling frequency is 50 rpm, and increments in work rate are based on the
child’s height. As an alternative, a new steep ramp cycling protocol (SRP) has demonstrated
both high reliability and validity for accurately assessing V̇O peak of children and adolescents
2
(Bongers et al. 2013). After a 3 min warm-up at a power output of 25 W, the ramp trial
begins with workload increments of either 10, 15, or 20 W per 10 sec; the increments are
determined by participant height (<120 cm, 120-150 cm, and >150 cm, respectively). The
ramp protocol continues until the pedaling cadence falls below 60 rpm and the participant
exhibits other signs of maximal exertion. The SRP was validated against a separate maximal
exertion cycling protocol with metabolic gas collection; the following equation was derived:
V̇O peak (ml·min ) = 8.262 × W +177.096 (R2 = .917; SEE = 237.4 ml·min ).
2 −1 SRP −1
The mean difference between predicted and measured V̇O peak was 0.3 ml·min , and no
2 −1
systematic bias was noted in the Bland and Altman analysis (1986). Test-retest comparison of
the SRP indicated high reproducibility of peak power output (ICC = .986).
Children, like adults, may not exhibit a plateau in oxygen consumption during ramp
protocol testing. Only 34% of the children undergoing metabolic gas analysis in the study by
Bongers and colleagues (2013) demonstrated a plateau in V̇O . Barker and colleagues (2011)
2
confirmed that children exert their maximal effort during a ramp cycling protocol. This
confirmation was made by having the children rest 15 min and then perform a supramaximal
cycling trial at 105% of the peak power output attained during the ramp protocol. Subsequent
analysis revealed similar V̇O peak values between the two cycling protocols. In addition to
2
noting a low incidence of a plateau during the ramp cycling trial, Barker and colleagues
(2011) commented that had they relied on the other secondary indicators of maximal exertion
(i.e., RER and HR), they would have underestimated V̇O max by 10% to 20%, on average,
2
in their sample of healthy 9 and 10 yr olds.

Field tests, such as the 1.0 mi (1.6 km) run/walk, are widely used to assess the
cardiorespiratory fitness of children 5 to 17 yr of age. These tests are part of the Physical Best
Program (American Alliance for Health, Physical Education, Recreation and Dance 1988),
Fitnessgram (Cooper Institute for Aerobics Research 1994), and the President’s Challenge
Test (President’s Council on Physical Fitness and Sports 1997), as well as national physical
fitness surveys of children and youth (Ross and Pate 1987). To estimate V̇O peak of 8 to 17
2
yr olds for the 1.0 mi run/walk test, you can use a generalized prediction equation (see table
204
4.7) (Cureton et al. 1995). You can also use a BMI-independent prediction equation for
adolescents 13 to 16 yr to estimate V̇O peak (Burns et al. 2016). For younger children (5-7
2
yr), the 0.5 mi (0.8 km) run/walk test is recommended (Rikli, Petray, and Baumgartner
1992). Criterion-referenced standards for the 1.0 mi test are available elsewhere (American
Alliance for Health, Physical Education, Recreation and Dance 1988; Cooper Institute for
Aerobics Research 1994). A review of literature and in-depth analysis of equations designed
to estimate V̇O max for young people (<18 yr) is available (Ferrar et al. 2014).
2
In Canada and Europe, the multistage 20 m shuttle run test, developed by Leger and
colleagues (1988), is a popular alternative to distance running/walking field tests to estimate
the aerobic fitness of children (8-19 yr) in educational settings. This test has been cross-
validated using other samples of European, Canadian, and American children (Anderson
1992; Mahar et al. 2011; van Mechelen, Holbil, and Kemper 1986).
For this test, children run back and forth continuously on a 20 m (indoor or outdoor)
course. The running speed is set using a sound signal emitted from a prerecorded tape. The
starting pace is 8.5 km·hr , and the speed is increased 0.5 km·hr each minute until they can
−1 −1
no longer maintain the pace. The maximal aerobic speed at this stage is used, in combination
with age, in the original equation to estimate V̇O max (ml·kg ·min ) as follows:
2 −1 −1
V̇O max = 31.025 + 3.238(speed, km·hr ) − 3.248(age, yr) + 0.1536(age × speed)

2 −1
Mahar and colleagues (2011) evaluated this and several other equations for a sample of
schoolchildren. In an attempt to improve the fitness category classification resulting from
these equations, they devised and cross-validated quadratic and linear equations that improve
both the prediction of V̇O max and the fitness level categorization of children aged 10 to 16
2
yr.
V̇O max = 41.76799 + (0.49261 × laps) − (0.00290 × laps ) − (0.61613 × BMI) + (0.34787 ×
2 2
gender × age), where boys = 1 and girls = 0; R = .75, R = .56, SEE = 6.17 ml·kg ·min
2 −1 −1
V̇O max = 40.34533 + (0.21426 × laps) − (0.79472 × BMI) + (4.27293 × gender) + (0.79444
2
× age); R = .74, R = .54, SEE = 6.29 ml·kg ·min

2 −1 −1
Two other incremental running tests have been validated against a graded treadmill test
and shown reliable in terms of test-retest determination of maximal heart rate. Bendiksen and
colleagues (2012) investigated the suitability of the modified Yo-Yo Intermittent Recovery
Level 1 test (YYIR1C) and the Anderson test (1992) for assessing the cardiorespiratory
health of children aged 6 to 10 yr. The criterion measure was the maximal heart rate attained
205
during an incremental treadmill test. Both tests require the children to run back and forth
between two cones (or lines). The maximal heart rate and number of laps completed are
recorded and used for interpretation. The YYIR1C uses a distance of 16 m. After returning
to the starting point, the child engages in a 10 sec active recovery by jogging around a third
cone located 4 m behind the starting location. The speed at which the child needs to
complete the 16 m laps up and back is controlled by the prerecorded YYIR1 disc. The 16 m
running pace becomes progressively faster, while the active recovery period remains at 10 sec.
The child runs until failing twice to complete the 16 m distance within the designated time
increment.
Similarly, in the Anderson test, children run between two cones (or lines) 20 m apart.
However, the children run as fast as possible and take one step beyond each demarcation
before turning and running back. The children run in this manner for 15 sec, at which time a
whistle is blown and the children stop as quickly as possible (within two steps) to rest for 15
sec. The last 3 sec of the rest period are counted off (e.g., “3, 2, 1, run”). According to
protocol, this pattern continues for 10 min. The average maximal heart rates from the
YYIR1C and Anderson tests were similar, at 207 and 206 bpm, respectively, and slightly
higher than that from the incremental treadmill test (203 bpm). Consequently, Bendiksen
and colleagues (2012) reported that these two field tests are sensitive enough to detect fitness-
based differences in this age group.
A stepping protocol that uses postexercise HR to identify a child’s cardiorespiratory fitness
level was investigated by Jankowski and colleagues (2015). Polish schoolchildren (6 to 12 yr)
performed the 3 min Kasch Pulse Recovery Test (KPR Test). Using a bench that was 30.5
cm (12 in.) in height, the children stepped up and down to the pace of a metronome set to 24
steps·min . The children sat immediately after the test ended so the 1 min recovery HRs
−1
could be determined; the average of electronically collected recovery HRs was computed. Age
and gender criterion tables were developed to classify a child’s fitness level based on the
percentile associated with postexercise HR. The authors commented that this simple protocol
requires minimal equipment and is suitable for screening large numbers of children for the
purpose of establishing their cardiorespiratory fitness levels. However, the HR categories have
not yet been validated against gold standard reference values of aerobic capacity. The details
of the gender- and age-specific criterion HRs are available elsewhere (Jankowski et al. 2015).
ASSESSING CARDIORESPIRATORY FITNESS OF OLDER ADULTS

Smith and colleagues (2016) reviewed and evaluated 13 submaximal exercise equations to
206
estimate V̇O max of apparently healthy older adults (mean age ≥65 yr). Modalities included
2
treadmills, recumbent steppers, and stationary stepping. Although they reported that walking
and running modalities are preferred, the most accurate (no significant differences between
measured and estimated V̇O max, r2 = .90) estimation of aerobic capacity was produced
2
using the self-paced stepping protocol developed by Petrella et al. (2001).

To assess the cardiorespiratory fitness of elderly clients, you can also use modified versions
of treadmill and cycle ergometer protocols. The following modifications for standard GXT
protocols are recommended:
Extend the warm-up to more than 3 min.

Set an initial exercise intensity of 2 to 3 METs; work increments should be 0.5 to
1.0 MET (e.g., Naughton treadmill protocol; see table 4.4).
Reduce the treadmill speed to the walking ability of your client when needed.
Extend the duration of each work stage (at least 3 min), allowing enough time for
the client to attain steady state.
Select a protocol likely to produce a total test time of 8 to 12 min.
Select treadmill protocols that increase grade instead of speed, especially for older clients
with poor ambulation. You can modify the standard Balke protocol (see figure 4.2) by having
the client walk at 0% grade and 3.0 mph (4.8 km·hr ) or slower initially and by increasing the
−1
duration of each stage to at least 3 min. If elderly clients are more comfortable holding on to
the handrails during a treadmill test, you can use the standard Bruce protocol and the
McConnell and Clark (1987) prediction equation to estimate their V̇O max (see table 4.5).
2
Alternatively, you could use cycle ergometer GXTs for older individuals with poor balance,
poor neuromuscular coordination, or impaired vision. You can also use field tests to estimate
the cardiorespiratory fitness of your older (60-94 yr) clients. The Senior Fitness Test battery
(Rikli and Jones 2013) includes two measures of aerobic endurance: the 6 min walking test
and the 2 min step test.
Self-Paced Step Test (Petrella et al. 2001)

Purpose: Quick assessment of the aerobic capacity of older adults in an office setting.
Application: Monitor functional aerobic capacity within the time constraints of a doctor’s
appointment.
Equipment: You will need a stopwatch, a valid HR monitor, and two contiguous steps,
207
20 cm each.
Test procedures: Instruct your client to fast for 2 hr or more before the test. Record your
client’s height and weight and convert those into a BMI value. A single stepping cycle
is completed by following the pattern of stepping up with one foot, up to the next step
with the other foot, together with both feet on the second step, down one step with
one foot, down to the floor with the other foot, together with both feet on the floor.
After the familiarization period (see the safety tips), let the client rest 5 min before
completing 20 steps at his normal stair climbing pace. Time (in sec) how long it takes
the client to complete the 20 steps. Record the HR immediately after completion of
the 20th stepping cycle.
Scoring: See table 4.7 for the gender-specific equations using normal stepping pace for
predicting V̇O max for this protocol. To compute the oxygen cost of stepping, use
2
the equation
O cost = [(F × step ht × 1.78 × 1.30 + 1/3F]

2
where F equals the stepping frequency.

Safety tips: Clinically screen the client to rule out known or possible silent ischemic heart
disease, unstable metabolic disease, orthopedic issues that may affect stepping,
unstable pulmonary disease, or medications that alter heart rate. Familiarize your
client to the step height and stepping procedure by having him climb up and down
the steps 10 times at a self-described slow pace while following the stepping pattern.
Stand close by to steady the client and prevent any falls as necessary.
Validity and reliability: There were strong correlations (r ≥ .90) between self-paced
stepping estimations and measured V̇O max (ml·kg ·min ; Balke ramp treadmill
2 −1 −1
protocol) for both men and women. No differences between predicted and measured
V̇O max values were found 2 to 4 wk after baseline assessment. Test-retest results 52
2
wk later produced strong correlations for V̇O max (r ≥ .97), heart rate (r ≥ .92), and
2
stepping time (r ≥ .90) for both sexes.
6 Min Walking Test

Purpose: Assess aerobic endurance.
208
00:00 / 00:00
Video 4.9
Application: Measure ability to perform activities of daily living such as walking, stair
climbing, shopping, and sightseeing.
Equipment: You will need a 5 × 20 yd (4.6 × 18.3 m) rectangular walking area, a
measuring tape, a stopwatch, four cones, masking tape, index cards, and chairs.
Test procedures: Use masking tape or chalk to mark 5 yd (4.6 m) lines on a flat,
rectangular course. Place cones on the inside corners of the rectangle. Instruct
participants to walk (not jog) as fast as possible around the course for 6 min. Partners
can keep track of the total number of laps and distance covered by marking the index
card each time a lap is completed. Administer one trial; measure total distance to the
nearest 5 yd. Test two or more people at a time for motivation.
Scoring: Calculate the total distance covered in 6 min. Each mark on the index card
represents 50 yd (45.6 m). Use table 4.9 to determine a client’s percentile ranking.
Safety tips: Place chairs around the outside of the walking course in case a client needs to
sit and rest during the test. Select a level, well-lit walking area with a nonslip surface.
Discontinue the test if the client shows signs of overexertion. Have the client cool
down by stepping in place for 1 min.
Validity and reliability: The 6 min walking distance was positively related (r = .78) to
submaximal treadmill walking time (Bruce protocol, time to reach 85% HRmax).
This walking test detects the expected performance declines across age groups and
discriminates between individuals with high and low physical activity levels and
functional ability test scores. The test-retest reliability was r = .94.
209
Casanova and colleagues (2011) followed standard procedure to evaluate the 6 min walking
test performance of 444 adults (40-80 yr) from seven countries. The effect of age on distance
walked was significant for ages ≥60 yr, regardless of gender. They found no difference in
distance walked based on self-reported activity levels (sedentary vs. physically active).
Casanova and associates reported geographic variations in the distance walked that could not
be explained by anthropometric variables. Consequently, they urge caution when using
existing predictive equations and standard curves when interpreting results of the 6 min
walking test.
2 Min Step Test

Purpose: Alternative test of aerobic endurance when time, space, or weather prohibits
administering the 6 min walking test.
Application: Measure ability to perform activities of daily living such as walking, stair
climbing, shopping, and sightseeing.
Equipment: You will need a stopwatch, a tape measure, masking tape, and a tally counter
210
to count steps.
Test procedures: Determine the minimum knee-stepping height of the client by
identifying the midpoint between the kneecap (midpatellar level) and iliac crest. Mark
this point on the anterior aspect of the client’s thigh and on a nearby wall or chair.
These marks are used to monitor knee height during the test. Ask the client to step in
place for 2 min, lifting the right knee as high as the target level marked on the wall.
Use the tally counter to count the number of times the right knee reaches the target
level. If the proper knee height cannot be maintained, ask the client to slow down or
stop until she can execute proper form; keep the stopwatch running. Administer one
trial.
Scoring: Count the number of times the right knee reaches the target level in 2 min. Use
table 4.10 to determine your client’s percentile ranking.
Safety tips: Clients with poor balance should stand close to a wall, doorway, or chair for
support in case they lose their balance during the test. Spot each client carefully. Have
the client cool down after the test by walking slowly for 1 min. Discontinue the test if
your client shows signs of overexertion.
Validity and reliability: The 2 min step test scores were moderately correlated (r =
.73-.74) with Rockport 1 mi walking scores and treadmill walking (Bruce protocol,
time to reach 85% HRmax) in older adults. This step test detected expected
performance declines across age groups and discriminated between exercisers and
nonexercisers. The test-retest reliability was r = .90.
211
Key Points
The best way to assess cardiorespiratory capacity (cardiorespiratory fitness) is through a GXT in which the
functional V̇O2max is measured.
Unless contraindications to exercise are observed or medical clearance is not granted, it is advised that you
administer a valid functional aerobic capacity test for your client before creating an exercise program.
Before, during, and after a maximal or submaximal exercise test, closely monitor the HR, BP, and RPE.
Treadmill, cycle ergometer, and bench stepping are the most commonly used modes of exercise for exercise
testing.
The choice of exercise mode and exercise test protocol depends on the purpose of the test and on the age,
gender, and health and fitness status of the individual.
Verification of maximal exercise effort is attained with short bouts of supramaximal exercise on the same
modality.
Self-paced protocols allow the individual to adjust workloads during graded maximal and submaximal tests.
212
Clamping stages of an exercise test by RPE values provides the individual with a stage-specific target during
a self-paced protocol.
Submaximal exercise tests are used to estimate the functional cardiorespiratory capacity by predicting the
V̇O2max of the individual. Failure to meet the assumptions underlying submaximal exercise tests produces
a ±10% to 20% error in the prediction of V̇O2max from submaximal HR data.
Field tests are the least desirable way of assessing aerobic fitness and should not be used for diagnostic
purposes. However, field tests are useful for assessing the cardiorespiratory fitness of large groups.
Commonly used field tests include distance runs, walking tests, step tests, and shuttle runs.
Distance runs should last at least 9 min to assess aerobic function. Distance runs usually range between 1
and 2 mi (1,600 and 3,200 m) or 9 and 12 min.
The validity of step tests for assessing cardiorespiratory fitness is highly dependent on obesity, height,
fitness level, and the accurate measurement of HR; step test validity is usually somewhat lower than the
validity of distance run tests.
For children and older adults, select a treadmill protocol that increases grade rather than speed.
The 6 min walking test or step tests can be used to assess cardiorespiratory fitness of older adults in field
settings.
Key Terms
absolute V̇O2
continuous exercise test

discontinuous exercise test
graded exercise test (GXT)
gross V̇O2
maximal exercise test

maximum oxygen uptake (V̇O2max)
net V̇O2
perceptually regulated exercise test (PRET)

ramp protocols
rating of perceived exertion (RPE)
relative V̇O2max
respiratory exchange ratio (RER)

RPE-clamped protocol
self-paced protocol
steep ramp cycling protocol (SRP)
submaximal exercise test
verification bout
213
V̇O2max
V̇O2peak
Review Questions
In addition to being able to define each of the key terms listed, test your knowledge and understanding of the material by
1. What is the most valid and direct measure of functional cardiorespiratory capacity?
2. What is the difference between absolute and relative V̇O2?
3. What is the difference between gross and net V̇O2?
4. What is the difference between V̇O2max and V̇O2peak?
5. What factors should you consider when choosing a maximal or submaximal exercise test protocol for your
client?
6. Identify the ACSM criteria for attainment of V̇O2max during a GXT.
7. Explain the pros and cons of requiring a plateau in V̇O2 consumption to call a test “maximal.”
8. How do you perform a verification bout to confirm whether the client gave maximal effort? Describe the
similarities and differences between self-paced and RPE-clamped protocols.
9. During a GXT, what three variables are monitored at regular intervals?
10. List three reasons for stopping a GXT.
11. What is active recovery, and why is it recommended for graded exercise testing?
12. How do the continuous, discontinuous, and ramp exercise testing protocols differ?
13. Calculate the gross V̇O2 for a 60 kg woman running on a treadmill at a speed of 6.0 mph and a grade of 10%.
14. Calculate the gross V̇O2 for an 80 kg man cycling on a Monark cycle ergometer at a pedaling frequency of 70
rpm and a resistance of 3.5 kg.
15. Calculate the energy expenditure for bench stepping using an 8 in. step and a cadence of 30 steps·min−1.
16. Name three types of field tests for estimating aerobic capacity.
17. Which type of testing, treadmill or cycle ergometer, should be used for assessing the cardiorespiratory
fitness of children?
18. How should standard GXT protocols be modified for testing older adults?
214
19. For whom is a GXT on an elliptical cross-trainer appropriate and why?
215
CHAPTER 5
Designing Cardiorespiratory Exercise Programs

KEY QUESTIONS
What are the basic components of an aerobic exercise prescription?
How is the aerobic exercise prescription individualized to meet each client’s goals and interests?
What methods are used to prescribe and monitor exercise intensity?
Which exercise modes are best suited for an aerobic exercise prescription?
How often does a client need to exercise to improve and maintain aerobic fitness?
How long does a client need to exercise to improve aerobic fitness?
Is discontinuous aerobic training as effective as continuous training?
How effective are multimodal cross-training programs?
What are the physiological benefits of aerobic exercise training?
Once you have assessed an individual’s cardiorespiratory fitness status, you are responsible
for planning an aerobic exercise program to develop and maintain the cardiorespiratory
endurance of that program participant—a program that will meet the individual’s needs and
interests, taking into account age, gender, physical fitness level, and exercise habits. Appendix
A.5, Lifestyle Evaluation, provides forms that will help you determine your clients’ exercise
patterns and preferences.
In designing the exercise prescription, keep in mind that some people engage in aerobic
exercise to improve their health status or reduce their disease risk, while others are primarily
interested in enhancing their physical fitness (V̇O max) levels or physical appearance. Given
2
that the quantity of exercise needed to promote health is less than that needed to develop and
maintain higher levels of physical fitness, you must adjust the exercise prescription according
to your client’s primary goal.
This chapter provides guidelines for writing individualized exercise prescriptions that
promote health status as well as develop and maintain cardiorespiratory fitness. It compares
various training methods and aerobic exercise modes and presents examples of individualized
216
exercise programs.
THE EXERCISE PRESCRIPTION

It is important to consider each client’s goals and purposes for engaging in an exercise
program. The primary goal for exercising may affect the mode, intensity, frequency, duration,
and progression of the exercise prescription. For example, the quantity of physical activity
needed to achieve health benefits or reduce one’s risk of illness and death is less than the
amount of activity typically prescribed when the client’s goal is to make substantial
improvements in cardiorespiratory fitness. When the primary goal for the exercise
prescription is improved health, refer to Guidelines for Exercise Prescription for Improved
Health.
On the other hand, when the primary goal for the exercise prescription is to improve
cardiorespiratory fitness, refer to ACSM (2018) Guidelines for Exercise Prescription for
Improved Health and Cardiorespiratory Fitness.
ELEMENTS OF A CARDIORESPIRATORY EXERCISE WORKOUT

Each exercise workout of the aerobic exercise prescription and program should include the
following phases:
Warm-up (5-10 min)

Endurance conditioning (20-60 min)
Cool-down (5-10 min)
Stretching (≥10 min)
The purpose of the warm-up is to increase blood flow to the working cardiac and skeletal
muscles, increase body temperature, decrease the chance of muscle and joint injury, and lessen
the chance of abnormal cardiac rhythms. During the warm-up, the tempo of the exercise is
gradually increased to prepare the body for a higher intensity of exercise performed during the
conditioning phase. The warm-up starts with 5 to 10 min of low-intensity (<40% V̇O reserve
2
[V̇O R]) to moderate-intensity (40%-60% V̇O R) aerobic activity (e.g., brisk walking for
2 2
clients who jog or slow jogging for clients who run during their endurance conditioning
phase).
During the endurance conditioning phase of the workout, aerobic exercise is performed
according to the exercise prescription following the FITT-VP principle (i.e., F = frequency; I
217
= intensity; T = time, duration; T = type, mode of activity; V = volume, quantity; P =
progression) (ACSM 2018). This phase usually lasts 20 to 60 min, depending on the exercise
intensity. Bouts of 10 min are acceptable as long as the client accumulates at least 30 min of
moderate-intensity or 20 min of vigorous-intensity exercise that day. The conditioning phase
is followed immediately by the cool-down phase.
A cool-down phase immediately after endurance exercise is needed to reduce the risk of
cardiovascular complications caused by stopping exercise suddenly. During cool-down, the
individual continues exercising (e.g., walking, jogging, or cycling) at a low intensity for 5 to
10 min. This light activity allows the heart rate (HR) and blood pressure (BP) to return to
near baseline levels, prevents the pooling of blood in the extremities, and reduces the
possibility of dizziness and fainting. The continued pumping action of the muscles increases
the venous return and speeds up the recovery process.
The stretching phase usually lasts at least 10 min and is performed after the warm-up or
cool-down phase. Static stretching exercises for the legs, lower back, abdomen, hips, groin,
and shoulders are usually included (for specific flexibility exercises, see appendix F.1).
Stretching exercises after the cool-down phase may help reduce the chance of muscle cramps
or muscle soreness.
Guidelines for Exercise Prescription for Improved Health
The following aerobic exercise guidelines are from the U.S. Department of Health and
Human Services (2015).
1. Mode: Select endurance-type physical activities.

2. Intensity: Prescribe at least moderate-intensity physical activities (3 to 6 METs
[metabolic equivalents of task]).
3. Frequency and duration: Schedule at least 150 to 300 min/wk (e.g., 30 min, 5
days/wk or 60 min, 3 days/wk). Duration varies according to the type and intensity of
activity (see Examples of Moderate-Intensity and Vigorous-Intensity Aerobic
Activities, chapter 1).
TYPES (MODES) OF EXERCISE

If the primary goal of the exercise program is to develop and maintain cardiorespiratory
fitness, prescribe aerobic activities using large muscle groups in a continuous, rhythmic
fashion. In the initial and improvement stages of the exercise program, it is important to
218
closely monitor the exercise intensity. Therefore, you should select modes of exercise that
allow the individual to maintain a constant exercise intensity and that are not highly
dependent on the participant’s skill. Type A activities require minimal skill or physical fitness
to perform. Activities such as walking, cycling, and aqua-aerobics are best suited for this
purpose. Type B activities are vigorous-intensity exercises that require minimal skill but
average physical fitness. Jogging and Spinning are examples of type B activities. You may
prescribe type B activities in the initial and improvement stages for individuals who exercise
regularly. Type C activities include endurance activities that require both skill and average
physical fitness levels. Swimming, skating, and cross-country skiing should be prescribed only
for individuals who have acquired these skills or who possess adequate physical fitness levels
to learn these skills. Type D activities are recreational sports that may improve physical
fitness. These should be performed in addition to the person’s regular aerobic exercise
program. Examples of type D activities are racket sports, hiking, soccer, basketball, and
downhill skiing. You should consider using type C and D activities to add variety in the later
stages (maintenance stage) of your clients’ exercise programs.
ACSM Guidelines for Exercise Prescription for Improved Health and Cardiorespiratory
Fitness (FITT-VP)
1. Frequency: Schedule moderate-intensity exercise at least 5 days/wk, vigorous-

intensity exercise at least 3 days/wk, or a combination of moderate- and vigorous-
intensity exercise 3 to 5 days/wk.
2. Intensity: Prescribe moderate-intensity (3.0-6.0 METs or 40%-<60% V̇O R or 2
HRR) or vigorous-intensity (>6.0 METs or ≥60%-89% V̇O R or HRR) or a 2
combination of moderate- and vigorous-intensity exercise. Intensity varies

depending on the client’s cardiorespiratory fitness classification.
3. Progression: Gradually adjust the exercise prescription for each client in accordance
with the conditioning effect, participant characteristics, new exercise test results, or
performance during the exercise sessions. The rate of progression depends on the
individual’s age, functional capacity, health status, and goals. For apparently healthy
adults, a reasonable progression increases duration by 5 to 10 min every 1 to 2 wk
for the first 4 to 6 wk of their exercise program. Typically, the aerobic exercise
prescription consists of three stages: initial conditioning, improvement, and
maintenance.
219
4. Time (duration): Schedule 30 to 60 min of moderate-intensity exercise (≥150
min/wk), 20 to 60 min of vigorous-intensity exercise (≥75 min/wk), or a
combination of moderate- and vigorous-intensity exercise to attain recommended
targeted volumes of exercise. Continuous exercise bouts of 10 min or more may be
accumulated throughout the day.
5. Type (mode): Select rhythmic aerobic activities that can be maintained
continuously and that involve large muscle groups and require little skill to perform
(see Classification of Aerobic Exercise Modalities). Introduce other aerobic
activities requiring more stamina or skill later in the program.
6. Volume (quantity): For most adults, target approximately 1,000 kcal·wk −1
moderate-intensity exercise or physical activity (150 min/wk at 3.0-6.0 METs or

40%-<60% V̇O R). When combined with the recommended duration, daily
2
pedometer step counts (≥5,400 to 7,900 steps·day ) fulfill this category. More steps
−1
per day are required for weight loss and subsequent weight maintenance. An energy
expenditure between 500 and 1,000 MET·min·wk is the recommended quantity of
−1
exercise or physical activity for most adults. To compute MET·min·wk , multiply

−1
the MET value of an activity by the number of minutes it is performed in the week.
Based on ACSM (2018).
In addition to walking, jogging, and cycling, other exercise modalities provide a sufficient
cardiorespiratory demand for improving aerobic fitness. Exercise modalities such as machine-
based stair climbing, elliptical training, and rowing offer your exercise program participants a
variety of options for their exercise prescription. Many individuals prefer to cross-train to add
variety and enjoyment to their aerobic workouts. But are these exercise modes just as effective
as traditional type A and B activities (walking, jogging, and cycling)? The answer to this
question is not simple, and it depends on the method (%V̇O max or perceived exertion) used
2
to equate different exercise modalities.

During exercise at a prescribed percentage of V̇O max, Thomas and colleagues (1995)
2
noted that six different aerobic exercise modes (treadmill jogging, Nordic skiing, shuffle
skiing, stepping, cycling, and rowing) produced relatively similar cardiovascular responses (see
figure 5.1), but that cycling resulted in a significantly higher rate of perceived exertion (RPE)
compared with the other modes. Likewise, other researchers have reported that the
relationship between HR and V̇O at constant submaximal intensities was similar for
2
treadmill jogging, in-line skating (Wallick et al. 1995), and aerobic dancing with arms used
220
extensively above the head or kept below the shoulders (Berry et al. 1992). In contrast, Parker
and colleagues (1989) reported that the average steady-state HR during 20 min of aerobic
dancing was significantly higher than that for treadmill jogging when the subjects exercised at
the same relative intensity (60% V̇O max). Likewise, Howley, Colacino, and Swensen (1992) 2
noted that HR response during electronic step ergometer exercise was systematically higher
than that from treadmill exercise at the same submaximal V̇O . Also, supporting the body 2
weight during step ergometer exercise significantly reduces the HR and oxygen consumption
compared with lightly holding on to the handrails for balance.
CLASSIFICATION OF AEROBIC EXERCISE MODALITIES
These lists contain examples of moderate amounts of physical activity. More vigorous activities, such as stair walking
and running, require less time (15 min). On the other hand, less vigorous activities like washing and waxing the car
require more time (45-60 min).
Note: Type A activities require minimal skill and physical fitness; type B activities require average physical fitness but minimal skill; type C activities require both skill and average physical fitness levels; and type D activities are
recreational sports that should be prescribed only in addition to a regular, aerobic exercise program.
*Machine-based activities.
FIGURE 5.1 Comparison of steady-state heart rate response at submaximal exercise intensities for various aerobic
exercise modes.
221
When exercise modes are equated using subjective RPEs, research suggests that treadmill
jogging may be superior to other aerobic exercise modes in terms of total oxygen
consumption and rate of energy expenditure (Hulsey et al. 2012; Kravitz, Robergs, and
Heyward 1996; Kravitz et al. 1997b; Zeni, Hoffman, and Clifford 1996). Subjects exercising
on seven different modalities at a somewhat hard (RPE = 13 or 14) intensity for 15 to 20 min
experienced a greater total oxygen consumption for treadmill jogging compared with
stepping, rowing, Nordic skiing, cycling, and shuffle skiing (Kravitz et al. 1997b; Thomas et
al. 1995). Also, the rate of energy expenditure during treadmill exercise was 20% to 40%
greater than during stationary cycling (Kravitz et al. 1997b; Zeni, Hoffman, and Clifford
1996) and 42% higher than arm crank exercise (Schrieks, Barnes, and Hodges 2011). Hulsey
and colleagues (2012) reported that kettlebell swinging intervals require 25% to 39% less
energy than does treadmill exercise at the same RPE. In addition, steady-state exercise HRs
were higher (see figure 5.2) for treadmill jogging compared with cycling and aerobic riding
(Kravitz et al. 1996; Kravitz et al. 1997b; Zeni, Hoffman, and Clifford 1996). Similarly, HR
and RPE were higher during kettlebell swings and sumo deadlifts compared with treadmill
walking at the same V̇O (Thomas et al. 2014). The average net energy expenditure of young
2
adults (18-28 yr; 29.1-55.2 ml·kg ·min ) was 5.56 kcal·min higher over 1,600 m when
−1 −1 −1
running (160 m·min ) compared with walking (86 m·min ) on a treadmill (Wilkin, Cheryl,
−1 −1
and Haddock 2012).
FIGURE 5.2 Comparison of steady-state heart rate response at somewhat hard intensity (rating of perceived exertion =
13 or 14) for various aerobic exercise modes.
222
When selecting aerobic exercise modes for a client’s exercise prescription, you should
consider how easily the exercise intensity can be graded and adjusted in order to overload the
cardiorespiratory system throughout the improvement stage. For aerobic dance, work rates
can be progressively increased by means of quicker cadences and upper body exercise using
light (1-4 lb [0.45-1.8 kg]) handheld weights (Kravitz et al. 1997a). The intensity of in-line
skating can be effectively graded by increasing the skating velocity (Wallick et al. 1995). The
intensity of rowing, stair climbing, and simulated whole-body climbing exercise can be
incremented progressively using a variety of exercise machine settings (Brahler and Blank
1995; Howley, Colacino, and Swensen 1992).
Prescribe rope-skipping activities with caution; the exercise intensity for skipping 60 to 80
skips·min is approximately 9 METs. This value exceeds the maximum MET capacity of
−1
most sedentary individuals. Also, the exercise intensity is not easily graded because doubling
the rate of skipping increases the energy requirement by only 2 or 3 METs. Town, Sol, and
Sinning (1980) reported an average energy expenditure of 11.7 to 12.5 METs for skipping at
rates of 125, 135, and 145 skips·min . They concluded that rope skipping is a strenuous
−1
exercise that may not serve well as a form of graded aerobic exercise.
When selecting exercise modes for your older clients, you need to consider their functional
aerobic capacity, musculoskeletal problems, and neuromuscular coordination (impaired vision
or balance). Select activities that are enjoyable and convenient. For many older adults,
walking is an excellent mode. Stationary cycling and aquatic exercise can be used for
individuals with impaired vision or balance. Research reviewed by Jahnke and colleagues
(2010) suggests that tai chi and qigong demonstrate similar health benefits pertaining to
balance improvement and fall prevention, cardiorespiratory fitness, physical function, and
quality of life in older adults.
INTENSITY OF EXERCISE
Exercise intensity is a key factor in determining physiological adaptations to the exercise
stimulus (Wolpern et al. 2015). Traditionally, exercise intensity has been expressed as a
straight percentage of the individual’s maximal aerobic capacity (V̇O max), peak oxygen
2
consumption (V̇O peak), or heart rate reserve (HRR). However, research has suggested that
2
the %V̇O max is not equivalent (1:1 ratio) to the %HRR for cycling and treadmill exercise
2
(Morán-Navarro et al. 2016; Swain and Leutholtz 1997; Swain et al. 1998). The ACSM
changed its recommendation regarding the method used to calculate exercise intensity for
223
aerobic exercise prescriptions. Instead of expressing relative intensity as a straight percentage
of V̇O max (%V̇O max) or HRmax (%HRmax), the ACSM (2018) recommends using the
2 2
percent V̇O reserve (%V̇O R). The V̇O R is the difference between the V̇O max and resting
2 2 2 2
oxygen consumption (V̇O rest). With this modification, percent values for the %V̇O R and
2 2
%HRR methods for prescribing exercise intensity are approximately equal, thereby improving
the accuracy of calculating a target V̇O , particularly for clients who are engaging in low-
2
intensity aerobic exercise (Swain 1999). There is individual variability in resting oxygen
consumption; this introduces questions regarding the assumed constant (1 MET = 3.5 ml·kg
·min ) ascribed to V̇O rest (Mansoubi et al. 2015). Consequently, when it is available, the
−1 −1 2
actual V̇O rest should be used when determining V̇O R.

2 2
Regardless of the method used, intensity and duration of exercise are indirectly related. In
other words, the higher the exercise intensity, the shorter the duration of exercise required
and vice versa. Before prescribing the exercise intensity for aerobic exercise, carefully evaluate
the individual’s initial cardiorespiratory fitness classification, goals for the program, exercise
preferences, and injury risks. Your client can improve cardiorespiratory fitness with either
lower-intensity, longer-duration exercise or higher-intensity, shorter-duration exercise.
Historically, low to moderate intensities of longer duration were recommended for most
individuals. Higher-intensity exercise may increase the risk of orthopedic injury and,
consequently, discourage continued participation in the exercise program. Regardless, high-
intensity interval training (HIT) is receiving interest as an increasingly popular and time-
efficient rival of endurance training for improving cardiorespiratory fitness. In a detailed
analysis and evaluation of studies comparing traditional endurance training with HIT, the
mean effect between the two training styles is small. Nevertheless, HIT’s influence on V̇O 2
max is greater than is that of endurance training (Milanović, Sporiš, and Weston 2015).
Part of the art of exercise prescription is being able to select an exercise intensity that is
adequate to stress the cardiovascular system without overtaxing it. According to the ACSM
(2018), the initial exercise intensity for apparently healthy adults is 40% to <90% V̇O R or 2
HRR, depending on their initial physical fitness classification (i.e., fair to excellent
cardiorespiratory fitness level). Lower-intensity exercise (30%-59% V̇O R or HRR) may be
2
sufficient to provide important health benefits for sedentary clients or older individuals with
poor initial cardiorespiratory fitness levels. For most individuals, intensities of 55% to 80%
V̇O R are sufficient to improve cardiorespiratory fitness. As a general rule, the more fit the
2
individual, the higher the exercise intensity needs to be to produce further improvement in
cardiorespiratory fitness. In fact, Morán-Navarro and colleagues (2016) reported that the
224
thresholds denoting the switch from aerobic to anaerobic metabolism for highly trained male
cyclists (20.5 ± 7.5 yr) occurred between 62% and 89% HRR. This finding suggests that
highly fit individuals may require a higher exercise intensity than what is generally prescribed
for the average adult. Exercise intensity can be prescribed using the V̇O reserve, HR, RPE, 2
or talk test method.
V̇O2Reserve (MET) Method

First, measure the client’s functional aerobic capacity (V̇O max or V̇O peak) using a graded 2 2
exercise test (see chapter 4). Express the client’s V̇O max in relative terms: ml·kg ·min or 2 −1 −1
METs (metabolic equivalents of task). The V̇O R calculations presented here assume that 1 2
MET is approximately equal to 3.5 ml·kg ·min . Therefore, given a V̇O max of 35 ml·kg−1 −1 2
·min , for example, the metabolic equivalent would be 10 METs (35 / 3.5 = 10 METs).
−1 −1
Next determine the V̇O reserve (V̇O R). As mentioned previously, the V̇O R is the
2 2 2
difference between V̇O max and V̇O rest (V̇O R = V̇O max − V̇O rest). The percentage of
2 2 2 2 2
V̇O R depends on the initial cardiorespiratory fitness level of the client. To calculate the
2
target V̇O (in METs) based on the V̇O R, use the following equation:
2 2
target V̇O = [relative exercise intensity (%) × V̇O R] + V̇O rest

2 2 2
For example, the target V̇O corresponding to 50% V̇O R for a client with a V̇O max of
2 2 2
10 METs is calculated as follows:
target V̇O = [0.50 × (10 − 1 MET)] + 1 MET = (0.50 × 9 METs) + 1 MET = 4.5 + 1.0
2
METs, or 5.5 METs
The exercise intensity (METs) for walking, jogging, running, cycling, and bench-stepping
activities is directly related to the speed of movement, power output, or mass lifted. Use the
ACSM equations (table 4.3) to calculate the speed or work rates corresponding to a specific
MET intensity for the exercise prescription. For example, to estimate how fast a woman
should jog on a level course to be exercising at an intensity of 8 METs, follow these steps:
1. Convert the METs to ml·kg ·min . −1 −1
V̇O = 8 METs × 3.5 ml·kg ·min = 28 ml·kg ·min

2 −1 −1 −1 −1
2. Substitute known values into the ACSM running equation and solve for speed.
28 ml·kg ·min = [speed (m·min ) × 0.2] + 3.5 ml·kg ·min

−1 −1 −1 −1 −1
28.0 ml·kg ·min − 3.5 = speed (m·min ) × 0.2 122.5 m·min = speed
−1 −1 −1 −1
225
3. Convert speed to mph.
1 mph = 26.8 m·min −1 122.5 m·min / 26.8 m·min = 4.57 mph

−1 −1
4. Convert mph to minute per mile pace.
pace = 60 min/hr / mph = 60 min/hr / 4.57 mph = 13.1 min·mi (or 8.1 min·km )
−1 −1
Average MET values for selected conditioning exercises, sports, and recreational activities
are presented in appendix E.3, Gross Energy Expenditure for Conditioning Exercises,
Sports, and Recreational Activities. When estimating MET values for children and
adolescents, use the compendium of energy expenditures (MET values) developed for youth
(see McMurray et al. 2015; Ridley, Ainsworth, and Olds 2008). Prescribing exercise intensity
using only MET values has certain limitations. The caloric costs (i.e., average MET values)
of conditioning exercises are only estimates of energy expenditure. The caloric costs of
activities, particularly type C activities, vary greatly with the individual’s skill level. Although
these MET estimates provide a starting point for prescribing exercise intensity,
environmental factors such as heat, humidity, altitude, and pollution may alter the HR and
RPE responses to exercise. Therefore, you should use the HR or RPE method along with the
MET method to ensure that the exercise intensity does not exceed safe limits.
Heart Rate Methods

There are three ways to prescribe exercise intensity for your clients using HR data. Each of
these approaches is based on the assumption that HR is a linear function of exercise intensity
(i.e., the higher the exercise intensity, the higher the HR).
Heart Rate Versus MET Graphing Method

When a submaximal or maximal graded exercise test (GXT) is administered, the client’s
steady-state HR response to each stage of the exercise test can be plotted (see figure 5.3). The
HRmax is the HR observed at the highest exercise intensity during a maximal GXT. For
submaximal GXTs, you can estimate a client’s HRmax using one of the age-predicted
HRmax formulas (e.g., 220 − age). From this graph, you can obtain HRs corresponding to
given percentages of the estimated functional capacity or V̇O max. In our example, the
2
functional capacity of the individual is 7.4 METs, and the HRmax is 195 bpm. The HRs
corresponding to exercise intensities of 4.8 and 6.4 METs (60%-85% V̇O R) are 139 and
2
175 bpm, respectively. During exercise workouts, the individual should measure the HR
using an HR monitor or palpation to verify that the appropriate exercise intensity is reached.
226
FIGURE 5.3 Plotting target heart rate zone using graded exercise test data (heart rate vs. METs). HRmax = maximal
heart rate; V̇O2R = oxygen reserve.
It is important to note that the HR response to graded exercise is dependent to some

extent on the mode of exercise testing. For example, compared with treadmill testing,
exercising on an electronic step ergometer elicits higher HRs, and stationary cycling typically
results in somewhat lower HRs at the same relative exercise intensities. When using this
method to obtain HRs for an exercise prescription, be sure to match the exercise testing and
training modes by selecting a testing mode that elicits HR responses that are similar to those
obtained for the training mode (see figure 5.1). For example, if your client chooses skating as
a training mode and you do not have a skating treadmill, you should administer an
incremental slide board GXT, given that the relationship between HR, V̇O , %V̇O max, 2 2
and ventilation at submaximal exercise intensities is similar for these two exercise modes
(Piucco et al. 2017).
Heart Rate Reserve Method

When HR data from a GXT are not available, you can use the Karvonen method, or percent
heart rate reserve (%HRR) method, to determine target HRs for a client’s exercise
prescription. The heart rate reserve (HRR) method takes into account the resting HR and
maximal HR. The HRR is the difference between the maximal HR and resting HR. A
percentage of HRR is added to the client’s resting HR to determine the target exercise HR:
target HR = [% exercise intensity × (HRmax − HRrest)] + HRrest
As previously mentioned, the percent values for the HRR method closely approximate the
percent values for the V̇O R method (Azevedo et al. 2011; Lounana et al. 2007; Swain and
2
Leutholtz 1997). The ACSM (2018) recommends using 40% to <90% HRR for most adults.
227
Deconditioned individuals may need to start at 30% HRR. For example, if
maximal HR = 178 bpm,
resting HR = 68 bpm, and
exercise intensity = 60% HRR, then
target exercise HR = 0.60(178 − 68) + 68 or 134 bpm.
Percentage of Maximal Heart Rate Method

You also can use a straight percentage of maximal HR (percent heart rate maximum,
%HRmax) to estimate exercise intensity and determine target exercise HR. This method is
based on the fact that the %HRmax is related to %V̇O R and %HRR. In table 5.1, you can
2
see that 67% and 94% HRmax correspond to exercise intensities of 45% and 85% V̇O R or 2
HRR. Using this method, you will typically prescribe target HRs between 64% and 96%
HRmax depending on the fitness level of your client.
With use of this technique, the actual maximal HR must be known or must be predicted
either from the HR response to submaximal workloads or from the HRmax prediction
equations, such as 220 − age or 206.9 − (0.67 × age). For example, if the age-predicted
maximal HR is 180 bpm and the exercise intensity is set at 70% HRmax, the target exercise
HR is equal to 126 bpm.
%HRmax × HRmax = target HR
0.70 × 180 bpm = 126 bpm
Compared with the Karvonen (%HRR) method, the %HRmax method tends to give a
lower value when the same relative intensity is used. If in our example the client’s resting HR
is 80 bpm, the target HR using the Karvonen method is 150 bpm [0.70 × (180 − 80) + 80
228
bpm] compared with 126 bpm for the %HRmax method.
Limitations of Heart Rate Methods

Exclusive use of HR to develop intensity recommendations for your clients’ exercise
prescriptions may lead to large errors in estimating relative exercise intensities (%V̇O R) for
2
some individuals. This is especially true when HRmax is predicted from age (220 − age)
instead of being directly measured. In about 30% of the population, an age-predicted
prescription of 60% HRR may be as low as 70% or as high as 80% of the actual HRmax
(Dishman 1994). Measured HRmax varies with exercise mode. Therefore, your clients’
perceived effort may differ among exercise modes even during exercise at the same
submaximal HR. Also, medications, emotional states, and environmental factors (e.g.,
temperature, humidity, and air pollution) can affect your clients’ exercise training HRs. You
should consider using RPEs to adjust the exercise intensity in such situations.
Rating of Perceived Exertion Method

In light of the limitations associated with using HR for setting exercise intensity, consider
using a combination of HR and RPE in developing prescriptions for your clients. You can
use RPEs to prescribe and monitor exercise intensity for healthy and clinical clientele (Reed
and Pipe 2016; Tang et al. 2016). The ACSM (2018) recommends using RPE values (10-
point scale) to prescribe exercise intensity for older adults (moderate intensity: 5 to 6;
vigorous intensity: 7 to 8). The RPE scales (see table 4.2 and appendix B.4) are valid and
reliable tools for assessing the level of physical exertion during continuous aerobic exercise
(Guidetti et al. 2011; Krause et al. 2012; Mays et al. 2010; Reed and Pipe 2016; Scherr et al.
2013; Tang et al. 2016).
During the GXT, the client rates the intensity of each stage of the test using an RPE scale.
You can use the intensities (METs) corresponding to somewhat hard (6 on OMNI scale or
12 on Borg RPE scale) to hard (8 on OMNI scale or 16 on Borg RPE scale) to set the
minimum and maximum training intensities for the exercise prescription. Compared with the
%HRR method, RPEs between 12 and 16 closely approximate 40% and 84% HRR,
respectively (Pollock et al. 1998). Strong correlations between the OMNI-RPE and V̇O (r = 2
.93-.96) as well as HR (r = .96-.97) and the Borg RPE scale (r = .96-.98) were reported
(Mays et al. 2010). Similarly, Scherr and colleagues (2013) reported a correlation between
RPE (6-20 scale) and blood lactate (r = .84 for a quadratic regression) that was higher than
that between RPE and HR (r = .74 for a linear regression); their sample consisted of 2,560
Caucasians (13-83 yr) who were classified as either sedentary (failed to meet the ACSM’s
229
recommended guidelines for physical activity) or athletic (performed at least 10 hr of exercise
weekly or were members of a national team). As reported by Scherr and colleagues (2013),
the relationships between RPE and associated exercise intensity variables (HR or blood
lactate) were strong and independent of gender, medical history, age, level of physical activity,
and testing modality (treadmill or stationary cycle).
With practice, an individual can learn to associate RPE with a specific target exercise HR,
especially at higher exercise intensities (Smutok, Skrinar, and Pandolf 1980). Thus, the RPE
can be used instead of HR, or in combination with HR, to monitor training intensity and to
adjust the exercise prescription for conditioning effects. Parfitt, Evans, and Eston (2012)
reported that sedentary clients are able to successfully use RPE to monitor their exercise
intensity. Those who exercised 3 days/wk at an RPE of 13 (somewhat hard) on the Borg 6-
20 scale improved their aerobic capacity by 17% in 8 wk. Moreover, the majority of the
exercise intervention group perceived their exercise sessions as being pleasant and reported
that their selected exercise intensity felt good (Parfitt, Evans, and Eston 2012). Interestingly,
Scherr and colleagues (2013) confirmed that an RPE in the range of 11 to 13 is appropriate
for untrained or less fit individuals, while those with higher levels of fitness would benefit
from aerobic training in the RPE range of 13 to 15. Compared with men, women are more
likely to overestimate RPE, especially if the women are infrequent exercisers. In contrast,
men and regular exercisers tend to underestimate their level of physical activity compared
with accelerometry data (Skatrud-Mickelson et al. 2011). Consequently, as an exercise
professional, you must remain aware that some of your clients may likely misestimate their
level of exertion.
One advantage of RPE as a method of monitoring exercise intensity is that your clients do
not need to stop exercising in order to check their HRs. Unfortunately, exercising at a given
RPE value produces very different metabolic responses when performing kettlebell swings
(34.1 ml·kg ·min ) compared with treadmill running (46.7 ml·kg ·min ) (Hulsey et al. 2012).
−1 −1 −1 −1
For an extensive review of research pertaining to the use of perceived exertion for prescribing
exercise intensity, see the studies of Dishman (1994) and Robertson (2004). Parfitt and
colleagues (2012) describe how allowing clients to select their exercise intensity based on
RPE theoretically supports the sense of self-determination and perception of exercise
autonomy, both of which may improve client adherence to an exercise prescription.
Monitoring Exercise Intensity

Throughout the aerobic exercise program, carefully monitor exercise intensity in order to
230
ensure your clients’ safety and to confirm that your clients are exercising at or near the
prescribed intensity. The HR and RPE methods can be used for this purpose. Teach your
clients how to monitor exercise intensity using HR palpation techniques (see chapter 2), HR
monitors, and the RPE scales (see table 4.2).
Research assessing the validity and reliability of using motion and physiological response
monitors to track exercise intensity for a variety of exercise modalities is ongoing. Some
exercise modalities are more suitable than others when monitoring HR via technology. In
addition to working well for land-based exercise, some HR monitors work well in fresh water
(e.g., swimming pools) but must be waterproofed for use in salt water (i.e., swimming in the
ocean). Raffaelli and colleagues (2012) reported that monitoring intensity during water
aerobics is better done by HR palpation than accelerometry.
Some clients prefer using a talk test to monitor their exertion. The talk test is a measure of
the ability to converse comfortably while exercising, and it is based on the relationship
between exercise intensity and pulmonary ventilation. Pulmonary ventilation, or the
movement of air into and out of the lungs, increases linearly with exercise intensity (V̇O ) up2
to a point. At the breaking point, known as the ventilatory threshold (VT), pulmonary
ventilation increases exponentially relative to the exercise intensity and rate of oxygen
consumed. At the ventilatory threshold, it becomes difficult to speak during exercise.
However, Quinn and Coons (2011) found that the talk test was more strongly associated
with lactate threshold (exercise intensity at which blood lactate value increases by at least 1
mmol·L compared with the previous blood sample) and RPE than with the VT in young
−1
men.
Studies of college-age students (Persinger et al. 2004), clinically stable cardiac patients
(Reed and Pipe 2014), and athletes (Jeans et al. 2011; Recalde et al. 2002) showed that
individuals who pass the talk test are exercising at intensities within the accepted guidelines
for the exercise prescription. Those failing the talk test are exercising at intensities that exceed
the prescribed level but that may be appropriate for high-intensity intervals. In a group of
competitive male cyclists (21-37 yr), Gillespie’s research team (2015) found that the point of
failure on the talk test (definitely unable to speak comfortably) equated to their cyclists’
ventilatory thresholds. The talk test provides a fairly precise and consistent method for
monitoring exercise during stationary cycling and treadmill exercise (Persinger et al. 2004).
The accuracy of the talk test to predict VT is improved by using speech passages that are ≥93
words in length (Schroeder et al. 2017).
Similarly, the counting talk test (CTT) is an objective method for monitoring exercise
231
intensity (Loose et al. 2012). The counting talk test is normalized relative to how far one can
count during rest; following a maximal inhalation, one begins counting at a comfortable pace
(i.e., one one-thousand, two one-thousand, and so on). The highest digit counted prior to a
second inhalation is the number (CTT ) used for future exercise intensity determinations.
rest
During exercise, the counting procedure is repeated, with the highest number spoken before
breathing again divided by the baseline value to derive a %CTT . Exercising at 30% to 40%
rest
CTT or 40% to 50% CTT is equivalent to being in the moderate- to vigorous-intensity

rest rest
range for those with a CTT of at least 25 or <25, respectively. The CTT is reliable as well as
rest
significantly and inversely related to %HRR and RPE (r = −.64 to −.77) for walking,
stationary cycling, elliptical training, and stair stepping (Loose et al. 2012).
FREQUENCY OF EXERCISE
The frequency of the exercise sessions depends on the client’s caloric goals, health and fitness
level, preferences, time constraints, and targeted exercise intensity. Health and fitness benefits
result from moderate-intensity aerobic exercise performed at least 5 days/wk. Vigorous-
intensity aerobic exercise performed more than 5 days/wk may result in overuse injuries for
individuals lacking moderate to high levels of aerobic fitness and variety in exercise
modalities. A combination of moderate- and vigorous-intensity aerobic exercise performed 3
to 5 days/wk is recommended (ACSM 2018) for attaining and retaining health and fitness
benefits. Exercising fewer than three times per week is not recommended even though some
health and fitness benefits may be realized by doing so. Individuals with poor
cardiorespiratory fitness levels should exercise at light to moderate intensities a minimum of 5
days/wk. Multiple daily exercise bouts of at least 10 min duration each may be prescribed for
sedentary clients having poor aerobic fitness.
In terms of improving V̇O max, the sequence of exercise sessions seems to be less
2
important than the total work (volume) performed during the training. Similar improvements
were noted for individuals who trained every other day (M-W-F) and three consecutive days
(M-T-W) (Moffatt, Stamford, and Neill 1977).
TIME (DURATION) OF EXERCISE

As an exercise specialist, you must prescribe an appropriate combination of exercise intensity
and duration so that the individual adequately stresses the cardiorespiratory system without
overexertion. As mentioned earlier, the intensity and duration of exercise are inversely related
(the lower the exercise intensity, the longer the duration of the exercise). The ACSM (2018)
232
recommends 20 to 60 min of continuous or intermittent activity per day. Healthy
asymptomatic individuals can usually sustain exercise intensities of 60% to <90% V̇O R for 20
2
to 30 min. To improve cardiorespiratory fitness levels, aerobic exercise of moderate intensity

and duration (30-60 min) is recommended for most adults (ACSM 2018). During the initial
4 to 6 wk of an aerobic exercise program, session duration can be increased by 5 to 10 min
every other week until the desired number of minutes per week are attained (ACSM 2018).
Poorly conditioned and older individuals may be able to exercise continuously at a low
intensity (<40% V̇O R) for only 5 to 10 min. They may need to perform multiple sessions
2
(e.g., three 10 min exercise bouts) in a given day to accumulate 30 min of aerobic exercise.
An alternative way of estimating the duration of exercise is to use the caloric cost of the
exercise. To achieve health benefits, ACSM (2018) recommends a minimum of 150 min/wk
of moderate-intensity exercise or at least 75 min/wk of vigorous-intensity exercise or a
combination thereof that results in the desired volume of exercise. Exercising at these
combinations of minimum duration and intensity is equivalent to a minimal weekly caloric
threshold of 1,000 kcal from physical activity or exercise. Consequently, you may target
caloric thresholds of 150 to 400 kcal·day ; however, be aware that the ACSM (2018)
−1
cautions against using absolute exercise intensities (e.g., kcal·min ) since they do not account
−1
for individual differences in body weight, fitness level, or gender.

During the initial stage of the exercise program, however, weekly exercise caloric
expenditure may be considerably lower (200-600 kcal·wk ). To attain 300 min/wk of
−1
moderate-intensity exercise in the improvement stage, your client’s caloric expenditure must
increase from 1,000 to 2,000 kcal·wk . This can be accomplished by gradually increasing the
−1
frequency, intensity, and duration of the exercise. For example, in order for a 60 kg (132 lb)
woman who is exercising at an intensity of 7 METs five times per week to reach a weekly net
caloric threshold of 1,500 kcal·wk , she needs to expend 300 kcal per exercise session (1,500
−1
kcal / 5 = 300 kcal). You can estimate the gross caloric cost of her exercise (kcal·min ) using
−1
the following formula:
gross caloric cost (kcal·min ) = METs × 3.5 × body mass in kg / 200

−1
To calculate the net caloric expenditure from her activity, subtract the resting oxygen
consumption (1 MET) from the gross V̇O (V̇O cost of exercise + V̇O rest) and substitute
2 2 2
this value (7 − 1 = 6 METs) into the equation:
net caloric cost = 6 METs × 3.5 × 60 kg / 200 = 6.3 kcal·min −1
Therefore, she needs to exercise approximately 48 min (300 kcal / 6.3 kcal·min ) five times
−1
233
per week in order to achieve her weekly net caloric expenditure goal of 1,500 kcal.
Santos and colleagues (2012) investigated the influence of body mass (60-100 kg) and
fitness levels (16.4-61.2 ml·kg ·min ) on the energy expenditure and exercise program
−1 −1
recommendations endorsed by the ACSM. They derived equations for estimating

individualized training intensity (%V̇O R), duration (min/wk), frequency (days/wk), and
2
weekly energy expenditure (kcal·wk ) to account for individual variability in a given exercise
−1
session. The energy expenditure equation suggested by the ACSM (2018) overestimated
energy expenditure for individuals with low aerobic fitness levels while underestimating
energy expenditure for everyone else. For additional information about their suggested
adjustments to the ACSM equations and how these adjustments were derived, see Santos and
colleagues (2012).
VOLUME OF EXERCISE
The frequency, intensity, and duration of exercise determine the quantity or volume of
exercise. The MET·min is an index of energy expenditure and is calculated by multiplying
the MET value of activities by the number of minutes the activity is performed per week
(e.g., 6 METS × 150 min = 900 MET·min·wk ). Using this measure of exercise volume, the
−1
total amount of physical activity can be standardized across individuals and types of activities
(ACSM 2018). The ACSM (2018) recommends ≥500-1,000 MET·min·wk as a target −1
volume of exercise for adults. This volume is equivalent to moderate-intensity exercise (3 to 6

METs) for about 150 min/wk.
In addition, pedometers (see chapter 3) can be used to quantify the amount of exercise.
Total step counts of 5,400 to 7,900 steps·day meet the physical activity recommendations
−1
(≥500 to 1,000 MET·min·wk ) for most adults. Walking 1 mi at a moderate intensity (100
−1
steps·min ) yields about 3,000 to 4,000 steps on average. It is best to use pedometer counts in
−1
combination with recommended time and duration of exercise (e.g., 100 steps·min ) for 30
−1
min and 150 min/wk.
PROGRESSION OF EXERCISE
Physiological changes associated with aerobic endurance training (see Physiological Changes
Induced by Cardiorespiratory Endurance Training) enable the individual to increase the total
work performed. The greatest conditioning effects occur during the first 6 to 8 wk of the
exercise program. Aerobic endurance may improve as much as 3% per week during the first
month, 2% per week for the second month, and 1% per week or less thereafter. For continued
234
improvements, the cardiorespiratory system must be overloaded through adjustments in the
intensity and duration of the exercise to the new level of fitness. The degree and rate of
improvement depend on the age, health status, and initial fitness level of the participant. For
the average person, aerobic training programs generally produce a 5% to 20% increase in V̇O 2
max (Pollock 1973). Sedentary, inactive persons may improve as much as 40% in aerobic
fitness, while elite athletes may improve only 5% because they begin at a level much closer to
their genetic limits. Do not expect older individuals entering the exercise program to improve
as quickly as younger individuals even when the initial fitness levels are the same.
STAGES OF PROGRESSION
As discussed in chapter 3, the three stages of progression for cardiorespiratory exercise
programs are the initial conditioning, improvement, and maintenance stages.
Initial Conditioning
The initial conditioning stage may last 1 to 6 wk, depending on the client’s rate of adaptation
to the exercise program. In this stage, each exercise session should include a warm-up,
moderate-intensity aerobic activity (3-6 METs), low-intensity muscular fitness exercises, and
a cool-down that emphasizes stretching exercises (ACSM 2006). Clients starting a moderate-
intensity aerobic conditioning program should exercise a minimum of 3 days/wk. The
duration of the aerobic exercise should be at least 20 min and progress to 30 min. After
clients are able to sustain aerobic activity at 55% to 60% HRR for 30 min, they progress to
the improvement stage.
Improvement
The improvement stage usually lasts 4 to 8 mo. During this stage, the rate of progression is
more rapid. Intensity, duration, and frequency of exercise should always be increased
independently. Either duration or frequency should be increased before intensity is increased.
Increase the duration no more than 10 min per session every week or two in the first month
until your clients are able to sustain moderate to vigorous exercise for 20 to 60 min.
Frequency should progress from 3 to 5 days/wk. Once the desired duration and frequency are
reached, the exercise intensity may be increased gradually to reduce the likelihood of injury,
soreness, and overtraining (ACSM 2018).
Rate of progression during this stage depends on a number of factors. Cardiac patients,
older adults, and less fit individuals may need more time for the body to adapt to a higher
conditioning intensity. Ultimately, older or less fit adults should strive to achieve 30 to 60
235
min/day of moderate-intensity activity (5 or 6 on a 10-point RPE scale) or 20 to 30 min/day
of vigorous-intensity activity (>6 on a 10-point RPE scale) or any equal combination thereof.
PHYSIOLOGICAL CHANGES INDUCED BY CARDIORESPIRATORY

ENDURANCE TRAINING
Cardiorespiratory System
Increases Decreases
Heart size and volume Resting heart rate
Blood volume and total hemoglobin Submaximal exercise heart rate
Stroke volume—rest and exercise Blood pressure (if high)
Cardiac output—maximum
V̇O2max
Oxygen extraction from blood
Lung volume
Musculoskeletal System
Increases
Mitochondria—number and size
Myoglobin stores
Triglyceride stores
Oxidative phosphorylation
Other Systems
Increases Decreases
Strength of connective tissues Body weight (if overweight)
Heat acclimatization Body fat
High-density lipoprotein cholesterol Total cholesterol
Mood Low-density lipoprotein cholesterol
Cognitive function Depression
Telomere length Incidence of Alzheimer’s disease
Maintenance
After achieving the desired level of cardiorespiratory fitness, an individual enters the
maintenance stage of the exercise program. This stage continues on a regular, long-term basis
236
if the individual has made a lifetime commitment to exercise.
The goal of this stage is to maintain the cardiorespiratory fitness level and the weekly
exercise caloric expenditure achieved during the improvement stage. Have your clients
accomplish this goal by engaging in aerobic activities 3 to 5 days/wk at the intensity and
duration that were reached at the end of the improvement stage. Reducing the training
frequency from 5 to 3 days/wk does not adversely affect V̇O max as long as the training
2
intensity remains the same. However, clients should participate in other activities an
additional 2 or 3 days/wk. To this end, a variety of enjoyable activities from the type C and D
classifications may be selected to counteract boredom and to maintain the interest level of the
participant. For example, an individual who was running 5 days/wk at the end of the
improvement stage may choose to run only 3 days/wk and substitute Zumba, in-line skating,
racquetball, or high-intensity circuit resistance training using body weight on the other 2
days.
AEROBIC TRAINING METHODS AND MODES

Either continuous or discontinuous training methods can improve cardiorespiratory
endurance. Continuous training involves one continuous aerobic exercise bout performed at
low to moderate intensities without rest intervals. Discontinuous training consists of several
intermittent low- to high-intensity exercise bouts interspersed with rest periods. Both
training methods produce significant improvements in V̇O max (Landram et al 2016;
2
Milanović, Sporiš, and Weston 2015). Results from research comparing improvements in
V̇O max resulting from high-intensity interval (discontinuous) training versus continuous
2
endurance training are mixed. However, a recent literature review and detailed analysis on the
topic indicate that high-intensity interval training is only slightly better than continuous
endurance training for improving V̇O max (Milanovic´, Sporiš, and Weston 2015). One
2
concern about high-intensity intermittent training is the possibility of exercise burnout.

Pollock and colleagues (1977) reported that the dropout rate of adults in a high-intensity
interval (discontinuous) training program was twice that of those in a continuous jogging
program. Thus, for the typical client, high-intensity interval training may be better suited for
stimulating short-term (e.g., 4 wk) improvements in cardiorespiratory fitness and for adding
variety to the exercise program. More research addressing the long-term health benefits of
interval training and its effects on exercise adherence for the general population is warranted.
CONTINUOUS TRAINING
237
All the exercise modes listed as type A or B activities (see Classification of Aerobic Exercise
Modalities earlier in this chapter) are suitable for continuous training. One advantage of
continuous training is that the prescribed exercise intensity (e.g., 75% HRR) is maintained
fairly consistently throughout the duration of the steady-paced exercise. Generally,
continuous exercise at low to moderate intensities is safer, more comfortable, and better
suited for individuals initiating an aerobic exercise program.
Walking, Jogging, and Cycling

The most popular modes of continuous training are walking, jogging or running, and cycling.
Exercise programs using walking, jogging, and cycling provide similar cardiorespiratory
benefits (Pollock, Cureton, and Greninger 1969; Pollock, Dimmick, et al. 1971; Pollock et al.
1975; Wilmore et al. 1980). Improvements in V̇O max are comparable for most commonly
2
used exercise modes. Pollock, Dimmick, and colleagues (1975) compared running, walking,
and cycling exercise programs of middle-aged men who trained at 85% to 90% HRmax. All
three groups showed significant improvements in V̇O max. These results indicate that
2
improvement in V̇O max is independent of the mode of training when frequency, intensity,
2
and duration of exercise are held constant and are prescribed in accordance with sound,
scientific principles.
Aerobic Dance
For nearly 50 yr, aerobic dance has been a popular mode of exercise for improving and
maintaining cardiorespiratory fitness. A typical aerobic dance workout begins with a low-
intensity warm-up (approximately 5 min) of the major muscle groups. This is followed by 20
to 30 min of either high- or low-impact (both feet simultaneously leaving the floor or one
foot always on the floor, respectively) aerobic dancing. The music and speed of movement
increase until the age-appropriate target training intensity is reached. A cool-down that
includes stretching concludes the session and typically lasts 5 min. Handheld weights (1-4 lb
[0.5-2 kg]) can also be used to increase exercise intensity. Heart rates should be monitored
frequently during exercise to ensure they stay within the target zone.
Zumba is a highly popular style of exercise that typically blends large muscle group
movements of the upper and lower body with Latin-style music (Domene et al. 2016). It can
be performed on land or in a swimming pool. Single-session energy expenditure is reported to
exceed the current recommendations (ACSM 2018) for improving and maintaining
cardiorespiratory fitness, making Zumba a moderate- to vigorous-intensity exercise enjoyed
by young, middle-aged, and older adults alike (Dalleck et al. 2015; Domene et al. 2016;
238
Luettengen et al. 2012). The average MET levels (4.3 ± 0.4 and 6.2 ± 0.3, respectively) in the
studies by Dalleck and colleagues (2015) and Domene and colleagues (2016) exceed that
corresponding to treadmill walking at 3.5 mph with no incline (3.8 METs). After a 16 wk
Zumba intervention (3 days/wk, 60 min/session), the estimated V̇O max values of the
2
participating sedentary and overweight or obese women had improved significantly as did
numerous other physiological variables associated with improved health (Krishnan et al.
2015).
Step Ergometry and Stair Climbing

Step ergometry (machine-based stair climbing) is a popular exercise modality in health and
fitness clubs. Research shows a linear HR response to graded submaximal exercise performed
on stair climbing ergometers. However, the MET levels displayed on the StairMaster 4000
PT overestimate the actual MET intensity of the exercise (Howley, Colacino, and Swensen
1992). When prescribing exercise intensity using this type of stair climber, be certain to adjust
the machine’s estimates for each MET level using the following equation:
actual METs = 0.556 + 0.745 (StairMaster MET setting)
MET level and %HRR estimations attained while ascending 100 steps or 17.3 m (17.3 cm
steps; 20 steps per floor) within 2 min indicate that stair climbing demands an effort
increasing from moderate (3.5 METs; 56%-61% HRR) to vigorous (7.6 METs; 69%-80%
HRR) intensity during ascent for both men and women, respectively. Descending the same
stairs requires approximately 3 METs of effort (Al Kandari et al. 2016). Consequently, your
clients may be able to introduce short bouts of moderate- to vigorous-intensity exercise
wherever an accessible stairwell is available. To estimate the V̇O requirement of stair
2
climbing at a given cadence (number of steps / time to complete) and step height, see table
4.3. Should climbing multiple flights of stairs initially prove too difficult for unfit clients,
Takaishi and associates (2014) recommend they perform multiple repetitions of ascending
and descending fewer flights of stairs. This will allow clients to accumulate the necessary 30
min of moderate-intensity exercise without the physiological stress of climbing continuously.
Acute, intense bouts of stair climbing produce similar physiologic results as equivalent bouts
(3 × 20 sec) of all-out cycling and, therefore, are suitable as a sprint interval modality (Allison
et al. 2017).
Elliptical Training
Elliptical training machines are popular in the fitness industry. Elliptical trainers are designed
239
for either upper body, lower body, or combined upper and lower body exercise. The lower
body motion during exercise on an elliptical trainer is a cross between the actions performed
with machine-based stair climbing and upright stationary cycling. With elliptical trainers, the
feet move in an egg-shaped, or elliptical, pattern, and the feet stay in contact with the
footpads of the device throughout the exercise. Unlike running or jogging, this form of
exercise may provide a high-intensity workout with low-impact forces comparable to those
for walking (Klein, White, and Rana 2016; Lu, Chien, and Chen 2007).
Kravitz and colleagues (1998) reported that the average energy expenditure during
forward-backward exercise with no resistance and against resistance for 5 min (125
strides·min ) was, respectively, 8.1 and 10.7 kcal·min . Exercise intensities ranged between
−1 −1
72.5% and 83.5% HRmax (age predicted). Compared against treadmill exercise, upper body
elliptical training at self-selected intensities produced similar V̇O , HR, and RPE responses
2
(Crommett et al. 1999). Although there was no difference in V̇O between combined upper
2
and lower body elliptical training and treadmill exercise, upper and lower body elliptical
training produced a significantly higher HR and RPE (Crommett et al. 1999). Compared
with steady-state treadmill exercise at self-identified training velocity (no incline), the RPE
response focusing on legs only was higher for traditional elliptical training (150-160
stridesˑmin at steady-state HR from prior treadmill bout). There were no differences by
−1
modality for upper body or whole-body RPEs. When matched for whole-body RPE from the
treadmill exercise, results regarding differences in HR responses, by modality, are equivocal
(Brown et al. 2010; Green et al. 2004). Mier and Feito (2006) reported significantly different
V̇O , ventilation, and RPE values when comparing traditional elliptical exercise performed
2
using legs only (V̇O : 18.7 ± 3.0 ml·kg ·min ; V : 38.9 ± 3.0 L·min ; RPE: 10.9 ± 1.9) with
2 −1 −1 E −1
elliptical training using both arms and legs combined (V̇O : 19.2 ± 3.0 ml·kg ·min ; V : 37.7
2 −1 −1 E
± 8.3 L·min ; RPE: 10.3 ± 1.9). Additionally, they commented on the large interindividual
−1
variability in V̇O at each stage of exercise, possibly related to the gender, body composition,
2
and elliptical training experience of the exerciser.

Traditional elliptical training is a viable substitute for treadmill and stair climbing exercise
(Brown et al. 2010; Green et al. 2004). Egaña and Donne (2004) reported similar and
significant increases in V̇O max values following a 12 wk intervention (30-40 minˑday ; 3
2 −1
days/wk; 70%-90% HRmax) of traditional elliptical training as compared against treadmill

running and stair climbing.
Outdoor elliptical bicycles (ebikes) are a cross between traditional elliptical trainers and
bicycles. As described by Klein, White, and Rana (2016), the pedal angle of the two styles of
240
elliptical trainers is different, with the ebike allowing for leg movement patterns more
reflective of running than cycling. Results from their crossover study comparing 4 wk of ebike
training against run-only training with experienced runners indicate similar significant
increases in VT compared with baseline for both modalities (Klein, White, and Rana 2016).
No differences were found between modalities for any of the performance variables of
interest. Ebike training provides a no-impact alternative to running that induces similar
physiological responses.
Water-Based Exercise
Water-based exercise, such as water aerobics or walking in waist-deep water, has been
promoted as an effective way to increase the cardiorespiratory fitness of young, middle-aged,
and older adults. Improvements in muscular strength may also be realized because of
resistance provided by the water as body segments move through it. This exercise is especially
popular among individuals who are older, overweight, or afflicted with orthopedic disabilities.
Bergamin and associates (2012) provide a comprehensive analysis of the effect of water-based
training on the physical fitness of elderly adults.
Although this will vary based on the style of water-based exercise, a typical exercise session
includes the following phases:
Warm-up—5 to 10 min of stretching before entering the pool, followed by walking

slowly in the water
Endurance phase—20 to 30 min of continuous walking, jogging, kicking, arm
swinging, and dancing in the water
Resistance phase—10 min of resistance exercises performed underwater with foam
noodles, dumbbells, barbell-like devices, and leg pads
Cool-down—5 to 10 min of stretching
In older women (60-75 yr) participating in water-based exercise training 3 days/wk for 12
wk, V̇O peak increased by 12% while total cholesterol and low-density lipoprotein cholesterol
2
decreased by 11% and 17%, respectively. Also, muscle strength and arm and leg power
increased significantly in response to exercising the limbs against the resistance of water
(Takeshima et al. 2002). For a thorough review of the influences of environmental conditions
on physiological responses during water-based exercise (with the head out and body
submerged), see the review by Barbosa and associates (2009).
241
Innovative Aerobic Exercise Modes
New and innovative modes of aerobic exercise are introduced every year by the fitness
industry in order to stimulate and maintain exercise participation of clients. Many of these
new programs combine traditional exercise modes (e.g., stationary cycling, stepping, tai chi,
and martial arts) with music. Fitness centers throughout the United States now offer group
exercise classes using programs such as BodyCombat, RPM, BodyJam, BodyPump,
BodyStep, and Tae Bo. BodyCombat is an aerobic workout that combines movements from
karate, boxing, taekwondo, and tai chi with fast-paced music. RPM is an indoor cycling
workout to music that includes warm-up, pace, hill, mixed terrain, interval, free spin,
mountain climb, and stretch segments. BodyJam integrates current dance styles with trending
music genres popular with young adults. BodyPump is a conditioning class of low-weight,
high-repetition workouts choreographed to music. Tae Bo is an aerobic exercise routine that
combines music with elements of taekwondo and kick boxing to promote aerobic fitness.
Rixon and colleagues (2006) compared exercise HRs and estimates of energy expenditure
for BodyCombat (73% HRmax; 9.7 kcal·min ), RPM (74.3% HRmax; 9.9 kcal·min ),
−1 −1
BodyStep (72.4% HRmax; 9.6 kcal·min ), and BodyPump (60.2% HRmax; 8.0 kcal·min )
−1 −1
routines. With the exception of BodyPump, the intensity and duration of these exercise
routines appear to be sufficient to meet physical activity recommendations for improving
health and for weight management. Eight inactive women (BMI: 29.9 ± 2.3 kg/m ) 2
participated in an 8 wk RPM studio cycling program (3 days/wk), increasing in session

duration from 20 min to 50 min by week 3 (Faulkner et al. 2015). The program was a mix of
low-, moderate-, and high-intensity cycling, and the average training intensity (83% HRmax)
met the ACSM (2018) criteria for vigorous intensity. Faulkner’s research team found that the
studio cycling program was an effective means for significantly improving maximal aerobic
capacity (11.8%), body fat levels (−13.6%), and blood lipid profile (LDL: −23%; total
cholesterol: −13%). Although the group exercise classes described by Rixon and colleagues are
taught in thousands of fitness clubs internationally, further research is needed to determine
the health benefits and effects of these exercise programs on aerobic fitness.
Kettlebell exercise is a popular whole-body, aerobic training modality. It involves the
rhythmic swinging and ballistic lifting of nonsymmetrical hand weights. The available
research on this modality is sparse and equivocal. Farrar, Mayhew, and Koch (2010) found
that kettlebell swinging is suitable for improving aerobic fitness. As expected, the average
%HRmax during kettlebell exercise (87% HRmax) was higher than the corresponding
average %V̇O max (65.3% V̇O max) (Farrar, Mayhew, and Koch 2010). Thomas and
2 2
242
colleagues (2014) compared a 30 min kettlebell routine against 30 min of moderate-intensity
treadmill walking (with incline) at the V̇O of the kettlebell routine. They reported that their
2
kettlebell routine required similar aerobic effort even though the HR and RPE were
consistently higher for the kettlebell routine. Jay and colleagues (2011) reported no significant
improvements in aerobic capacity following an 8 wk kettlebell training intervention for a
predominantly (85%) female sample of adults (44 yr and 23 kg·m on average). Conversely, a
-2
6% improvement in maximal aerobic capacity was reported for a group of top-tier female
collegiate soccer players who performed high-intensity kettlebell snatches at a 1:1 work-to-
rest ratio for 20 min three times a week for 4 wk (Falatic et al. 2015). Beltz and colleagues
(2013) reported a 13% increase in aerobic capacity for young adults completing kettlebell
classes (two classes a week for 8 wk) in a university setting.
DISCONTINUOUS TRAINING
As mentioned previously, discontinuous training involves a series of low- to high-intensity
exercise bouts interspersed with rest or relief periods. All of the exercise modes listed as type
A and type B activities (see Classification of Aerobic Exercise Modalities earlier in this
chapter) are suitable for discontinuous training. Because of the intermittent nature of this
form of training, the exercise intensity and total amount of work performed can be greater
than with continuous training, making discontinuous training a versatile method that is
widely used by athletes, as well as individuals with low cardiorespiratory fitness. In fact, the
ACSM (2018) recommends the use of discontinuous (intermittent) training for symptomatic
individuals who are able to tolerate only low-intensity exercise for short periods of time (3-5
min). Interval training, treading, Spinning, and circuit resistance training are examples of
intermittent, or discontinuous, training.
Interval Training
Interval training involves a repeated series of exercise work bouts interspersed with rest or
relief periods. This method is popular among athletes because it allows them to exercise at
higher relative intensities during the work interval than are possible with longer-duration
continuous training. Interval training programs can also be modified to improve speed and
anaerobic endurance, as well as aerobic endurance, simply by changing the exercise intensity
and length of the work and relief intervals.
An example of interval training is work intervals run at a pace such that a distance of 1,100
yd (1,005 m) is covered in 3 to 4 min. Each work interval is then followed by a rest-relief
interval of 1.5 to 2 min. This sequence is repeated three times. During the rest-relief interval,
243
the individual may walk or jog while recovering from the work bout. For aerobic interval
training, the ratio of work to rest-relief is usually 1:1 or 1:0.5. Each work interval is 3 to 5
min and is repeated three to seven times. The exercise intensity usually ranges between 70%
and 85% V̇O max. The overload principle is applied by increasing the exercise intensity or
2
length of the work interval, decreasing the length of the rest-relief interval, or increasing the
number of work intervals per exercise session. For a discussion of interval training and sample
programs, including programs for developing speed and anaerobic endurance, refer to the
work of Janssen (2001).
For a review of studies investigating the similarities and differences between endurance
training and high-intensity interval training (HIT), see the article by Milanović, Sporiš, and
Weston (2015). Their key points identify the influence of the comparative group’s fitness
status on the magnitude of change induced through the two styles of training. Regardless,
HIT is reported as improving aerobic capacity more so than does traditional continuous
endurance training.
As highlighted in a review regarding the potential of HIT programs, Kessler, Sisson, and
Short (2012) differentiated between sprint interval training (SIT) and aerobic interval
training (AIT). SIT is typically based on iterative combinations of 30 sec maximal exertion
sprints and extended recovery interludes (approximately 4 min) on a stationary cycle. AIT is
based on iterations of near maximal (80%-95% V̇O max) 4 min bouts of treadmill or cycling
2
exercise followed by 3 to 4 min recovery periods, and it appears to have broader application
for nonathlete, sedentary, and clinical populations. However, the SIT and AIT protocols tend
to vary widely in exercise session volume and number of exercise sessions. Both SIT and AIT
protocols show similar if not larger increases in maximal aerobic capacity and insulin
sensitivity compared with counterparts engaging in the standard continuous moderate-
intensity exercise. This trend is pervasive even though the SIT and AIT groups exercise just a
fraction of the time recommended by the ACSM and American Heart Association (150
min/wk). For example, Gillen and associates (2016) reported that 12 wk of stationary cycling
increases V̇O peak by 19% regardless of group assignment. Of note, their moderate-intensity
2
continuous (MIC) cycling group exercised five times longer each week than did their SIT
cycling group. However, the MIC cycling group (3.75-5.0 hr/wk) in the 6 wk study
conducted by Fisher and colleagues (2015) had greater improvements in their
cardiorespiratory fitness than did the HIT cycling group (60 min/wk) even though both
cycling programs improved cardiometabolic parameters in overweight, inactive men.
HDL-C and body fat percentage have been favorably altered by AIT, as has blood pressure
244
in those not already undergoing treatment for hypertension. However, a dose-response
relationship is evident as the duration of the HIT protocols on these cardiovascular risk
factors varies. Kessler, Sisson, and Short (2012) also outlined the need for exercise session
supervision early on in the HIT programs, which are rigorous and may not be appropriate for
everyone. On the other hand, Gosselin and colleagues (2012) reported that HIT exercise is as
physiologically taxing as moderate-intensity (70% V̇O max) steady-state exercise for
2
physically active adults (20-30 yr).

Thirteen inactive yet healthy men (35-45 yr; BMI: 25-30 kg/m ) underwent an AIT
2
intervention that consisted of a 10 min warm-up (70% HRmax) and four 4 min bouts of
treadmill running at incline and at 90% HRmax. Treadmill activity (3 min bouts at 70%
HRmax) separated the near maximal periods of exertion. The study period covered 10 wk,
with three sessions per week. Aerobic capacity increased by 13%, and systolic and diastolic
blood pressures decreased, as did fasting blood glucose. However, only body fat, total
cholesterol, and LDL-cholesterol values had decreased significantly after 10 wk (Tjønna et al.
2013).
Recreationally active men served as their own controls in an investigation comparing high-
intensity (90% V̇O max) interval treadmill running (6 reps × 3 min·rep ) interspersed with
2 −1
moderate-intensity (50% V̇O max) active recovery (6 reps × 3 min·rep ) against a 50 min
2 −1
continuous treadmill running bout at moderate intensity (70% V̇O max). Along with the 7
2
min warm-up and cool-down periods, the high-intensity protocol involved approximately 50
min of exercise. Although the %HRmax, %V̇O max, and total energy expenditure were
2
similar, RPE and perceived enjoyment were significantly higher for the high-intensity
interval running protocol (Bartlett et al. 2011). An increased sense of enjoyment with exercise
may lead to increased exercise adherence, although this needs to be investigated in less fit
individuals. Consequently, research on longer duration interventions are needed to determine
if adherence and long-term physiological gains are possible with AIT and SIT programs.
An Interval Training Prescription to Develop Aerobic Endurance
Sets: One
Repetitions: Three to seven
Distance: 1,100 yd (1,105 m)
Intensity: 70% to 85% V̇O max 2
Time: 3 to 5 min
245
Rest-relief interval: 1.5 to 2 min
Treading and Spinning

Treading and Spinning are two examples of interval training that are popular offerings in
fitness clubs because of the variety and enjoyment they offer. These group classes involve
walking, jogging, and running at various speeds and grades on a treadmill (treading) or
stationary cycling at various cadences and resistances (Spinning). A typical treading or
Spinning workout consists of 1:1 or 1.5:1 work-recovery intervals, or stages, that are repeated
for a specified duration. For example, a 30 min treading class may consist of six stages. Each
stage lasts 5 min (i.e., 3 min work interval and 2 min recovery interval). Participants can
advance the intensity of the work interval by increasing the treadmill speed or grade. During
the recovery interval, both the speed and grade of the treadmill are decreased (e.g., 2.5 mph
[4 km·hr ] and 0% grade). Instructors individualize and adapt the workouts for their clients
−1
by adjusting the duration of the work-recovery intervals and varying the speed and grade.
Caution is urged for novice Spinning participants as the leg muscles may experience a trauma
that releases cell contents into the bloodstream. This condition, exertional rhabdomyolysis,
appears in medical literature and is being identified in some individuals following their first
Spinning class (Brogan et al. 2017). Some researchers have suggested separate beginner-level
familiarization classes to provide a controlled adaptation to the rigors of Spinning (de Melo
dos Santos et al. 2015).
In one study, researchers designed 30 min treading workouts for walkers and runners
(Nichols, Sherman, and Abbott 2000). They reported that the average intensity of the
walking protocol was 40% to 49% V̇O max for male and female walkers, respectively. For the
2
running protocol, the average intensity of the work intervals was 76% to 80% V̇O max for 2
male and female runners, respectively. The researchers suggest that these average intensities,
as well as the duration of the workout (30 min), are sufficient to meet ACSM
recommendations for an aerobic exercise prescription. Although both treading and Spinning
classes are offered in fitness centers coast to coast, there is very little research about their acute
and long-term training effects on cardiorespiratory fitness.
Circuit Resistance Training

Use of circuit resistance training for the development of aerobic fitness, as well as muscular
strength and tone, has received much attention. An example of a circuit resistance training
program is presented in chapter 7 (see figure 7.1). Circuit resistance training usually consists
246
of several circuits of resistance training with a minimal amount of rest between the exercise
stations (15-20 sec). Alternatively, instead of rest, you can have your clients perform 1 to 3
min of aerobic exercise between each station. The aerobic stations may include activities such
as stationary cycling, jogging in place, rope skipping, stair climbing, bench stepping, and
rowing. This modification of the circuit is known as super circuit resistance training.
Gettman and Pollock (1981) reviewed the research on the physiological benefits of circuit
resistance training. Because it produces only a 5% increase in aerobic capacity as compared
with a 15% to 25% increase from other forms of aerobic training, the authors concluded that
circuit resistance training is more suited for the maintenance stage of an aerobic exercise
program than for developing aerobic fitness. Newer research indicates that 5 wk of high-
intensity circuit resistance training (three sessions per week) using body weight exercises
improves aerobic capacity (11%) for young sedentary women (Myers et al. 2015). The
improvement in aerobic capacity (~8%) in recreationally active women completing 4 wk of
circuit resistance training using body weight exercises was similar to the ~7% improvement
reported for women performing 30 min of treadmill running at 85% HRmax (McRae et al.
2012). Both groups in the McRae study exercised four times per week. In addition to
increasing the aerobic capacity of young women, high-intensity circuit resistance training
using body weight exercises increases muscular endurance.
PERSONALIZED EXERCISE PROGRAMS

The aerobic exercise prescription should be individualized to meet each client’s training goals
and interests. To do this, you need to consider the client’s age, gender, physical fitness level,
and exercise preferences. This section presents a sample case study and examples of
individualized exercise prescriptions to illustrate how the exercise prescription may be
personalized for each client.
CASE STUDY
Like any preventive or therapeutic intervention, exercise should be prescribed carefully. You
must be able to evaluate a client’s medical history, medical condition, physical fitness status,
lifestyle characteristics, and interests before designing the exercise program. In addition, to
test your ability to extract, analyze, and evaluate all pertinent information needed to design a
safe exercise program for the client, many professional certification examinations require that
you be able to analyze a case study. For these reasons, this section includes a sample case
study.
247
A case study is a written narrative that summarizes client information you will need in
order to develop an accurate and safe individualized exercise prescription (Porter 1988).
Important elements to focus on when reading and analyzing a case study are listed in
Essential Elements of a Case Study. First, determine if medical clearance is needed for the
client by assessing signs and symptoms of cardiovascular, metabolic, and renal disease (see
Appendix A.3) and physical activity history in the past 3 mo. Then identify the client’s
coronary heart disease (CHD) risk factors by focusing on information provided about age,
family history of CHD, blood lipid profile (total cholesterol, high- and low-density
lipoprotein cholesterol [HDL-C and LDL-C]), blood glucose levels, resting BP, physical
activity, body fat level, and smoking. Become familiar with ideal or typical values for various
blood chemistry tests so you will be able to recognize normal or abnormal test results.
Remember that each of the following factors places individuals at greater risk for CHD:
Triglycerides ≥150 mg·dl −1
Total cholesterol ≥200 mg·dl −1
LDL-cholesterol ≥130 mg·dl −1
HDL-cholesterol <40 mg·dl −1
Total cholesterol/HDL ratio >5.0

Blood glucose ≥110 mg·dl −1
Systolic BP ≥130 or diastolic BP ≥80 mmHg
Pay close attention to information about the client’s medical history and physical
examination results. These may reveal signs or symptoms of CHD, particularly if shortness of
breath, chest discomfort, or leg cramps are reported or if high BP is detected. It is also
important to note the types of medication the client is using. Drugs such as digitalis, beta-
blockers, diuretics, vasodilators, bronchodilators, and insulin may alter the body’s
physiological responses during exercise and could affect the HR and BP responses reported
for the GXT. Keep in mind that exercise programs need to be modified for individuals with
musculoskeletal disorders such as arthritis, low back pain, osteoporosis, and chondromalacia.
Next, be certain to key in on information regarding the client’s lifestyle. Factors such as
smoking, lack of physical activity, or diets high in saturated fats or cholesterol increase the
risk of CHD, atherosclerosis, and hypertension. You can often target these factors for
modification; they also help you assess the likelihood of the client’s adherence to the exercise
248
program (see table 3.3).
Examine the BP, HR, and RPE data for the GXT used to assess the client’s functional
aerobic capacity and cardiorespiratory fitness level. You need to be acutely aware of the
normal and abnormal physiological responses to graded exercise. After assessing the client’s
CHD risk and cardiorespiratory fitness level, you can design an aerobic exercise program
using a personalized exercise prescription of intensity, frequency, duration, mode, volume,
and progression. To write the exercise prescription, use the results from the GXT (HR, RPE,
functional MET capacity).
The sample case study is provided to test your ability to evaluate the need for medical
clearance prior to exercise, risk factors, and GXT results and to prescribe an accurate and safe
aerobic exercise program for this individual. See the results of the case study analysis in
appendix B.5.
SAMPLE CASE STUDY
A 28 yr old female police officer (5 ft 5 in. or 165.1 cm; 140 lb or 63.6 kg; 28% body fat) has enrolled in the adult fitness
program. Her job demands a fairly high level of physical fitness—a level she was able to achieve 6 yr ago when she passed
the physical fitness test battery used by the police department. Before becoming a police officer, she jogged 20 min,
usually three times a week. Since starting her job, she has had little or no time for exercise and has gained 15 lb (6.8 kg).
She is divorced, and she works 8 hr a day and takes care of two children, ages 7 and 9. At least three times a week, she and
the children dine out, usually at fast food restaurants like Burger King and Taco Bell. She reports that her job, along with
the sole responsibility for raising her two children, is quite stressful. Occasionally she experiences headaches and a
tightness in the back of her neck. Usually in the evening she has one glass of wine to relax.
Her medical history reveals that she smoked one pack of cigarettes a day for 4 yr while she was in college. She quit
smoking 3 yr ago. Over the past 2 yr, she has tried some quick weight loss diets, with little success. She was hospitalized
on two occasions to give birth to her children. She reports that her father died of heart disease when he was 52 and that
her older brother has high blood pressure. Recently she had her blood chemistry analyzed because she was feeling light-
headed and dizzy after eating. In an attempt to lose weight, she eats only one large meal a day, at dinnertime. Results of
the blood analysis were total cholesterol = 220 mg·dl−1; triglycerides = 98 mg·dl−1; glucose = 82 mg·dl−1; high-density
lipoprotein cholesterol = 37 mg·dl−1; and total cholesterol/high-density lipoprotein cholesterol ratio = 5.9.
The exercise evaluation yielded the following data:
Mode, protocol: Treadmill, modified Bruce
Resting data: HR = 75 bpm; BP = 140/82 mmHg
Endpoint: Stage 4 (2.5 mph [4 km·hr−1], 12% grade). Test terminated because of fatigue.
249
Analysis
1. Evaluate the client’s need for medical clearance and the client’s CHD risk profile. Be certain to address each
of the positive and negative risk factors.
2. Describe any special problems or limitations that need to be considered in designing an exercise program for
this client.
3. Were the HR, BP, and RPE responses to the GXT normal? Explain.
4. What is the client’s functional aerobic capacity in METs? Categorize her cardiorespiratory fitness level (see
table 4.1).
5. Plot the HR versus METs on graph paper.
6. From the graph, determine the client’s target HR zone for the aerobic exercise program. What HRs and
RPEs correspond to 60%, 70%, and 75% of the client’s V̇O2R?
7. The client expressed an interest in walking outside on a level track to develop aerobic fitness. Calculate her
walking speed for each of the following training intensities: 60%, 70%, and 75% V̇O2R. Use the ACSM
equations presented in table 4.3.
8. In addition to starting an aerobic exercise program, what suggestions do you have for this client for
modifying her lifestyle?
See appendix B.5 for answers to these questions.
SAMPLE CYCLING PROGRAM
Client Data
Age: 27 yr
Gender: Female
Body weight: 70 kg (154 lb)
Resting heart rate: 67 bpm
Maximal heart rate: 195 bpm (measured)
V̇O2max: 26 ml·kg−1·min−1 (measured); 7.4 METs
Graded exercise test: Cycle ergometer
Initial cardiorespiratory fitness level: Poor
250
Mode: Stationary cycling
60%-80% V̇O2R
Intensity: 16.8-21.4 ml·kg−1·min−1

4.8-6.1 METs
139 bpm minimum

Exercise heart rates (from figure 5.3):
168 bpm maximum
RPE: 5-8 (OMNI scale)
Duration: 40-60 min
Frequency: 4 or 5 days/wk
SAMPLE CYCLING PROGRAM

The sample cycling program later in this chapter shows a personalized program for a 27 yr
251
old female who was given a maximal GXT on a stationary cycle ergometer. Her measured
V̇O max is 7.4 METs. The exercise intensity is based on a percentage of her V̇O reserve
2 2
(%V̇O R), and the target exercise HRs corresponding to 60% (4.8 METs) and 80% V̇O R
2 2
(6.1 METs) are 139 bpm and 168 bpm, respectively (see figure 5.3). Thus, the training
exercise HR should fall within this HR range. During the initial stage of the exercise
program, the woman will cycle at a work rate corresponding to 60% V̇O R (4.8 METs) for 2 2
wk.
During weeks 1 and 2, the exercise duration is increased by 5 min/wk (from 40 to 45 min).
During the third week, relative exercise intensity rather than duration is increased by 5%
(from 60% V̇O R to 65% V̇O R). The work rate corresponding to an exercise intensity is
2 2
calculated using the ACSM formulas for leg ergometry (see table 4.3). For example, the work
rate corresponding to 60% V̇O R (4.8 METs or 16.8 ml·kg ·min ) is calculated as follows:
2 −1 −1
V̇O (ml·kg ·min ) = W / M × 1.8 + 3.5 + 3.5

2 −1 −1
where W = work rate in kgm·min and M = body mass in kg.

−1
16.8 = W / 70 kg × 1.8 + 7.0
16.8 − 7.0 = W / 70 kg × 1.8
9.8 × 70 kg / 1.8 = 381 kgm·min −1
To calculate the resistance setting corresponding to 381 kgm·min for a cycling cadence of
−1
50 rpm, divide the work rate by the total distance the flywheel travels: 381 / 50 rpm × 6 =
1.27 kg, or 1.3 kg.
To calculate the net energy cost (kcal·min ) of cycling, subtract the resting V̇O (1 MET)
−1 2
from the gross V̇O for each intensity. Convert this net MET value to kcal·min using the
2 −1
following formula:
kcal·min = METs × 3.5 × body mass (kg) / 200

−1
(e.g., 4.8 − 1.0 = 3.8 METs; 3.8 × 3.5 × 70 kg / 200 = 4.7 kcal·min ) −1
In the initial stages of the program, the weekly net energy expenditure is between 752 and
1,040 kcal. In the improvement stage, the exercise intensity, duration, and frequency are
progressively increased, and the weekly net caloric expenditure ranges between 1,040 and
1,874 kcal. Only one variable—intensity, duration, or frequency—should be increased at a
time. The variable that is increased during each stage of the progression for this exercise
252
program is indicated by boldface. During the improvement stage, this client’s net caloric
expenditure due to exercise meets the caloric threshold of between 1,000 and 2,000 kcal·wk
−1
from physical activity recommended by the ACSM (2018). In the maintenance phase, tennis
and aerobic dancing are added to give variety and to supplement the cycling program. The
ACSM (2018) guidelines were followed to calculate each component of this exercise
prescription.
ESSENTIAL ELEMENTS OF A CASE STUDY
Demographic Factors
Age
Gender
Ethnicity
Occupation
Height
Body weight
Family history of coronary heart disease
Medical History
Present Symptoms
Dyspnea or shortness of breath
Angina or chest pain
Leg cramps or claudication
Musculoskeletal problems or limitations
Medications
Past History
Diseases
Injuries
Surgeries
Lab tests
Lifestyle Assessment
253
Alcohol and caffeine intake
Smoking
Nutritional intake, eating patterns
Physical activity patterns and interests
Sleeping habits
Occupational stress level
Mental status, family lifestyle
Physical Examination
Blood pressure
Heart and lung sounds
Orthopedic problems or limitations
Laboratory Tests (Ideal or Typical Values)
Triglycerides (<150 mg·dl−1)
Total cholesterol (<200 mg·dl−1)
LDL-cholesterol (<100 mg·dl−1)
HDL-cholesterol (>40 mg·dl−1)
Total cholesterol/HDL-cholesterol (<3.5) •
Blood glucose (60-99 mg·dl−1)
Hemoglobin:
13.5-17.5 g·dl−1 (men)
11.5-15.5 g·dl−1 (women)
Hematocrit:
40%-52% (men)
36%-48% (women)
Potassium (3.5-5.5 meq·dl−1)
Blood urea nitrogen (4-24 mg·dl−1)
Creatinine (0.3-1.4 mg·dl−1)
254
Iron:
40-190 mg·dl−1 (men)
35-180 mg·dl−1 (women)
Calcium (8.5-10.5 mg·dl−1)
Physical Fitness Evaluation
Cardiorespiratory fitness (HR, BP, V̇O2max)
Body composition (% body fat)
Musculoskeletal fitness (muscle and bone strength)
Flexibility
Balance
SAMPLE JOGGING PROGRAM

The sample jogging program later in this chapter is designed for a 29 yr old male who has an
excellent cardiorespiratory fitness level. Since a GXT could not be administered, the V̇O max
2
was predicted from performance on the 12 min distance run test. The maximal HR was
predicted using the formula 220 − age. Because this client is accustomed to jogging and his
cardiorespiratory fitness level is classified as excellent, he is exempted from the initial stage
and enters the improvement stage of the program immediately. During this time (20 wk), the
exercise intensity is increased from 70% to 85% of the estimated V̇O R. The speed
2
corresponding to each MET intensity is calculated using the ACSM formulas for running on
a level course (see table 4.3).
The intensity, duration, and frequency of the exercise sessions provide a weekly net caloric
expenditure between 1,010 and 2,170 kcal. During the first 4 wk of the program, this client’s
net rate of energy expenditure due to exercise is 10.2 kcal·min (8.3 METs × 3.5 × 70 kg /
−1
200 = 10.2 kcal·min ); thus, he will expend approximately 1,010 kcal, jogging 33 min at a
−1
pace of 11:06 min·mi three times per week (33 min × 10.2 kcal·min × 3). To figure the
−1 −1
distance covered, the exercise duration is divided by the running pace: 33 min / 11.1 min·mi −1
= 3 mi (5 km). During the improvement stage, the frequency of exercise sessions gradually
progresses from 3 to 5 days/wk. During the maintenance stage, the running is reduced to 3
days/wk, and handball and basketball are added to the aerobic exercise program. The ACSM
255
(2018) guidelines were followed to calculate each component of this exercise prescription.
SAMPLE MULTIMODAL EXERCISE PROGRAM

Some clients prefer to engage in a variety of exercise modes (cross-training) to develop their
cardiorespiratory fitness (see the example in the Sample Multimodal Exercise Program later
in the chapter). In these cases, it is difficult to systematically prescribe increments in exercise
intensity using METs or target HRs. Although MET equivalents for various activities are
available (see appendix E.3), typically a range of values is given, making it difficult for you to
accurately prescribe work rates corresponding to specific intensity recommendations in an
exercise prescription. Also, the HR response to a given MET level is highly dependent on the
exercise mode.
The degree of muscle mass involved in the activity, as well as whether the body weight is
supported during exercise, can affect the HR response to a prescribed exercise intensity. For
example, whole-body exercise modes, such as Nordic skiing and aerobic dancing, involve both
upper and lower body musculature. These produce higher submaximal HRs than lower body
exercise modes (e.g., cycling and jogging). Also, at any given exercise intensity, the HR
response during weight-bearing exercise such as jogging is greater than that for non-weight-
bearing exercise (e.g., cycling).
Therefore, you should use RPEs to progressively increase exercise intensity throughout the
improvement stage of a multimodal aerobic exercise program (see table 4.2). To use the RPE
safely and effectively, you will need to teach your clients to focus on and learn to monitor
important exertional cues such as breathing effort (rate and depth of breathing) and muscular
sensations (e.g., pain, warmth, and fatigue). Guidelines for developing multimodal exercise
prescriptions are presented in this section.
For multimodal exercise programs, you should set exercise frequency and weekly net
caloric expenditure goals for each client (see the example in Sample Multimodal Exercise
Program later in the chapter). Provide your clients with estimates of net energy expenditure
(kcal·min ) for each of the aerobic activities they select for their exercise prescriptions. The
−1
exercise duration to achieve a specified weekly net caloric expenditure goal will vary
depending on the activity mode chosen for each exercise session. Any combination of type A,
B, or C activities can be used, provided the client is able to maintain the prescribed RPE
intensity for at least 20 min.
Flexibility is the key to successful multimodal exercise prescriptions. Clients should be free
not only to select exercise modes of interest but also to decide on various combinations of
256
frequency and duration as long as they meet the caloric thresholds specified in their exercise
prescriptions for each week.
The primary advantages of multimodal exercise programs over single-mode (e.g., jogging
or cycling) programs for many of your clients are
greater likelihood of engaging in a safe and effective exercise program,

overall greater enjoyment of physical activity and exercise,
better understanding of how their bodies respond to exercise,
more direct involvement and sense of control in developing and monitoring their
exercise programs, and
increased likelihood of incorporating physical activity and exercise into their
lifestyles.
Guidelines for Multimodal Exercise Prescriptions
Modes: Select at least three per week from type A and B activities.
Frequency: Three to seven sessions a week. Engage in either type A, B, or C
activities at least three times per week.
Intensity: Rating of perceived exertion between 5 and 9 on 10-point OMNI scale.
Duration: At least 15 min, preferably 20 to 30 min. Duration depends on energy
cost (kcal·min ) of exercise mode.
−1
Caloric expenditure: 1,000 to 2,000 kcal·wk . Group C and D activities can be

−1
used to reach the weekly caloric expenditure goal, but they cannot be counted as
one of the required aerobic activities.
SAMPLE HIT EXERCISE PROGRAM

The sample HIT program later in this chapter is designed for a 34 yr old, recreationally active
male who has a fair cardiorespiratory fitness level and a limited amount of time per week to
dedicate to aerobic training. Since he is satisfied with his body weight, his main goal is to
improve his aerobic capacity. His V̇O max was predicted from a multistage treadmill test, and
2
his maximal HR was predicted using the formula 220 − age. Because this client is
undertaking his first HIT exercise program and his cardiorespiratory fitness level is classified
257
as fair, he is starting his program 5% above the exercise intensity targeted by the end (60%
V̇O R) of the standard initial conditioning stage. During the first 2 wk of his treadmill
2
routine, the work and active recovery (rest) intervals are performed at 65% and 35% of his
estimated V̇O R, respectively. With the work and rest intervals both being 1 min in duration,
2
his work-to-rest ratio is 1:1; he is initially scheduled to complete 15 repetitions per session
three times per week. While the frequency and total duration of his exercise sessions remain
constant, the intensity and duration of the work interval are systematically manipulated
throughout the remaining 11 wk of the program, as is the work-to-rest ratio (principle of
progression). The average net kcal·min expended in week 1 is calculated as follows: Work:
−1
[(7 METs × 3.5 ml kg ·min × 97.7 kg) / 200] = 11.97 kcal·min ; Rest: [(4.3 METs × 3.5 ml
−1 −1 −1
kg ·min × 97.7 kg) / 200] = 7.35 kcal·min ; average net kcal·min = [(11.97 × 15 min) +
−1 −1 −1 −1
(7.35 × 15 min)] / 30 min = 9.7. This represents the respective contributions of the work and
rest intervals.
The 2 wk block arrangement of exercise sessions provides a weekly net caloric expenditure
between 869 and 1,041 kcal and the opportunity to manipulate one programmatic variable at
a time. As he advances through the improvement stage, adjustments are made to the intensity
of the work interval, work-to-rest ratio, and number of repetitions. His ability to tolerate the
progression must be closely monitored and his program adjusted accordingly. A reassessment
of his estimated V̇O max is recommended after the sixth week. For additional information on
2
the potential improvements in aerobic capacity or cardiometabolic disease risk following

high-intensity interval training interventions, refer to the work of Bacon and colleagues
(2013) and Kessler, Sisson, and Short (2012), respectively.
SAMPLE JOGGING PROGRAM
Client Data
Age: 29 yr
Gender: Male
Body weight: 70 kg (154 lb)
Maximal heart rate: 191 bpm (age predicted)
V̇O2max: 45 ml·kg−1·min−1 (predicted); 12.9 METs
Graded exercise test: None
Initial cardiorespiratory
fitness level: Excellent
258
Mode: Jogging and running
Intensity: 70%-85% V̇O2R
32.5-38.8 ml·kg−1·min−1
9.3-11.1 METs
Exercise heart rates: 149 bpm minimum
(70% HRR)
170 bpm maximum
(85% HRR)
RPE: 6-9 (OMNI scale)
Duration: 33-35 min
Frequency: 3-5 days/wk
SAMPLE MULTIMODAL EXERCISE PROGRAM
Client Data
Age: 44 yr
259
Sex: Female
Weight: 68 kg (150 lb)
Maximal heart rate: 170 bpm
V̇O2max: 30 ml·kg−1·min−1
8.6 METs
Treadmill maximal GXT

Graded exercise test:
(Bruce protocol)
Initial cardiorespiratory fitness level: Fair
Stationary cycling (100 W): 5.5 METs; 5.4
kcal·min−1
Rowing (100 W): 7.0 METs; 7.1 kcal·min−1

Swimming (moderate effort): 7.0 METs; 7.1
kcal·min−1
Modes and estimates of gross caloric expenditure (METs) and net
Stair climbing (machine): 9.0 METs; 9.5
caloric expenditure (kcal·min−1)a:
kcal·min−1
In-line skating: 12.5 METs; 13.7 kcal·min−1
Hiking: 6.0 METs; 5.9 kcal·min−1

Resistance training (free weights, machines): 3.0
METs; 2.4 kcal·min−1
Intensity: RPE: 5-9 (OMNI scale)
Duration: 20-60 min
Weekly caloric expenditure: 500-1,250 kcal·wk−1
260
SAMPLE HIT PROGRAM
Client Data
Age: 34 yr
261
Gender: Male
Body weight: 97.7 kg (215 lb)
Maximal heart rate: 186 bpm (predicted)
V̇O2max: 36 ml·kg−1·min−1
(predicted);10.3 METs
Graded exercise test: Treadmill
Initial cardiorespiratory fitness level: Fair
Mode: Treadmill
Intensity: Work periods: 65% to 75% V̇O2R
Rest periods: 35% V̇O2R
Work periods: 15-20 min
Rest periods: 10-15 min
Frequency: 3 days/wk
Weekly caloric expenditure: 869-1,041 kcal·wk−1
Key Points
Always personalize cardiorespiratory exercise programs to meet the needs, interests, and abilities of each
participant.
The exercise prescription includes mode, frequency, intensity, duration, volume, and progression (FITT-
VP principle) of exercise.
262
Aerobic endurance activities involving large muscle groups are well suited for developing cardiorespiratory
fitness. Type A and B activities such as walking, jogging, and cycling allow the individual to maintain
steady-state exercise intensities and are not highly dependent on skill.
Exercise intensity can be prescribed using the HR, V̇O2R, or RPE methods, or a combination of these
methods.
For the average healthy person, the traditional cardiorespiratory exercise program should be at a moderate
intensity of 40% to <60% V̇O2R, a duration of 30 to 60 min, and a frequency of 5 days/wk.
More fit individuals can exercise at a vigorous intensity of ≥60% to 90% V̇O2R, 20 to 60 min/day, 3 day/wk.
The cardiorespiratory exercise program includes three stages of progression: initial conditioning,
improvement, and maintenance.
Each exercise session includes a warm-up, aerobic conditioning exercise, and a cool-down.
Continuous and discontinuous training methods are equally effective for improving cardiorespiratory
fitness.
AIT and SIT training programs may provide similar or better improvements in cardiometabolic factors in
less time compared with continuous moderate-intensity programs. Additionally, they may improve
adherence and increase enjoyment of exercise.
Multimodal exercise prescriptions use a variety of type A, B, and C aerobic activities to improve
cardiorespiratory endurance.
Key Terms
aerobic interval training (AIT)

caloric threshold
continuous training
counting talk test (CTT)
cross-training
discontinuous training
FITT-VP principle
heart rate reserve (HRR)
high-intensity interval training
interval training
Karvonen method
lactate threshold
MET·min
multimodal exercise program
percent heart rate maximum (%HRmax)
percent heart rate reserve (%HRR) method
percent V̇O2max reserve (%V̇O2R)
263
pulmonary ventilation
Spinning
sprint interval training (SIT)
super circuit resistance training
talk test
treading
type A activities
type B activities
type C activities
type D activities
ventilatory threshold
volume of exercise
V̇O2reserve (V̇O2R)
Review Questions
1. Name the four components of any aerobic exercise prescription.
2. What are the guidelines for an exercise prescription for improved health?
3. What are the guidelines for an exercise prescription for cardiorespiratory fitness?
4. Identify the three parts of an aerobic exercise workout and state the purpose of each part.
5. To classify an aerobic exercise mode as either type A, B, C, or D activity, what criteria are used?
6. Give three examples each for type A, B, C, and D aerobic activities.
7. Describe three methods used to prescribe intensity for an aerobic exercise prescription.
8. Describe potential advantages of mixing non-load-bearing aerobic exercises into a load-bearing aerobic
exercise program.
9. Using the V̇O2reserve method, calculate the target V̇O2 for a client whose V̇O2max is 12 METs and
relative exercise intensity is 70% V̇O2R.
10. Which method of prescribing intensity (%HRR or %HRmax) corresponds 1:1 with the % V̇O2R method?
11. What are the limitations of using HR methods to monitor intensity of aerobic exercise?
12. Describe how RPEs can be used to prescribe and monitor the intensity of aerobic exercise.
264
13. Describe how your clients can use the talk test to monitor exercise intensity during their aerobic exercise
workouts.
14. How does the talk test differ from the counting talk test?
15. What target caloric thresholds are recommended by ACSM for aerobic exercise workouts and weekly caloric
expenditure from physical activity and exercise?
16. What is the recommended frequency of activity and exercise for improved health benefits? For improved
cardiorespiratory fitness?
17. Name the three stages of a cardiorespiratory exercise program. For the average individual, what is the typical
length (in weeks) of each stage?
18. What is the difference between continuous and discontinuous aerobic exercise training? Give examples of
continuous and discontinuous training methods.
19. Compare the health benefits of aerobic interval training, sprint interval training, and continuous moderate-
intensity exercise training programs.
20. What are the essential elements of a client case study?
265
CHAPTER 6
Assessing Muscular Fitness

KEY QUESTIONS
How are strength, muscular endurance, and power assessed?
How does the type of muscle action (concentric, eccentric, static, or isokinetic) affect force production?
What test protocols can be used to assess a client’s muscular fitness?
What are the advantages and limitations of using free weights and exercise machines to assess muscular
strength?
What are sources of measurement error for muscular fitness tests, and how are they controlled?
What are the recommended procedures for administering 1-RM strength tests?
Is it safe to give 1-RM strength tests to children and older adults?
What tests can be used to assess the functional strength of older adults?
Muscular strength and endurance are two important components of muscular fitness.
Minimal levels of muscular fitness are needed to perform activities of daily living, to maintain
functional independence as one ages, and to partake in active leisure-time pursuits without
undue stress or fatigue. Adequate levels of muscular fitness lessen the chance of developing
low back problems, osteoporotic fractures, and musculoskeletal injuries. Traditionally,
muscular power has not been routinely assessed in health and fitness programs. However, the
American College of Sports Medicine (2018) now includes power along with muscular
strength and endurance to collectively define muscular fitness. In addition to being critical for
success in many athletic endeavors, muscular power might actually be more important than
muscular strength for preventing slip-related falls (Han and Yang 2015).
This chapter describes a variety of laboratory and field tests for assessing all forms of
muscular strength, endurance, and muscular power. In addition, the chapter compares types
of exercise machines, addresses factors affecting muscular fitness tests, discusses sources of
measurement error, and provides guidelines for testing muscular fitness of children and older
adults.
266
DEFINITION OF TERMS
Muscular strength is defined as the ability of a muscle group to develop maximal contractile
force against a resistance in a single contraction. The force generated by a muscle or muscle
group, however, is highly dependent on the velocity of movement. Maximal force is produced
when the limb is not rotating (i.e., zero velocity). As the speed of joint rotation increases, the
muscular force decreases. Thus, strength for dynamic movements is defined as the maximal
force generated in a single contraction at a specified velocity (Knuttgen and Kraemer 1987).
Muscular endurance is the ability of a muscle group to exert submaximal force for extended
periods. Muscular power is the muscle’s ability to exert force per unit of time, or the rate of
performing work.
Figure 6.1 summarizes the different types of muscle actions. Both strength and muscular
endurance can be assessed for static and dynamic actions. If the resistance is immovable, the
action is static or isometric (iso, same; metric, length), and there is no visible movement of the
joint. Dynamic actions, in which there is visible joint movement, are classified as either
auxotonic, isokinetic, or variable resistance.
FIGURE 6.1 Summary of muscle actions.
00:00 / 00:00
267
Video 6.1
Traditional resistance training with free weights is classified as isotonic (iso, same; tonic,
tension) in many textbooks. However, the term isotonic muscle action is a misnomer because
the tension produced by the muscle group fluctuates greatly even though the resistance is
constant throughout the range of motion (ROM). This fluctuation in muscular force is due
to the change in muscle length and angle of pull as the bony lever is moved, creating a
strength curve that is unique for each muscle group. For example, the strength of the knee
flexors is maximal at 160° to 170° (see figure 6.2). The correct term for describing the muscle
action when lifting with free weights is auxotonic, defined as variable tensions caused by
changing velocities and joint angles.
FIGURE 6.2 Strength variations in relation to knee joint angle.
Auxotonic muscle action can be either concentric or eccentric (see figure 6.3a). If the
resistance is less than the force produced by the muscle group, the muscle action is
concentric, allowing the muscle to shorten as it exerts tension to move the bony lever. The
muscle is also capable of exerting tension while lengthening. This is known as eccentric
muscle action, and it typically occurs when the muscles produce a braking force to decelerate
rapidly moving body segments or to resist gravity (e.g., slowly lowering a barbell).
268
00:00 / 00:00
Video 6.2
00:00 / 00:00
Video 6.3
In auxotonic (concentric and eccentric) dynamic exercise, because of the change in

mechanical and physiological advantage as the limb is moved, the muscle group is not
contracting maximally throughout the ROM. Thus, the greatest resistance that can be used
during regular, dynamic exercise is equal to the maximum weight that can be moved at the
weakest point in the ROM.
In an attempt to overcome this deficiency, equipment manufacturers have designed
variable-resistance machines that vary the resistance during the ROM. These machines have
a moving connection (i.e., lever, cam, or pulley) between the resistance and the point of force
application. As the weight is lifted, the mechanical advantage of the machine decreases.
Therefore, more force must be applied to continue moving the resistance. The variable-
resistance mode of exercise attempts to match the force capability of the musculoskeletal
system throughout the ROM. However, many variable-resistance exercise machines fail to
match the strength curves of different muscle groups.
Isokinetic muscle action (see figure 6.3b) is a maximal contraction of a muscle group at a
constant velocity throughout the entire range of joint motion (iso, same; kinetic, motion). The
velocity of contraction is controlled mechanically so that the limb rotates at a set velocity
(e.g., 120°·sec ). Electromechanical devices vary the resistance to match the muscular force
−1
produced at each point in the ROM. Thus, isokinetic exercise machines allow the muscle
269
group to encounter variable but maximal resistances during the movement.
FIGURE 6.3 (a) Concentric and eccentric components of auxotonic muscle action and (b) isokinetic muscle action.
STRENGTH AND MUSCULAR ENDURANCE ASSESSMENT

Measures of static or dynamic strength and endurance are used to establish baseline values
before training, monitor progress during training, and assess the overall effectiveness of
resistance training and exercise rehabilitation programs. Static strength and muscular
endurance are measured using dynamometers. Free weights (barbells and dumbbells), as well
as constant-resistance, variable-resistance, and isokinetic exercise machines, are used to assess
dynamic strength and endurance (see table 6.1). The testing procedures vary depending on
the type of test (i.e., strength or endurance) and equipment.
Table 6.1 Strength Training Modes
Testing mode Equipment Measure*

Static Isometric dynamometers MVC (kg or N)
Dynamic
Free weights (barbells and dumbbells) and exercise machines 1-RM (lb or kg)
Constant resistance
Exercise machines NA
Variable resistance
Isokinetic Isokinetic machines Peak torque (Nm or ft-lb)
*MVC = maximum voluntary contraction; N = newton; NA = not applicable; Nm = newton-meter; ft-lb = foot-pound.
ISOMETRIC MUSCLE TESTING USING DYNAMOMETERS

Isometric strength is measured as the maximum force exerted in a single contraction against
an immovable resistance (i.e., maximum voluntary contraction, or MVC). For many years,
270
spring-loaded dynamometers have been used to measure static strength and endurance of grip
squeezing muscles and leg and back muscles (see figure 6.4). The handgrip dynamometer has
an adjustable handle to fit the size of the hand and measures forces between 0 and 100 kg in 1
kg increments (0-220 lb in 2.2 lb increments). The back and leg dynamometer consists of a
scale that measures forces ranging from 0 to 2,500 lb in 10 lb increments (0-1,134 kg in 4.5
kg increments). As force is applied to the dynamometer, the spring is compressed and moves
the indicator needle a corresponding amount.
FIGURE 6.4 Spring-loaded dynamometers for measuring static strength and endurance: (a) handgrip dynamometer
and (b) back and leg dynamometer.
An alternative to spring-loaded dynamometers, hydraulic dynamometers can also be used

to measure isometric grip strength. These instruments have a sealed hydraulic system that
measures force (in lb or kg) on a gauge dial. The Jamar grip dynamometer is widely used and
has excellent validity and reliability (Roberts et al. 2011). The American Society of Hand
Therapists (ASHT; 1992) recommends the following standardized testing procedures:
271
1. Client is seated.
2. Shoulders are adducted and neutrally rotated. The elbow of the testing arm is flexed
at 90° with the forearm in a neutral position.
3. Wrist is dorsiflexed between 0° and 30°.
4. Administer three trials for each hand and record the mean of three trials.
However, Roberts and colleagues (2011) reported that testing protocols for assessing grip
strength vary widely. Therefore, they proposed a more detailed testing protocol based on the
ASHT guidelines. This revised protocol standardizes leg and forearm position,
encouragement and assessor training, and summary measures (i.e., use the best score from six
trials). Norms for the Jamar grip dynamometer are available for women and men, ages 20 to
80+ yr (Bohannon et al. 2006; Peters et al. 2011).
The Jamar is widely regarded as the gold standard for handgrip dynamometers among
clinicians. However, the resolution of the Jamar is too large to detect small changes in
strength, and it may not be appropriate for individuals with a weak MVC. Hogrel (2015)
recently reported on a dynamometer called the Myogrip with a 10 g resolution and an
accuracy of 50 g. This dynamometer is recommended for very weak clients, and Hogrel
provided norms for males and females aged 5 to 80 yr.
Grip Strength Testing Procedures

Before using the handgrip dynamometer, adjust the handgrip size to a position that is
comfortable for the individual. Alternatively, you can measure the hand width with a caliper
and use this value to set the optimum grip size (Montoye and Faulkner 1964). The individual
stands erect, with the arm and forearm positioned as follows (Fess 1992): shoulder adducted
and neutrally rotated, elbow flexed at 90°, forearm in the neutral position, and wrist in slight
extension (0° to 30°). For some test protocols, however, the client must keep the arm straight
and slightly abducted for measurement of the grip strength of each hand (Canadian Society
for Exercise Physiology 2003). The individual squeezes the dynamometer as hard as possible
using one brief maximal contraction and no extraneous body movement. Administer three
trials for each hand, allowing a 1 min rest between trials, and use the best score as the client’s
static strength.
272
00:00 / 00:00
Video 6.4
Grip Endurance Testing Procedures

Once the grip size is adjusted, instruct the client to squeeze the handle as hard as possible and
to continue squeezing for 1 min. Record the initial force and the final force exerted at the end
of 1 min. The greater the endurance, the lower the rate and degree of decline in force. The
relative endurance score is the final force divided by the initial force times 100.
00:00 / 00:00
Video 6.5
Alternatively, you can assess static grip endurance by having your client exert a submaximal
force, which is a given percentage of the individual’s maximum voluntary contraction (MVC)
strength (e.g., 50% MVC). The relative endurance score is the time this force level is
maintained. During the test, the client must watch the dial of the dynamometer and adjust
the amount of force exerted as necessary in order to maintain the appropriate submaximal
force level.
Leg Strength Testing Procedures

Using the back and leg dynamometer, the individual stands on the platform with trunk erect
and the knees flexed to an angle of 130° to 140°. The client holds the hand bar using a
pronated grip and positions it across the thighs by adjusting the length of the chain (see
figure 6.4b). If a belt is available, attach it to each end of the hand bar after positioning the
belt around the client’s hips. The belt helps stabilize the bar and reduces the stress placed on
273
the hands during the leg lift. Without using the back, the client slowly exerts as much force as
possible while extending the knees. The maximum indicator needle remains at the peak force
achieved. Administer two or three trials with a 1 min rest interval. Divide the maximum score
(in pounds) by 2.2 to convert it to kilograms.
Back Strength Testing Procedures

Using the back and leg dynamometer, the individual stands on the platform with the knees
fully extended and the head and trunk erect. The client grasps the hand bar using a pronated
grip with the right hand and a supinated grip with the left. Position the hand bar across the
client’s thighs. Without leaning backward, the client pulls the hand bar straight upward using
the back muscles. Instruct the client to roll the shoulders backward during the pull, to avoid
flexing the trunk, and to keep the head and trunk erect during the test. Administer two trials
with a 1 min rest between the trials. Divide the maximum score (in pounds) by 2.2 to convert
it to kilograms.
Static Strength Norms for Spring-Loaded Dynamometers

Grip strength norms for each hand are presented in table 6.2. You can also use norms
developed for men and women to assess your clients’ static strength for each dynamometric
test item (see table 6.2). Calculate the total strength score by adding the right grip, left grip,
leg strength, and back strength scores. Before doing this, convert the leg and back strength
scores (measured in pounds) to kilograms. To calculate the relative strength score, divide the
total strength score by body mass (expressed in kilograms).
274
ISOMETRIC MUSCLE TESTING USING DIGITAL HANDHELD
DYNAMOMETRY
Handheld dynamometry is a convenient method for measuring the isometric strength of the
upper and lower body musculature. Compared against isokinetic testing (Kin-Com, Biodex,
and Cybex), handheld dynamometry has moderate to good validity (Stark et al. 2011) and
excellent reliability for most muscle groups (Lu et al. 2011). You can use handheld
dynamometers that provide a digital display of force production to assess the isometric
strength of 11 muscle groups (see figure 6.5, a and b). This handheld dynamometer digitally
displays force measurements up to a maximum of 1334 newtons (300 lb in 0.1 lb increments).
For this type of testing, place the dynamometer on the limb and hold it stationary while the
client exerts maximum force against it. Administer two trials and use either the average or
best score for each muscle group. Appendix C.1 describes standardized test protocols for the
11 muscle groups. Performance norms for adults (20-79 yr) and children (4-16 yr) are
available (see Andrews, Thomas, and Bohannon 1996; Beenakker et al. 2001; Bohannon
1997; van den Beld et al. 2006).
275
FIGURE 6.5 (a) Handheld dynamometer for measuring isometric strength and (b) the hand being tested.
Courtesy of Hoggan Scientific, LLC.
ISOMETRIC MUSCLE TESTING USING CLINICAL METHODS

Several clinical tests have been developed to measure the isometric endurance of core muscles
used to stabilize the spine. Two of the most popular tests are the Kraus-Weber test for the
trunk flexors and the Sorensen test for the trunk extensors (Biering-Sorensen 1984; Kraus
1970). For these tests, clients must maintain their trunk and lower extremities in a certain
position for as long as possible. Over the years, the tests have been modified to reduce the risk
of low back pain or injury resulting from hyperextension of the lumbar spine during testing
(Ito et al. 1996; McGill, Childs, and Liebenson 1999; Reiman et al. 2010).
To assess the isometric endurance of the trunk flexors, you can use the V-sit test. For this
test, the trunk is reclined 60° above the horizontal plane, the knees and hips are flexed 90°,
and the feet are stabilized. A wooden triangular supporting wedge is positioned behind the
client to ensure that the torso is reclined to 60°. To start the test, the supporting wedge is
moved away from the client. The client maintains the V-sit position for as long as possible.
Endurance time (in seconds) is measured with a stopwatch (McGill, Childs, and Liebenson
1999). The test-retest reliability of the V-sit test is high (r = .92).
276
For the trunk extensor test, the client lies prone on the bench with the lower body strapped
to the bench at the ankles, knees, and hips. The upper body is extended over the edge of the
bench. The bench height is 25 cm. The arms are folded across the chest and the upper body
is lifted until the trunk is horizontal to the floor. The client maintains this position for as long
as possible. Endurance time (in seconds) is measured with a stopwatch. The test is terminated
when the client’s upper body touches the floor (McGill, Childs, and Liebenson 1999).
In addition to the trunk flexors and extensors, the lateral flexors of the spine are important
for lumbar stabilization. The side bridge test can be used to assess the isometric endurance of
the lateral flexors. For this test, the client elevates the torso from a side-lying position and
supports this position on one elbow and forearm. The upper leg crosses in front of the lower
leg for additional support. The non-weight-bearing arm is held across the chest, with the
hand placed on the opposite shoulder. The client maintains a straight line position with the
hips off the mat for as long as possible. Endurance time (in seconds) is measured with a
stopwatch. The test is terminated when the hips return to the mat.
00:00 / 00:00
Video 6.6
One disadvantage of the side bridge test is that some clients terminate the test because of
upper extremity fatigue or pain. To avoid this shortcoming, Greene and colleagues (2012)
developed a novel side-support test with the feet elevated on a 15 cm padded stool. To ensure
that the torso is aligned properly, a horizontal reference rod of the alignment apparatus is
placed on the greater trochanter of the top leg and fixed at this height. The client is
instructed to maintain contact with the reference rod and hold this position for as long as
possible during the test. The test is terminated when contact with the rod is lost for longer
than 2 sec or when the client lowers the hips to the mat. Moderate to high correlations (r =
.59-.75) were reported between this test and the traditional side bridge test, and the test-
retest reliability was good. This modification may be a suitable alternative for clients with
upper extremity pain or weakness.
277
For average endurance times for the trunk flexor, trunk extensor, and trunk lateral flexor
tests for healthy men and women, see the work by McGill, Childs, and Liebenson (1999).
Additionally, they provide average ratios of endurance times, normalized to the trunk
extensor, that may be used to identify muscle endurance imbalances around the torso. Table
6.3 presents percentile norms for time to fatigue for the forearm plank for university students
(Strand et al. 2014).
Table 6.3 Percentile Scores for the Forearm Plank
Percentile Male Female

90th 201 142
80th 157 108
70th 137 95
60th 122 84
50th 110 72
40th 97 63
30th 89 58
20th 79 48
10th 62 35
Note: Score is measured in seconds.
Adapted by permission from S.L. Strand et al., “Norms for an Isometric Muscle Endurance Test,” Journal of Human Kinetics 40 (2014): 93-102.
DYNAMIC MUSCLE TESTING USING CONSTANT-RESISTANCE

AND VARIABLE-RESISTANCE MODES
Although either a constant-resistance (auxotonic muscle action) or a variable-resistance
exercise mode can be used to assess dynamic (concentric and eccentric) muscle strength and
endurance, you will be better served if you use either free weights or constant-resistance
exercise machines.
A major disadvantage of free weights, dumbbells, and constant-resistance exercise
machines, however, is that they measure dynamic strength only at the weakest point in the
ROM. The reason is that the resistance cannot be varied to account for fluctuations in
muscular force caused by the changing mechanical advantage (angle of pull of muscle) and
physiological advantage (length of muscle) of the musculoskeletal system during the
movement.
A major disadvantage of variable-resistance machines is that it is difficult to assess the
client’s maximal force or strength because the resistance is modified by the levers, pulleys, and
cams, causing the movement velocity to vary. Variable-resistance exercise machines,
therefore, have limited usefulness for maximal testing. Still, these types of machines are well
suited for resistance training.
Although free weights and constant-resistance exercise machines are generally
278
recommended for muscular fitness testing, there are advantages and limitations to each of
these modalities. Compared with exercise machines, free weights require more neuromuscular
coordination in order to stabilize body parts and maintain balance during lifting of the barbell
or dumbbell. Although exercise machines may reduce the need for spotting during the test,
these machines limit the individual’s range of joint motion and plane of movement. Also,
some exercise machines have relatively large weight plate increments, so you must attach
smaller weights to the weight stack in order to accurately measure a client’s strength.
Last, some machines cannot accommodate individuals with short limbs; you may need to
use child-sized machines to standardize their starting positions for testing. Clients with long
limbs or large body and limb circumferences (e.g., some bodybuilders or obese clients) also
may have difficulty using standard exercise machines. Body size and weight increments are
less of a problem with free weights.
To overcome some of these limitations, free-motion machines that provide constant and
variable resistance in multiple planes have been developed. These machines have adjustable
seats, lever arms, and cable pulleys that can be set to exercise muscle groups in multiple
planes. They are easy to get in and out of, they can accommodate smaller or larger
individuals, and they have smaller weight increments (5 lb or 2.3 kg) than do older standard
machines (typically 10 lb or 4.5 kg). When using free-motion exercise machines for muscular
fitness testing, take care to adjust the plane of movement and the seat so that you simulate
the starting and ending body positions that were used to develop test norms for older
constant-resistance machines. If you use free-motion machines to monitor the progress of
your clients, make certain that you use the same settings (i.e., seat and plane-of-movement
adjustments) for each test session.
Dynamic Strength Tests

Force plates paired with linear transducers are used to obtain direct (i.e., gold standard)
measures of muscular force and power. Cost of equipment, however, limits the usefulness of
this method beyond the laboratory setting. With improved technology, new devices for
assessing dynamic muscle force and power are being developed and validated against gold
standard measures. Two such devices are the Tendo Weightlifting Analyzer System and the
Myotest accelerometer.
The Tendo system is a linear transducer that can be attached to the end of a barbell.
Measurements of peak velocity, average velocity, peak power, and average power were
evaluated simultaneously by Tendo and an isoinertial dynamometer to assess the reliability
279
and validity of Tendo during bench press and squat exercises. The intra-class correlation
coefficients for reliability were .922 to .988 and for validity were .853 to .989 (Garnacho-
Castano, Lopez-Lastra, and Mate-Munoz 2015). The researchers also noted that the random
errors and biases were low, and they concluded that the Tendo system was reliable and valid
for measuring movement velocity and estimating power during resistance exercises. Similarly,
the Myotest triaxial accelerometer has excellent validity (r = .85-.99) for calculating force,
velocity, and power during dynamic exercise (Casartelli, Muller, and Maffiuletti 2010;
Crewther et al. 2011; Thompson and Bemben 1999). This accelerometer also demonstrated
high concurrent validity and reliability for measuring dynamic strength and power of men and
women performing squat and bench press exercises (Comstock et al. 2011). In light of their
small size, ease of use, and portability, the Tendo and Myotest are practical devices you can
use in the field to evaluate lifting velocity, muscle force, and power.
However, more commonly in field settings, dynamic strength is measured as the one-
repetition maximum (1-RM), which is the maximum weight that can be lifted for one
complete repetition of the movement. The 1-RM strength value is obtained through trial and
error.
Although 1-RM strength tests can be safely administered to individuals of all ages, you
should take precautions to decrease the risk of injury when clients attempt to lift maximal
loads. Be certain that your clients warm up before attempting the lift, and start with a weight
that is below their expected 1-RM. When you administer these tests, you should spot your
clients and closely monitor their lifting technique and breathing. The National Strength and
Conditioning Association (NSCA) outlines guidelines for spotting (see Tips for Spotting
Free Weight Exercise).
The American College of Sports Medicine (2018) recommends the bench press and leg
press (upper plate of constant-resistance exercise machine) for assessing strength of the upper
and lower body, respectively. To determine relative strength, divide the 1-RM values by the
client’s body mass. Norms for men and women are provided in tables 6.4 and 6.5.
280
281
Another test of dynamic strength includes six test items: bench press, arm curl, latissimus
pull, leg press, leg extension, and leg curl. For each exercise, express and evaluate the 1-RM
as a percentage of body mass. For example, if a 120 lb (54.5 kg) woman bench presses 60 lb
(27.2 kg), her ratio of strength to body mass is 0.50 (60 divided by 120), and she scores 3
points for that exercise. Follow this procedure for each exercise, and then add the total points
to determine the individual’s overall strength and fitness category. Strength-to-body-mass
ratios with corresponding point values for college-age men and women are presented in table
6.6.
TIPS FOR SPOTTING FREE WEIGHT EXERCISE
1. The primary role of the spotter is to help protect the client from injury.
2. With the exception of power exercises, free weight exercises performed with the bar moving over the head,
on the back, in front of the shoulders, or passing over the face require one or more spotters (e.g., bench
press, lying triceps extensions, and front squat).
282
3. The spotter should be at least as strong and at least as tall as the client performing the exercise.
4. Overhead exercises and exercises where the bar is placed on the back or in front of the shoulders should be
performed inside a power rack.
5. When spotting over-the-face exercises, use an alternated grip that is narrower than the client’s when
grasping the bar to lift or lower it. Use a supinated grip to spot the bar during the exercise.
6. When spotting heavy loads, establish a stable base of support and a flat-back position.
7. For dumbbell exercises, spot as close to the dumbbell as possible (e.g., for dumbbell flys, spot at the wrists,
not at the elbows).
8. Spotters typically help the client move the barbell or dumbbells to the proper starting position (i.e., liftoff or
moving the bar from the upright supports to client’s hands and extended elbows).
9. Most clients need just enough help to successfully complete a repetition. During 1-RM attempts, however,
the spotter should be prepared to take the bar immediately if the client cannot complete the repetition.
Based on National Strength and Conditioning Association (2016).
00:00 / 00:00
Video 6.7
283
Dynamic Muscle Endurance Tests
You can assess your clients’ dynamic muscle endurance by having them perform as many
repetitions as possible using a weight that is a set percentage of their body weight or
maximum strength (1-RM). Pollock, Wilmore, and Fox (1978) recommend using a weight
that is 70% of the 1-RM value for each exercise. Although norms for this test have not been
established, these authors suggest, on the basis of their testing and research findings, that the
average individual should be able to complete 12 to 15 repetitions.
The YMCA (Golding 2000) recommends using a bench press test to assess dynamic
muscular endurance of the upper body. For this absolute endurance test, use a flat bench and
barbell. The client performs as many repetitions as possible at a set cadence of 30 repetitions
per minute. Use a metronome to establish the exercise cadence. Male clients lift an 80 lb
284
(36.4 kg) barbell, whereas female clients use a 35 lb (15.9 kg) barbell. Terminate the test
when the client is unable to maintain the exercise cadence. Table 6.7 presents norms for this
test.
Alternatively, you can use a test battery consisting of seven items to assess dynamic
muscular endurance. Select the weight to be lifted using a set percentage of the individual’s
body mass. The client lifts this weight up to a maximum of 15 repetitions. Table 6.8 provides
percentages for each test item, as well as the scoring system and norms for college-age men
and women.
285
Steps for 1-RM Maximum Testing
The following basic steps are recommended for 1-RM testing.
Have your client warm up by completing 5 to 10 repetitions of the exercise at 40%

to 60% of the estimated 1-RM.
Have the client rest for 1 min. This is followed by 3 to 5 repetitions of the exercise
at 60% to 80% of the estimated 1-RM.
Have the client rest for 2 min and attempt the 1-RM lift. If successful, increase the
weight conservatively. Both the ACSM (2018) and the NSCA (2016) recommend
increases of 5% to 10% for upper body exercise and 10% to 20% for lower body
exercise. The client should rest 2 to 4 min before attempting the next weight
increment. Follow this procedure until the client fails to complete the lift. The 1-
RM typically is achieved within three to five trials.
Record the 1-RM value as the maximum weight lifted for the last successful trial.
286
00:00 / 00:00
Video 6.8
DYNAMIC ISOKINETIC MUSCLE TESTING

Isokinetic dynamometers provide an accurate and reliable assessment of strength, endurance,
and power of muscle groups (see figure 6.6). The speed of limb movement is kept at a
constant preselected velocity. Any increase in muscular force produces an increased resistance
rather than increased acceleration of the limb. Thus, fluctuations in muscular force
throughout the ROM are matched by an equal counterforce, or accommodating resistance.
FIGURE 6.6 Cybex II isokinetic dynamometer.
287
Isokinetic dynamometers measure muscular torque production at speeds of 0° to 300°·sec . −1
From the recorded output, you can evaluate peak torque, total work, and power. Some less
expensive isokinetic dynamometers lack this recording capability, but these are suitable for
training and rehabilitation exercise. Table 6.9 summarizes isokinetic test protocols for
assessing strength, endurance, and power.
00:00 / 00:00
Video 6.9
CALISTHENIC-TYPE STRENGTH AND MUSCULAR ENDURANCE

TESTS
In certain field situations, you may not have access to dynamometers, free weights, or exercise
machines to assess muscular fitness. As an alternative, you may use calisthenic-type strength
and endurance tests to assess your clients’ strength and muscular endurance.
Calisthenic-Type Strength Tests

You can measure dynamic strength using calisthenic-type exercises by determining the
maximum weight, in excess of body mass, that an individual can lift for one repetition of the
movement. Because strength is related to the size and body mass of the individual, Johnson
and Nelson (1986) recommend using relative strength scores. For each test, attach weight
plates (2.5, 5, 10, and 25 lb or 1, 2.3, 4.5, and 11.4 kg) to the individual. The relative strength
288
score is the amount of additional weight divided by the body mass. For example, if a 150 lb
(68.2 kg) man successfully performs one pull-up with a 30 lb (13.6 kg) weight attached to the
waist belt, his relative strength score is 0.20 (30 lb / 150 lb). Test protocols and performance
norms for the pull-up, sit-up, and bench squat, as well as dip strength, are described
elsewhere (Johnson and Nelson 1986).
Calisthenic-Type Endurance Tests

You can assess dynamic muscular endurance by measuring the maximum number of
repetitions of various calisthenic exercises. Pull-up, push-up, and trunk curl (partial curl-up)
tests are widely used for this purpose.
Pull-Up Tests
Pull-up tests may be used to measure the dynamic endurance of the arm and shoulder girdle
muscles for individuals who are able to lift their body weight. For clients who are unable to
perform even one pull-up, you can use modified pull-up and flexed-arm hang tests.
Baumgartner (1978) developed a modified pull-up that uses an incline board (at a 30° angle
to the floor) with a pull-up bar at the top. A modified scooter board slides along garage door
tracks attached to the incline board (Baumgartner et al. 1984). While lying prone on the
scooter board, the client pulls up until the chin is over the pull-up bar. Detailed testing
procedures, equipment designs, and performance norms for children, adolescents, and
college-age women and men are available (see Baumgartner 1978; Baumgartner et al. 1984).
00:00 / 00:00
Video 6.10
The flexed-arm hang test is scored as the amount of time the client maintains the flexed-
arm hanging position (i.e., supporting the body weight with the chin over the pull-up bar).
Traditionally, a pronated grip on the pull-up bar is used (i.e., overgrip); however, variations of
the flexed-arm hang test include using a supinated grip (i.e., undergrip). Although the flexed-
arm hang tests isometric endurance of the arm and shoulder girdle musculature, it has been
used for more than three decades as a measure of upper body strength. One study of college
289
women showed that flexed-arm hang time relates more to relative strength (1-RM / body
mass) than to absolute strength (1-RM) or to dynamic muscle endurance (measured as
repetitions to failure at 70% 1-RM) (Clemons et al. 2004).
Push-Up Tests
The ACSM (2018) and Canadian Society for Exercise Physiology (CSEP; 2013) recommend
using a push-up test to assess endurance of the upper body musculature. To start, clients lie
prone on the mat with their legs together and hands pointing forward under the shoulders.
Clients push up from the mat by fully extending the elbows and by using either the toes (for
males) or the knees (for females) as the pivot point. The upper body should be kept in a
straight line and the head should be kept up. The client returns to the down position,
touching the chin to the mat. The stomach and thighs should not touch the mat. Clients
perform as many consecutive repetitions (no rest between repetitions) as possible; there is no
time limit. Repetitions not meeting the stated criteria should not be counted. Terminate the
test when the client strains forcibly or is unable to maintain proper push-up technique over
two consecutive repetitions; record the total number of correctly executed repetitions. Table
6.10 provides age-gender norms for the push-up test.
00:00 / 00:00
Video 6.11
290
Trunk Curl Tests
Traditionally, abdominal muscle endurance tests (e.g., trunk curls, partial curl-ups, and sit-
ups) were commonly included in health-related fitness test batteries to identify clients at risk
for low back pain or injury because of weak abdominal muscles. However, the validity of
these tests as measures of abdominal strength or endurance and as predictors of low back pain
is questionable. Most trunk curl tests are poorly related to abdominal strength (r = −.21-.36)
x,y
and only moderately related to abdominal endurance (r = .46-.50) (Knudson 2001; Knudson
x,y
and Johnston 1995). Also, Jackson and colleagues (1998) found no relationship between sit-
up test scores and incidence of low back pain. For these reasons, the current ACSM
guidelines (2018) no longer include the curl-up test as a measure of muscular endurance.
Core Stability Tests

The Sahrmann Core Stability Test (Sahrmann 2002) is an excellent tool for assessing the
core stability of your clients. Core stability is the ability of the trunk musculature to stabilize
the spine and maintain optimal spinal alignment during movement. The transversus
abdominis and multifidus are two deep-seated abdominal and spinal muscles. Weakness in
these muscles may be linked to low back pain and injury.
The Sahrmann Core Stability Test (see table 6.11) may be used to assess and grade your
clients’ levels of core stability. The test consists of series of movements that are performed
while maintaining lumbopelvic stability in a neutral position. You may use a blood pressure
cuff under the client’s back to determine if there is any movement of the spine by watching
291
for pressure changes during the movement. Alternatively, you can have the client place the
hands beneath the lower back to feel for any changes in pressure during the movement.
Table 6.11 Sahrmann Core Stability Test

Begin in supine, hook-lying position while hollowing abdomen.
Level 1 Slowly raise 1 leg to 100° of hip flexion with comfortable knee flexion.
Bring opposite leg up to same position.
From hip-flexed position, slowly lower 1 leg until heel contacts ground.
Level 2 Slide out leg to fully extend the knee.
Return to starting flexed position.
From hip-flexed position, slowly lower 1 leg until heel is 12 cm above ground.
From hip-flexed position, slowly lower both legs until heel contacts ground.
Level 4 Slide out legs to fully extend the knee.
From hip-flexed position, slowly lower both legs until heels are 12 cm above ground.
MUSCULAR POWER ASSESSMENT

A baseline measure of muscular power, followed by subsequent tests, can be used to monitor
the progress of a client as well as the effectiveness of a training program for improving this
muscular fitness variable. Power is the rate of work and is calculated as work divided by time.
The vertical jump and standing long jump, or broad jump, are often used as field measures of
muscular power, even though time is not typically measured during these assessments.
VERTICAL JUMP
The vertical jump test can be performed without specialized equipment. A jumping client
with colored chalk on the fingers touches a point as high as possible on a wall; use a
measuring tape to determine jump height. Using a commercially available device, such as the
Vertec, makes measurement easier. The Vertec resembles a volleyball standard with colored,
movable horizontal plastic vanes spaced at 0.5 in. increments. For ease of rapid measurement,
red vanes are spaced 6 in. apart, blue vanes are at 1 in. increments, and white vanes denote a
0.5 in. change. There are several variations of the vertical jump, such as the drop-step jump
and the static jump, but the countermovement jump (CMJ) is the most commonly used
version for assessing muscular power. The CMJ using the Vertec is explained here (see Steps
for Vertical Jump Testing Using the Vertec), and norms for the CMJ are available in table
6.12.
292
00:00 / 00:00
Video 6.12
Alternatively, a switch mat or contact mat can be used to assess vertical jump rather than
the Vertec. The client simply stands on the mat and performs the CMJ as previously
described, landing back on the mat. The contact mat automatically measures the flight time
and the jump height based on the amount of time the client was in the air. Consequently, it is
293
important to instruct the jumper to avoid tucking the knees while airborne. Additionally,
accelerometers, such as the Myotest previously described in the Dynamic Strength Tests
section, can be used to measure vertical jump.
Researchers have compared the various technologies for measuring the CMJ. Nuzzo,
Anning, and Scharfenberg (2011) compared the reliability of the Myotest, Vertec, and Just
Jump, a contact mat. The best intrasession and intersession reliability occurred with the
Myotest. Intrasession reliability was .91 (intraclass correlation) and .95 for females and males,
respectively, and intersession reliability was .88 to .92. They noted that better jumpers tended
to have greater fluctuations in jump scores across testing sessions, and this was more
pronounced with the Vertec. Validity of the CMJ varies by the technology used. Leard and
colleagues (2007) reported that both the Vertec (r = .906) and Just Jump contact mat (r =
.967) were highly correlated with motion analysis, but the mean jump heights were
significantly less for the Vertec (0.3937 m) compared with the contact mat (0.4420 m) and
the motion analysis (0.4369 m). One research team reported that both the contact mat and
Vertec recorded significantly lower jump heights than their criterion method of a laboratory
force plate (Buckthorpe, Morris, and Folland 2012), while another research group found that
the methods that rely on flight time (contact mat and accelerometer) systematically record
lower values than the Vertec (Magnusdottir, Porgilsson, and Karlsson 2014). In yet another
comparison study, Whitmer and colleagues (2015) reported that a jump mat, but not a force
plate, was in agreement with the Vertec. When taken together, these studies suggest that the
CMJ is a reliable measure of muscular power, but the same testing device should be used
when comparing subsequent tests against baseline measures.
Typically, the client’s jump height is sufficient information for most examiners. However,
prediction equations are available to convert jump height into peak power. Normative data for
peak power from the CMJ are available in table 6.12. Sayers and colleagues (1999) derived
the following equation to estimate peak power from a CMJ:
peak power (W) = 51.9 × CMJ (cm) + 48.9 × body mass (kg) – 2,007
Unfortunately, there is considerable variability in this equation (R = .78; SEE = 561.5 W).
2
Consequently, the researchers recommend using a static jump with a pause at the bottom of
the bent-knee position before jumping, as this reduces the variability observed in CMJ. The
static jump equation has less variability (R = .88; SEE = 372.9 W) and is as follows:
2
peak power (W) = 60.7 × static jump (cm) + 45.3 × body mass (kg) – 2,055
294
Steps for Vertical Jump Testing Using the Vertec
The following basic steps are recommended for CMJ testing.
1. The examiner adjusts the height of the movable vanes to be within the client’s
reach height, with the bottom vane at a known height.
2. The client stands flat-footed and reaches as high as possible with the dominant
hand to push the vanes forward. This is recorded as the reach height.
3. The examiner readjusts the height of the vane stack to be within the estimated
jump height of the client, again with the bottom vane at a known height.
4. Without a preparatory step or a step backward, the client performs a ballistic
countermovement by quickly flexing the hips and knees and swinging the arms
backward before exploding upward. During the jump, the dominant arm should
reach as high as possible and swat or tap the vanes at the maximal height of reach.
5. The examiner records the height of the highest vane that was moved. This is the
jump height. The client’s vertical jump is the difference between the jump height
and the reach height.
6. The best of three trials to the nearest 0.5 in. (each vane) is used.
STANDING LONG JUMP

An alternative power test to the vertical jump is the standing long jump. For this test, the
client places both feet behind a taped line. Without any preparatory steps or drop-steps, the
client swings the arms backward and then propels himself forward, jumping horizontally as
far as possible. The client must stick the landing; the trial is discarded if the client falls
forward. The jump distance is measured from the starting line to the back of the heel of the
foot that landed closest to the starting line. The best of three trials is used. The standing long
jump is moderately correlated with other power tests, such as the vertical jump and peak
torque knee extension, as well as track and field performances in the sprint and jumping
events (Almuzaini and Fleck 2008).
SOURCES OF MEASUREMENT ERROR IN MUSCULAR FITNESS

TESTING
The validity and reliability of strength, muscular endurance, and power measures are affected
by client factors, equipment, technician skill, and environmental factors. You must control
295
each of these factors to ensure the accuracy and precision of muscular fitness scores.
CLIENT FACTORS
Before measuring a client’s strength, muscular endurance, or power, familiarize the individual
with the equipment and testing procedures. Clients with limited or no prior weightlifting
experience need time to practice each lift to control for the effects of learning on
performance. You should give even experienced weightlifters time to practice so you can
correct any improper lifting techniques prior to testing.
Muscular fitness tests require clients to give a maximal effort. Therefore, clients should get
adequate sleep before performing these tests, and you should restrict the use of drugs and
medications that may adversely affect their performance. It is also important that you
motivate your clients during testing by encouraging them to do their best and giving them
positive feedback after each trial. Adequate rest between trials is necessary in order for clients
to obtain scores that truly represent their maximal effort.
EQUIPMENT
The design of testing equipment may also affect test scores. Most of the dynamic strength
and muscular endurance protocols and norms presented in this chapter were developed using
constant-resistance exercise machines. Therefore, you should not use free weights or variable-
resistance machines when administering these tests. It is also important to calibrate the
equipment and make sure it is in proper working condition prior to testing. Inspection and
maintenance of equipment will increase accuracy and decrease risk of accidents. When
selecting exercise machines, make sure the equipment can be properly adjusted to
accommodate varying limb lengths and body sizes. Use equipment specifically designed for
smaller individuals when testing children and smaller adults.
TECHNICIAN SKILL
All strength testing should be done by qualified, trained technicians who are knowledgeable
about proper lifting and spotting techniques and familiar with standardized testing
procedures. Explain and demonstrate the proper lifting technique and then correct any
performance errors you see as the client practices. During the test, clients may inadvertently
cheat by moving extraneous body parts to help lift the weight. Carefully observe the client
during the test, focusing on the grip used and the starting position. The type of grip
(pronated vs. supinated) has a substantial effect on performance. For example, using a narrow
grip instead of a wide grip during a lat pull-down exercise increases the amount of weight
296
that can be lifted. Likewise, the client will be able to produce more force during an arm curl
using a supinated grip compared with a pronated grip.
The client’s starting position may also affect strength scores. During the bench press, for
example, eccentric movement (i.e., lowering the weight) prior to the concentric phase of the
lift will increase maximal muscular force because of the stretch reflex and the tendency for the
client to bounce the weight off the chest. To obtain accurate assessments of the client’s
strength, it is important to standardize starting positions and to follow all testing procedures
carefully.
ENVIRONMENTAL FACTORS
Factors such as room temperature and humidity may affect test scores. The room temperature
should be 70 to 74 °F (21 to 23 °C) to maximize subject comfort during testing. Ideally, you
want a quiet, clean environment with limited distractions (not an overcrowded weight room,
for example). When assessing improvements due to training, remember to pretest and
posttest your clients at the same time of day to control for diurnal variations in strength.
ADDITIONAL CONSIDERATIONS FOR MUSCULAR FITNESS

TESTING
This section addresses a number of additional factors and questions regarding the testing and
evaluation of muscular fitness.
How can I estimate my clients’ 1-RM?

Although 1-RM tests can be safely administered to clients of all ages, sometimes it is
preferable to estimate the 1-RM. One-repetition maximum testing can be time consuming,
especially for a large group of clients. Some clients may take 15 min to complete a 1-RM test
(multiple attempts and rests). Also, the 1-RM may be underestimated for clients with little or
no exercise experience because they are unaccustomed to or may be apprehensive about lifting
heavy loads. In these cases, it may be more suitable and practical to estimate 1-RM.
You can estimate the 1-RM of your clients from submaximal muscle endurance tests.
Research demonstrates a strong relationship between muscle endurance (measured as the
number of repetitions to fatigue) and the percentage of 1-RM lifted (Brzycki 1993).
Muscular strength (1-RM) therefore can be predicted from muscular endurance tests with a
fair degree of accuracy (Ball and Rose 1991; Braith et al. 1993; Desgorces et al. 2010;
Invergo, Ball, and Looney 1991; Kuramoto and Payne 1995; Mayhew et al. 1992). The most
frequently used prediction equations assume an inverse linear relationship between the %1-
297
RM and number of repetitions performed, and they are typically based on the number of
repetitions to fatigue in one set. For example, the Brzycki (1993) equation can be used to
estimate 1-RM of men. This equation can be used for any combination of submaximal
weights and repetitions to fatigue providing the repetitions to fatigue do not exceed 10.
1-RM = weight lifted (lb) / [1.0278 − (reps to fatigue × 0.0278)]
For example, if your client completes seven repetitions to fatigue during a bench press
exercise using a 100 lb (45 kg) barbell, the estimated 1-RM is calculated as follows:
1-RM = 100 lb / [1.0278 − (7 reps × 0.0278)] = 120 lb (54.5 kg)
Brzycki (2000) also suggested using a prediction equation based on the number of
repetitions to fatigue obtained in two submaximal sets to estimate 1-RM. Any two
submaximal sets can be used as long as the number of reps to fatigue does not exceed 10. For
example, you can determine the client’s 5-RM value, or the maximum weight that can be
lifted for 5 reps (e.g., 120 lb [55 kg] for 5 reps), and the 10-RM value (e.g., 80 lb [36 kg] for
10 reps) and use them in the following equation:
predicted 1-RM = [(SM − SM ) / (REP − REP )] × (REP − 1) + SM

1 2 2 1 1 1
= [(120 − 80) / (10 − 5)] × (5 − 1) + 120
= 152 lb
In this equation, SM and REP represent the heavier submaximal weight (120 lb) and the
1 1
respective number of repetitions (5) completed, and SM and REP correspond to the lighter
2 2
submaximal weight (80 lb) and the respective number of repetitions (10) performed.
Alternatively, you can use the average number of repetitions corresponding to various
percentages of 1-RM (see table 6.13). This technique and the Brzycki (1993) equation yield
similar 1-RM estimates for lifts between 2-RM and 10-RM. To estimate the 1-RM from 2-
RM to 10-RM values, divide the weight lifted by the respective %1-RM, expressed as a
decimal (%1-RM / 100). For example, a client lifting 100 lb (45.4 kg) for 8 repetitions would
have an estimated 1-RM of 125 lb (56.7 kg):
1-RM = 100 lb / 0.80 or 125 lb (56.7 kg)
Also, gender-specific prediction equations can be used to estimate upper body strength
(i.e., the 1-RM bench press) from the YMCA bench press test (see table 6.7) in younger
clients (22-36 yr) (Kim, Mayhew, and Peterson 2002):
298
For Men
predicted 1-RM (kg) = (1.55 × YMCA test repetitions) + 37.9
r = .87; SEE = 8.0 kg
For Women
predicted 1-RM (kg) = (0.31 × YMCA test repetitions) + 19.2
r = .87; SEE = 3.2 kg
For example, if a 25 yr old female’s YMCA bench press test score is 30 reps, her estimated
1-RM bench press strength is calculated as follows:
predicted 1-RM (kg) = (0.31 × 30 reps) + 19.2 = 28.5 kg (62.8 lb)
Additionally, Desgorces and colleagues (2010) developed equations to predict %1-RM

from the number of repetitions to failure for bench press exercise. They noted that predictive
accuracy was improved using a model based on a nonlinear relationship (curve fitting two-
function exponential decay) between %1-RM values and number of repetitions to failure,
with a reduced number of repetitions (1-12 reps), performed at relatively high intensities
(75%-85% 1-RM), yielding the best prediction. They developed two specific %1-RM
prediction equations for male athletes—one for high-strength athletes (powerlifters and
racquetball players) and another for high-endurance athletes (swimmers and rowers). A third
equation based on the total population combined was also developed (r = .97; SEE = 3.4). 2
%1-RM = 79.3412 exp(-0.0302 × reps to failure) + 20.7706
r = .98 and SEE = 3.4 % 1-RM
Mayhew and colleagues (2011) reported that the Desgorces prediction equation accurately
(<5% error) predicted strength changes (%1-RM bench press) of untrained men and women
following a 12 wk periodization resistance training program.
Table 6.13 Average Number of Repetitions and %1-RM Values
Repetitions %1-RM*
1 100
2 95
3 93
4 90
5 87
6 85
299
7 83
8 80
9 77
10 75
12 70
14 65
15-20 60
*These values may vary slightly for different muscle groups and ages.
Adapted by permission from J.M. Sheppard and N.T. Triplett, Program Design for Resistance Training. In Essentials of Strength Training and Conditioning, 4th ed., edited by G.G. Haff and N.T. Triplett for the National
Strength and Conditioning Association (Champaign, IL: Human Kinetics, 2016), 452.
How is muscle balance assessed?

Muscle strength is important for joint stability; however, a strength imbalance between
opposing muscle groups (e.g., quadriceps femoris and hamstrings) may compromise joint
stability and increase the risk of musculoskeletal injury. For this reason, experts recommend
maintaining a balance in strength between agonist and antagonist muscle groups.
Muscle balance ratios differ among muscle groups and are affected by the force-velocity of
muscle groups at specific joints. To control limb velocity during muscle balance testing, you
will do best to use isokinetic dynamometers. In field settings, however, you may obtain a
crude index of muscle balance by comparing 1-RM values of muscle groups. Based on
isokinetic tests of peak torque production at slow speeds (30°-60°·sec ), the muscle balance −1
ratios shown in table 6.14 are recommended for agonist and antagonist muscle groups.
Table 6.14 Muscle Balance Ratios
Muscle groups Muscle balance ratio

Hip extensors and flexors 1:1
Elbow extensors and flexors 1:1
Trunk extensors and flexors 1:1
Ankle inverters and evertors 1:1
Shoulder flexors and extensors 2:3
Knee extensors and flexors 3:2
Shoulder internal and external rotators 3:2
Ankle plantar flexors and dorsiflexors 3:1
Muscle balance between other pairs of muscle groups is also important. The difference in
strength between contralateral (right vs. left sides) muscle groups should be no more than
15%, and the strength-to-body-mass (BM) ratio of the upper body (bench press 1-RM /
BM) should be at least 40% of lower body relative strength (leg press 1-RM / BM). If you
detect imbalances, prescribe additional exercises for the weaker muscle groups.
Can strength or muscular endurance be assessed by a single test?
Strength and endurance are specific to the muscle group, the type of muscular contraction
(static or dynamic), the speed of muscular contraction (slow or fast), and the joint angle being
300
tested (static contraction). There is no single test to evaluate total body muscle strength or
endurance. Minimally, the strength test battery should include a measure of abdominal, lower
extremity, and upper extremity strength. In addition, if the individual trains dynamically,
select a dynamic, not static, test to assess strength or endurance levels before and after
training.
You should also use caution in selecting test items to measure muscle strength. The
maximum number of sit-ups, pull-ups, or push-ups that an individual can perform measures
muscular endurance, yet maximum-repetition tests have been included in some strength test
batteries. This may lead to misinterpretation of the test results.
Should absolute or relative measures be used to classify a client’s muscle strength?

A direct relationship exists between body size and muscle strength. Generally, larger
individuals have more muscle mass, and therefore greater strength, than smaller individuals
with less muscle mass.
Because strength directly relates to the body mass and lean body mass of the individual,
you should express the test results in relative terms (e.g., 1-RM / BM). This is especially true
in comparing a client’s score to group norms and in comparing groups or individuals differing
in body size and composition (e.g., men vs. women or older vs. younger adults).
Use relative strength scores for assessing individual improvement from training. As a result
of resistance training, some individuals may gain body weight while others may lose weight,
especially if they are using resistance training as part of a program for weight gain or loss. If
you compare the client’s relative strength scores (from pre- and posttest training), you will be
able to evaluate the change in strength that is independent of a change in body weight.
How can the influence of strength on muscular endurance be controlled?
Performance on some endurance tests (e.g., pull-ups and push-ups) is highly dependent on
the strength of the individual. It is recommended that you use relative endurance tests that
are proportional to the individual’s body mass or maximum strength to assess muscle
endurance. You cannot use a pull-up test to assess muscular endurance if the individual is not
strong enough to lift the body weight for one repetition of that exercise. Therefore, select a
modified or submaximal (percentage of body weight) endurance test.
Are there comprehensive norms that can be used to classify muscular fitness levels of
diverse population subgroups?
Strength norms for women (20-82 yr) were developed for the bench press (1-RM), leg press
301
(1-RM), static grip strength, and push-up tests (Brown and Miller 1998). These norms are
based on data obtained from 304 independent-living women attending wellness classes at a
university medical center. However, there is a lack of up-to-date endurance norms for men
and strength and endurance norms for older men. New norms need to be established for this
population in particular.
MUSCULAR FITNESS TESTING OF OLDER ADULTS

It is important to accurately assess the muscular fitness of older individuals. Adequate
strength in the upper and lower body lessens risk of falls and of injuries associated with
falling, reduces age-related loss of bone mineral, maintains lean body tissue, improves glucose
utilization, and prevents obesity. Moderate to high levels of muscular strength enable older
adults to maintain their functional independence and to perform activities of daily living as
well as fitness and recreational activities. This section addresses tests you can use to assess the
muscular strength and physical performance of older clients.
STRENGTH TESTING OF OLDER ADULTS

Experts agree that it is safe to administer 1-RM tests to older adults if proper procedures (see
Steps for 1-RM Maximum Testing earlier in the chapter) are followed (Shaw, McCully, and
Posner 1995). The risk of injury is low, with only 2.4% of older adults (55-80 yr)
experiencing an injury during 1-RM assessment (Salem, Wang, and Sigward 2002; Shaw,
McCully, and Posner 1995). Salem and colleagues (2002) suggested that at least one
pretesting session (i.e., a practice 1-RM test session) is necessary to establish stable baseline
1-RM values for older adults.
Alternatively, you can estimate the 1-RM of older clients from submaximal muscular
endurance tests. Kuramoto and Payne (1995) developed prediction equations to estimate 1-
RM from a submaximal endurance test in middle-aged and older women. For this endurance
protocol, the client completes as many repetitions as possible using a weight equivalent to
45% of her body mass. To estimate 1-RM, use the following equations:
Middle-Aged Women (40-50 yr)
1-RM = (1.06 × weight lifted in kg) + (0.58 × reps) − (0.20 × age) − 3.41
r = .94; SEE = 1.85 kg
Older Women (60-70 yr)
302
1-RM = (0.92 × weight lifted in kg) + (0.79 × reps) − 3.73
r = .90; SEE = 2.04 kg
Knutzen, Brilla, and Caine (1999) tested the validity of selected 1-RM prediction
equations for older women (mean age = 69 yr) and men (mean age = 73 yr). On average, these
prediction equations underestimated the actual 1-RM for 11 different constant-resistance
machine exercises. For exercises such as the biceps curl, the lateral row, the bench press, and
ankle plantar and dorsiflexion, the predicted values were on average 0.5 to 3.0 kg less than the
actual 1-RM values. However, larger differences (as much as a 10 kg underestimation) were
noted for the triceps press-down, the supine leg press, and the hip flexion, extension,
abduction, and adduction exercises. The Brzycki (1993) equation gave a closer estimate of
actual 1-RMs for hip exercises (extension, flexion, adduction, and abduction) than the other
equations evaluated; the Wathen (1994) equation, 1-RM = 100 × weight lifted / [48.8 +
53.8 ], most closely estimates 1-RM for all upper body exercises, the leg press, and
−0.075 (reps)
dorsiflexion exercises. The authors concluded that the actual and predicted 1-RM are close
enough to warrant using these prediction equations to determine resistance training
intensities (i.e., %1-RMs) for older adults. In addition, given that the predicted 1-RM values
were consistently less than the actual 1-RM values, the resistance training intensity will not
likely exceed the prescribed value.
FUNCTIONAL FITNESS TESTING OF OLDER ADULTS

Functional fitness is the ability to perform everyday activities safely and independently
without undue fatigue (Rikli and Jones 2013). Functional fitness is multidimensional,
requiring aerobic endurance, flexibility, balance, agility, and muscular strength. Older
individuals with moderate to high functional fitness have the ability to perform normal
activities of daily living (ADLs) such as getting out of a chair or car, climbing stairs,
shopping, dressing, and bathing; these individuals are able to stay strong, active, and
independent as they age.
The Senior Fitness Test (Rikli and Jones 2013) assesses the physical capacity and
functional fitness of older adults (60-94 yr). This test battery includes two measures of
muscular strength: (1) an arm (biceps) curl for upper body strength (figure 6.7) and (2) a 30
sec chair stand for lower body strength (figure 6.8). The ACSM (2018) recommends using
these two test items to safely assess the muscular fitness of most older adults.
303
FIGURE 6.7 Arm curl test for older adults.
ARM CURL TEST

Purpose: Assess upper body strength.
Application: Measure ability to perform ADLs such as lifting and carrying groceries,
grandchildren, and pets.
Equipment: You will need a folding or straight-back chair, a stopwatch, and a 5 lb (2.27
kg) dumbbell for women or an 8 lb (3.63 kg) dumbbell for men.
Test procedures: The client sits in the chair with the back straight and the feet flat on the
floor. The client holds the dumbbell in the dominant hand using a neutral
(handshake) grip and lets this arm hang down at the side (see figure 6.7). For each
repetition, the client curls the weight by fully flexing the elbow while supinating the
forearm and returns the weight to the starting position by fully extending the elbow
and pronating the forearm. Instruct your client to keep the upper arm in contact with
the trunk during the test. Have your client perform as many repetitions as possible in
30 sec. Administer one trial.
Scoring: Count the number of repetitions executed in 30 sec. If the forearm is more than
halfway up when the time expires, count the move as a complete repetition. Use table
6.15 to determine your client’s percentile ranking.
Safety tips: Before testing, demonstrate the exercise for your client. Have your client
perform one or two repetitions of the exercise without a dumbbell to check body
position and lifting technique. Stop the test if the client complains of pain.
Validity and reliability: Arm curl test scores were moderately related (r = .84 for men
x,y
and .79 for women) to combined 1-RM values for the chest, upper back, and biceps
(criterion-related validity). Average arm curl test scores of physically active older
304
adults were significantly greater than those of sedentary older adults (construct
validity). Test-retest reliability was .81.
30 SEC CHAIR STAND TEST

Purpose: Assess lower body strength.
Application: Measure ability to perform ADLs such as climbing stairs, walking, and
getting out of a chair, bathtub, or car.
Equipment: You will need a folding or straight-back chair (seat height = 17 in. or 43 cm)
and a stopwatch.
Test procedures: Place the chair against a wall to prevent slipping. Instruct your client to
sit erect in the chair with the feet flat on the floor and the arms crossed at the wrists
and held against the chest (see figure 6.8). For each repetition, the client rises to a full
stand and then returns to the fully seated starting position. Have your client perform
as many repetitions as possible in 30 sec. Administer one trial.
305
Scoring: Count the number of repetitions executed in 30 sec. If the client is more than
halfway up when the time expires, count the move as a full stand. Use table 6.16 to
determine the client’s percentile ranking.
Safety tips: Brace the chair against a wall, watch for balance problems, and stop the test if
the client complains of pain. Before testing, demonstrate the movement slowly to
show proper form. Have your client perform one or two repetitions to check body
position (fully standing and fully seated) for the test.
Validity and reliability: Scores for the chair stand test were moderately related to the 1-
RM leg press (criterion-related validity) in older men (r = .78) and women (r = .71).
x,y x,y
Average scores were lower for older adults (80+ yr) than for relatively younger adults
(60-69 yr) and higher for physically active older adults compared with sedentary older
adults (construct validity). Test-retest reliability was .86 and .92 for older men and
women, respectively.
FIGURE 6.8 30 sec chair stand test for older adults
306
LOWER BODY POWER TEST
Purpose: Assess lower body power.
Application: Muscular power declines at a faster rate than muscular strength and
endurance, and power is a stronger predictor of impaired mobility and functional
limitations than these other muscle fitness variables (Reid and Fielding 2012). Having
more power may help prevent slip-related falls (Han and Yang 2015).
Equipment: You will need a Tendo (described in Dynamic Strength Tests section earlier
in the chapter), a belt, a folding or straight-back chair, and a scale to measure body
mass.
Test procedures: Measure the client’s body mass. Attach the Tendo to the client’s waist
with the belt. Place the chair against the wall to prevent slipping, and have the client
sit in the chair with arms crossed over the chest. Similar to the 30 sec chair stand test,
have the client rise to a full standing position, but this time do only one repetition as
quickly as possible. The client completes at least three repetitions, with complete
recovery (60 sec) between each effort.
307
Scoring: Power is assessed from the Tendo unit from the vertical velocity (m·sec ) and−1
the mass moved (kg). Record the best trial.

Safety tips: Brace the chair against a wall, watch for balance problems, and stop the test if
the client complains of pain. Before testing, demonstrate the movement slowly to
show proper form. Have your client perform one or two repetitions to check body
position (fully standing and fully seated) for the test.
Validity and reliability: Norms have not yet been established. However, Gray and
Paulson (2014) administered this test to 20 older adults (>65 yr). Relative power
measured by Tendo (5.34 ± 1.67 W·kg ) was similar to power obtained by measuring
−1
changes in center of mass from motion analysis (5.39 ± 1.73 W·kg ), and the two
−1
measures were strongly correlated (r = .76). Cronbach’s alpha for 10 repeated trials
was .98, suggesting excellent reliability.
MUSCULAR FITNESS TESTING OF CHILDREN

In the past, experts questioned whether or not it was safe to use 1-RM tests to evaluate
children. A major concern was the risk of growth plate fractures when the children attempted
to lift heavy weights. Experts now agree it is safe to administer 1-RM tests to children (6-12
yr) if appropriate procedures are followed (Faigenbaum, Milliken, and Westcott 2003).
Results from 1-RM tests may be used to establish baselines for evaluating the progress of
children in resistance training programs. You can also use these values to plan a personalized
resistance training program for each child, to identify muscle imbalances, and to provide
motivation. One shortcoming of 1-RM testing is that it must be closely supervised (one on
one) to ensure safety, which limits its usefulness in physical education classes and youth sport
programs. Also, child-sized exercise machines must be used; the safety of 1-RM testing using
other modes (e.g., dumbbells or barbells) has not been adequately established.
Generally speaking, the same tests described in this chapter for assessing the muscular
strength, endurance, and power of adults can be applied to youth, albeit with some
modifications and norms that are age appropriate. In a study that assessed the power, speed,
and agility of athletic preadolescent youth, Jones and Lorenzo (2013) suggested a test battery
that included vertical jump, standing long jump, proagility shuttle run, and 20 yd sprint. In
another study that compared a variety of field-based muscular fitness tests against the
criterion measure of isokinetic strength in 126 adolescents (14.4 ± 1.7 yr), Artero, España-
Romero, et al. (2012) determined that handgrip strength and standing long jump had the
highest associations to the criterion measure. For standing long jump norms for youth (10-18
308
yr), see Saint-Maurice and colleagues (2015) and Catley and Tomkinson (2013).
1-RM TESTING GUIDELINES FOR CHILDREN
The following steps are recommended for 1-RM testing of children (Faigenbaum, Milliken, and Westcott 2003):
1. Have a certified, experienced exercise professional administer and closely supervise (one on one) all tests.
2. Before testing, familiarize the child with proper lifting techniques (i.e., proper breathing and controlled
movements), allow him to practice these techniques, and answer any questions he may have.
3. Have the child warm up by performing 10 min of low- to moderate-intensity aerobic exercise and stretching.
4. Use dynamic, constant-resistance exercise machines designed specifically for children or individuals with
small body frames.
5. Before the 1-RM lift, instruct the child to perform six repetitions with a relatively light load followed by
three repetitions with a heavier load. Then gradually increase the weight and have the child attempt the 1-
RM lift. Allow at least 2 min of rest between the series of single repetitions with increasing loads. Follow
this procedure until the child fails to complete the full ROM of the exercise for at least two attempts. The 1-
RM is typically achieved within 7 to 11 trials.
6. Record the 1-RM as the maximum weight lifted for the last successful trial.
7. After testing, have the child stretch the exercised muscle groups for 5 min.
Key Points
Strength is the ability of a muscle group to exert maximal contractile force against a resistance in a single
contraction.
Muscular endurance is the ability of a muscle group to exert submaximal force for an extended duration.
Muscular power is the ability of a muscle group to exert force rapidly.
Both strength and muscular endurance are specific to the muscle group and to the type of muscle action—
static, concentric, eccentric, or isokinetic.
The greatest resistance that can be used during dynamic, concentric muscle action with a constant-
resistance exercise mode is equal to the maximum weight that can be moved at the weakest point in the
ROM.
Dynamometers are used to measure static strength and endurance.
Constant-resistance modes of exercise (free weights and exercise machines) are used to assess dynamic (i.e.,
concentric and eccentric) strength and endurance.
The accommodating-resistance mode of exercise is used to assess isokinetic strength, endurance, and
power.
309
Free-motion machines allow muscle groups to be exercised in multiple planes.
Calisthenic-type exercise tests provide a crude index of strength and endurance but can be used when other
equipment is not available.
Strength should be expressed relative to the body mass or lean body mass of the individual.
Muscular endurance tests should take into account the body mass or maximal strength of the individual.
The countermovement vertical jump test is a commonly used field method to assess muscular power.
Test batteries should include a minimum of three items that measure upper body, lower body, and
abdominal strength or endurance.
It is important to follow standardized testing procedures and to control extraneous variables (e.g.,
motivation level, time of testing, isolation of body parts, and joint angles) when assessing strength,
muscular endurance, and power.
It is safe to give 1-RM strength tests to children and older adults if appropriate testing procedures are
followed.
Although strength can be predicted from submaximal endurance tests, 1-RM assessments are preferable.
Use the arm curl test and the 30 sec chair stand test to assess the functional strength of older clients.
Key Terms
accommodating-resistance exercise
activities of daily living (ADLs)
auxotonic muscle action
concentric muscle action
constant-resistance exercise
countermovement jump (CMJ)
dynamic muscle action
eccentric muscle action
free-motion machines
functional fitness
isokinetic muscle action
isometric muscle action
isotonic muscle action
maximum voluntary contraction (MVC)
muscular endurance
muscular power
muscular strength
one-repetition maximum (1-RM)
relative strength
310
static muscle action
variable-resistance exercise
Review Questions
1. During dynamic movement, why does muscle force production fluctuate throughout the ROM?
2. Name two methods for assessing static strength and muscular endurance.
3. How do constant-resistance, variable-resistance, accommodating-resistance, and free-motion exercise

machines differ?
4. Why are strength test scores typically expressed relative to the client’s body mass?
5. Describe the recommended procedures for administering 1-RM strength tests.
6. Describe the recommended procedures for administering the vertical jump test.
7. Identify three sources of measurement error for muscular fitness testing. What can you do to control these
potential errors?
8. Is it safe to give 1-RM tests to children and older adults?
9. Describe two tests that can be used to assess the functional strength of older adults.
10. Why is it important to assess muscle balance?
11. In terms of the specificity principle, explain why a single test cannot be used to adequately assess your
clients’ overall strength. Minimally, what muscle groups should be tested to evaluate overall strength?
12. Identify the test items recommended by ACSM for assessing your clients’ upper and lower body strength.
13. For certain clients, you may choose not to administer 1-RM strength tests. Describe how you could obtain
an estimate of their strength instead.
311
CHAPTER 7
Designing Resistance Training Programs

KEY QUESTIONS
How do training principles specifically apply to the design of resistance training programs?
How are resistance training programs modified to optimize the development of strength, muscular
endurance, muscle power, or muscle size?
What factors do I need to consider when designing individualized exercise prescriptions?
Is resistance training recommended for children, adolescents, and older adults?
What methods can be used to design advanced resistance training programs?
What are the outcomes and health benefits derived from resistance training?
What is the cause of delayed-onset muscle soreness, and can it be prevented?
Muscular strength and endurance are important for the overall health and physical fitness of
your clients, enabling them to engage in physically active leisure-time pursuits, to perform
activities of daily living more easily, and to maintain functional independence later in life.
Resistance training is a systematic program of exercise for development of the muscular
system. Although the primary outcome of resistance training is improved strength and
muscular endurance, a number of health benefits are also derived from this form of exercise.
Resistance exercise builds bone mass, thereby counteracting the loss of bone mineral
(osteoporosis) and risk of falls as one ages. This form of training also lowers blood pressure in
hypertensive individuals, reduces body fat levels, and may prevent the development of low
back syndrome.
Moderate-intensity aerobic exercise receives most of the attention for lowering the risk of
all-cause mortality (see chapter 1), but some public health professionals are promoting a
paradigm shift to higher-intensity activity with a greater emphasis on resistance training
(Steele et al. 2017).
Although resistance training has long been widely used by bodybuilders, powerlifters, and
competitive athletes to develop strength and muscle size, participation in weightlifting by
312
individuals of all ages and levels of athletic interest has increased dramatically over the past 30
yr. The popularity and widespread appeal of weightlifting exercise for general muscle
conditioning gives exercise specialists and personal trainers the challenge of developing
resistance training programs that can meet the diverse needs of their clients.
This chapter shows you how to apply basic training principles (see chapter 3) to the design
of resistance training programs for novice, intermediate, and advanced weightlifters. The
chapter also presents guidelines for developing muscle strength, muscle endurance, muscle
size, and muscle power. The chapter addresses various models of periodization, functional
training exercise progressions, and guidelines for youth resistance training.
TYPES OF RESISTANCE TRAINING

Muscular fitness can be improved using various types of resistance training—isometric
(static), dynamic (concentric and eccentric), and isokinetic. Although there are general
guidelines for designing isometric, dynamic, and isokinetic resistance training programs, each
exercise prescription should be individualized to meet the specific needs and goals of your
clients.
ISOMETRIC TRAINING
In 1953, Hettinger and Muller reported that people produce significant gains in isometric
strength (5% per week) by holding one 6 sec contraction at two-thirds of maximum intensity,
5 days/wk. This type of training became popular in the late 1950s and early 1960s because the
exercises could be performed anywhere and at any time with little or no equipment. A major
disadvantage is that strength gains are specific to the joint angle used during training. Thus,
to increase strength throughout the range of motion, the exercise needs to be performed at a
number of different joint angles (e.g., 30°, 60°, 90°, 120°, and 180° of knee flexion).
Isometric exercise is widely used in rehabilitation programs to counteract strength loss and
muscle atrophy, especially in cases in which the limb is temporarily immobilized. This type of
training, however, is contraindicated for coronary-prone and hypertensive individuals because
the static contraction may produce large increases in intrathoracic pressure. This reduces the
venous return to the heart, increases the work of the heart, and causes a substantial rise in
blood pressure.
After further research, Hettinger and Muller modified their original exercise prescription.
Table 7.1 presents the general guidelines for designing training programs for isometric
strength and endurance development. For descriptions and illustrations of isometric exercises
313
for various muscle groups, see appendix C.3.
DYNAMIC RESISTANCE TRAINING

Dynamic resistance training is suitable for developing muscular fitness of men and women of
all ages, as well as children. This type of resistance training involves concentric and eccentric
actions of the muscle group performed against a constant or variable resistance. Typically, free
weights (barbells and dumbbells) and constant- or variable-resistance machines are used for
resistance training.
Several important concepts used to prescribe dynamic resistance training programs are
intensity, repetitions, sets, training volume, and order of exercises (Fleck and Kraemer 2014).
Intensity is expressed either as a percentage of the individual’s one-repetition maximum (%1-
RM) or as the repetition maximum (RM), which is the maximum weight the person can lift
for a given number of repetitions of an exercise (e.g., 8-RM equals the maximum weight the
person can lift for 8 repetitions). For the number of repetitions (1-15) corresponding to
various percentages of 1-RM (60%-100%), see table 6.15.
Intensity is inversely related to repetitions. In other words, individuals are able to perform
more repetitions using lighter resistance or weights and fewer repetitions using heavier
resistance. A set consists of a given number of consecutive repetitions of the exercise.
Training volume is the total amount of weight lifted during the workout and is calculated by
summing the products of the weight lifted, repetitions, and sets for each exercise.
The optimal training stimulus for developing muscular strength or endurance is
controversial. Some research supports the conventional prescription of high-intensity–low-
repetition resistance exercise for strength development and low-intensity–high-repetition
exercise for muscular endurance (Kraemer and Ratamess 2004; Ratamess et al. 2009). To
develop muscle strength and muscle mass, the American College of Sports Medicine
(ACSM; 2018) recommends selecting a resistance that allows the individual to complete 8 to
12 repetitions per set; to improve muscular endurance, a lower resistance (≤50% 1-RM) and
314
higher number of repetitions (15-25) are recommended. Table 7.2 summarizes the ACSM
(2018) guidelines for the resistance training of healthy populations.
Although this training stimulus may be sufficient for beginner and novice lifters, experts
recommend that resistance training programs be tailored to the specific goals of intermediate
and advanced lifters (Kraemer and Ratamess 2004; Ratamess et al. 2009). You can design
programs to optimize the development of muscle strength, size (hypertrophy), endurance, or
power by varying the intensity, repetitions, sets, and frequency of training. Tables 7.3 through
7.5 present guidelines for designing programs for novice, intermediate, and advanced
weightlifters. For descriptions of dynamic resistance training exercises, see appendix C.4.
Also, see the online video for additional information on grip and body position variations and
common weightlifting errors and corrections.
315
00:00 / 00:00
Video 7.1
316
00:00 / 00:00
Video 7.2
00:00 / 00:00
Video 7.3
00:00 / 00:00
Video 7.4
00:00 / 00:00
Video 7.5
317
00:00 / 00:00
Video 7.6
00:00 / 00:00
Video 7.7
00:00 / 00:00
Video 7.8
Intensity
As previously mentioned, the %1-RM and RM are widely used to estimate intensity for
resistance training programs. The %1-RM, however, may not accurately estimate intensity
because the number of repetitions performed at a given %1-RM varies among single-joint
and multijoint exercises and among upper body and lower body exercises (Marocolo et al.
2016). Still, many experts endorse the %1-RM to prescribe intensity (Ratamess et al. 2009).
Alternatively, Naclerio and colleagues (2011) demonstrated that the intensity of bench press
exercise can be controlled and monitored using the OMNI-resistance exercise RPE scale (see
appendix B.4)
318
The mean optimal intensity for developing strength ranges between 60% and 100% 1-RM.
At these intensities, most individuals are able to perform 1 to 12 repetitions (1-RM to 12-
RM). The client’s experience with resistance training dictates the optimal intensity for
developing strength. Generally, you should prescribe intensities of 60% to 70% 1-RM for
novice lifters, 70% to 80% 1-RM for intermediate lifters, and 80% to 100% 1-RM for
advanced lifters (Kraemer and Ratamess 2004; Ratamess et al. 2009). Meta-analyses support
these recommendations. Rhea and colleagues (2003a) reported that the optimal intensity for
strength gains in untrained (<1 yr of resistance training) and trained (>1 yr) lifters differs
(60% 1-RM and 80% 1-RM, respectively). For competitive athletes (college and
professional), the optimal training intensity is 85% 1-RM (Peterson, Rhea, and Alvar 2004).
Keep in mind that these intensities are averages. Throughout the strength training program,
intensity needs to be varied for continued improvement.
To develop muscular endurance, prescribe an intensity of ≤50% 1-RM (American College
of Sports Medicine 2018). Although low to moderate intensity best suits muscle endurance
and toning, it also brings some strength gains. The degree and rate of strength gain, however,
will be less than that experienced with a program that optimizes strength development
(specificity principle).
Sets
The optimal number of sets for improving muscular strength is controversial and depends on
the client’s goal; one to three sets for children, older adults, and novice lifters are
recommended (Ratamess et al. 2009). A major advantage of single-set programs is that they
require much less time for a training session than do multiple-set programs (20 vs. 50 min),
potentially increasing your clients’ compliance. Some studies suggest that single sets (one set
per exercise) are just as effective as multiple sets (two or three sets per exercise) for increasing
the strength of untrained and recreational lifters during the first 3 to 4 mo of resistance
training (Feigenbaum and Pollock 1999; Frohlich, Emrich, and Schmidtbleicher 2010; Hass
et al. 2000).
However, the results from analyses of resistance training studies do not support prescribing
single-set programs to develop strength (Rhea et al. 2003a) or hypertrophy (Krieger 2010) in
untrained and trained recreational lifters. Traditionally, a set refers to the number of
consecutive repetitions performed for a specific exercise; however, Rhea and colleagues
(2003a) noted that the total number of sets performed for a specific muscle group is a better
indicator of training stress than sets per exercise. Using this definition of sets, they reported
319
that an average of four sets during each training session optimizes strength development in
untrained and trained lifters. For single-set programs, the authors suggest prescribing
multiple exercises for a specific muscle group in order to reach the goal of four sets. The
ACSM (2018) stated that each set should be performed to the point of volitional fatigue for
each exercise (see table 7.2).
Multiple sets using periodization are recommended for serious athletes, powerlifters, and
bodybuilders engaging in advanced strength training and hypertrophy programs (Frohlich,
Emrich, and Schmidtbleicher 2010; Ratamess et al. 2009). To optimize the strength gains of
collegiate and professional athletes, an average of eight sets per muscle group is recommended
(Peterson, Rhea, and Alvar 2004).
Frequency
Muscular fitness may improve from exercising just 1 day/wk, especially in clients with below-
average muscular fitness. Recent research, however, suggests that the optimal frequency of
strength training for untrained individuals is 3 days/wk. For healthy populations, the ACSM
(2018) recommends 2 or 3 nonconsecutive days per week. For advanced lifters, four to six
training sessions per week and split routines are recommended (Ratamess et al. 2009). To
optimize the strength gains of trained recreational lifters and competitive athletes, each
muscle group should be exercised twice a week (Rhea et al. 2003a; Peterson, Rhea, and Alvar
2004; Ratamess et al. 2009). Advanced lifters and competitive athletes who train 4 to 6
days/wk can accomplish this goal by using split routines (see Variations for Frequency). You
should prescribe 48 hr of rest between workouts of the same muscle groups to allow the
muscles to recuperate and to prevent injury from overtraining.
Volume
Training volume is the sum of the repetitions performed during each training session
multiplied by the resistance used (Ratamess et al. 2009). Throughout a resistance training
program, volume and intensity must be systematically increased (progression principle) to
avoid plateaus and to ensure continued strength improvements. You can alter training volume
by changing the number of exercises performed for each session, the number of repetitions
performed for each set, or the number of sets performed for each exercise. Several models of
periodized training can be used to systematically vary volume and intensity (see
Periodization).
Order of Exercises
320
A well-rounded resistance training program should include at least one exercise for each of
the major muscle groups in the body. In this way, muscle balance—that is, the ratio of
strength between opposing muscle groups (agonists vs. antagonists), contralateral muscle
groups (right vs. left side), and upper and lower body muscle groups—can be maintained.
Order the exercises so that the client first executes multijoint exercises—such as the seated leg
press, bench press, and lat pull-down—that involve larger muscles (e.g., gluteus maximus,
pectoralis major, and latissimus dorsi) and more muscle groups. Then have the client progress
to single-joint exercises for smaller muscle groups (see table 7.6). To avoid muscle fatigue in
novice weightlifters, arrange the order so that successive exercises do not involve the same
muscle group. This allows time for the muscle to recover.
Rest
The amount of rest taken between sets and exercises is another variable to consider when
designing resistance training programs. The recommended rest between sets and exercises
depends on exercise intensity: A lower intensity requires shorter rests and a higher intensity
longer rests (see table 7.7). Short rest intervals may compromise the number of repetitions
that can be completed (Ratamess et al. 2009), and the ACSM recommends a rest interval of
2 to 3 min between sets (American College of Sports Medicine 2018). In strength or power
training, rests should last 3 to 5 min to allow resynthesis of adenosine triphosphate (ATP)
and creatine phosphate (CrP) and to prevent metabolic acidosis (Kraemer 2003).
321
Table 7.7 Exercise Intensity and Recommended Rest Periods
Intensity %1-RM Length of rest

>13-RM <65% <1 min
11-RM to 13-RM 65%-74% 1-2 min
8-RM to 10-RM 75%-80% 2-3 min
5-RM to 7-RM 81%-87% 3-5 min
<5-RM >87% >5 min
Data from Kraemer 2003.
Dynamic Resistance Training Methods

You can use a variety of methods to design dynamic resistance training programs. The
majority of these methods are best suited for advanced programs. Each uses a different
approach for prescribing sets, order of exercises, or frequency of workouts.
Variations for Sets

You can use either a single set or multiple sets of exercise. For multiple sets, you may choose
to have your clients consecutively perform a designated number of sets (usually three or more)
at a constant intensity (e.g., 10-RM) for each exercise. Alternatively, you may have your
clients perform one set of three different exercises for the same muscle group. For example,
instead of three consecutive sets of barbell curls for the elbow flexors, you may prescribe one
set of incline dumbbell curls, one set of hammer curls, and one set of barbell curls. This adds
variety to the program and changes the training stimulus because different muscles or parts of
a muscle are used to perform each of these exercises.
A client performing multiple sets of a given exercise may choose to lift the same weight for
each set or to vary the intensity of each set by lifting progressively heavier weights (light to
heavy sets) or lighter weights (heavy to light sets). Pyramiding is a light to heavy system in
which the client performs as many as six sets of each exercise. In the first set, the client lifts a
relatively lighter weight for 10 to 12 repetitions (10-RM to 12-RM). In subsequent sets the
individual lifts progressively heavier weights (i.e., 8-RM, 6-RM, and 4-RM). Because this
involves such a large volume of work, you should prescribe the pyramid system for
experienced weightlifters only. Bodybuilders commonly use this system to develop muscle
size. Although pyramiding adds variety to a resistance training program, it might not be any
more effective than performing multiple sets with the same weight. When training volume is
equal, there is no difference between multiple sets and pyramiding for muscular strength,
muscle damage, or hormonal responses in either healthy young men (Charro et al. 2010) or
older women (Ribeiro, Schoenfeld, et al. 2017).
Variations for Order and Number of Exercises
322
Exercise scientists generally recommend ordering the exercises so that large muscle groups are
exercised at the beginning of the workout, with progression to smaller muscle groups later in
the workout. To maximize the overload of muscle groups, however, some clients may choose
to preexhaust muscle groups by reversing this order. To do this, the individual fatigues
smaller muscles by using single-joint exercises prior to performing multijoint exercises.
When you prescribe two or more exercises for a specific muscle group, instruct the average
individual to alternate muscle groups so that the muscle can rest and recover between
exercises. For example, your client should not perform leg press and leg extension exercises
consecutively because the quadriceps femoris is used in both of these exercises. Instead,
intersperse one or more exercises using different muscle groups between these two exercises.
In contrast, many advanced weightlifters prefer to do compound sets or tri-sets in order to
completely fatigue a targeted muscle group. To use this training system, the client performs
two exercises (compound sets) or three exercises (tri-sets) consecutively for the same muscle
group, with little or no rest between the exercises.
Many bodybuilders also use a training system called supersetting. For supersets, the client
exercises agonist and antagonist muscle groups consecutively without resting. For example, to
superset the hamstrings and quadriceps femoris, follow a leg curl set immediately with a leg
extension set. Balsamo and colleagues (2012) compared the effects of different superset
exercise sequences for the quadriceps femoris (leg extensions) and hamstrings (leg curls) on
total training volume and perceived exertion. They reported that total training volume is
increased and the RPE is decreased when leg curls preceded leg extensions. Further research
is warranted to identify optimal exercise sequences for other agonist-antagonist muscle
groups. Weakley and colleagues (2017) suggested that supersets and tri-sets can enhance
training efficiency and reduce training time, but these routines are more fatiguing than
normal multiset routines and may require additional posttraining recovery.
Variations for Frequency

Traditionally for advanced resistance training programs, exercise scientists have
recommended resistance training 3 days/wk on alternate days (e.g., M-W-F) to give the
muscles time to recover. For individuals who want to resistance train 4 to 6 days/wk,
prescribe a split routine. With a split routine, you are targeting different muscle groups on
consecutive days, thereby allowing at least 1 day of recovery for each muscle group. For
example, a bodybuilder may exercise the chest and shoulders on Monday and Thursday, the
hips and legs on Tuesday and Friday, and the back and arms on Wednesday and Saturday.
323
Ribeiro, Schoenfeld, Silva, and colleagues (2015) demonstrated that the muscular strength
and fat-free mass gains of elite bodybuilders were similar for four sessions per week compared
with six sessions per week.
Periodization
Periodization systematically varies the intensity and volume of resistance training. The goal
of periodization is twofold: (1) to maximize the response of the neuromuscular system (i.e.,
gains in strength, endurance, power, and hypertrophy) by systematically changing the training
or exercise stimulus and (2) to minimize overtraining and injury by planning rest and
recovery. A recent meta-analysis showed that periodized resistance training programs are
indeed more effective than nonperiodized training plans for increasing 1-RM (Williams et al.
2017). The training stimulus may be varied by manipulations in one or more of the following
program elements:
Training volume (number of sets, repetitions, or exercises)

Training intensity (amount of resistance)
Type of muscle action (concentric, eccentric, or isometric)
Training frequency
Given the number of variables, there are numerous possibilities for designing periodized
programs. Researchers have identified combinations that optimize the training stimulus for
developing strength and muscular endurance (Rhea et al. 2002, 2003b).
Three common periodization models are linear periodization (LP), reverse linear
periodization (RLP), and undulating periodization (UP). All periodized training programs
are divided into periods, or cycles; however, the duration and the training stimulus differ
depending on the model used.
Classic Linear Periodization Model

The classic linear periodization (LP) model is divided into three types of cycles. The
macrocycle (usually 9-12 mo) is divided into mesocycles that last 3 to 4 mo. Mesocycles are
subdivided into microcycles lasting 1 to 4 wk. Within and between cycles, training intensity
increases as training volume decreases. For example, a 3 mo (12 wk) mesocycle can be divided
into three 4 wk microcycles as follows: During weeks 1 through 4, three sets are performed at
12-RM, or 70% 1-RM; during weeks 5 through 8, three sets are performed at 10-RM, or
75% 1-RM; and during weeks 9 through 12, three sets are performed at 8-RM, or 80% 1-
324
RM (see Sample Linear Periodized Resistance Training Program for Intermediate Lifter
later in the chapter). The training intensity increases from 70% 1-RM (12-RM) to 80% 1-
RM (8-RM) while the training volume systematically decreases because of the progressive
reduction in the number of repetitions (from 12 to 8) performed during each microcycle.
Reverse Linear Periodization Model

The reverse linear periodization (RLP) model reverses the progression of the LP training
stimulus. Between and within cycles, training intensity decreases as training volume increases.
The RLP configuration of the mesocycles and microcycles is as follows: weeks 1 through 4,
three sets at 80% 1-RM (8-RM); weeks 5 through 8, three sets at 75% 1-RM (10-RM); and
weeks 9 through 12, three sets at 70% 1-RM (12-RM). As you can see, the training intensity
decreases from 80% to 70% 1-RM (8-RM to 12-RM) as the training volume increases (from
8-12 reps) during the three progressive microcycles.
Undulating Periodization Model

Compared with those in LP and RLP, the microcycles for undulating periodization (UP) are
considerably shorter (biweekly, weekly, or even daily) so that the training stimulus (intensity
and volume) changes frequently. The client may progress from high volume, low intensity to
low volume, high intensity in the same week. For example, in a 3 days/wk UP program, the
individual may perform three sets of 8-RM (high volume, low intensity) on day 1, three sets
of 6-RM on day 2, and three sets of 4-RM on day 3 (low volume, high intensity). In
subsequent microcycles (each week), this training stimulus could be repeated or could be
varied to change the order of the training stimulus (e.g., day 1 = 4-RM, day 2 = 6-RM, and
day 3 = 8-RM). Frequently changing the volume and intensity subjects the exercising muscles
to a different training stimulus on a daily or weekly basis. As such, UP may prevent plateaus
in training and maintain the client’s interest and motivation for long-term resistance training.
Circuit Resistance Training

Circuit resistance training is a method of dynamic resistance training that increases strength,
muscular endurance, and cardiorespiratory endurance (Gettman and Pollock 1981). Circuit
resistance training compares favorably with traditional resistance training programs for
increasing muscle strength of untrained adults, especially if low-repetition, high-resistance
exercises are used (Gettman et al. 1978; Wilmore et al. 1978). Recent reviews concluded that
circuit training can be effective for increasing muscular strength of adults 18-65 yr (Muñoz-
Martinez et al. 2017) as well as middle-aged and older adults (Buch et al. 2017).
Additionally, Alcaraz and colleagues (2011) reported that high-resistance training (3-RM to
325
6-RM; six sets; 35 sec interset rest) was as effective as traditional resistance training for
improving upper and lower body 1-RM strength and power in resistance-trained men.
A circuit resistance training program usually has 10 to 15 stations per circuit (see figure
7.1). The circuit is repeated two or three times so that the total time of continuous exercise is
20 to 30 min. At each exercise station, select a resistance that fatigues the muscle group in
approximately 30 sec (as many repetitions as possible at approximately 40% to 55% of 1-
RM). Include a 15 to 20 sec rest period between exercise stations. Circuit resistance training
is usually performed 3 days/wk for at least 6 wk. This method of training is ideal for clients
with a limited amount of time for exercise. As mentioned in chapter 5, you can add aerobic
exercise stations to the circuit between each weightlifting station (i.e., super circuit resistance
training) to obtain additional cardiorespiratory benefits.
FIGURE 7.1 Sample circuit resistance training program. 1-RM = 1-repetition maximum.
Eccentric Training
Traditional dynamic resistance training includes both concentric and eccentric muscle
actions. However, one can train with higher forces and velocities when the load is limited to
eccentric muscle action only (Cowell, Cronin, and Brughelli 2012). Eccentric training has
the potential to improve strength, hypertrophy, and performance as well as aid in tendon and
muscle injury rehabilitation (Cowell, Cronin, and Brughelli 2012). Thus, it is not surprising
326
that several manufacturers have designed eccentric training devices. One such device is the
Eccentron, which looks like a recumbent stair stepper. However, instead of pushing down on
the pedals, the exerciser must resist the movement of the pedals toward him. This action
simulates downhill walking or running. Numerous other eccentric training machines have
been developed. These range from isokinetic devices that apply an eccentric force, to weight
machines that reduce the load during the concentric phase and increase it during the eccentric
phase, to eccentric cycle ergometers. See the review by Tinwala and colleagues (2017) for an
explanation of the technology as well as the advantages and disadvantages of various negative-
resistance training devices. Unfortunately, there is still a lack of peer-reviewed literature
comparing these devices against traditional resistance training.
00:00 / 00:00
Video 7.9
Several researchers have proposed training plans using eccentric training devices. Eccentric
training is appealing because of the potential for high force production at a very low energy
cost (e.g., the energy cost of walking downhill is only about a quarter of the cost of walking
uphill) (Hoppeler 2016; LaStayo et al. 2014). This mode of training is distinctively different
from other forms of eccentric exercise, such as plyometrics or eccentric overload with free
weights. Hoppeler (2016) coined the term moderate-load eccentric exercise to describe
progressive exercise performed on motorized ergometers that allow for controlled application
of eccentric loads. Hoppeler recommended initial negative loads of 50 to 75 W for 5 to 10
min, eventually progressing to negative loads of 400 to 500 W for 20 to 30 min, three times
per week, in rehabilitation settings. In comparison, plyometric exercises can produce negative
loads of several thousand watts and are inherently more dangerous, placing the exerciser at
increased risk for muscle damage. According to Hoppeler (2016), delayed-onset muscle
soreness (DOMS) can largely be prevented with the progressive protocols of moderate-load
eccentric training. LaStayo and colleagues (2014) described the potential rehabilitative
applications of this type of eccentric training in older adults, cancer survivors, and those with
327
cardiopulmonary, metabolic, neurologic, and orthopedic disorders.
CORE STABILITY AND FUNCTIONAL TRAINING

Core stability training is widely promoted in fitness settings to improve functional capacity
(activities of daily living and occupational tasks) and sport skills performance of healthy
individuals. Core stability is the ability to maintain the ideal alignment of the neck, spine,
scapulae, and pelvis while performing an exercise or a sport skill. Abdominal bracing is more
effective than abdominal hollowing in optimizing spinal stability. Traditional core stability
training includes resistance exercises performed on unstable surfaces (e.g., wobble board,
balance disc, and Swiss ball).
Compared with exercise on stable surfaces (e.g., ground-based free weights), exercising on
unstable surfaces is thought to enhance the activation of the core and limb muscles but may
decrease force, power, velocity, and range of motion during the resistance exercise. In
contrast, some researchers believe that multijoint free weight exercises actually engage the
core more than core-specific exercises (Martuscello et al. 2013). Regardless, core stability
training performed on unstable surfaces may be better suited for developing muscular
endurance than muscle strength and power (Willardson 2008). Instability resistance training,
however, can play an important role in periodized training programs and rehabilitation
programs. Also, nonathletes who prefer not to use ground-based free weights to achieve
muscular fitness may receive health-related benefits from resistance training on instability
devices (Behm et al. 2010b). For more on this topic, see Core Stability Testing and Training
for Low Back Pain in chapter 11. The muscles and their functional contribution to core
stability are presented in Core Stability Muscles and Function.
For years, functional training has been widely used in physical rehabilitation programs to
improve joint stability, neuromuscular control, flexibility, and muscular fitness (strength and
endurance) of injured clients. Functional training programs typically include four types of
exercise:
1. Spinal stabilization exercises to improve stability of the spine during movement

2. Proprioception and balance exercises to enhance neuromuscular coordination
3. Resistance exercises to develop muscular fitness
4. Flexibility exercises to regain range of movement
Functional training has gained popularity and recognition, especially in health and fitness
328
clubs. Usually the goal of functional training is to train and develop muscles so that
performing everyday activities is easier, safer, and more efficient (Yoke and Kennedy 2004).
However, some studies have examined the efficacy of functional training for improving sport
performance (Thompson, Cobb, and Blackwell 2007).
Functional training is a system of exercise progressions for specific muscle groups that uses
a six-step approach developed by Yoke and Kennedy (2004). The difficulty level (strength)
and skill level (balance and coordination) of specific exercises are rated, with 1 representing
the least difficult exercises (requiring less strength and skill) and 6 the most difficult exercises
(requiring more strength and skill). As the difficulty of the exercises progresses, greater
strength, balance, core stability, and coordination are required. The hardest exercises (6
rating) require the most core stability. To maintain proper postural alignment, the strength of
the core muscle groups (erector spinae and abdominal prime movers and stabilizers) needs to
be developed (core strengthening). Because core stability is dynamic, changing with body
position during exercise, isolated core strengthening does not automatically increase core
stability unless it is accompanied by motor skill training (Yessis 2003). Functional exercise
progressions develop the strength and function of all muscle groups, not just core muscles.
For an outline and example of functional exercise progressions, see Functional Exercise
Progressions: Six-Step Approach and Example.
It is not necessary for every client to progress to the most difficult levels (5 and 6) on the
exercise continuum. Safety is of utmost importance. Be certain that your clients are able to
perform exercises with proper form and postural alignment for the duration of the set before
progressing to the next level. Your clients’ ability to perform each level of exercise depends on
their fitness and skill levels. Level 6 exercises should challenge competitive athletes or very fit
individuals with excellent balance, strength, motor skill, and core stability. Although
functional training potentially adds variety and challenge to workouts, research is needed to
compare its effectiveness against conventional strength and muscular endurance training.
Improvements in strength, endurance, balance, flexibility, and coordination as well as in
functional performance of everyday tasks need to be evaluated. For more information,
detailed descriptions, and illustrations of functional exercise progressions for all muscle
groups, see Yoke and Kennedy (2004).
CORE STABILITY MUSCLES AND FUNCTION
Muscles Location Function

Multifidus, rotators, intertransversales, interspinalis Between adjacent vertebrae Maintain core stability by contracting in response to sudden changes in posture
Transverse processes of lumbar Stabilize the spine by drawing in umbilicus and increasing compressive forces between bodies of lumbar
Transversus abdominis, internal abdominal obliques, quadratus lumborum
vertebrae vertebrae
329
Rectus abdominis, external abdominal obliques, erector spinae, latissimus Maintain core stability during performance of heavy ground-based movements with free weights (e.g.,
Pelvic girdle and rib cage
dorsi squats)
Hip flexors, extensors, adductors, and abductors Pelvis and lumbar vertebrae to femur Produce pelvic tilt that results in movement of lumbar spine, affecting core stability
EXTREME CONDITIONING PROGRAMS

High-intensity functional training, or extreme conditioning programs, typically consists of
high-intensity and high-volume activities with short recovery periods between exercises
(Knapik 2015). Additionally, these programs place an emphasis on multijoint exercises,
functional movements, body resistance, and variety to target multiple joint angles. Examples
of extreme conditioning programs include CrossFit, Insanity, and P90X. Insanity and P90X
are home-based exercise programs; exercisers follow a series of DVDs for the workouts.
CrossFit-affiliated gyms are available worldwide, but daily workouts are posted online for
those who want to train on their own. Given that high-intensity interval training, body
weight training, strength training, and functional fitness were ranked numbers 1, 4, 5, and 10,
respectively, on the list of worldwide fitness trends for 2018 (Thompson 2017), it is not
surprising that extreme conditioning programs that incorporate aspects of all these trends are
popular.
Despite the popularity of these programs and anecdotal reports of their effectiveness, peer-
reviewed research comparing extreme functional training against traditional resistance
training is limited. Heinrich and colleagues (2014) compared previously inactive CrossFit
participants with a group engaged in moderate-intensity aerobic and resistance training. Each
group exercised three times a week. At the conclusion of the 8 wk study, CrossFit
participants reported higher enjoyment and planned to continue the same exercise more so
than those engaged in the moderate-intensity workouts. Additionally, in a randomized
controlled trial conducted in a secondary school in Australia, researchers concluded that a
CrossFit program was feasible, enjoyable, and effective for improving health-related fitness in
adolescents (Eather, Morgan, and Lubans 2016).
FUNCTIONAL EXERCISE PROGRESSIONS: SIX-STEP APPROACH AND

EXAMPLE
330
Some fitness experts, including a consortium for health and military performance and the
ACSM (Bergeron et al. 2011), have questioned the safety of extreme conditioning programs.
Epidemiological injury surveys of CrossFit exercisers in the United States (Weisenthal et al.
2014) and Brazil (Sprey et al. 2016) reported injury rates of 19% and 31%, respectively.
Although these injury statistics sound alarming, recent research indicates that the injury rates
for participants of extreme conditioning programs are comparable to other resistance training
programs (Aune and Powers 2017; Grier et al. 2013; Montalvo et al. 2017); the injury
incidence reported for CrossFit ranges from 2.1 to 2.3 per 1,000 athlete training hours
(Montalvo et al. 2017; Moran et al. 2017). Weisenthal et al. (2014) noted that the injury rate
was significantly reduced when a trainer was involved in the workout.
As with all training programs, a well-planned stepwise approach to increasing frequency,
331
intensity, and duration is a prudent strategy when prescribing an extreme conditioning
program. With careful planning, extreme conditioning programs can be safe alternatives or
supplements to traditional resistance training.
ISOKINETIC TRAINING
Isokinetic exercise combines the advantages of dynamic (full range of motion) and static
(maximum force exerted) exercise. Since the resistance is accommodating, isokinetic training
overcomes the problems associated with using either a constant- or variable-resistance
exercise mode. You can use isokinetic training to increase strength, power, and muscular
endurance. Isokinetic training involves dynamic shortening contractions of the muscle group
against an accommodating resistance that matches the force produced by the muscle group
throughout the entire range of motion. The speed of the movement is controlled
mechanically by the isokinetic exercise device. Isokinetic dynamometers are used for
isokinetic training. If this equipment is not available, exercises can be done with a partner
who offers accommodating resistance to the movement. The speed of the movement,
however, is not precisely controlled.
Isokinetic training is done at speeds that vary between 24° and 300°·sec , depending on the
−1
needs of the individual. The carryover effect appears to be greater when a person trains at
faster speeds (180°-300°·sec ) as compared with slower speeds (30°-60°·sec ). In some studies,
−1 −1
strength gains have been limited to velocities at or below the training velocity (Lesmes et al.
1978; Moffroid and Whipple 1970). Other researchers have reported significant strength
gains at all testing velocities (30°-300°·sec ) for high-velocity training groups (240°-300°·sec )
−1 −1
(Coyle et al. 1981; Jenkins, Thackaberry, and Killian 1984). Recent isokinetic research with
older adults resulted in velocity-specific training improvements in both low-velocity (75°·sec )−1
and high-velocity (240°·sec ) groups, but the carryover of improvements into other velocities
−1
was greater for the high-velocity group (Englund et al. 2017). Table 7.8 presents general
guidelines for designing isokinetic training programs for the development of strength and
endurance.
A major advantage of isokinetic training over traditional forms of training is that little or
332
no muscle soreness results because the muscles do not contract eccentrically when performing
traditional isokinetic training, and eccentric muscle action is thought to produce more muscle
soreness than other muscle actions (see Muscular Soreness later in this chapter). For example,
when performing knee extension and flexion on an isokinetic device, the client performs a
concentric muscle action of the quadriceps (kicking the leg out) followed by a concentric
muscle action of the hamstrings (forcibly pulling the leg back to the starting position) for
extension and flexion, respectively. This differs from auxotonic dynamic training with free
weights in which an eccentric action of the quadriceps is needed to act as a braking force as
the lower leg returns to the starting position. However, when the goal of training is an
increase in muscle size, traditional isokinetic exercise that involves only concentric muscle
actions is not the best choice. Although muscle hypertrophy can be achieved with concentric
exercise only (Cadore et al. 2014; Moore, Young, and Phillips 2012), research by Farthing
and Chilibeck (2003) demonstrated that eccentric muscle action performed at a fast velocity
is the most effective method for muscle hypertrophy. A combination of eccentric and
concentric actions is recommended when the goal is to increase muscle hypertrophy
(Schoenfeld et al. 2017).
DEVELOPING RESISTANCE TRAINING PROGRAMS

Before designing a resistance training program for a client, review training principles and
determine how each of these principles can be incorporated into your client’s program. The
training program needs to be individualized. By varying the combination of intensity,
duration, and frequency of exercise, you can develop programs that meet the unique goals and
needs of each client. Be sure to follow guidelines and recommendations for resistance training
programs (see tables 7.2 through 7.5), as well as specific recommendations and precautions,
when developing resistance training programs for children and older adults. See Steps for
Developing a Resistance Training Program.
Steps for Developing a Resistance Training Program
The following steps, used to design the sample dynamic resistance training programs, provide
an outline of how you should proceed.
1. In consultation with your client, identify the primary goal of the program (i.e.,
strength, muscular endurance, muscle size, or muscle toning), and ask the client
how much time she is willing to commit to this program.
333
2. Based on your client’s goal, time commitment, and access to equipment, determine
the type of resistance training program (i.e., dynamic, static, or isokinetic).
3. Using results from your client’s muscular fitness assessment, identify specific muscle
groups that need to be targeted in the exercise prescription.
4. In addition to core exercises for the major muscle groups, select exercises for those
muscle groups targeted in step 3.
5. For novice weightlifters, order the exercises so the same muscle group is not
exercised consecutively.
6. Based on your client’s goal, determine appropriate starting loads, repetitions, and
sets for each exercise.
7. Set guidelines for progressively overloading each muscle group.
APPLICATION OF TRAINING PRINCIPLES TO RESISTANCE

EXERCISE
To develop effective resistance training programs, you must apply each of the training
principles presented in chapter 3 (see Basic Principles for Exercise Program Design). This
section reviews some of the more pertinent training principles and outlines how they are
applied to the design of resistance training programs.
Specificity Principle
The development of muscular fitness is specific to the muscle group that is exercised, the type
of muscle action, and training intensity. To increase the dynamic strength of the elbow
flexors, for example, you must select exercises that involve the concentric and eccentric
actions of that particular muscle group. For strength, the person performs exercises at a high
intensity with low repetitions; exercising at a low intensity with high repetitions stimulates
the development of muscular endurance.
Strength and endurance gains are also specific to the speed and range of motion used
during the training. With isometric training, strength gains at angles other than the training
angle are typically 50% less than those at the exercised angle. Similarly, as previously noted,
strength gains in isokinetic training may be limited to velocities at or below the training
velocity (Lesmes et al. 1978; Moffroid and Whipple 1970).
Overload Principle
To promote strength and endurance gains, it is necessary to exercise the muscle group at
334
workloads that are greater than normal for the client. The exercise intensity should be at least
60% of maximum to stimulate the development of strength. Clients may achieve more rapid
strength gains, however, by exercising the muscle at or near maximum (80%-100%)
resistance. To stimulate endurance gains, intensities as low as 30% of maximum may be used;
however, at low intensities the muscle group should be exercised to the point of fatigue.
Progression Principle
Generally, throughout a resistance training program, you must periodically increase the
training volume, or total amount of work performed, to continue overloading the muscle so
that the person can make further improvements in strength and muscular endurance. The
progression needs to be gradual because doing too much too soon may cause musculoskeletal
injuries and excessive muscle soreness. Typically you progressively overload muscle groups by
increasing the resistance or amount of weight lifted. As clients adapt to the training stimulus,
they will be able to perform more repetitions at the prescribed resistance. Thus, the number
of repetitions a client is able to perform will indicate when it is necessary to increase the
resistance throughout the training program. In addition to increasing resistance, you may
progressively overload muscle groups by increasing the total number of repetitions performed
at a selected intensity, altering the speed of movement (slow, moderate, fast pace), and
varying the duration of rest periods between sets of exercises (Ratamess et al. 2009).
Additional Principles
Individuals with lower initial strength will show greater relative gains and a faster rate of
improvement in response to resistance training than those starting out with higher strength
levels (principles of initial values and interindividual variability). However, the rate of
improvement slows, and eventually plateaus, as clients progress through the program and
move closer to their genetic ceiling (principle of diminishing returns). Also, when an
individual stops resistance training, the physiological adaptations and improvements in
muscle structure and function are reversed (principle of reversibility). Using periodization
techniques (see Periodization), you can lessen the effects of detraining on athletes and
maintain strength gains during the competitive period by manipulating the intensity and
volume of the resistance training exercise (see Haff 2016).
GENERAL PROCEDURES AND SAMPLE RESISTANCE TRAINING

PROGRAMS
After assessing a client’s muscular fitness, you can individualize the resistance training
335
exercise prescription to meet the client’s individual needs and interests by using the steps
outlined in this section.
The first example (see Sample Resistance Training Program for Older Adult) describes a
beginning resistance training program developed for a 70 yr old man with no previous
weightlifting experience. The primary goal for this program is to develop adequate muscular
fitness so the client can retain functional independence. During the first 4 wk of training,
low-intensity (30%-40% 1-RM), high-repetition (15-20 reps) exercises familiarize the client
with weightlifting exercise and reduce the chance of injury and excessive muscle soreness. The
client gradually increases the resistance so that by the end of this phase, the exercise intensity
is 50% 1-RM. After 8 wk, the intensity starts at 50% 1-RM and gradually increases to 75%
1-RM. The client does one or two sets of 10 to 15 repetitions for each exercise. To overload
the muscles during this phase, he increases the resistance gradually, but only after he is able to
complete 15 or more repetitions at the prescribed relative intensity. This program includes
multijoint exercises, weight-bearing calisthenics, and stair climbing. The client exercises two
times a week, allowing at least 2 days of rest between each workout. Additionally, muscle
power training can be incorporated using light to moderate loads (30%-60% 1-RM) for one
to three sets of 6 to 10 repetitions (American College of Sports Medicine 2018).
The second program (see Sample Linear Periodized Resistance Training Program for
Intermediate Lifter) is for a 25 yr old woman whose primary goal is to improve muscle
strength. This client is an experienced weightlifter. Results from her 1-RM tests indicated
that her upper body strength (particularly the shoulder flexor and forearm flexor muscle
groups) is below average. Therefore, two exercises are prescribed for each of the weaker
muscle groups. The strength of all other muscle groups is average or above average; therefore,
only one exercise is prescribed for each of these muscle groups. Given her initial strength
levels and weightlifting experience, the prescription is for three sets of each exercise; and the
exercise intensity is set at 70% to 80% 1-RM to maximize the development of strength. The
client completes about 8 to 12 repetitions at the prescribed intensity for each microcycle. She
devotes 50 to 60 min, 3 days/wk, to her workouts.
SAMPLE RESISTANCE TRAINING PROGRAM FOR OLDER ADULT
Client Data
Age: 70 yr
Gender: Male
Body weight: 160 lb (72.7 kg)
336
Program goal: Muscle fitness and functional independence
Time commitment: 20-30 min per workout
Equipment: Exercise machines
Intensity: 30%-50% 1-RM for first 8 wk; 50%-75% 1-RM thereafter
Frequency: 2 days/wk; at least 48 hr between workouts
Duration: 16 wk or longer
Overload: Increase reps first; increase resistance only when able to complete >15 reps
Rest: 2-3 min between exercises
337
SAMPLE LINEAR PERIODIZED RESISTANCE TRAINING PROGRAM FOR
INTERMEDIATE LIFTER
Client Data
Age: 25 yr
Gender: Female
Program goal: Muscle strength
338
Equipment: Variable resistance machines and free weights
Cycles: 3; each microcycle = 4 wk
Intensity: 70%-80% 1-RM
Repetitions: 8-12
Sets: 3
Rest: 1-2 min for 70% 1-RM; 2-3 min for 75%-80% 1-RM
Frequency: 3 days/wk, alternate days
The third example (see Sample Undulating Periodized Resistance Training Program for
Bodybuilder) illustrates an advanced resistance training program developed for an experienced
weightlifter (28 yr old male with superior strength) whose long-term goal is competitive
bodybuilding. He engages in a high-volume UP training program. The intensity (70%-85%
1-RM) and moderate repetitions (6-12) vary systematically throughout each macro- and
microcycle to maximize the development of muscle size. To achieve a high training volume,
339
he performs three exercises for each muscle group and three or four sets of each exercise. To
effectively overload the muscles, he performs the exercises for each muscle group
consecutively (tri-sets) with little or no rest between the sets. He lifts weights 6 days/wk,
splitting the routine so he is not exercising the same muscle groups on consecutive days. With
this routine, each muscle group is exercised two times a week.
SAMPLE UNDULATING PERIODIZED RESISTANCE TRAINING PROGRAM

FOR BODYBUILDER
Client Data
Age: 28 yr
Gender: Male
Program goal: Hypertrophy
Time commitment: 90 min per workout
Equipment: Free weights and exercise machines
Mesocycles: 4; each mesocycle = 1 mo
Microcycles: 4; each microcycle = 1 wk
Intensity: 70%-85% 1-RM
Repetitions: 6-12
Sets: 3 or 4
Rest: 1 min rest between tri-sets
Frequency: 6 days/wk, split routine
UP Mesocycles and Microcycles
Intensity Volume
MONTH 1
Week 1 70% 1-RM 3 or 4 sets; 12 reps
Intensity Volume
MONTH 2
MONTH 3
340
MONTH 4
Split Routine Using Tri-Sets
Exercises 1-RM (lb)c Muscles

MONDAY AND THURSDAYa
Chestb
Flat bench press (barbell) 250 Pectoralis major (midsternal portion); triceps brachii
Incline dumbbell fly 80 Pectoralis major (clavicular portion); anterior deltoid
Decline bench press (barbell) 180 Pectoralis major (lower sternal portion)
Shoulders b
Upright row (barbell) 140 Middle deltoid
Front dumbbell raises 80 Anterior deltoid
Posterior cable pull (horizontal plane) 100 Posterior deltoid
TUESDAY AND FRIDAYa
Hips and thighs a

First tri-set
Squats (Smith machine) 300 Gluteus maximus; quadriceps femoris; upper hamstrings
Leg extension (machine) 150 Quadriceps femoris
Leg curl (standing, unilateral, machine) 90 Hamstrings (mid to lower portions)
Second tri-set
Leg press (seated) 400 Gluteus maximus; quadriceps femoris; upper hamstrings
Leg curl (lying) 130 Hamstrings (mid to lower portions)
Glute-ham raise — Gluteus maximus; hamstrings
Legs and calvesb
Standing calf (heel) raise 250 Gastrocnemius; soleus
Ankle flexion exercise (seated) 90 Tibialis anterior
Seated calf raise 180 Soleus; gastrocnemius
WEDNESDAY AND SATURDAYa
Back b
Lat pull-down (wide grip) 225 Latissimus dorsi (lateral portion); biceps brachii; brachialis
Seated row (narrow grip) 240 Latissimus dorsi (mid portion); biceps brachii; brachialis
Dumbbell row 90 Latissimus dorsi (mid portion); biceps brachii; brachialis
Elbow flexors b
Standing barbell curl 130 Biceps brachii; brachialis; brachioradialis
Preacher curl (dumbbells) 100 Biceps brachii (mid portion); brachialis
Hammer curl (dumbbells) 80 Brachioradialis; brachialis
Elbow extensors b
Lying triceps extension (barbell) 120 Triceps brachii (long head)
Triceps push-down (cables) 150 Triceps brachii (short and lateral heads)
Triceps pull-down with lateral flair (cables) 130 Triceps brachii (lateral head)
aOther exercises that work the same muscles may be substituted on the second day to add variety to the program (see appendix C.4, Dynamic Resistance Training Exercises).
bFor tri-sets, the three exercises listed are performed consecutively without rest, then the tri-set is repeated for the prescribed number of sets for that muscle group (1 min rest between sets).
c1 lb = 0.45 kg.
Several excellent references deal with the design of advanced resistance training programs
(Fleck and Kraemer 2014; Kraemer and Fleck 2007; National Strength and Conditioning
341
Association 2016, 2017).
DESIGNING RESISTANCE TRAINING PROGRAMS FOR

CHILDREN
Children and adolescents can safely participate in resistance training if special precautions and
recommended guidelines are carefully followed. Because children are anatomically and
physiologically immature, high-resistance training programs are not typically recommended
for them. Most experts agree that to lessen the risk of injury (e.g., epiphyseal growth plate
fractures) to developing bones and joints, exercise intensity should not exceed 80% 1-RM,
which equates to 8 to 15 repetitions per set. Faigenbaum and colleagues (1999) reported that
high-repetition–moderate-intensity training (one set, 13-RM to 15-RM) was more effective
than low-repetition–high-intensity training (one set, 6-RM to 8-RM) for improving the
strength and muscle endurance of children (5-12 yr) during the initial training phase (8 wk).
Strength gains in resistance-trained children result from neural adaptations (e.g., increased
activation of motor units and coordination) rather than from hypertrophy (Guy and Micheli
2001). In addition, resistance training positively affects the bone mineral density of the
femoral neck in adolescent girls ages 14 to 17 yr (Nichols, Sanborn, and Love 2001), and
there appears to be a dose-response relationship between weight-bearing activity and bone
health in this population (Ishikawa et al. 2013). Furthermore, experts recommend strength
training for youth to develop an adequate strength foundation prior to power training (e.g.,
plyometrics) (Behm et al. 2017). There is no evidence that children lose flexibility when they
resistance train (Guy and Micheli 2001). Resistance training is safe and beneficial for youth,
especially when the established training guidelines are followed (see Youth Resistance
Training Guidelines). These guidelines are based primarily on recommendations outlined in
the Canadian Society for Exercise Physiology position paper on resistance training for
children and adolescents (Behm et al. 2008) and the National Strength and Conditioning
Association position paper on youth resistance training (Faigenbaum et al. 2009).
Youth Resistance Training Guidelines
Provide qualified instruction and supervision.

Provide an exercise environment that is safe and free of hazards.
Teach clients about the benefits and risks of strength training.
Design a comprehensive program that focuses on developing muscular fitness and
342
motor skills.
Begin each workout with a 5 to 10 min warm-up.
Select 8 to 12 multijoint exercises for major muscle groups; include exercises for the
abdominal muscles and lower back.
Use equipment that is appropriate for the size, strength, and maturity of the child.
Start with one or two sets of 8 to 15 repetitions with light to moderate load (~60%
1-RM) for each exercise.
Slowly progress to three or four sets at 60% to 80% 1-RM, or 8-RM to 15-RM,
depending on the child’s needs and goals; as strength improves, increase the
number of repetitions before increasing resistance.
Increase resistance gradually and only when the child can perform the specified
number of repetitions with good form.
Reduce the resistance for prepubescent children who cannot perform a minimum of
eight repetitions with good form.
Prescribe low-repetition exercises (fewer than eight reps) for mature adolescents
only.
Focus on correct exercise technique (slow and smooth movements and breathing)
instead of amount of weight lifted.
Train two or three times per week on nonconsecutive days.
Closely supervise the child in the event of a failed repetition.
Monitor progress (e.g., use workout logs), listen to the child’s concerns, and answer
questions.
Systematically vary the training program to keep it fresh and challenging by adding
new exercises, changing the number of sets and repetitions, and incorporating
calisthenics as well as exercises using elastic tubing and fitness balls.
Focus on participation and provide positive reinforcement.
Adapted from Behm et al. 2008.
DESIGNING RESISTANCE TRAINING PROGRAMS FOR OLDER

ADULTS
Resistance training provides many health benefits, especially for older adults. The primary
goal of the resistance training program is to develop sufficient muscular fitness so that older
adults may carry out activities of daily living (ADLs) without undue stress or fatigue and may
343
retain their functional independence. To achieve these goals, age-related losses in muscle
mass (sarcopenia) and muscle strength (dynapenia) must be counteracted. Experts agree that
resistance training is the most effective mode of exercise to maintain and to improve strength
and muscle mass in older adults (Garber et al. 2011; Peterson et al. 2010; Peterson and
Gordon 2011; Romo-Perez, Schwingel, and Chodzko-Zajko 2011; Tremblay et al. 2011).
Candow and associates (2011) reported that 22 wk of whole-body resistance training (3
days/wk) was sufficient not only in attenuating age-related deficits in lean body tissue and
upper and lower body strength of older (60-71 yr) men but also in realizing strength levels
comparable to those of untrained, younger men.
Over the years, researchers have discovered that muscular strength in older adults can be
improved by lifting 1, 2, or 3 days/wk (Taaffe et al. 1999), at low or high intensities (Vincent
et al. 2002), and with either nonperiodized or UP programs (Hunter et al. 2001). Using
results from meta-analyses of studies investigating resistance training and strength gains,
Peterson and colleagues (2010, 2011) concluded that the higher the training volume, the
greater the absolute and relative improvement in strength and lean body mass of older adults.
In a more recent meta-analysis, Borde, Hortobagyi, and Granacher (2015) concurred that a
dose-response relationship exists, with the largest effects for the longest training periods (50-
53 wk). The most effective resistance training program for older adults appears to consist of
two or three sessions a week of seven to nine repetitions of each exercise (two or three sets), at
an intensity of 51% to 69% of 1-RM, with 60 to 120 sec rest between sets.
Muscle power (strength × speed of contraction) is a significant predictor of ability to
perform ADLs. With aging, both strength and power decline because of atrophy of slow and
fast muscle fibers. Muscle power declines at a relatively faster rate (3%-4% per yr after age 60)
than strength (1%-2% per yr). Some experts suggest that resistance training of older persons
should emphasize the development of power by using fast-velocity resistance exercise (Forbes,
Little, and Candow 2012; Porter 2006). In fact, a recent meta-analysis indicates that fast-
velocity resistance training is superior to traditional resistance training for increasing lower
body muscle power in middle-aged and older adults (Straight et al. 2016). For fast-velocity
exercise, the concentric phase of the exercise is performed as quickly as possible, and the
eccentric phase should take about 2 sec.
In addition to increasing strength, power, and muscular endurance, resistance training may
improve the performance of functional tasks such as lifting and reaching, rising from the floor
or a chair to a standing position, stair climbing, and walking (Henwood and Taaffe 2003;
Messier et al. 2000; Schot et al. 2003; Vincent et al. 2002). Also, the postural sway and
344
balance of older, osteoarthritic adults were improved by participation in either long-term
resistance training or aerobic walking (Messier et al. 2000). Muscle strength and power likely
contribute to balance performance; however, a cause-and-effect relationship between muscle
function and balance performance cannot yet be made (Orr 2010). Improved strength and
balance may help prevent falls and injuries in older adults.
For older adults, the ACSM (Garber et al. 2011) recommends resistance training 2
days/wk at moderate intensity (40%-50% 1-RM; 10-15 reps) to improve strength and at a
lower intensity (20%-50% 1-RM) to improve power. Although resistance training guidelines
for older adults vary among organizations, the consensus is that older adults should exercise
the major muscle groups of the body at least 2 days/wk on nonconsecutive days. Most suggest
prescribing two or three sets of 8 to 10 different exercises at intensities ranging between 8-
RM and 15-RM (Peterson and Gordon 2011; Romo-Perez, Schwingel, and Chodzko-Zajko
2011; Tremblay et al. 2011). As with your younger clients, the training volume needs to be
varied and gradually increased over time (progression principle). For a detailed example of a 6
mo progressive resistance training program for healthy, older adults, see Peterson (2010) or
Peterson and Gordon (2011).
The ACSM (2018) recommends moderate-intensity (rating of perceived exertion [RPE] =
5 or 6) to vigorous-intensity (RPE = 7 or 8) exercise at least 2 days/wk to improve the
muscular fitness of older adults; prescribe one to three sets of 8 to 12 repetitions for 8 to 10
different exercises each workout.
In addition to the general guidelines for designing resistance training programs for healthy
adults (see table 7.2), the following guidelines and precautions are recommended for older
adults:
During the first few weeks of training, use minimal resistance for all exercises.
Instruct older adults about proper weightlifting and breathing techniques.
Trained exercise leaders who have experience working with older adults should
closely supervise and monitor each client’s weightlifting techniques and resistance
training program during the first few exercise sessions.
Prescribe multijoint, rather than single-joint, exercises.
Use exercise machines to stabilize body position and control the range of joint
motion. Avoid using free weights with older adults.
Each exercise session should last approximately 20 to 30 min and should not exceed
345
60 min.
Older adults should rate their perceived exertion during exercise. Ratings of
perceived exertion should be 5 or 6 (moderate) or 7 or 8 (vigorous).
Prescribe at least two sets of 8 to 15 repetitions for 8 to 10 different exercises for
the major muscle groups.
Train at least 2 days/wk, allowing at least 48 hr of rest between the exercise
workouts.
Discourage clients with arthritis from lifting weights when they are actively
experiencing joint pain or inflammation.
When clients are returning to resistance training following a layoff of more than 3
wk, they should start with a low resistance that is less than 50% of the weight they
were lifting prior to the layoff.
COMMON QUESTIONS ABOUT RESISTANCE TRAINING

Because of the popularity of resistance training, there is an overwhelming amount of
information about the subject in professional journals as well as in popular magazines and
newspapers. This section presents common questions that exercise professionals may have
about designing resistance training programs and addresses questions and concerns your
clients may pose.
PROGRAM DESIGN
There are numerous ways to make small alterations to resistance training programs.
Considerable deliberation exists, even among fitness professionals, whether or not these
variations offer any meaningful benefit over traditional weightlifting. This section addresses
the efficacy of some of the most commonly applied variations to traditional resistance training
programs.
Which resistance training method, nonperiodized or periodized, is better?

The answer depends on the client’s initial training status and goals. During the first stage (4
wk) of resistance training, both nonperiodized and periodized multiple-set programs increase
the muscular fitness of untrained and novice lifters (Baker, Wilson, and Carlyon 1994);
however, a varied training stimulus is needed for continued improvements in muscle strength
and endurance during long-term (>4 wk) training (Fleck 1999; Marx et al. 2001). Kell (2011)
reported that periodized training (12 wk; 3 or 4 days/wk) significantly improved the strength
346
of men and women who had nonperiodized training experience. In contrast, nonperiodized,
LP, and UP resistance training programs were equally effective at improving the physical
function and health (systolic blood pressure, body composition, maximal strength, functional
capacity, balance confidence, and blood biomarkers) of untrained older adults (Conlon et al.
2016). Periodized training is highly recommended for intermediate and advanced lifters;
nonperiodized training may be more appropriate for clients just starting a weightlifting
program or who are primarily interested in maintaining strength and muscle tone. Varying
workouts daily (UP training) helps prevent boredom and maintain exercise compliance.
Which periodization model is best?

The answer depends on the client’s training goal. One research team conducted two studies
to assess the effectiveness of different types of periodized programs (LP, RLP, and daily UP)
for increasing the strength and local muscular endurance of young, resistance-trained women
and men (Rhea et al. 2002, 2003b). The researchers reported that daily UP was superior to
LP for developing the strength of young men who trained 3 days/wk for 12 wk. For
endurance gains, there were no statistically significant differences across the three plans.
Analysis of effect sizes, however, indicated that RLP was more effective than either LP or
daily UP for increasing the muscular endurance of women and men who trained 2 days/wk
for 15 wk. Other researchers also noted that daily UP programs are more effective than LP
programs for increasing strength, muscle endurance, and muscle thickness in untrained males
(Miranda et al. 2011; Simao et al. 2012). However, a more recent meta-analysis revealed no
significant differences in the effectiveness of LP versus UP on upper or lower body strength
(Harries, Lubans, and Callister 2015).
Is single-set training as effective as multiple-set training?
Some research suggests that single-set training is as effective as multiple-set training for
increasing the strength of untrained individuals during the initial stage of resistance training.
However, the majority of recent research on this topic indicates a dose-response relationship
for training volume and strength gains. Ribeiro, Schoenfeld, Pina, et al. (2015) reported
greater strength gains in the chest press (26.6% vs. 20.3%) and knee extension (23.9% vs.
16.2%) when three sets were executed compared with a single-set resistance training program
(12 wk) for older women. Likewise, Radaelli and colleagues (2015) reported a dose-response
relationship for one, three, and five sets for strength gains, muscular endurance, and
hypertrophy in previously untrained men who lifted three times a week for 6 mo. Finally, a
recent meta-analysis by Ralston and associates (2017) concluded that moderate and high
347
weekly sets were superior to low weekly sets for producing strength increases in novice,
intermediate, and advanced lifters.
Is it better to train using fixed-form or free-form exercise machines?
Both fixed-form and free-form resistance exercise machines may be used to improve muscular
fitness. Fixed-form devices limit the range of motion and plane of motion during the
resistance exercise (e.g., a leg extension machine that allows flexion and extension in sagittal
plane only). In contrast, free-form exercise machines allow movement in multiple planes (e.g.,
chest fly machine that allows press or fly movements in horizontal and oblique planes). One
study compared the effects of 16 wk of fixed-form training and free-form training on strength
and balance of sedentary men and women (Spennewyn 2008). The improvement in overall
strength of the free-form training group (116%) was significantly greater than that of the
fixed-form training group (58%). Also, overall balance performance improved 245% and 49%,
respectively, for the free-form and fixed-form training groups. In contrast, Balachandran and
colleagues (2016) recently reported that although both standing cable training and seated
machine training improved physical performance in adults ≥ 65 yr, there were no significant
differences between training interventions.
Are abdominal training devices more effective than traditional calisthenic exercises for
strengthening abdominal muscles?
There is little scientific evidence justifying manufacturers’ claims that abdominal training
devices improve strength more effectively than simply performing calisthenic exercises
without these devices (e.g., curl-ups). These devices purportedly overload the abdominal
muscles by adding resistance (e.g., abdominal belts) and isolate the abdominal musculature by
supporting the head, neck, or back. However, studies using electromyography (EMG) show
that exercising with these devices does not increase the muscle activity of the abdominal
prime movers (rectus abdominis and external abdominal oblique muscles) more than
exercising without the devices (American Council on Exercise 1997; Demont et al. 1999;
Francis et al. 2001). Although research does not support the use of abdominal trainers, they
can add variety to conventional abdominal exercises and may even improve some clients’
adherence to the abdominal exercise regimen.
To progressively overload (increase the training stimulus of) the abdominal muscles, you
can have your clients modify body position (e.g., perform abdominal curls on a decline bench
rather than on a flat bench), hold a weight across the chest, or change arm positions.
Abdominal exercises become more difficult as the arms move from along the sides to behind
348
the head to overhead.
How can stability balls, medicine balls, resistance bands, and suspension systems be used
to improve a client’s fitness?
Stability balls, medicine balls, resistance bands, and suspension training can be used in a
variety of ways to improve muscular strength, power, core stability, flexibility, and static and
dynamic balance. For example, performing a plank from a suspended position using the TRX
suspension system resulted in greater abdominal muscle activation than a plank done on the
floor (Byrne et al. 2014), and performing a pike on instability devices elicited higher
percentages of MVC than when done on stable ground (Snarr et al. 2016). Calisthenic
exercises such as abdominal crunches and back extensions can be performed while clients are
lying on the ball; dumbbell exercises can be performed while they are lying supine or prone or
sitting on the ball. Stability and medicine ball exercises are used to train the body as a linked
system, starting with the core muscle groups. Use of resistance bands and tubing allows the
individual to train the muscles with exercises that simulate the movement patterns of a
specific sport. For more information about stability ball, resistance band, and suspension
training, see Goldenberg and Twist (2016), Page and Ellenbecker (2011), and Dawes (2017),
respectively.
Does performing the curl-up on an unstable surface increase the challenge for the
abdominal muscles?
Another way to increase the training stimulus for developing abdominal muscular fitness is to
perform curl-up exercises on an unstable surface. Vera-Garcia, Grenier, and McGill (2000)
studied the EMG activity of the abdominal muscles (upper and lower rectus abdominis and
internal and external abdominal obliques) during four types of curl-ups: curl-ups on a stable
bench, curl-ups on a gym ball with feet flat on the floor, curl-ups on a gym ball with feet on a
bench, and curl-ups on a wobble board. Curl-ups performed with instability devices (gym ball
and wobble board) doubled the EMG activity of the rectus abdominis and quadrupled the
activity of the external oblique muscles. In terms of maintaining whole-body stability, the
curl-up on the gym ball with the feet flat on the floor was the most demanding, as evidenced
by increased EMG activity in all the abdominal muscles. Curl-ups with the upper body
supported on the wobble board produced the most EMG activity in the upper rectus
abdominis. Although exercising on an unstable surface increases abdominal muscle activity
and coactivation, it also increases loads on the spine. In rehabilitation programs, curl-ups on
movable surfaces should be used only with clients who can tolerate higher spinal loads (Vera-
349
Garcia, Grenier, and McGill 2000).
What is whole-body vibration training and how does it work?
Over 50 yr ago, scientists explored the idea of using vibration loading to prevent bone mineral
loss and muscle atrophy in astronauts during space travel. Today whole-body vibration
(WBV) exercise devices can be found in fitness and rehabilitation centers throughout the
world. WBV exercise involves positioning the body on a motorized platform that produces
vibratory signals at a set frequency and amplitude. Frequency is measured in hertz (Hz) and
usually ranges from 20 to 60 Hz. At 35 Hz, for example, the targeted muscles will contract
and relax 35 times per second. Amplitude, or the vertical displacement of the platform during
vibrations, is measured in millimeters. Intensity is a direct function of the frequency and
amplitude. These oscillating vibrations are transmitted to the weight-bearing muscles and
bones. The body parts in direct contact with the surface of the platform will receive the
greatest amount of vibration. Typically, the client stands on the platform holding the handles
and performs lower body exercises such as squats, lunges, calf raises, or light jumping.
Alternatively, the arms or feet may be placed on the platform for upper body and abdominal
exercises such as push-ups, triceps dips, side support, abdominal planks, and static stretches.
Vibration devices vary in how the oscillating signals are delivered to the body. For
synchronous WBV devices, the vibration is applied to the right and left foot simultaneously,
whereas side-alternating models apply the vibration sequentially to the right and left foot. In
the fitness setting, low-magnitude vibratory platforms are usually used. For these devices, the
magnitude of acceleration due to gravity (1 g = 9.81 m·sec ) is less than 1 g. High-magnitude
-2
devices provide an acceleration greater than 1 g and may cause musculoskeletal and neural
damage, posing a health risk (Abercromby et al. 2007; Judex and Rubin 2010). These high-
intensity WBV devices are not regulated by the Food and Drug Administration and are more
commonly used in clinical rehabilitation settings. WBV exercise, even at low intensities, is
contraindicated for pregnant women or individuals with thrombosis, seizures, pacemakers, or
other electronic implants (Albasini, Krause, and Rembitzki 2010).
The frequency, intensity, and duration of WBV sessions used in training studies vary
greatly. Frequency of training sessions varies between one and seven per wk and their
duration can last from 6 wk to 18 mo. The peak acceleration of the vibration platform is
usually less than 1 g, with intensity of the oscillating signals varying from 10 to 60 Hz
(frequency) and 0.05 to 8 mm (amplitude). The vibration signal is delivered in bouts lasting
anywhere from 30 sec to 10 min (Lau et al. 2011). The disparity in training protocols and
350
lack of standardization of methods complicate the synthesis, application, and ability to
generalize research findings. To address this issue, the International Society of
Musculoskeletal and Neuronal Interactions developed a set of recommendations for
describing methods used in WBV training intervention studies (Rauch et al. 2010).
Vibration loading produces small changes in muscle length that stimulate a tonic vibration
reflex. This reflex activates muscle spindles and alpha motor neurons, causing the muscles to
contract (Torvinen et al. 2002). Torvinen and colleagues examined the long-term (4 mo)
effects of vibration training combined with unloaded static and dynamic exercises on
strength, power, and balance. They noted that the greatest relative gains in isometric leg
extension strength and in leg power (measured by the vertical jump) occurred after the first 2
mo of training. Gains in strength and power during the last 2 mo of training were minimal.
Thus, it appears that vibration training elicits a neural response and adaptation (recruitment
of motor units through the activation of muscle spindles) similar to that observed during the
early stages of conventional resistance training. When compared against a standard fitness
program (combined aerobic and resistance training) and conventional resistance training
(exercise machines) in women, vibration training during unloaded static and dynamic
exercises produced similar gains in isometric, isokinetic, and dynamic strength over 3 to 4 mo
(Delecluse, Roelants, and Verschueren 2003; Roelants et al. 2004). However, Abercromby
and colleagues (2007) reported that more than 10 min a day of whole-body vibration training
may have adverse health effects. Vibration training warrants further study, especially to
determine its applicability in improving strength, flexibility, and possibly even balance in
elderly individuals in order to prevent falls, as well as to identify any long-term potential
health hazards for this form of training.
Is whole-body vibration training as effective as traditional resistance training for

improving musculoskeletal fitness of my clients?
Over the past two decades, research has examined the potential of using whole-body
vibration as a method for improving muscular strength, explosive power, bone density, body
composition, balance, mobility, and postural control (McBride et al. 2010). Additionally, the
usefulness of WBV for attenuating muscle soreness due to eccentric exercise and for reducing
low back pain has been addressed. Only studies dealing with effects of WBV on
musculoskeletal parameters are summarized in this section. Findings relative to body
composition, balance, mobility, postural control, and low back pain are addressed in later
chapters. Much of the research focuses on older adults, in light of age-related declines in
351
muscle strength (dynapenia), muscle mass (sarcopenia), and bone mineral (osteoporosis). For
specific guidelines for using WBV in the treatment of low back pain, osteoporosis and
osteopenia, balance disorders, sarcopenia, and dynapenia, see Albasini, Krause, and
Rembitzki (2010).
For older clients, the addition of WBV to resistance training augments the positive effects
of resistance training on muscle strength (Bemben et al. 2010; Bogaerts et al. 2009) and
muscle hypertrophy (Machado et al. 2010). Vibration training is suggested as a “skilling up”
activity in older people with low function until they are able to perform conventional exercises
(Rogan et al. 2015). In some cases, WBV training is even more effective than resistance
training alone for increasing muscle strength and power of older women (Lau et al. 2011; von
Stengel et al. 2012). Medium frequency and medium duration (40 Hz × 360 sec) was more
effective than low frequency and long duration (20 Hz × 720 sec) or high frequency and short
duration (60 Hz × 240 sec) for improving the isokinetic knee extension of older adults (Wei
et al. 2016).
For younger, well-trained adults, supplementing resistance training with WBV does not
augment strength gains or corticospinal excitability induced by resistance training alone
(Artero, Espada-Fuentes, et al. 2012; Weier and Kidgell 2012). WBV, however, does have an
additive effect on muscular power in well-trained athletes (Fort et al. 2012; Ronnestad et al.
2012).
The effects of WBV training on bone mineral density (BMD) vary. In a study of older
postmenopausal women, von Stengel, Kemmler, Bebenek, and colleagues (2011) reported
that the BMD of the lumbar spine increases significantly following 12 mo of WBV training.
In contrast, others have shown little or no change in BMD of the femur, spine, and hip with
WBV training. In fact, a number of review articles and meta-analyses of published literature
concluded that WBV training does not lead to a clinically important increase in BMD of
postmenopausal women (Cheung and Giangregorio 2012; Lau et al. 2011; von Stengel,
Kemmler, Engelke, et al. 2011; Wysocki et al. 2011; Xu, Lombardi et al. 2016). At this time,
WBV should not replace usual treatments for osteoporosis; however, further research should
examine the efficacy of using WBV as an adjunct therapy for osteoporosis, sarcopenia, and
dynapenia.
Is kettlebell training a safe and effective method to enhance my clients’ muscular fitness?
In the United States, kettlebell exercise has emerged as a popular mode of exercise training
with purported claims of improved muscular strength and endurance as well as
352
cardiorespiratory fitness and body composition. Otto and colleagues (2012) reported that 6
wk of kettlebell training significantly improves the muscular strength (1-RM back squat) and
power (1-RM power clean) of healthy young men; however, traditional resistance training
(weightlifting) produces greater improvements in strength compared with kettlebell training.
Likewise, Lake and Lauder (2012) noted that 6 wk of kettlebell training (12 min bouts; 30
sec exercise, 30 sec rest using 12 kg [BW <70 kg] and 16 kg [BW >70 kg] kettlebells)
produces a 9.8% increase in maximum strength (1-RM half-squat) and a 19.8% increase in
explosive strength (height of vertical jump) in young men.
Because of the unique shape of kettlebells, the client needs to learn how to control and
stabilize the weight of the kettlebell during exercise. Thus, some kettlebell exercises are well
suited for functional training of strength and stability for carrying a suitcase or grocery bag
(Liebenson 2011). For an overview of the effects of kettlebell training on strength, power,
and aerobic fitness, as well as the biomechanics of this unique mode of muscular fitness
training, see the review by Beardsley and Contreras (2014).
Given that kettlebell exercises involve a lot of swinging and bending movements, is
kettlebell exercise contraindicated for clients with neck and low back pain?
Jay and colleagues (2011) investigated the efficacy of using kettlebell training to improve
trunk extensor strength and to lessen low back and neck pain of adults engaging in
occupations with a high prevalence of musculoskeletal pain symptoms. The training group
performed full-body ballistic kettlebell exercises, 3 days/wk for 8 wk. Compared with a
control group, kettlebell training reduced pain in the neck and shoulders (−2.1 points) and
low back (−1.4 points). The training group also showed a significant increase in trunk
extensor strength. Although an isolated lumbar extension generates a greater level of lumbar
fatigue, kettlebell swings are still an effective method for strengthening the lumbar extensors
(Edinborough, Fisher, and Steele 2016).
McGill and Marshall (2012) measured spinal compression and shear loads during kettlebell
swings. This exercise creates a hip-hinge squat pattern with cycles of rapid muscle activation
and relaxation for the low back extensors (50% MVC [maximum voluntary contraction]) and
gluteal muscles (80% MVC) when using a 16 kg kettlebell. Unlike the anterior shear
produced during traditional lower body weightlifting exercises, kettlebell swings create a
posterior shear of the L4 vertebrae on the L5 vertebrae. This observation lends support to
anecdotal reports that kettlebell exercise may be useful in restoring and improving low back
health and function (McGill and Marshall 2012).
353
CLIENT CONCERNS
This section poses typical questions that clients have for their fitness professionals regarding
strength training and muscle hypertrophy.
Is it OK to lift weights every day?

During weightlifting, you are exercising your muscles at greater than normal workloads,
producing microscopic tears in the muscle cells and connective tissues. Your body responds by
producing new muscle proteins, which causes muscle growth and increased strength. For
these changes to occur, you need to rest the exercised muscles between workouts. Most
people show substantial improvements in strength when they lift weights every other day, just
two or three times a week. If you lift weights every day, you run the risk of overtraining your
muscles. Overtraining may cause muscle strains, tendinitis, bursitis, and other muscle and
joint injuries. Experienced weightlifters who work out every day split their exercise routine so
they do not exercise the same muscle groups on consecutive days. A split routine reduces the
risk of excessive muscle soreness and overuse injuries if you lift weights every day.
Can I use calisthenic exercises like push-ups and pull-ups to improve my strength?
You can use calisthenic exercises to increase your strength. Exercise professionals often
prescribe push-ups and pull-ups in addition to free weight and machine exercises to
strengthen the chest, arm, and back muscles. When you do calisthenics, your body weight
provides the resistance. If you are unable to lift your body weight, you will need to modify the
calisthenic exercise. For example, doing push-ups with your body weight supported by your
knees and hands is easier than doing standard push-ups with your body fully extended and
your weight supported by your hands and feet. As your strength improves, you may increase
the difficulty of the push-up by placing your hands wider than shoulder-width apart.
If you are unable to lift your body weight, you can modify pull-ups by using a spotter or
resistance band. As you pull up, assist your movement by extending your knees as the spotter
supports your lower legs or ankles. Alternatively, stretching a resistance band from the bar to
your knees or feet can help propel you upward during a pull-up. To increase the difficulty of a
pull-up, place your hands wider than shoulder-width apart and use an overhand (pronated)
grip instead of an underhand (supinated) grip.
I have followed my exercise prescription closely, but over the last several weeks I haven’t
seen any change in my strength. What should I do?
At the beginning of your program, your strength gains were dramatic and rapid because your
354
initial strength level was less than it is now. As your muscles adapt to the training stimulus,
you may reach a plateau, or a point where you can’t seem to improve further. It may be
helpful if you periodically alter the training stimulus more frequently (weekly or even daily) by
changing your combination of intensity, repetitions, and sets (ask your personal trainer about
a periodized program). For example, if you are presently doing high-intensity–low-repetition
exercises during each workout, you may want to decrease your intensity (from 80% to 70% 1-
RM) and increase your repetitions (from 6-8 to 10-12) for several days. Selecting different
exercises for the muscle groups may also help.
Will I become muscle bound and lose flexibility if I lift weights?

It is a common misconception that resistance training decreases joint flexibility. Studies of
elite bodybuilders and powerlifters indicate that these athletes have excellent flexibility.
Resistance training has been shown to improve or at least preserve flexibility in young men
and women (Ribeiro, Campos-Filho et al. 2017). One study showed that resistance training
actually increased the flexibility of elderly women, and training three times a week resulted in
greater hip flexion than twice a week (Carneiro et al. 2015). The key to remaining flexible
during resistance training is to perform each exercise throughout the entire range of motion.
Also, statically stretching the muscle groups after each workout may help you maintain
flexibility.
Will resistance training help me lose weight and fat?
Resistance training positively alters your body composition and preserves your lean body
tissues. Although your body weight may not change, your lean body mass (muscle and bone)
increases and your body fat decreases. Given that muscle tissue is more metabolically active
(burns more calories) than fat tissue, the increase in muscle size and lean body mass helps
maintain your resting metabolic rate when you are on a weight loss diet. Exercise science and
nutrition professionals recommend using resistance training combined with aerobic exercise
to maximize the loss of body fat and to maintain lean body tissues.
Will my strength improve if I train aerobically at the same time that I am resistance
training?
If you concurrently participate in aerobic and resistance training, your muscle growth and
strength improvement may be lessened because of the increased energy demands and protein
requirements of endurance training. In a meta-analysis of studies addressing this question,
Wilson and associates (2012) reported that doing resistance training concurrently with
355
running significantly lessens strength gains and hypertrophy. The frequency and duration of
endurance training were negatively related to hypertrophy, strength, and power. However,
recent research suggests that concurrent training does not necessarily interfere with muscular
strength and hypertrophy (Murach and Bagley 2016). The type and sequence of training
might moderate any interference effect. For example, combining HIT with resistance
training did not diminish knee extensor or elbow flexor strength gains in premenopausal
women training for 8 wk compared with those who participated only in resistance training
(Gentil et al. 2017). Additionally, there is some evidence that performing resistance training
before aerobic training during concurrent sessions aids in retaining muscle strength (Alves et
al. 2016; Murlasits, Kneffel, and Thalib 2017). Although the potential interference is an
important consideration for competitive bodybuilders and power athletes, your decision to
participate in both forms of training depends on your overall exercise program goal. If your
goal is improved health or weight loss, experts recommend including both aerobic and
resistance training in your program.
Are protein and amino acid supplements necessary to maximize muscle growth and
strength during resistance training?
Although the protein needs of resistance-trained individuals (1.6-1.8 g·kg each day) are
−1
higher than the recommended dietary allowance for inactive individuals (0.8 g·kg each day),
−1
for most individuals, a well-balanced diet containing 12% to 25% protein will meet increased
protein needs during resistance training. However, if your goal is to augment muscle
hypertrophy and strength gains beyond those produced from resistance training alone, whole
protein or amino acid supplementation, consumed close to the time you engage in resistance
exercise, may dramatically enhance the acute anabolic response to the exercise (Hayes and
Cribb 2008). There is a synergistic relationship between resistance training and protein intake
that triggers muscle protein synthesis and muscle hypertrophy (Guimaraes-Ferreira et al.
2014). According to a review by Stark and colleagues (2012), pre- and postworkout protein
supplementation increases lean mass, muscle hypertrophy, strength, physical performance,
and recovery.
The timing of protein intake is critical for optimizing muscle growth in response to
resistance training. In a frequently cited study of elderly men who resistance trained over 12
wk, those who took a protein-carbohydrate supplement immediately after exercise (within 5
min) had greater gains in muscle hypertrophy, lean body mass, and muscular strength than
those who ingested the supplement 2 hr after the training session (Esmarck et al. 2001).
356
What types of protein and amino supplements are most effective for augmenting muscle
and strength development in response to resistance training?
The type of protein consumed may influence the anabolic response to resistance training.
Whey protein supplements (i.e., >80% protein concentrates or >90% protein isolates) are
widely used among athletes to increase muscle mass. Whey protein supplements are the
richest source of branched-chain amino acids, particularly leucine, which is a regulator of
muscle protein synthesis (Hayes and Cribb 2008). In a study comparing the effects of whey
protein and casein supplements in athletic individuals engaging in a 10 wk resistance training
program, the group taking whey protein isolates (1.5 g·kg ·day ) had a fivefold better gain in
−1 −1
fat-free mass and better gains in strength compared with the group taking an equivalent daily
dose of casein supplements (Cribb et al. 2006). The optimal postworkout supplement for
protein synthesis provides at least 3 g of leucine per serving, combined with a fast-acting
carbohydrate such as maltodextrin or glucose; the preworkout supplement should combine
dextrose with essential amino acids (Stark et al. 2012).
Will creatine supplements enhance strength and muscle size during resistance training?
According to the International Society for Sports Nutrition, creatine monohydrate is an
effective ergogenic supplement for increasing lean body mass and high-intensity exercise
capacity in athletes (Kreider et al. 2010). Over 300 studies have tested the effects of creatine
supplementation on performance. Overall, the data suggest that creatine supplementation can
improve the performance of high-intensity exercise lasting less than 30 sec (Branch 2003;
Rawson and Clarkson 2003). Studies demonstrate that creatine supplementation combined
with resistance training increases muscular strength, body mass, fat-free mass, muscle fiber
size, and training volume in healthy young adults as well as in older women and men (Brose,
Parise, and Tarnopolsky 2003; Cribb et al. 2007; Nissen and Sharp 2003). However,
differences in skeletal muscle morphology may affect hypertrophy responses (i.e., changes in
lean body mass, fiber-specific hypertrophy, and contractile protein content) to resistance
training (Cribb et al. 2007). Creatine supplements increase muscle creatine, but there is much
interindividual variability in the response (Rawson and Clarkson 2003). Theoretically, an
increase in muscle creatine enhances training volume and decreases the amount of recovery
time needed between sets and exercises. The increased training stimulus improves the
physiological adaptation to resistance training for some individuals (i.e., they experience a
greater gain in muscle mass and strength).
In addition, researchers have compared the separate and combined effects of creatine
357
monohydrate and whey protein supplementation on strength and muscle hypertrophy
improvements with resistance training. After 10 or 11 wk of resistance training, both creatine
and whey protein supplements resulted in significant improvements in strength compared
with values in a control group. However, the addition of creatine monohydrate (0.1-0.3 g·kg
·day ) to the whey protein supplement (1.5 g·kg ·day ) produced much greater gains in body
−1 −1 −1 −1
weight, lean body mass, and muscle hypertrophy than whey protein alone (Cribb, Williams,
and Hayes 2007; Cribb et al. 2007). Thus, if the goal of the resistance training program is to
maximize gains in muscle mass and body weight along with strength improvement, the
addition of creatine monohydrate to a whey protein supplement is recommended (Hayes and
Cribb 2008). In addition to creatine monohydrate, other creatine forms are now being
promoted as potential ergogenic aids. For a summary of these other creatine forms, see the
review by Andres and colleagues (2017).
Is it safe to take creatine supplements?
Creatine is a widely studied supplement. Although anecdotal reports associate creatine

supplementation with muscle cramping, gastrointestinal distress, soft tissue injuries, and
impaired renal function, the available evidence from controlled studies indicates that creatine
is safe when taken in recommended doses (Andres et al. 2017; Cooper et al. 2012). In a
recent review that cites policy statements from several food safety agencies, Andres and
colleagues (2017) concluded that a 3 g·day intake of creatine is unlikely to pose any health
−1
risk, and even much higher doses associated with creatine loading (5-10 g·day ) have been
−1
used with no adverse effects. However, they recommend that people with impaired kidney
function, pregnant or breastfeeding women, and children obtain medical advice before
supplementing with creatine. Similarly, Cooper and associates (2012) concluded that creatine
supplementation appears to be safe, but they cautioned that the effects of prolonged use are
still largely unknown.
Do β-hydroxy-β-methylbutyrate (HMB) supplements increase lean body mass and muscle

strength?
β-hydroxy-β-methylbutyrate (HMB) is a metabolite of the amino acid leucine. HMB has a
potent anticatabolic effect on skeletal muscle by inhibiting muscle protein breakdown and
enhancing protein synthesis (Zanchi et al. 2011). HMB also reduces muscle damage
associated with resistance training. In a meta-analysis of dietary supplements, Nissen and
Sharp (2003) reported that HMB is one of only two supplements (the other being creatine)
that significantly increases the lean body mass and muscular strength of individuals engaging
358
in resistance training. Several double-blind, placebo-controlled studies were all in agreement
that HMB was effective at increasing lean mass and strength in trained individuals during a
12 wk supplementation and strength training period (Ferreira et al. 2013, 2015; Lowery et al.
2016; Wilson et al. 2014). Furthermore, these different investigators all discovered that
HMB appears to attenuate markers of muscle damage (e.g., creatine kinase) during periods of
overreaching or intense training (Ferreira et al. 2013, 2015; Lowery et al. 2016; Wilson et al.
2014).
Studies have examined the effect of combining HMB with creatine (Jowko, Ostaszewski,
and Jank 2001; O’Connor and Crowe 2007) or whey protein (Kraemer et al. 2015; Shirato et
al. 2016), with varying results. Over a 3 wk strength training program, Jowko, Ostaszewski,
and Jank (2001) found that a group taking the HMB-creatine combination had greater gains
in strength and lean body mass than participants taking only creatine or only HMB. In
contrast, O’Connor and Crowe (2007) reported that neither HMB alone or in combination
with creatine was effective at increasing strength, power, or lean mass of trained athletes over
a 6 wk training period. The combination of HMB with whey protein offered no benefit over
HMB alone or whey protein alone at inhibiting muscle strength loss and soreness or
decreasing muscle damage markers following a bout of eccentric exercise (Shirato et al. 2016).
However, Kraemer and colleagues (2015) reported that the recovery benefits of whey protein
are enhanced with the addition of HMB. Compared with whey protein only, the HMB-whey
combination resulted in reductions in markers of muscle damage (creatine kinase and
interleukin-6) induced by 3 consecutive days of intense resistance exercise.
EFFECTS OF RESISTANCE TRAINING PROGRAMS

Resistance training improves muscular fitness by increasing strength, muscular endurance,
and power. This section addresses the morphological, biochemical, and neurological effects of
MORPHOLOGICAL EFFECTS OF RESISTANCE TRAINING ON

THE MUSCULOSKELETAL SYSTEM
Resistance training leads to morphological adaptations in skeletal muscles and bone.
Structural changes in muscle fibers account for a large portion of the strength gains resulting
from resistance training. Increases in bone mineral content and bone density improve bone
health. The following questions deal with these adaptations.
What is exercise-induced muscle hypertrophy?
359
One effect of strength training is an increase in the size of the muscle tissue. This adaptation,
known as exercise-induced hypertrophy, results from an increase in the total amount of
contractile protein, the number and size of myofibrils per fiber, and the amount of connective
tissue surrounding the muscle fibers (Goldberg et al. 1975). With heavy resistance training,
fast (type II) muscle fibers show a twofold greater increase in size than slow (type I) fibers
(Kosek et al. 2006). An increase in protein synthesis and myogenic satellite cell proliferation
are two major processes leading to hypertrophy. Although these two processes are initiated
immediately following a client’s first bout of resistance training exercise, it typically takes 4 to
6 wk of intensive training to observe a measurable amount of hypertrophy in untrained adults
(Seynnes, de Boer, and Narici 2007). For more information on how resistance training
influences gene and protein synthesis leading to muscle hypertrophy, see the review by
McGlory, Devries, and Phillips (2017).
Is it possible to increase the number of muscle fibers by resistance training?

Heavy resistance training has been reported to produce an increase in the number of muscle
fibers (i.e., hyperplasia) in animals due to longitudinal splitting and satellite cell proliferation
(Antonio and Gonyea 1993; Edgerton 1970; Gonyea, Ericson, and Bonde-Petersen 1977).
Such processes, however, have not been clearly demonstrated in human skeletal muscle tissue
(Taylor and Wilkinson 1986; Tesch 1988). Although some data suggest that human skeletal
muscle has the potential to increase muscle fiber number (Alway et al. 1989; Sjostrom et al.
1992), hyperplasia probably contributes less than 5% to overall muscle growth in response to
heavy resistance training (Kraemer, Fleck, and Evans 1996). The major factor contributing to
exercise-induced hypertrophy for humans, apparently, is an increase in the size of existing
muscle fibers.
Does resistance training alter muscle fiber type from slow-twitch to fast-twitch?
One way to classify muscle fibers is by identifying the myosin heavy chain isoforms present in
individual fibers. Three different isoforms of myosin heavy chain (MHC) proteins are MHC
I, MHC IIA, and MHC IIX (formerly called type IIB). Pure muscle fibers contain only one
type of isoform. Hybrid muscle fibers contain a mix of MHC I and MHC IIA or MHC IIA
and MHC IIX. MHC I fibers are the slowest contracting fibers, and MHC IIX are the
fastest contracting fibers; the contractile speed of hybrid fibers is somewhere between these
two (Andersen and Aagaard 2010; Harridge 2007). Heavy resistance training decreases the
expression of MHC IIX and simultaneously increases the expression of MHC IIA fibers, but
MHC I fibers are relatively unaffected (Fry 2004; Andersen and Aagaard 2000). Thus,
360
strength training appears to affect only the relative amount and size of MHC IIA and IIX
fast fibers, with no change in the contractile characteristics of MHC I slow fibers (Andersen
and Aagaard 2010).
Is the relationship between muscle size and strength the same for men and women?
Muscle strength is directly related to the cross-sectional area of the muscle tissue. Ikai and
Fukunaga (1968) noted that the static strength per unit of cross-sectional area of the elbow
flexors was similar for young men and women. These values ranged between 4.5 and 8.9
kg·cm ; average values were 6.2 and 6.7 kg·cm for women and men, respectively. Cureton
2 2
and colleagues (1988) also reported that the dynamic strength per unit of cross-sectional area
(CSA) was similar for men and women. Posttraining ratios of elbow flexor or extensor
strength to upper arm CSA were 1.65 kg·cm and 1.85 kg·cm , respectively, for men and
2 2
women. Likewise, the posttraining ratios for leg strength to thigh CSA were 1.10 kg·cm for 2
men and 0.90 kg·cm for women.

2
Is there a limit to the degree of hypertrophy in response to resistance training?
A ceiling, or plateau, effect appears to exist in the degree of hypertrophy attainable. As

mentioned previously, the two major processes responsible for muscle hypertrophy are protein
synthesis and satellite cell proliferation. This ceiling effect may be related to the client’s ability
to activate the pool of satellite cells and to add new nuclei to the muscle cells. The amount of
cellular hypertrophy for moderate and extreme responders following a 16 wk resistance
training program typically ranges between 25% to 75% of the cross-sectional area of the
muscle tissue (Petrella et al. 2008). In light of the amount of interindividual variation in the
degree of hypertrophy in response to resistance training, it is important to closely examine the
client’s training background and to modify the resistance training exercise prescription
accordingly (Andersen and Aagaard 2010).
How much do women’s muscles hypertrophy in response to resistance training?
In the past, it was believed that resistance training produced less muscle hypertrophy in
women than in men even though their relative strength gains were similar, but muscle
hypertrophy was assessed indirectly using anthropometric and body composition measures.
However, Cureton and colleagues (1988), using computerized tomography to directly assess
muscle hypertrophy in a heavy resistance training program (70%-90% 1-RM, 3 days/wk for
16 wk), found significant increases in CSA of the upper arms of women (5 cm or 23%) as 2
well as men (7 cm or 15%). Although absolute change in muscle volume was greater in men,
2
361
the relative degree of hypertrophy (% change) was similar for men and women (Cureton et al.
1988). Research confirms this observation. Walts and colleagues (2008) reported that 10 wk
of strength training resulted in similar relative gains in muscle volume of the knee extensors
of Caucasian and African-American men (9%) and women (7.5%). Today experts agree that
the relative increases in fiber size are similar for women and men when the training stimulus
is the same (Deschenes and Kraemer 2002).
Is it possible for older adults to increase the size of their muscles by resistance training?
For many years it was thought that strength gains from resistance training in older individuals
were due primarily to neural adaptation rather than muscle hypertrophy (Moritani and
deVries 1979). However, it is now well accepted that exercise-induced hypertrophy appears to
be an important mechanism underlying strength gains in older women and men. This implies
that older adults can effectively counter age-related loss in muscle mass by participating in a
vigorous resistance training program. However, these size gains take time to become
apparent. Recently, Lixandrao and colleagues (2016), using ultrasound, took weekly cross-
sectional measurements of the vastus lateralis muscle of older adults who were doing four sets
of 10 repetitions of the leg press at 70% to 80% 1-RM twice a week. Muscle mass accrual was
not observable until after 18 sessions (9 wk).
Raue and colleagues (2012) identified and compared gene sets responsible for eliciting a
growth response to resistance training in young (24 yr) and old (84 yr) adults. Approximately
660 genes are affected by resistance training during the 1st and 36th training sessions. These
genes are termed the transcriptome signature of resistance exercise (Raue et al. 2012) and are
correlated with gains in muscle size and strength. The number of genes responding to acute
resistance exercise in untrained and trained muscles decreased in young adults but stayed
fairly constant in old adults, thereby suggesting a lack of training response in older adults.
The skeletal muscle of young adults was more responsive to resistance exercise at the gene
level compared with that of older adults. After 12 wk of resistance training, however, a
greater number of genes changed expression in old vs. young adults. This finding indicated
that some cell types in old muscle are capable of adapting to resistance training. The
resistance exercise gene response was more pronounced in MHC IIA fibers than in MHC I
fibers. This study provides insight into understanding the molecular basis for increases in
muscle size in response to resistance exercise (hypertrophy), as well as decreases in muscle size
(atrophy) due to aging and lack of physical exercise.
Does resistance training improve bone health and joint integrity?
362
Resistance training has beneficial effects on bone health that may decrease the risk of
osteoporosis and bone fractures, particularly in women. This form of training may help
achieve the highest possible peak bone mass in premenopausal women and may aid in
maintaining and increasing bone in postmenopausal women and older adults (Layne and
Nelson 1999). In a recent systematic review and meta-analysis, Xu, Lombardi, and colleagues
(2016) concluded that combining impact exercise with resistance training was the best
exercise strategy to preserve and improve bone mineral density in pre- and postmenopausal
women. In a long-term study of postmenopausal women (45-65 yr), muscle strength and
bone mineral density improved significantly (25%-75%) after 1 yr of resistance training (two
sets; 6-RM to 8-RM; 70%-80% 1-RM; 2 days/wk). Women who lifted weights consistently
for over 4 yr had significant changes in bone mineral density at the femur and lumbar spine
sites. The researchers concluded that women who maintained bone density lifted weights two
or more times per week (Metcalfe 2010). Evidence suggests that resistance training and
higher-intensity weight-bearing activities (not walking) may slow the decline in bone loss
even if there is no significant increase in bone mineral density. Improvements in bone mineral
density appear to be site specific; the greater changes occur in bones to which the exercising
muscles attach. Experts agree that resistance training has a more potent effect on bone health
than do weight-bearing aerobic exercises such as walking and jogging (Layne and Nelson
1999); this is true for men as well as women (Bolam, Van Uffelen, and Taaffe 2013).
SUMMARY OF EFFECTS OF RESISTANCE TRAINING
Morphological Factors
Muscle hypertrophy due to increase in contractile proteins, number and size of myofibrils, connective
tissues, and size of MHC II muscle fibers
No change in relative amounts of MHC I and MHC II muscle fibers
Conversion of MHC IIX to MHC IIA fast fibers
Little or no change in the number of muscle fibers (<5%)
Increase in size and strength of ligaments and tendons
Increase in bone density and bone strength
Increase in muscle capillary density
Neural Factors
363
Increase in motor unit activation and recruitment
Increase in discharge frequency of motor neurons
Decrease in neural inhibition
Increase in corticospinal excitability
Biochemical Factors
Minor increase in ATP and CrP stores
Minor increase in creatine phosphokinase (CPK), myosin adenosine triphosphatase (ATPase), and
myokinase activity
Decrease in mitochondrial volume density
Increase in testosterone, growth hormone, insulin-like growth factor (IGF-I), and catecholamines during
resistance training exercises
Enhanced fat oxidation and fat availability during submaximal cycle ergometer exercise following resistance
exercise
Additional Factors
Little or no change in body mass
Increase in fat-free mass
Decrease in fat mass and relative body fat
Improved bone health increases with exercise intensity
Changes in the transcriptome signature of genes
Resistance training also improves the size and strength of ligaments and tendons
(Edgerton 1973; Fleck and Falkel 1986; Tipton et al. 1975). These changes may increase
joint stability, thereby reducing the risk of sprains and dislocations.
BIOCHEMICAL EFFECTS OF RESISTANCE TRAINING

The morphological changes in skeletal muscles due to resistance training are caused by
hormones. This section addresses questions regarding hormonal responses to resistance
exercise, as well as changes in the metabolic profile of skeletal muscles.
What causes the increase in muscle size with resistance training?
Exercise-induced hypertrophy occurs through hormonal mechanisms. Anabolic (protein

building) hormones such as testosterone, growth hormone, and insulin-like growth hormone
364
increase in response to heavy resistance exercise and interact to promote protein synthesis.
The magnitude of testosterone and growth hormone release, however, appears to be related
to the size of the muscle groups used, the exercise intensity (%1-RM), and the length of rest
between sets, with larger increases observed for high-intensity (5-RM to 10-RM) exercise
and short (1 min) rest periods involving large muscle groups (Kraemer et al. 1991). In men,
high-intensity resistance training produces significant increases in testosterone and growth
hormone, but testosterone appears to be the principal muscle-building hormone (Deschenes
and Kraemer 2002). Levels of catecholamines (norepinephrine, epinephrine, and dopamine),
which augment the release of testosterone and insulin-like growth factor, also increase in men
in response to heavy resistance exercise (Kraemer et al. 1987). In women, growth hormone is
likely the most potent muscle-building hormone (Deschenes and Kraemer 2002). For more
information on insulin-like growth factor, growth hormone, and testosterone and their role in
skeletal muscle hypertrophy, see the review by Schoenfeld (2013).
Does resistance training alter the metabolic profile of skeletal muscles?
Although high-intensity resistance training results in substantial increases in muscle proteins,

it appears to have little or no effect on muscle substrate stores and enzymes involved with the
generation of adenosine triphosphate (ATP). Although stores of ATP and CrP may increase
significantly in response to strength training (MacDougall et al. 1979), the changes are not
large enough to have practical significance. Strength training produces only minor alterations
in myosin adenosine triphosphatase (ATPase) activity (Tesch 1992) and other ATP turnover
enzymes, such as creatine phosphokinase (CPK), in response to strength training (Costill et
al. 1979; Komi et al. 1978; Thorstensson et al. 1976). Strength training using heavy
resistance and explosive exercises results in decreased activities for hexokinase, myofibrillar
ATPase, and citrate synthase (Tesch 1988).
Does resistance training decrease aerobic capacity and endurance performance?
This is somewhat controversial. The mitochondrial volume density following heavy resistance
training has been reported to decrease as a consequence of a disproportionate increase of
contractile protein in comparison with mitochondria. In theory, this could be detrimental to
aerobic capacity and endurance performance. In similar studies of untrained men, one
research team found that V̇O peak increased equally in groups who trained only aerobically or
2
with a combination of strength and endurance training (McCarthy et al. 1995), while another
research team reported that V̇O peak increased only in the endurance training group
2
(Glowacki et al. 2004). For young elite cyclists, V̇O max remained unchanged after 16 wk of
2
365
concurrent strength and endurance training, but 45 min time trial performance improved 8%
only with concurrent training, not endurance training (Aagaard et al. 2011). They attributed
the improved endurance capacity to an increased proportion of type IIA muscle fibers, while
capillarization remained unchanged. Similarly, adding lower body resistance exercise to well-
trained female duathletes improved their running and cycling performance compared with an
endurance-only training group (Vikmoen et al. 2017). However, Psilander and colleagues
(2015) found that concurrent training did not enhance aerobic capacity or endurance in
moderately trained cyclists. Their combined endurance-strength training group increased leg
strength (19%), sprint power (5%), and short-term endurance (9%), but only the endurance
group increased muscle citrate synthase activity (11%), lactate threshold (3%), and long-term
endurance (4%).
NEUROLOGICAL EFFECTS OF RESISTANCE TRAINING

In addition to muscle hypertrophy, neural adaptations significantly contribute to strength
gains, especially during the initial stages of resistance training. This section addresses
questions regarding neural adaptations to short- and long-term resistance training.
What changes in neural function occur in response to resistance training?
The nervous system responds to resistance training by increasing the activation and
recruitment of motor units (the alpha motor neuron and all the muscle fibers it innervates)
and by decreasing the coaction of antagonistic muscle groups (Sale 1988). Recruiting
additional motor units as well as increasing the frequency of firing results in greater muscular
force production. Some evidence suggests that the central drive from higher neural centers
(e.g., motor cortex of the brain) changes and that the number of neurotransmitters and
postsynaptic receptors at the neuromuscular junction increases (Deschenes and Kraemer
2002). These changes facilitate the activation and recruitment of additional motor units,
thereby increasing force production.
Transcranial magnetic stimulation (TMS) has been used to assess the strength of neural
signals between the motor cortex and skeletal muscles (Kidgell and Pearce 2011). With this
technique, adaptations in the central nervous system in response to strength training can be
studied. In one study using TMS, strength improved after 4 wk of isometric strength training
because of decreased cortical inhibition, thereby improving the corticospinal drive to the
motor unit pool (Carroll et al. 2009). This increase in MVC with a decrease in cortical
inhibition that coincides with resistance training has been observed in both young and older
adults (Christie and Kamen 2014). Kidgell and colleagues (2010) reported that strength gains
366
(28% in trained arm and 19% in untrained contralateral arm) in response to resistance
training (four sets; six to eight repetitions at 80% 1-RM) are related to increased corticospinal
excitability (53% and 33% increase, respectively, in trained and untrained arms). Findings
from TMS studies suggest that neural adaptations in response to strength training occur at
the cortical, spinal, and motor unit levels. Manipulation of the load, timing of repetitions,
and precision of movement (ballistic vs. controlled) modulate central nervous system
adaptations (Kidgell and Pearce 2011).
At what stage during resistance training does neural adaptation occur?

In the past, it was believed that neural adaptations are primarily responsible for strength gains
only during the initial stage (first 2-8 wk) of resistance training. At about 8 to 10 wk of
resistance training, muscle hypertrophy contributes more than neural adaptation to strength
gains, but hypertrophy eventually levels off (Sale 1988). Evidence suggests that muscle
hypertrophy is finite and may be limited to no more than 12 mo (Deschenes and Kraemer
2002). Given that long-term resistance training (>6 mo) continues to increase strength
without hypertrophy, experts now believe a secondary phase of neural adaptation is most
likely responsible for strength gains occurring between 6 and 12 mo of training (Deschenes
and Kraemer 2002).
What role do neural factors play in age-related loss of muscle strength?
The term sarcopenia, or age-related loss in muscle mass, has also been used to define age-
related loss in muscle strength. This implies that changes in muscle mass are fully responsible
for changes in strength. According to Clark and Manini (2008), longitudinal studies indicate
that age-related changes in muscle mass account for less than 5% of the change in strength
with aging. Changes in muscle mass and strength do not follow the same time course,
suggesting that neural factors, along with changes in muscle factors (e.g., muscle architecture,
fiber type transformations, and electro-contractile coupling), may modulate age-related loss
of strength. They recommend using the term dynapenia to refer to age-related loss in
strength, and they proposed a screening algorithm to help define and identify dynapenia
(Manini and Clark 2012). Although it is difficult to identify specific neural mechanisms
associated with dynapenia, changes in supraspinal drive, coactivation of antagonist muscles,
muscle synergism, and maximal spinal cord output may mediate strength loss with aging
(Clark and Manini 2008).
MUSCULAR SORENESS
367
Muscular soreness may develop as a result of resistance training because isolated muscle
groups are being overloaded beyond normal use. Acute-onset muscle soreness occurs during
or immediately following the exercise and is usually caused by ischemia and the accumulation
of metabolic waste products in the muscle tissue. The pain and discomfort may persist up to 1
hr after the cessation of the exercise.
In delayed-onset muscle soreness (DOMS), the pain occurs 24 to 48 hr after exercise.
Although the causes of DOMS are not known (Armstrong 1984; Smith 1991), it appears to
be related to the type of muscle action. Eccentric exercise produces a greater degree of
delayed muscular soreness than either concentric or isometric exercise (Byrnes, Clarkson, and
Katch 1985; Schwane et al. 1983; Talag 1973). Little or no muscular soreness occurs with
isokinetic exercise (Byrnes, Clarkson, and Katch 1985). This most likely reflects the fact that
isokinetic exercise devices offer no resistance to the recovery phase of the movement and
therefore the muscle does not contract eccentrically. For a better understanding of eccentric
muscle action and its role in DOMS, see the review by Hyldahl and Hubal (2014).
THEORIES OF DELAYED-ONSET MUSCLE SORENESS

Although the precise causes of DOMS remain unclear, several theories have been proposed.
The more widely recognized theories suggest that exercise, particularly eccentric exercise,
causes damage to skeletal muscle cells and connective tissues, producing an acute
inflammation.
Connective Tissue Damage

Abraham (1977) extensively studied the factors related to DOMS produced by resistance
training. He suggested that DOMS most likely results from disruption in the connective
tissue of the muscle and its tendinous attachments. Abraham noted that urinary excretion of
hydroxyproline, a specific by-product of connective tissue breakdown, was higher in subjects
who experienced muscular soreness than in those who did not. Because a significant rise in
urinary hydroxyproline levels indicates an increase in both collagen degradation and synthesis,
he concluded that more strenuous exercise damages the connective tissue, which increases the
degradation of collagen and creates an imbalance in collagen metabolism. To compensate for
this imbalance, the rate of collagen synthesis increases.
Skeletal Muscle Damage

Researchers have assessed skeletal muscle damage induced through exercise. Exercise-
induced muscle damage (EIMD) may occur when individuals engage in eccentric exercise or
368
exercise to which they are unaccustomed. The muscle damage results in decreased force
production and increased passive tension, as well as increased muscle soreness, swelling, and
intramuscular proteins in the blood (Howatson and van Someren 2008). Much of the
research on EIMD has focused on the effects of eccentric exercise on muscle damage and
soreness. Regardless of the speed or intensity of muscle action, eccentric exercise injures both
the contractile and cytoskeletal components of myofibrils as well as the excitation coupling
system; this is especially true for novel exercise (Howatson and van Someren 2008).
Friden, Sjostrom, and Ekblom (1983) observed structural damage to myofibrillar Z bands
resulting from eccentric exercise. Proske and Morgan (2001) pointed out that disruption of
the sarcomere organization within the skeletal muscle is most likely the cause of the decreased
active tension and force production that follows a series of intense eccentric muscle actions.
Mackey and colleagues (2008) reported that electrically stimulated isometric muscle actions
may also produce muscle damage at the sarcomere level. Z-line disruption and microphage
infiltration provided direct evidence of damage to myofibers and sarcomeres. More research is
needed to assess the effects of various types of muscle action, as well as high- and low-impact
eccentric exercise (e.g., downhill running and eccentric cycle exercise), on muscle damage
(Friden 2002).
Researchers have also examined markers of muscle damage such as serum CPK, lactate
dehydrogenase, and myoglobin. Schwane and colleagues (1983) noted a significant increase in
plasma CPK levels produced by downhill running. They suggested that the mechanical stress
from eccentric exercise causes cellular damage, resulting in an enzyme efflux. Clarkson and
colleagues (1986) reported similar increases in serum CPK levels following concentric
(37.6%), eccentric (35.8%), and isometric (34%) arm curl exercises. They concluded that
muscle damage occurred with all three types of muscle actions; however, the subjects
perceived greater muscle soreness with eccentric and isometric exercises. Likewise, Byrnes,
Clarkson, and Katch (1985) observed that both concentric and eccentric resistance training
elevated serum CPK levels, but that individuals who trained concentrically did not develop
DOMS. In contrast to all of this evidence implicating muscle damage, recent research
suggests that muscle fiber damage is not essential for DOMS; rather neurotrophic factors
produced by satellite cells play a key role (Mizumura and Taguchi 2016).
Armstrong’s Model of Delayed-Onset Muscle Soreness

On the basis of an extensive literature review, Armstrong (1984) proposed the following
model of the development of DOMS:
369
1. The structural proteins in muscle cells and connective tissue are disrupted by high
mechanical forces produced during exercise, especially eccentric exercise.
2. Structural damage to the sarcolemma alters the permeability of the cell membrane,
allowing a net influx of calcium from the interstitial space. Abnormally high levels
of calcium inhibit cellular respiration, thereby lessening the cell’s ability to produce
ATP for active removal of calcium from the cell.
3. High calcium levels within the cell activate a calcium-dependent proteolytic
enzyme that degrades Z discs, troponin, and tropomyosin.
4. This progressive destruction of the sarcolemma (postexercise) allows intracellular
components to diffuse into the interstitial space and plasma. These substances
attract monocytes and activate mast cells and histocytes in the injured area.
5. Histamine, kinins, and potassium accumulate in the interstitial space because of the
active phagocytosis and cellular necrosis. These substances, as well as increased
tissue edema and temperature, may stimulate pain receptors, resulting in the
sensation of DOMS.
Acute Inflammation Theory

Smith (1991) suggested that acute inflammation, in response to muscle cell and connective
damage caused by eccentric exercise, is the primary mechanism underlying DOMS. Many of
the signs and symptoms of acute inflammation, such as pain, swelling, and loss of function,
are also present with DOMS. On the basis of research about acute inflammation and
DOMS, Smith proposed the following sequence of events:
1. Connective tissue and muscle tissue disruption occurs during eccentric exercise,
especially when the individual is not accustomed to eccentric exercise.
2. Within a few hours, neutrophils in the blood are elevated and migrate to the site of
injury for several hours postinjury.
3. Monocytes also migrate to the injured tissues for 6 to 12 hr postinjury.
4. Macrophages synthesize prostaglandins (series E).
5. The prostaglandins sensitize type III and IV pain afferents, resulting in the
sensation of pain in response to intramuscular pressure caused by movement or
palpation.
6. The combination of increased pressure and hypersensitization produces the
370
sensation of DOMS.
In summary, it appears that there is no single mechanism or theory to explain DOMS. In a

review article, Lewis, Ruby, and Bush-Joseph (2012) concluded that the culmination of six
different mechanisms underlie muscle soreness. DOMS starts with microtrauma to the
muscles and connective tissues. This trauma is followed by inflammation and shifts of fluids
and electrolytes, causing the pain and discomfort associated with DOMS. Furthermore,
interindividual genetic variation exists in response to EIMD such that individuals with
certain genotypes experience greater muscle damage following exercise (Baumert et al. 2016).
PREVENTION OF EXERCISE-INDUCED MUSCLE DAMAGE AND

MUSCULAR SORENESS
Given that eccentric muscle action is an integral part of human locomotion, physical
activities, and sport, researchers have explored myriad intervention strategies to lessen the
negative effects of eccentric muscle actions and to treat EIMD. These approaches include
nutrition (e.g., antioxidants, carbohydrate-protein supplements, and β-hydroxy-β-
methylbutyrate) and pharmacological strategies (e.g., aspirin, ibuprofen, and naproxen);
manual (e.g., massage and cryotherapy), neuromuscular (e.g., transcutaneous electrical nerve
stimulation [TENS] and ultrasound), and whole-body vibration therapies; and exercise (e.g.,
prior bouts of isometric or eccentric exercise and stretching). Some evidence suggests that
cold water immersion (cryotherapy) may reduce DOMS after exercise (Bleakley et al. 2012;
Lynch and Barry 2012). Likewise, whole-body vibration therapy prior to eccentric exercise
reduces muscle inflammation, strength loss, and DOMS symptoms (Aminian-Far et al. 2011;
Broadbent et al. 2010; Imtiyaz, Veqar, and Shareef 2014). Performing isometric MVCs in
the weeks preceding a bout of maximal eccentric contractions reduced soreness and creatine
kinase concentration (Tseng et al. 2016). Additionally, a single bout of low-volume, high-
intensity eccentric exercise has been consistently shown to have a positive effect on reducing
EIMD. Howatson and van Someren (2008) provide an excellent review of research dealing
with the prevention and treatment of EIMD.
For many years, slow static stretching exercises were recommended to warm up major
muscle groups at the start of a resistance training workout. It was believed that this form of
stretching prevented muscle injury and soreness (deVries 1961). However, evidence suggests
that stretching prior to physical activity does not prevent injury (Pope et al. 2000). Also,
stretching before, after, or before and after exercise does not produce clinically significant
371
reductions in delayed-onset muscle soreness (Henschke and Lin 2011; Herbert, de Noronha,
and Kamper 2011). In fact, stretching before resistance exercise may actually decrease
strength and force production (Rubini, Costa, and Gomes 2007). Therefore, stretching
immediately prior to resistance exercise is not recommended. Instead of performing static
stretching, your clients should warm up by completing 5 to 10 repetitions of the exercise at a
low intensity (e.g., 40% 1-RM).
Law and Herbert (2007) reported that low-intensity exercise (i.e., warm-up) prior to
unaccustomed eccentric exercise (e.g., walking backward downhill on an inclined treadmill
for 30 min) reduced muscle soreness up to 48 hr after exercise. In contrast, neither low-
intensity cool-down exercise nor stretching after exercise reduces muscle soreness (Herbert
and de Noronha 2007; Herbert, de Noronha, and Kamper 2011; Law and Herbert 2007).
Using a gradual progression of exercise intensity at the beginning of a resistance training
program may also help prevent muscular soreness. Some experts suggest using 12-RM to 15-
RM during the beginning phases of strength training. Make sure your clients gradually
increase exercise intensity throughout the resistance training program. Avoiding eccentric
actions during dynamic resistance training may also lessen the chance of muscular soreness.
An assistant or exercise partner should return the weight to the starting position.
Key Points
The specificity principle states that muscular fitness development is specific to the muscle group, type of
muscle action, training intensity, speed, and range of movement.
The overload principle states that the muscle group must be exercised at greater than normal workloads to
promote development of muscular strength and endurance.
For nonperiodized resistance training programs, the training volume must be progressively increased to
overload the muscle groups for continued gains in strength and muscular endurance.
In most programs, resistance training exercises should be ordered so that successive exercises do not involve
the same muscle group. For advanced programs, however, exercises for the same muscle group should be
done consecutively.
Dynamic resistance training can be used to develop muscular strength, power, size, or endurance by
modifying the intensity, repetitions, sets, and frequency of the exercise.
Periodization programs can result in greater changes in strength than nonperiodized resistance training
programs.
Strength and endurance gains resulting from resistance training are due to morphological, neurological,
and biochemical changes in the muscle tissue.
372
Eccentric exercise produces a greater degree of DOMS than either concentric, isometric, or isokinetic
exercise.
Little or no muscular soreness is produced by isokinetic training.
The precise cause of DOMS is unknown; however, connective tissue and muscle damage, as well as acute
inflammation, have been proposed as possible causes.
Key Terms
acute-onset muscle soreness

β-hydroxy-β-methylbutyrate (HMB)
compound sets
core stability
core strengthening
delayed-onset muscle soreness (DOMS)
dynapenia
eccentric training
exercise-induced hypertrophy
exercise-induced muscle damage (EIMD)
functional training
high intensity–low repetition
kettlebell training
linear periodization (LP)
low intensity–high repetition
macrocycle
mesocycle
microcycle
muscle balance
periodization
pyramiding
repetition maximum (RM)
repetitions
reverse linear periodization (RLP)
sarcopenia
set
split routine
supersetting
tonic vibration reflex
training volume
transcranial magnetic stimulation (TMS)
373
transcriptome signature of resistance exercise
tri-sets
undulating periodization (UP)
whole-body vibration (WBV)
Review Questions
1. What are the health benefits of resistance training?
2. Name three general types of resistance training. Which one is best suited for physical therapy rehabilitation
programs?
3. Describe the ACSM guidelines for designing resistance training programs for healthy adults. What
modifications are necessary when you are planning resistance training programs for children and older
adults?
4. Describe how the basic exercise prescriptions for strength training and muscular endurance training
programs differ.
5. Describe how you can increase training volume for advanced strength training and hypertrophy programs.
6. Describe two methods of varying sets for advanced strength training programs.
7. Explain two methods an advanced weightlifter can use to completely fatigue a targeted muscle group.
8. Describe three periodization models. How do they differ?
9. How does moderate-load eccentric training differ from plyometric training?
10. What is the major advantage of isokinetic training compared with traditional forms of resistance training?
11. Explain how the specificity, overload, and progression principles are applied in the design of resistance
training programs.
12. What will you tell your clients if they ask about supplementing their resistance training with creatine?
13. Explain what causes the exercise-induced hypertrophy resulting from resistance training. In the time course
of a resistance training program, when is this morphological adaptation most likely to occur?
14. Describe the potential effects of resistance training on bone health.
15. What neural adaptations account for initial strength gains during resistance training? When are these
changes most likely to be observed during the time course of resistance training?
374
16. Define sarcopenia and dynapenia. Identify muscle morphological and neurological mechanisms responsible
for dynapenia.
17. Describe one theory of DOMS. What can you instruct your clients to do to help prevent and relieve muscle
soreness caused by resistance training?
375
CHAPTER 8
Assessing Body Composition

KEY QUESTIONS
Why is it important to measure body composition, and how are body composition measures used by health
and fitness professionals?
What are the standards for classifying body fat levels?
What is the difference between two-component and multicomponent body composition models?
What are the guidelines and limitations of the hydrostatic weighing method?
Is air displacement plethysmography as accurate as hydrostatic weighing?
Is dual-energy X-ray absorptiometry considered a gold standard method for measuring body composition?
What are the guidelines, limitations, and sources of measurement error for the skinfold method?
Is ultrasound a suitable alternative to the skinfold method for assessing body composition in field settings?
What is bioelectrical impedance analysis? What factors affect the accuracy of this method?
Can circumferences and skeletal diameters be used to accurately assess body composition?
What anthropometric indices can be used to identify at-risk individuals?
Body composition is a key component of an individual’s health and physical fitness profile.
Obesity is a serious health problem that contributes to reduced life expectancy by increasing
one’s risk of developing coronary artery disease, hypertension, type 2 diabetes, obstructive
pulmonary disease, osteoarthritis, and certain types of cancer. Too little body fat also poses a
health risk because the body needs a certain amount of fat for normal physiological functions.
Essential lipids, such as phospholipids, are needed for cell membrane formation; nonessential
lipids, like triglycerides found in adipose tissue, provide thermal insulation and store
metabolic fuel (free fatty acids). In addition, lipids are involved in the transport and storage of
fat-soluble vitamins (A, D, E, and K) and in the functioning of the nervous system, the
menstrual cycle, and the reproductive system, as well as in growth and maturation during
pubescence. Thus, too little body fatness, as found in individuals with eating disorders (e.g.,
anorexia nervosa), exercise addiction, and certain diseases such as cystic fibrosis, can lead to
376
serious physiological dysfunction.
This chapter describes standardized testing procedures for reference methods (hydrostatic
weighing, air displacement plethysmography, and dual-energy X-ray absorptiometry) and
field methods (skinfold, ultrasound, bioimpedance, and anthropometry) for assessing body
composition. For each method, you will learn to identify potential sources of measurement
error as well as ways to minimize these errors.
CLASSIFICATION AND USES OF BODY COMPOSITION

MEASURES
To classify level of body fatness, the relative body fat (%BF) is used. Table 8.1 presents %BF
ranges, by decade of life, for men and women. It is important to recall that these data are
based on skinfolds, and the skinfold measures are reported to be within ± 3.5% BF of a
hydrostatic weighing reference. The minimal, average, and obesity fat values vary with age,
gender, and activity status. For example, the average or median %BF values for adult men and
women (20-29 yr) are approximately 15.0% for men and 20% for women. The corresponding
minimal recommended fat values are 3% and 10%, respectively. Body fat percentages above
22% for men and 28% for women exceed what is believed to be healthy.
In addition to classifying your client’s %BF and disease risk, body composition measures
377
are useful for
estimating a healthy body weight and formulating nutritional considerations and

exercise prescriptions (see chapter 9);
estimating competitive body weight for athletes participating in sports that use
body weight classifications for competition (e.g., wrestling and bodybuilding);
monitoring the growth of children and adolescents and identifying those at risk
because of under- or overfatness; and
assessing changes in body composition associated with aging, malnutrition, and
certain diseases, as well as the effectiveness of nutrition and exercise interventions
in counteracting these changes.
BODY COMPOSITION MODELS

To make the most valid assessment of body composition for your clients, it is necessary to
understand the underlying theoretical models. You may recall that the body is composed of
water, protein, minerals, and fat. The two-component model of body composition (Brozek et
al. 1963; Siri 1961) divides the body into a fat component and a fat-free body (FFB)
component. The FFB consists of all residual chemicals and tissues including water, muscle
(protein), and bone (mineral). The two-component model of body composition makes the
following five assumptions:
1. The density of fat is 0.901 g·cc .

−1
2. The density of the FFB is 1.100 g·cc .−1
3. The densities of fat and the FFB components (water, protein, mineral) are the same
for all individuals.
4. The densities of the various tissues composing the FFB are constant within an
individual, and their proportional contribution to the lean component remains
constant.
5. The individual being measured differs from the reference body only in the amount
of fat; the FFB of the reference body is assumed to be 73.8% water, 19.4% protein,
and 6.8% mineral.
This two-component model is the foundation for the hydrodensitometry (underwater
378
weighing) method. With use of the assumed proportions of water, mineral, and protein and
their respective densities, equations were derived to convert an individual’s total body density
(Db) from hydrostatic weighing into relative body fat proportions (%BF). Two commonly
used equations are the Siri (1961) equation, %BF = (4.95 / Db − 4.50) × 100, and the
equation of Brozek and colleagues (1963), %BF = (4.57 / Db − 4.142) × 100. These two
equations yield similar %BF estimates for body densities ranging from 1.0300 to 1.0900 g·cc . −1
For example, if a client’s measured Db is 1.0500 g·cc , the %BF estimates obtained by
−1
plugging this value into the Siri and Brozek equations are 21.4% and 21.0%, respectively.
Generally, two-component (2C) model equations provide accurate estimates of %BF as
long as the basic assumptions of the model are met. However, there is no guarantee that the
FFB composition of an individual within a certain population subgroup will exactly match
the values assumed for the reference body. Researchers have reported that FFB density varies
with age, gender, ethnicity, level of body fatness, and physical activity level, depending mainly
on the relative proportion of water and mineral composing the FFB (Baumgartner et al.
1991; Williams, Going, et al. 1993). For example, the average FFB density of black women
and black men (~1.106 g·cc ) is greater than 1.10 g·cc because of their higher mineral
−1 −1
content (~7.3% FFB) or relative body protein (or both) (Cote and Adams 1993; Ortiz et al.
1992; Wagner and Heyward 2001). Because of this difference in FFB density, the body fat of
blacks will be systematically underestimated when 2C model equations are used to estimate
%BF. In fact, negative %BF values were reported for professional football players whose
measured Db exceeded 1.10 g·cc (Adams et al. 1982). Likewise, the FFB density of white
−1
children is estimated to be only 1.086 g·cc because of their relative lower mineral values
−1
(5.2% FFB) and higher body water values (76.6% FFB) compared with the reference body
(Lohman, Boileau, and Slaughter 1984). Also, the average density of the FFB of elderly
white men and women is 1.098 g·cc because of the relatively low body mineral value (6.2%
−1
FFB) in this population (Heymsfield et al. 1989). Thus, the relative body fat of children and
persons who are elderly will be systematically overestimated using 2C model equations.
For certain population subgroups, therefore, scientists have applied multicomponent
models of body composition based on measured total body water and bone mineral values.
With the multicomponent approach, you can avoid systematic errors in estimating body fat
by replacing the reference body with population-specific reference bodies that take into
account the age (e.g., for children, for persons who are elderly), gender, and ethnicity of the
individual. Table 8.2 provides population-specific formulas for converting Db to %BF. You
will note that population-specific conversion formulas do not yet exist for all age groups
379
within each ethnic group. You may have to use the age-specific conversion formula developed
for white males and females in these cases. Also, you can use the population-specific
conversion formulas for anorexic and obese females only when it is obvious that your client is
either anorexic or obese.
380
REFERENCE METHODS FOR ASSESSING BODY COMPOSITION
In many laboratory and clinical settings, densitometry and dual-energy X-ray absorptiometry
381
are used to obtain reference measures of body composition. For densitometric methods, total
body density (Db) is estimated from the ratio of body mass to body volume (Db = BM / BV).
Body volume is usually measured using either hydrostatic weighing or air displacement
plethysmography.
HYDROSTATIC WEIGHING
Hydrostatic weighing (HW) is a valid, reliable, and widely used laboratory method for
assessing total Db. Hydrostatic weighing provides an estimate of total body volume (BV)
from the water displaced by the body’s volume. According to Archimedes’ principle, weight
of a body under water is directly proportional to the volume of water displaced by the body’s
volume. For calculating Db, body mass is divided by body volume. The total Db is a function
of the amounts of muscle, bone, water, and fat in the body.
Using Hydrostatic Weighing

Determine BV by totally submerging the body in an underwater weighing tank or pool and
measuring the underwater weight (UWW) of the body. To measure UWW, you can use
either a chair attached to an HW scale (see figure 8.1) or a platform attached to load cells (see
figure 8.2). Given that the weight loss under water is directly proportional to the volume of
water displaced by the body’s volume, the BV is equal to the body mass (BM) minus the
UWW (see figure 8.3). The net UWW is the difference between the UWW and the weight
of the chair or platform and its supporting equipment (i.e., tare weight). The BV must be
corrected for the volume of air remaining in the lungs after a maximal expiration (i.e.,
residual volume or RV), as well as the volume of air in the gastrointestinal tract (GV). The
GV is assumed to be 100 ml.
382
FIGURE 8.1 Hydrostatic weighing using scale and chair.
FIGURE 8.2 Hydrostatic weighing using load cells and platform.
383
FIGURE 8.3 Hydrostatic weighing data collection form.
From A.L. Gibson, D.R. Wagner, and V.H. Heyward, 2019, Advanced Fitness Assessment and Exercise Prescription, 8th ed. (Champaign, IL: Human Kinetics).
The RV is commonly measured using helium dilution, nitrogen washout, or oxygen

dilution techniques. The RV is measured in liters and must be converted to kilograms (kg) in
order to correct UWW. This is easy to do because 1 L of water weighs approximately 1 kg;
therefore, the water weight per liter of RV is 1 kg. To correct the BV, you subtract the
equivalent weight of the RV and the GV (100 ml or 0.1 kg). Since water density varies with
water temperature, the BV is corrected for water density (see figure 8.3). Under normal
circumstances, the water temperature of the underwater weighing tank or swimming pool will
be between 34 and 36 °C. The resulting equation for BV is
BV = [(BM − net UWW) / density of water] − (RV + GV)
Calculate body density (Db in g·cc ) by dividing BM by BV: Db = BM / BV. After you−1
calculate Db, you can convert it into percent body fat (%BF) by using the appropriate
population-specific conversion formula (see table 8.2).
You should adhere to established guidelines when using the HW technique (see
Guidelines for Hydrostatic Weighing).
384
In addition to the HW testing guidelines, following the suggestions in Tips for
Minimizing Error in Hydrostatic Weighing may improve the accuracy of your underwater
weighing measurements.
GUIDELINES FOR HYDROSTATIC WEIGHING
Pretest Guidelines for Clients
Do not eat or engage in strenuous exercise for at least 4 hr before your scheduled appointment.
Avoid ingesting any gas-producing foods or beverages (e.g., baked beans, diet soda) for at least 12 hr before
your test.
Bring a towel and a tight-fitting, lightweight swimsuit.
Testing Procedure Guidelines
Carefully calibrate the body weight scale and underwater weighing scale. To determine the accuracy of the
underwater weighing scale, hang calibrated weights from the scale and check the corresponding scale values.
To calibrate a load cell system, place weights on the platform and check the recorded values.
Have your client use the restroom to void and change into a swimsuit.
Measure the underwater weight of the chair or platform and of the supporting equipment and weight belt;
the total is the tare weight.
Measure your client’s dry weight (weight in air) to the nearest 50 g.
Check and record the water temperature of the tank just before the test; it should range between 34 and 36
°C. Use the constant values in figure 8.3 to determine the density of the water at that temperature.
Instruct your client to shower and then enter the tank slowly, so that the water stays calm. Have the client
gently submerge without touching the chair or weighing platform and rub hands over the body to eliminate
air bubbles from the swimsuit, skin, and hair.
Have the client kneel on the underwater weighing platform or sit in the chair. Your client may need to wear
a scuba diving weight belt to facilitate the kneeling or sitting position. If RV is being measured
simultaneously, insert the mouthpiece at this time. If RV is measured outside of the tank, administer the
RV test prior to the HW test and before the client changes clothes and showers.
Have the client take a few normal breaths and then exhale maximally while slowly bending forward at the
waist to submerge the head. Check to make certain that the client’s head, back, and hair are completely
underwater and that the arms and feet are not touching the sides or bottom of the tank. Instruct the client to
continue exhaling until RV is reached. The client needs to remain as still as possible during this procedure.
A relaxed and motionless state under water will aid in an accurate reading of UWW.
Record the highest stable weight with the client fully submerged at RV, then signal to the client that the
trial is completed.
385
Administer as many trials as needed to obtain three readings within ±100 g. Most clients achieve a
consistent and maximal UWW in four or five trials (Bonge and Donnelly 1989). Average the three highest
trials and record this value as the gross UWW.
Determine the net UWW by subtracting the tare weight from the gross UWW. The net UWW is used to
calculate body volume (see figure 8.3).
00:00 / 00:00
Video 8.1
TIPS FOR MINIMIZING ERROR IN HYDROSTATIC WEIGHING
Make sure clients adhere to all pretesting guidelines.
Before each test session, check the calibration of the BW and UWW scales or load cells, and carefully
calibrate the gas analyzers used to measure RV.
Precisely measure BW to ±50 g, UWW to ±100 g, and RV to ±100 ml.
Coach the client to maximally exhale and remain motionless under the water.
Steady the underwater weighing apparatus as the client submerges, but remove your hand from the scale
before actually reading the UWW.
If possible, use a load cell system and measure RV and UWW simultaneously.
Carry the calculated Db value out to five decimal places. Rounding off a Db of 1.07499 g·cc−1 to 1.07 g·cc−1
corresponds to a difference of 2.2% BF when converted with the Siri (1961) 2C model formula.
If you are estimating %BF from Db with a 2C model, use the appropriate population-specific conversion
formula (see table 8.2).
Special Considerations
Some clients may have difficulty performing the HW test using these standardized
procedures. Accurate test results are highly dependent on the client’s skill, cooperation, and
motivation. This section addresses the use of modified HW procedures as well as other
386
questions and concerns about this method.
What should I do when my client is unable to blow out all the air from the lungs or remain
still while under water?
You will likely come across clients who are uncomfortable expelling all the air from their
lungs during HW. In such cases, you can weigh these individuals at functional residual
capacity (FRC) or total lung capacity (TLC) instead of RV. Thomas and Etheridge (1980)
underwater-weighed 43 males, comparing the body densities measured at FRC (taken at the
end of normal expiration while the person was submerged) and at RV (at the end of maximal
expiration). The two methods yielded similar results. Similarly, Timson and Coffman (1984)
reported that Db measured by HW at TLC (vital capacity + RV) was similar (less than 0.3%
BF difference) to that measured at RV if TLC was measured in the water. However, when
the TLC was measured out of the water, the method significantly overestimated Db. When
using these modifications of the HW method, you must still measure RV in order to calculate
the FRC or TLC of your client. Also, be certain to substitute the appropriate lung volume
(FRC or TLC) for RV in the calculation of BV.
People uncomfortable under water tend to have difficulty being still while fully submerged.
Your client’s movement under water causes the arm of the scale to move. In addition to
prolonging the time your client is under water, it may preclude your ability to confidently
determine your client’s underwater weight. The damping technique as described by Moon
and colleagues (2011) reduces the magnitude of the swings in the scale arm until the client
and chair become stable under water. Damping is performed by temporarily holding the
moving part of the scale (where the chair attaches) to apply an upward force that counters the
motion associated with submersion or movement in the chair. Gently releasing the hold prior
to the end of the maximal exhalation maneuver allows the scale arm to stabilize for a more
accurate measurement. The damping technique produced similar underwater weights
compared with hydrodensitometric assessments made via load cell and without damping
(Moon et al. 2011).
Because of their lower Db, clients with greater amounts of body fat are more buoyant than
leaner individuals; therefore, they have more difficulty remaining motionless while under the
water. To correct this problem, place a weighted scuba belt around the client’s waist. Be
certain to include the weight of the scuba belt when measuring and subtracting the tare
weight of the HW system.
What should I do when my clients are afraid to put their face in the water or are not
387
flexible enough to get their backs and heads completely submerged?
Occasionally, you will encounter clients who are extremely fearful of being submerged, who
dislike facial contact with water, or who are unable to bend forward to assume the proper
body position for HW. In such cases, a satisfactory alternative would be to weigh your clients
at TLC while their heads remain above water level. Donnelly and colleagues (1988)
compared this measure (i.e., TLCNS, or total lung capacity with head not submerged) to the
criterion Db obtained from HW at RV for 75 men and 67 women. Vital capacity was
measured with the subject submerged in the water to shoulder level. Regression analysis
yielded the following equations for predicting Db at RV, using the Db determined at
TLCNS as the predictor:
Males
Db at RV = 0.5829(Db at TLCNS) + 0.4059
r = .88; SEE = 0.0067 g·cc −1
Females
Db at RV = 0.4745(Db at TLCNS) + 0.5173
r = .85; SEE = 0.0061 g·cc −1
The correlations (r) between the actual Db at RV and the predicted Db at RV were high,
and the standard errors of estimate (SEE) were within acceptable limits. These equations
were cross-validated for an independent sample of 20 men and 20 women. The differences
between the Db from HW at RV and the predicted Db from weighing at TLCNS were quite
small (less than 0.0014 g·cc or 0.7% BF). This method may be especially useful for HW of
−1
older adults, obese individuals with limited flexibility, and people with physical disabilities.
Will the accuracy of the HW test be affected if I estimate RV instead of measuring it?
Several prediction equations have been developed to estimate RV based on an individual’s

age, height, gender, and smoking status (see appendix D.1, Prediction Equations for Residual
Volume). However, these RV prediction equations have large prediction errors (SEE = 400-
500 ml). When RV is measured, the precision of the HW method is excellent (≤1% BF).
However, this precision error increases substantially (±2.8%-3.7% BF) when RV is estimated
(Morrow et al. 1986). Therefore, always measure RV when you are using the HW method.
When is the best time during the menstrual cycle to hydrostatically weigh my female
388
clients?
Some women, particularly those whose body weight fluctuates widely during their menstrual
cycles, may have significantly different estimates of Db and %BF when weighed
hydrostatically at different times in their cycles. Bunt, Lohman, and Boileau (1989) reported
that changes in total body water values due to water retention partly explain the differences in
body weight and Db during a menstrual cycle. On the average, the relative body fat of the
women was 24.8% at their lowest body weights, compared with an average of 27.6% BF at
their peak body weights during their menstrual cycles. Because their low and peak body
weights occurred at different times during the menstrual cycle (varied from 0 to 14 days prior
to the onset of the next menses), the effect of total body water fluctuations cannot be
routinely controlled by using the same day of the menstrual cycle for all women. However,
when you are monitoring changes in body composition over time or establishing healthy body
weight for a female client, it is recommended that you hydrostatically weigh her at the same
time within her menstrual cycle and outside of the period of her perceived peak body weight.
AIR DISPLACEMENT PLETHYSMOGRAPHY

Air displacement plethysmography (ADP) is a method to assess body volume and density.
This method uses air displacement instead of water displacement to estimate volume. Because
ADP is quick (usually 5-10 min) and requires minimal client compliance and minimal
technician skill, it is considered a viable alternative to hydrostatic weighing. The ADP
method requires a whole-body plethysmograph such as the Bod Pod. The Bod Pod is a large,
egg-shaped fiberglass chamber that uses air displacement and pressure-volume relationships
to estimate body volume (see figure 8.4).
389
FIGURE 8.4 Air displacement plethysmograph.
The Bod Pod system consists of two chambers: a front chamber in which the client sits
during the measurement and a rear reference chamber. A molded fiberglass seat forms the
wall between the two chambers, and a moving diaphragm mounted in this wall oscillates
during testing (figure 8.5). The oscillating diaphragm creates small volume changes between
the two chambers. These changes are equal in magnitude but opposite in sign, and they
produce small pressure fluctuations. The pressure-volume relationship is used to calculate the
volume of the front chamber when it is empty and when the client is sitting in it. Body
volume is calculated as the difference in the volume of the chamber with and without the
client inside.
390
FIGURE 8.5 Two-chamber Bod Pod system.
The principle underlying ADP centers on the relationship between pressure and volume.
At a constant temperature (isothermal condition), volume (V) and pressure (P) are inversely
related. According to Boyle’s law,
P /P =V /V,
1 2 2 1
where P and V represent one paired condition of pressure and volume and P and V
1 1 2 2
represent another paired condition. P and V correspond to the pressure and volume of the
1 1
Bod Pod when it is empty; P and V represent the pressure and volume of the Bod Pod when
2 2
the client is in the chamber.

One assumption of the ADP method is that the Bod Pod controls the isothermal effects of
clothing, hair, thoracic gas volume, and body surface area in the enclosed chamber. Bod Pod
clients are tested while wearing minimal clothing (e.g., a swimsuit) and a swim cap to
compress the hair. An estimate of the body surface area, calculated from the height and
weight of the client, is used to correct for the isothermal effects at the body’s surface.
Thoracic gas volume (TGV) is the volume of air in the lungs (functional residual capacity) at
midexhalation. The TGV is either directly measured or estimated by the Bod Pod to account
for the isothermal conditions in the lungs. The Bod Pod software calculates the functional
residual capacity at end exhalation by subtracting one-half of the exhaled tidal volume from
the FRC measured at midexhalation.
Numerous studies have assessed the accuracy of the Bod Pod for measuring Db. Several
researchers reported only small differences in average Db (≤0.002 g·cc ) measured by the Bod
−1
391
Pod and HW (Fields et al. 2001; Vescovi et al. 2001; Yee et al. 2001). Some studies have
reported slightly higher and statistically significant differences (0.003-0.007 g·cc ) in adults
−1
(Collins et al. 1999; Demerath et al. 2002; Dewit et al. 2000; Millard-Stafford et al. 2001;
Wagner, Heyward, and Gibson 2000). In high school male athletes and collegiate track and
field female athletes, the Bod Pod significantly underestimated average body density
(Bentzur, Kravitz, and Lockner 2008; Moon et al. 2008).
Several studies, however, showed good group prediction errors (SEE ≤ 0.008 g·cc ) for −1
adults (Fields, Hunter, and Goran 2000; Nunez et al. 1999; Wagner, Heyward, and Gibson
2000). Compared with multicomponent body composition models, the Bod Pod and HW
methods have similar predictive accuracy (Fields et al. 2001). However, against a
multicomponent reference model, ADP significantly overestimates fat-free mass of elite male
rowers (Kendall et al. 2017). Regardless, since the Bod Pod is more accommodating than
HW, there is much interest in further exploring the validity of the ADP method for
estimating %BF in clinical populations and special populations such as children and older
adults (Heyward and Wagner 2004).
Compared with dual-energy X-ray absorptiometry (DXA), ADP was strongly correlated (r
= .88) and similar, on average, for body fat estimation; however, there may be large individual
differences between the two methods, suggesting they are not interchangeable for young
healthy women (Edwards, Simpson, and Buchholz 2011). Hurst and associates (2016) found
no differences between %BF from DXA (Hologic QDR Discovery A) and Bod Pod for their
large sample of adults (19-71 yr). However, the individual differences reflected by the limits
of agreement were large (−6.1%-6.9% BF). Sex-specific differences were also noted. The Bod
Pod underestimated the DXA %BF values for the lean men and overestimated the DXA
%BF values for women with high levels of body fat. When looking at the entire sample, the
opposite is true. ADP overestimated %BF for the lean participants while underestimating the
DXA results for those having higher body fat levels (Hurst et al. 2016). Having recruited
adults from the underweight, normal weight, and overweight/obese BMI categories, Lowry
and Tomiyama (2015) compared %BF values between DXA (Lunar Prodigy) and ADP.
Their results align somewhat with those of Hurst and colleagues by indicating that ADP
overestimates %BF from DXA for those in the lower BMI ranges but, conversely,
underestimates the DXA %BF of those in the higher BMI ranges. The magnitude of the
significant mean differences was 7.6% and 2.1% for the underweight and overweight/obese
participants, respectively.
More recently, Gibby and colleagues (2017) compared %BF values from ADP and HW
392
against those from whole-body computed tomography (CT) for a sample that was
predominantly male. Correlations between CT and the densitometry methods were strong
(>.95), and the only reported mean differences involved comparisons with HW. On average,
%BF from HW compared with ADP was lower by approximately 3.1%. The confidence
interval (limits of agreement) for the densitometry method comparison was 1.28% to 4.28%
BF, with ADP resulting in the higher %BF values. The two %BF conversion algorithms used
with the CT significantly overestimated %BF from HW by 2.32% and 1.94%, respectively
(Gibby et al. 2017).
Using the ADP Method

The Bod Pod is user friendly, providing computer prompts for each step of the procedure.
Air displacement plethysmography is faster and easier than HW; researchers reported better
compliance with ADP and a preference for ADP over HW among participants, including
among children (Demerath et al. 2002; Dewit et al. 2000; Lockner et al. 2000). To increase
the likelihood of accurate body composition assessments with ADP, give your client
pretesting instructions before the scheduled appointment. These instructions are similar to
those for HW (see Guidelines for Hydrostatic Weighing). For step-by-step instructions for
Bod Pod testing, see Testing Procedures for the Bod Pod.
Special Considerations for the ADP Method

Accurate test results from the Bod Pod depend on a number of factors. The following
questions address these factors.
How will the test results be affected if my client has excess body hair?
As mentioned earlier, isothermal air trapped in body hair may affect test results. For clients
with beards, %BF may be underestimated by 1%; when scalp hair is exposed (no swim cap),
relative body fat is underestimated by about 2.3% BF (Higgins et al. 2001). Wearing a tight-
fitting swim cap and shaving excess facial and body hair ensure the most accurate estimate of
body volume and Db.
Can I use the Bod Pod to measure the body composition of children?
During the 20 sec test, the client must remain very still, as the body volume estimate from the
ADP method can vary if the client moves during testing. Fields and Goran (2000)
commented that it took twice as long to measure children compared with adults, primarily
because children move during the test. This was substantiated by Crook and colleagues
(2012). They reported that the BV measurement took 50 sec for their sample of children aged
393
3 to 5 yr. As a result of children’s tendency to move during the assessment, the test-retest
reliability of the Bod Pod is lower in children (r = .90) than in adults (r = .96) (Demerath et
al. 2002).
Also, several researchers commented that body size may affect Bod Pod estimates, with the
largest effects seen in the smallest clients (Demerath et al. 2002; Lockner et al. 2000; Nunez
et al. 1999; Rosendale and Bartok 2012). The Pea Pod was developed to assess the body
composition of infants younger than 6 mo and weighing less than 8 kg. A pediatric option for
the Bod Pod is now commercially available to address the smaller body size of young
children. The pediatric option utilizes a smaller calibration cylinder (19.993 L), a booster
seat, and special software modifications. Research, however, is not unanimous in terms of the
accuracy of the pediatric modifications. Crook and associates (2012) reported that the
pediatric option %BF estimates for children (3-5 yr) were weakly correlated (r < .18) to the
reference measure calculated following isotope dilution. They also reported that the limits of
agreement were large. Conversely, Fields and Allison (2012) reported that compared with a
four-component (4C) reference measure, the pediatric option is reliable, precise, and accurate
for estimating body fat levels of children aged 2 to 6 yr. It is important to note, however, that
the BV parameter included in the 4C model was derived from the Bod Pod assessment
(Fields and Allison 2012). This area requires further investigation.
TESTING PROCEDURES FOR THE BOD POD
Instruct the client to change into dry, form-fitting swimwear and to completely void the bladder and bowels.
Measure the client’s height to the nearest centimeter and body weight to the nearest 5 g using the Bod Pod
scale. These measures are used to calculate body surface area.
Perform the two-point calibration: (a) baseline calibration with the chamber empty and (b) phantom
calibration with a 50 L calibration cylinder. Be careful when handling the calibration cylinder; a dent in the
cylinder alters its volume. If measuring TGV, attach the microbial filter and breathing tube according to the
manufacturer’s guidelines.
Instruct your client to sit still and upright in the chamber, with the back against the wall and feet on the
floor. Remind your client to breathe normally during the upcoming 20 sec test. Then close the door tightly.
Follow the prompts and then open the door and close it again tightly; repeat the 20 sec test. If the two tests
disagree by more than 150 ml, perform additional tests until two results agree within 150 ml; average these
and use them to calculate raw BV.
Open the door and instruct the client about the steps they need to follow during the TGV measurement. Be
sure you give your client a nose clip or instructions on pinching the nostrils shut during the TGV
measurement. All air exchange during the TGV measurement needs to take place via the single-use
394
microbial filter and breathing tube.
Instruct your client to follow the breathing cycle prompts and close the door. After a few tidal volume
(normal) breathing cycles, the airway is occluded by the Bod Pod software. Guide your client through the
puffing maneuver. If the computer-calculated figure of merit (indicating similar pressure signals in the
airway and chamber) is not met, repeat this step.
00:00 / 00:00
Video 8.2
Is it absolutely necessary that my client wear a swimsuit and swim cap during the Bod Pod
test?
The original investigators of the Bod Pod recognized that the isothermal effect of clothing
leads to an underestimation of body volume; they recommended that clients wear only a
swimsuit and swim cap during testing to minimize this effect (Dempster and Aitkens 1995;
McCrory et al. 1995). Silicone swim caps more thoroughly compress scalp hair compared
with Lycra swim caps; for a sample of Caucasian women, the body fat percentage was an
average of 1.2% higher when wearing the silicone swim cap (Peeters and Claessens 2011).
More or loose-fitting clothing leads to a larger layer of isothermal air and a greater
underestimation of body volume. For example, wearing a hospital gown instead of a swimsuit
lowers %BF by about 5% (Fields et al. 2000). Thus, the clothing recommendation needs to be
followed.
As long as they sit still, do I need to closely monitor how my clients are positioned inside
the Bod Pod?
Deviations from the upright seated position may affect the volume of isothermal air in the
pulmonary tree. This may cause an incorrect calculation of raw body volume. To test the
influence of body position, Peeters (2012) assessed the body volume (at measured TGV) of
young men in the standard seated position and in a forward leaning position (slight hip
flexion, shoulders hanging, and back curved). Although the measured TGV did not differ by
395
position, body volume was significantly smaller in the forward leaning position; the 86 ± 122
ml difference in body volume resulted in a small (0.5% ± 0.7%) yet significant difference in
body fat estimations. Consequently, to increase the test-retest reliability of your assessments,
standardize your instructions to the client regarding body position (Peeters 2012). Adhering
to this recommendation may prove critical in research studies.
Do I need to measure my client’s TGV or can I use a predicted TGV?

Although McCrory and colleagues (1998) reported an insignificant difference (54 ml)
between measured and predicted TGV, the SEE was large (442 ml). Some researchers have
reported larger mean differences (344-400 ml) and SEEs (650 ml) (Collins et al. 1999;
Lockner et al. 2000). Given that only 40% of the TGV value is used to calculate body
volume, using a predicted TGV has a relatively smaller effect on Db and %BF compared with
using a predicted RV for the HW method. Nevertheless, a measured TGV maximizes
accuracy.
For top-level collegiate athletes at the extremes of the height distributions, Wagner (2015)
recommends measuring TGV instead of predicting it. Although no significant difference
between measured and predicted TGVs was reported for the athletes, there was a significant
negative bias. This means the predicted TGV was larger than the measured TGV at small
volumes, with the opposite being true for high volumes.
If I measure TGV, should I take one or more measurements?
To date, few researchers mention averaging multiple measurements of thoracic gas volume.
Some that do, however, are suggesting that at least two TGV measurements be taken and
averaged (Gibson, Roper, and Mermier 2016; Noreen and Lemon 2006; Tucker,
Lechiminant, and Bailey 2014) even though doing so may increase the amount of time
required. Notable individual between-trial differences in Db, BV, and %BF have been
reported. If using Db from ADP in a multicomponent model, it is recommended that the
average of multiple TGV measurements be used for Db calculations.
Do I need to closely monitor how my client breathes during the body volume and TGV
measurement procedures?
Software revisions now guide the client through the tidal volume breathing cycles during
TGV assessment. The client breathes in synchronization with the in and out prompts
displayed on the computer screen, even if this is not her normal breathing pattern. However,
if your Bod Pod does not have this software upgrade, then deviations in normal tidal
396
breathing can affect body volume and %BF estimations.
For example, Tegenkamp and colleagues (2011) instructed their subjects to alter their tidal
volume breathing patterns and found significant (p < .001) differences in %BF estimations as
a result. During the body volume measurement procedure, deeper-than-normal breathing
resulted in an average body fat percentage that was 2.1% lower. On the other hand, breathing
more shallowly resulted in a %BF that was 2.2% higher. Differences in the opposite direction
were recorded when breathing patterns were altered only during the TGV measurement.
Shallower breathing underestimated body fat by an average of 3.4%, while deeper breathing
increased body fat estimations by 3.7% (Tegenkamp et al. 2011). Standardizing instructions
to your client regarding the need to inhale and exhale as normally as possible throughout the
body volume and TGV measurements is important to obtain the best estimates of their body
fat percentage.
Does the Bod Pod yield a valid and reliable measure of functional residual capacity?
Davis and colleagues (2007) compared FRC measures obtained from the Bod Pod and
traditional gas dilution techniques in healthy males and females (18-50 yr). The FRC at
midexpiration as measured by the Bod Pod was corrected to an end-exhalation volume by
subtracting approximately one-half of the measured tidal volume. The mean difference
between FRC from the Bod Pod and gas dilution FRC measures was −32 ml for males (r =
.925; SEE = 0.246 L) and −23 ml for females (r = .917; SEE = 0.216 L). The test-retest
reliability of the Bod Pod FRC was excellent (r = .95-.97). These results suggest that the Bod
Pod provides a valid and reliable measure of FRC in healthy adults.
If I use both hydrostatic weighing and the Bod Pod to measure my client’s body
composition, which test should I give first?
The Bod Pod manufacturer recommends testing clients under resting conditions and when
the body is dry. Although there are no known studies indicating the amount of error that may
occur if these guidelines are violated, experts suggest adhering to these recommendations
(Fields, Goran, and McCrory 2002). Thus, if a test battery includes both HW and ADP,
administer the Bod Pod test first. If doing so is not possible, make certain your client is
completely dry and fully recovered from the HW test before you administer the Bod Pod test.
Does exercise before Bod Pod testing affect the results?
The effect of an acute bout of exercise prior to body composition assessment via ADP was
investigated by Harrop and Woodruff (2015). Compared with preexercise, 30 min of cycling
397
at 75% HRmax (age predicted) resulted in numerous significant and sex-specific differences
postexercise and during the repeat testing 2 hr postexercise. Similarly, Grossman and Deitrick
(2015) compared ADP results at baseline against those acquired 2 hr after a resistance
training workout lasting approximately 1 hr. Their finding of significant differences in %BF,
body mass, BV, and FM supports the findings of Harrop and Woodruff (2015). The Bod
Pod manufacturer’s recommendation is that clients refrain from exercise for 2 hr before their
ADP test. However, significant changes in body composition variables 2 hr postexercise
indicate that refraining from exercise for only 2 hr is insufficient. Consequently, the accuracy
of the ADP results may be affected.
Which model and equation should I use to convert Db to %BF?

Using a multicomponent model and a population-specific conversion formula increases the
group and individual accuracy of %BF estimates. The default equation in the Bod Pod
software is the Siri (1961) two-component model formula for non-black adults. A formula
for black adults is also available. In field settings, these 2C conversion formulas may be
appropriate for some clients with certain demographic characteristics. For other clients, you
may need to select an appropriate population-specific 2C model formula (see table 8.2).
DUAL-ENERGY X-RAY ABSORPTIOMETRY

Dual-energy X-ray absorptiometry (DXA) is increasingly used as a reference method for
body composition research (see figure 8.6), especially in clinical settings. This method yields
estimates of bone mineral, fat, lean soft tissue, and visceral adipose tissue (VAT) mass. It is
also possible to obtain estimates of these variables at the whole-body level and the regional
(trunk and appendicular) level. Dual-energy X-ray absorptiometry is an attractive alternative
to HW because it is safe and rapid, it requires minimal client cooperation, and, most
important, it accounts for individual variability in bone mineral content. The time required
per whole-body scan ranges from approximately 3 to 20 min, depending on the model, type
of scan beam, and age of the DXA machine as well as the stature of the client (Bazzocchi et
al. 2016).
398
FIGURE 8.6 Dual-energy X-ray absorptiometer.
© 2006 General Electric Company
The basic principle underlying DXA technology is that the attenuation of X-rays with high
and low photon energies is measurable and dependent on the thickness, density, and chemical
composition of the underlying tissue. The attenuation, or weakening, of X-rays through fat,
lean tissue, and bone varies because of differences in the densities and chemical compositions
of these tissues. The attenuation ratios for the high and low X-ray energies are thought to be
constant for all individuals (Pietrobelli et al. 1996).
It is difficult to assess the validity of the DXA method because each of the three
manufacturers of DXA instruments (General Electric, Hologic, and Norland) has developed
its own models and software over the years. As researchers and clinicians have discovered,
body composition results vary with manufacturer, model, and software version. Thus, some of
the variability reported in DXA validation studies may be due to the different DXA scanners
and software versions. Thus experts who have reviewed DXA studies have called for more
standardization among manufacturers (Genton et al. 2002; Lohman 1996).
Some researchers have reported that the predictive accuracy of DXA is better than that of
HW (Fields and Goran 2000; Friedl et al. 1992; Prior et al. 1997; Wagner and Heyward
2001; Withers et al. 1998). However, the opposite finding (that HW is more accurate than
DXA) has also been reported (Bergsma-Kadijk, Baumeister, and Deurenberg 1996; Goran,
Toth, and Poehlman 1998; Millard-Stafford et al. 2001). In a review of DXA studies,
Lohman and colleagues (2000) concluded that DXA estimates of %BF are within 1% to 3%
of multicomponent model estimates.
Although some body composition prediction equations have been developed and validated
with DXA as the reference method, further research is needed before DXA can be firmly
established as the best reference method. Toombs and colleagues (2012), in their review of
the metamorphosis of DXA technology, support the call for additional research using 4C and
399
human cadaver criterion measures before labeling DXA as a gold standard for body
composition assessment. Still, the DXA method is widely used in light of its availability, ease
of use, and low radiation exposure (Yee and Gallagher 2008). Regardless, caution is urged
when interpreting the results of DXA comparison studies given equivocal findings such as
large intra-individual and significant group mean differences in body fat percentage compared
with 4C measures (Toombs et al. 2012).
Using the DXA Method

The DXA method requires minimal client cooperation during the scan and minimal
technical skill. However, to use the scanner to get precise and accurate DXA scans, proper
training and client preparation are essential. Many states require that a licensed X-ray
technician perform the scan under direct supervision of a medical doctor. For general
procedures for DXA testing, see Basic Testing Procedures for DXA.
In terms of client preparation for DXA scanning, give the client a reminder call or send an
email or text 24 hr before the appointment. In this reminder, instruct your client to arrive in a
rested, fasted state with no fluid intake prior to the scan and to refrain from exercise until
after the scan (Nana et al. 2015).
Special Considerations
The accuracy of DXA results depends on a number of factors. The following questions
address some of these factors.
Will my client’s body size and hydration state affect the test results?
In the past, the DXA method was not recommended for assessing the body composition of
clients whose body dimensions exceed the length or width of the scanning bed. Techniques
and software options are now available to accommodate people whose height or width is
outside the scanning field. These options require repositioning the body on the scan bed and
performing a half-body (hemiscan procedure) analysis (Bazzocchi et al. 2016) or multiple
partial body scans (Nana et al. 2015). The results of these partial scans need to be summed in
order to estimate body composition.
BASIC TESTING PROCEDURES FOR DXA
Before testing, calibrate the DXA scanner with a calibration marker provided by the manufacturer.
Have the client void the bladder and bowels and remove all jewelry.
400
Measure the client’s height and weight, with the client wearing minimal clothing (e.g., underwear, wireless
crop top, hospital gown) and no shoes. There should be no chlorine or salt in the clothing.
Carefully guide the client into a supine position on the scanner bed for a head-to-toe anteroposterior scan.
Use a skeletal anthropometer to accurately determine body thickness (see the Sagittal Abdominal Diameter
section later in this chapter).
Some scanners have different acquisition modes so scan speeds can be adjusted for body thickness. Such
adjustments are usually made automatically by new model scanners. Slow speed scans (~10 to 15 min) are
appropriate for clients with sagittal abdominal diameters exceeding 27 cm (10.6 in.). GE Lunar scanners
alert the technician to switch to “thick” scan mode if the client surpasses the manufacturer’s cutoff for body
weight. Hologic scanners can switch to the “high power whole-body” mode (International Atomic Energy
Association 2010). The hemiscan procedure (positioning the client off center on the scan table so one side
of the body is completely within the scan field) can be used for clients too wide for the scan table. Resulting
values are doubled to derive total body values (International Atomic Energy Association 2010).
Adapted from Nana et al. 2015; International Atomic Energy Association 2010.
In terms of hydration, DXA algorithms assume the adult FFB is consistently 73% water
(Toomey, McCormack, and Jakeman 2017). Research has shown that small fluctuations in
hydration have little effect on DXA estimates of fat mass or bone (Toomey, McCormack,
and Jakeman 2017). Conversely, Nana and colleagues (2012) reported that many dietary
factors affect regional body composition estimates more than they affect total and lean mass
estimates. Consequently, DXA scan precision may be maximized if the client arrives in a
fasted (Nana et al. 2015) and euhydrated (Rodriguez-Sanchez and Galloway 2015) state. For
athletes, lean tissue mass assessment will be more accurate if they strictly adhere to pretest
instructions regarding exercise, euhydration, and postexercise nutritional compensation
(Toomey, McCormack, and Jakeman 2017).
Does it matter if my client has fasted and is rested when having a DXA scan?
As just mentioned, fasting prior to a DXA scan increases the accuracy of the body
composition assessment and provides a level of standardization that is important for repeat
measurements. An investigation of young adults revealed that performing activities of daily
living (meal consumption and typical physical activity) before a DXA scan does not
significantly influence body fat estimates (Nana et al. 2012). However, other regional and
total body composition results are affected. Substantial increases in the mean estimate and
typical error were noted in the regional and total lean mass as well as total body mass. For the
women, eating prior to the DXA scan substantially increased the mean bone mineral content
(BMC) values of the total body, trunk, and arms. To learn more about the influence of usual
daily activities, meal consumption, and within- and between-day variability on DXA
401
measurements, see the work of Nana and colleagues (2012). Nana and associates (2015) offer
a best practice protocol for whole-body scanning of athletes and active people. This
positioning protocol differs somewhat from the protocol used by NHANES (Centers for
Disease Control and Prevention 2013). Kerr and associates (2016) reported that the two
protocols are not interchangeable and that the measurement precision of the arms and trunk
are increased with the positioning protocol of Nana and colleagues.
For client comfort and compliance, is DXA better than other reference methods?
According to anecdotal feedback on positioning protocols, participants in the study by Kerr’s
research team (2016) preferred the protocol of Nana and associates (2015) over the
NHANES protocol. Compared with a 4C model reference, the new iDXA scanner (GE
Healthcare Lunar) differed significantly in the calculations of fat mass (FM) for a sample of
adults (Watson, Venables, and Murgatroyd 2017). The iDXA overestimates the reference
FM of the participants whose FM values are below 32 kg and underestimates the reference
when FM values exceed 32 kg.
Compared with other reference methods, DXA requires little client participation. The
client does not need to perform the breathing maneuvers required for measuring RV for
hydrostatic weighing and TGV for air displacement plethysmography. The BV component of
a 4C model traditionally comes from a densitometric method (HW or ADP). Recently, a 4C
model deriving its BV component from ADP was compared against a 4C model with the BV
being derived from DXA (Smith-Ryan et al. 2017). There were no differences between 4C
models for %BF, FM, and lean mass, although the limits of agreement for %BF were large
(−5.33 to 7.01 %BF). In addition to eliminating client errors in UWW or ADP’s thoracic gas
volume measurements, the DXA body volume method may eventually provide all but the
water component for a 4C reference measure of body fat. Additional research and cross-
validation using each style of DXA machine is required before eliminating the densitometric
assessment of body volume for multicomponent models.
How do the various DXA machines and software versions affect test results?
As mentioned earlier, variability among DXA technologies is a major source of error.
Although all DXA equipment uses the same underlying physical principles, the instruments
differ in their generation of high- and low-energy beams (filter or switching voltage), imaging
geometry (pencil beam, fan beam, or narrow fan beam), X-ray detectors, calibration
methodology, and algorithms (Genton et al. 2002). Comparisons of older (Lunar Prodigy)
and newer (iDXA) scanners made by GE Healthcare indicate the correlation between the
402
devices is strong, and there is no significant difference between means for whole-body values
in samples of adults spanning the BMI range (Morrison et al. 2016; Reinhardt et al. 2017).
However, in the Bland and Altman plots of individual variation, the %BF from iDXA is
higher than from the Lunar Prodigy in lean adults; the opposite is true for heavier adults
(Reinhardt et al. 2017). Reports of significant interdevice differences in regional (arms, legs,
and torso) values also appear in the recent literature (Morrison et al. 2016; Oldroyd,
Treadgold, and Hind 2017).
BODY COMPOSITION ASSESSMENT USING 3D BODY SURFACE SCANNING
Historically used in the clothing industry, whole-body scanning with three-dimensional (3D) body surface scanners is
gaining recognition as a quick, noninvasive, and precise automated method for quantifying anthropometric measures of
length, circumference, and volume (Löffler-Wirth et al. 2016; Ng et al. 2016; Soileau et al. 2016). Device reliability is
high regardless of operator skill level (Zancanaro et al. 2015). More expensive high-resolution laser-based systems are
useful in clinical settings but may soon be rivaled by systems derived from inexpensive Microsoft Corporation Kinect
devices. Soileau and colleagues (2016) compared 3D whole-body scanning results from a Kinect-based system against a
laser-based system reference, ADP, and stadiometry for a sample of healthy adults and children. Differences in estimated
height, waist circumference, body volume, and body surface area were small compared with reference measures.
Conversely, smaller and more distal features had much larger differences. Although there are some anatomical feature
acquisition issues yet to be resolved, whole-body 3D scanning holds promise as a valid and reliable method to quickly
assess anthropometric measurements without the need to identify bony landmarks. In the future, using a Kinect-based
system may prove suitable for field-based epidemiological use.
Because of technological differences, you should use the same DXA device and software
version for longitudinal assessments or cross-sectional comparisons of body composition. If
upgrading DXA machines or software, perform same-day scans of a representative sample
using both versions. By doing so, you will have the information necessary to create a
prediction equation useful in converting the older data, bringing it into alignment with results
from the upgrade (Camhi et al. 2011). Equations to convert %BF values between the Lunar
Prodigy and iDXA have been created and validated by multiple research teams (Oldroyd,
Treadgold, and Hind 2017; Reinhardt et al. 2017). Likewise, Xu, Chafi, and colleagues
(2016) developed equations to convert %BF, BMD, BMC, and total mass values between the
Hologic Discovery and iDXA scanners.
Is the DXA method safe for my clients, given that it uses X-rays to estimate body
composition?
Even though two X-ray energies are passed through the body during a scan, the radiation
exposure is very low. For example, a standard chest X-ray has a radiation dose of 50
403
microSeiverts (μSv), and a whole-body DXA scan is in the range of 0.2 to 0.5 μSv (Nana et
al. 2015). Even so, the International Society for Clinical Densitometry does not recommend
DXA scans for pregnant women. The radiation exposure depends on the scanner’s
manufacturer, model, and mode of scan. The scan mode is based on the anteroposterior
thickness (sagittal abdominal diameter) of the client. Mode options are thin, standard, and
thick. Mode selection is typically done automatically based on BMI, but it can be changed by
the technician. The thicker the client, the slower the scan; this may slightly increase radiation
exposure (Nana et al. 2015).
Is the DXA method recommended for estimating visceral adipose tissue (VAT)?
Computed tomography and magnetic resonance imaging (MRI) are considered the gold
standards for VAT assessment, but both increase radiation exposure as compared with DXA.
The Lunar Prodigy and iDXA fan-beam technology provides the ability to automatically and
separately identify VAT from subcutaneous adipose tissue (SAT). In the VAT estimation
study by Cheung’s research team (2016), the Lunar Prodigy significantly underestimated
VAT as compared with MRI for their sample of older men (61.6 ± 6.5 yr). The DXA-MRI
and DXA-CT correlations were strong (r > .80); no comparison of mean differences between
DXA and the CT was reported (Cheung et al. 2016). After comparing the two methods in a
sample of 40 adults, Reinhardt and associates (2017) concluded the iDXA is a suitable
alternative to MRI for assessing VAT in research studies. Consequently, both the Lunar
Prodigy and iDXA offer a less expensive and safer option for VAT assessment.
FIELD METHODS FOR ASSESSING BODY COMPOSITION

In field settings, you can use more practical methods to estimate your clients’ body
composition. Your choices include skinfolds, ultrasound, bioelectrical impedance, and other
types of anthropometric prediction equations. To use these methods and equations
appropriately, you need to understand the basic assumptions and principles as well as the
potential sources of measurement error for each method. You must closely follow
standardized testing procedures, and you must practice in order to perfect your measurement
techniques. For more detailed information about these field methods and how they are
applied to various population subgroups, see Heyward and Wagner (2004) and Wagner
(2013).
SKINFOLD METHOD
A skinfold (SKF) indirectly measures the thickness of subcutaneous adipose tissue. When
404
you use the SKF method to estimate total Db in order to calculate relative body fat (%BF),
certain basic relationships are assumed:
• A SKF is a good measure of subcutaneous fat. Research has demonstrated that the
subcutaneous fat value obtained by SKF measurements at 12 sites is similar to the value
obtained from magnetic resonance imaging (Hayes et al. 1988).
• The distribution of fat subcutaneously and internally is similar for all individuals within each
gender. The validity of this assumption is questionable. There are large interindividual
differences in the patterning of subcutaneous adipose tissue within and between genders
(Martin et al. 1985). Older subjects of the same gender and Db have proportionately less
subcutaneous fat than their younger counterparts. Also, lean individuals have a higher
proportion of internal fat, and the proportion of fat located internally decreases as overall
body fatness increases (Lohman 1981).
• Because there is a relationship between subcutaneous fat and total body fat, the sum of several
SKFs can be used to estimate total body fat. Research has established that SKF thicknesses at
multiple sites measure a common body fat factor (Jackson and Pollock 1976; Quatrochi et al.
1992). It is assumed that approximately one-third of the total fat is located subcutaneously in
men and women (Lohman 1981). However, there is considerable biological variation in
subcutaneous, intramuscular, intermuscular, and internal organ fat deposits (Clarys et al.
1987), as well as in essential lipids in bone marrow and the central nervous system. Age,
gender, and degree of fatness all affect variation in fat distribution (Lohman 1981).
• There is a relationship between the sum of SKFs (∑SKF) and Db. This relationship is linear
for homogeneous samples (population-specific SKF equations) but nonlinear over a wide
range of Db (generalized SKF equations) for both men and women. A linear regression line
depicting the relationship between the ∑SKF and Db will fit the data well only within a
narrow range of body fatness values. Thus, you will get an inaccurate estimate if you use a
population-specific equation to estimate the Db of a client who is not representative of the
sample used to develop that equation (Jackson 1984).
• Age is an independent predictor of Db for both men and women. Using age and the quadratic
expression of the sum of skinfolds (∑SKF ) accounts for more variance in Db of a
2
heterogeneous population than using the ∑SKF alone (Jackson 1984).

2
Using the Skinfold Method

Skinfold prediction equations are developed using either linear (population specific) or
quadratic (generalized) regression models. There are well over 100 population-specific
405
equations for predicting Db from various combinations of SKFs, circumferences, and bony
diameters (Jackson and Pollock 1985). These equations were developed for relatively
homogeneous populations, and they are assumed to be valid only for individuals having
similar characteristics, such as age, gender, ethnicity, or level of physical activity. For example,
an equation derived specifically for 18 to 21 yr old sedentary men would not be valid for
predicting the Db of 35 to 45 yr old sedentary men. Population-specific equations are based
on a linear relationship between SKF fat and Db (linear model); however, research shows a
curvilinear relationship (quadratic model) between SKFs and Db across a large range of body
fatness (see figure 8.7). Population-specific equations will tend to underestimate %BF in
fatter subjects and overestimate it in leaner subjects.
FIGURE 8.7 Relationship of sum of skinfolds to body density.

Reprinted from A.S. Jackson and M.L. Pollock, “Generalized Equations for Predicting Body Density of Men,” British Journal of Nutrition 40 no. 3 (1978): 497-504. © 1978 The Nutrition Society. Reprinted with the permission
of Cambridge University Press.
Using the quadratic model, Jackson and colleagues (Jackson and Pollock 1978; Jackson,
Pollock, and Ward 1980) developed generalized equations applicable to individuals varying
greatly in age (18-60 yr) and body fatness (up to 45% BF). These equations also take into
account the effect of age on the distribution of subcutaneous and internal fat. An advantage
of the generalized equations is that you can use one equation, instead of several, to accurately
estimate your clients’ %BF.
Given that these generalized SKF equations were developed on predominately white
adults, Jackson and colleagues (2009) cross-validated the equations with samples of young
white, Hispanic, and African-American men and women (17-35 yr). The DXA method was
406
used to obtain reference measures of %BF for 706 women and 423 men. Although the
generalized SKF equations were highly correlated (r = .91) with %BF , these equations
DXA
lacked accuracy when applied to racially and ethnically diverse samples. New race-specific
equations have been developed and reported. Practitioners who use the new DXA-based
equations are urged to use ones that have undergone independent cross-validation.
Most equations use two or three SKFs to predict Db. Experts recommend using equations
that have SKF measures from a variety of sites, including both upper and lower body sites
(Martin et al. 1985). The Db is then converted to %BF using the appropriate population-
specific conversion formula (see table 8.2). Table 8.3 presents commonly used population-
specific and generalized SKF prediction equations. Select the appropriate SKF equation and
population-specific conversion formula in table 8.2 to estimate %BF based on physical
demographics (e.g., age, gender, ethnicity, and physical activity level) of your clients. Using
these equations, you can accurately estimate the %BF of your clients within the
recommended value, ±3.5% BF (Lohman 1992).
407
Alternatively, nomograms exist for some SKF prediction equations. The nomogram in
figure 8.8 was specifically developed for the Jackson sum-of-three-SKFs equations. To use
this nomogram, plot the sum of three skinfolds (∑3SKF) and age in the appropriate columns,
and use a ruler to connect these two points. The corresponding %BF is read at the point
where the connecting line intersects the %BF column on the nomogram.
FIGURE 8.8 Nomogram to estimate percent body fat of college-age men and women using the Jackson sum-of-three-
skinfolds equations.
From A nomogram for the estimate of percent body fat from generalized equations, by W.B. Baun, M.R. Baun, and P.B. Raven, 1981, Research Quarterly for Exercise and Sport, 52(3), pg. 382. Copyright 1981 by American Alliance
for Health, Physical Education, and Dance, 1900 Association Drive, Reston, VA 20191.
Although nomograms are potential time-savers, you should be aware that this nomogram
is based on a two-component body composition model, using the Siri equation to convert Db
to %BF. In general, use this nomogram only to calculate %BF of clients with an estimated
fat-free body density of 1.100 g·cc (see table 8.2). −1
Skinfold Technique
It takes a great deal of time and practice to develop your skill as a skinfold technician.
Following standardized procedures (see Standardized Procedures for Skinfold Measurements)
408
will increase the accuracy and reliability of your measurements.
You will also be able to increase your skill by following the recommendations (see
Recommendations for Skinfold Technicians) made by experts in the field (Jackson and
Pollock 1985; Lohman et al. 1984; Pollock and Jackson 1984).
In addition to perfecting your technical skills, you should develop your interpersonal skills
when administering SKF and other anthropometric tests. For suggestions about developing
interpersonal skills, see Tips for Developing Interpersonal Skills.

The accuracy and precision of SKF measurements and the SKF method are affected by the
technician’s skill, the type of SKF caliper, and client factors. The following questions and
responses address these sources of measurement error.
Is there high agreement among SKF values when the measurements are taken by two
different technicians?
A major source of measurement error is differences between SKF technicians. Objectivity, or

between-technician reliability, is improved when SKF technicians follow standardized testing
procedures, practice taking SKFs together, and mark the SKF site (Pollock and Jackson
1984). A major cause of low intertester reliability is improper location and measurement of
the SKF sites (Lohman et al. 1984). The amount of between-technician error depends on the
SKF site, with larger errors reported for the abdomen (8.8%) and thigh (7.1%) sites than for
the triceps (~3.0%), subscapular (~3.0%-5.0%), and suprailiac (~4%) sites (Lohman et al.
1984).
Are the anatomical descriptions for specific SKF sites the same for all SKF equations?
In the past, for some SKF sites, the anatomical location and direction of the fold have varied.
For example, Behnke and Wilmore (1974) recommend measuring the abdominal SKF using
a horizontal fold adjacent to the umbilicus; Jackson and Pollock (1978), however, recommend
measuring a vertical fold taken 2 cm (0.8 in.) lateral to the umbilicus.
Inconsistencies such as this have led to confusion and lack of agreement among SKF
technicians. As a result, groups of experts in the field of anthropometry have developed
standardized testing procedures and detailed descriptions for identification and measurement
of SKF sites (Harrison et al. 1988; Ross and Marfell-Jones 1991). Appendix D.2,
Standardized Sites for Skinfold Measurements, summarizes some of the most commonly
used sites as described in the Anthropometric Standardization Reference Manual.
409
Although the objective is to have all SKF technicians follow standardized procedures and
recommendations for site location and SKF measurements, you may not be able to do so
under all circumstances. For example, if you are using the generalized equations of Jackson
and Pollock (1978) and Jackson and colleagues (1980), the chest, midaxillary, subscapular,
abdominal, and suprailiac SKFs will be located at sites that differ from those described in the
Anthropometric Standardization Reference Manual. The descriptions for the sites used in these
equations are presented in appendix D.3, Skinfold Sites for Jackson’s Generalized Skinfold
Equations.
How many measurements do I need to take at each SKF site?

A lack of intratechnician reliability or consistency of measurements by the SKF technician is
another source of error for the SKF method. You need to practice your SKF technique on 50
to 100 clients to develop a high degree of skill and proficiency (Jackson and Pollock 1985).
Take a minimum of two measurements at each site using a rotational order. If values vary
from each other by more than 10%, take additional measurements and average the two trials
that meet this criterion. Use this average value in the SKF prediction equation. The ±10%
value for duplicate measurements at each site is recommended as the standardized procedure
in the Anthropometric Standardized Reference Manual.
Standardized Procedures for Skinfold Measurements
1. Take all SKF measurements on the right side of the body.

2. Carefully identify, measure, and mark the SKF site, especially if you are a novice
SKF technician (see appendix D.2, Standardized Sites for Skinfold Measurements).
3. Grasp the SKF firmly between the thumb and index finger of your left hand. Lift
the fold 1 cm (0.4 in.) above the site to be measured.
4. Lift the fold by placing the thumb and index finger 8 cm (~3 in.) apart on a line
that is perpendicular to the long axis of the SKF. The long axis is parallel to the
natural cleavage lines of the skin. For individuals with extremely large SKFs, you
will need to separate your thumb and finger more than 8 cm in order to lift the
fold.
5. Keep the fold elevated while you take the measurement.
6. Place the jaws of the caliper perpendicular to the fold, approximately 1 cm below
the thumb and index finger and halfway between the crest and the base of the fold.
410
Release the jaw pressure slowly.
7. Take the SKF measurement 3 sec after the pressure is released. The American
College of Sports Medicine (ACSM 2018) recommends that you wait only 1 to 2
sec before reading the caliper.
8. Open the jaws of the caliper to remove it from the site. Close the jaws slowly to
prevent damage or loss of calibration.
Recommendations for Skinfold Technicians
Be meticulous when locating the anatomical landmarks used to identify the SKF
site, when measuring the distance, and when marking the site with a surgical
marking pen.
Read the dial of the caliper to the nearest 0.1 mm (Harpenden or Holtain), 0.5 mm
(Lange), or 1 mm (plastic calipers).
Take a minimum of two measurements at each site. If values vary from each other
by more than 2 mm, take additional measurements and use the average of two
measurements within that range.
Take SKF measurements in a rotational order (circuits) rather than taking
consecutive readings at each site.
Take the SKF measurements when the client’s skin is dry and lotion free.
Do not measure SKFs immediately after exercise because the shift in body fluid to
the skin tends to increase the size of the SKF.
Practice taking SKFs on 50 to 100 clients.
Avoid using plastic calipers if you are an inexperienced SKF technician. Instead use
metal calipers.
Train with skilled SKF technicians and compare your results.
Use a training videotape that demonstrates proper SKF techniques (Lohman 1987;
Human Kinetics 1995).
Seek additional training through workshops held at state, regional, and national
conferences or through distance education courses.
411
TIPS FOR DEVELOPING INTERPERSONAL SKILLS
Before the scheduled test session, instruct your clients to wear loose clothing that allows easy access to the
measurement sites, such as shorts and a T-shirt or two-piece exercise gear.
Often clients are apprehensive about having their SKFs measured, particularly when they are meeting you
for the first time. During the testing, put your clients at ease by establishing good rapport (e.g., talk about
some unrelated topic), projecting a sense of relaxed confidence, and creating a test environment that is
friendly, private, safe, and comfortable.
Perform the test in an uncluttered private room that holds a small table for calipers, pens, and clipboards
and a chair for clients who are unstable standing or need to rest during the testing.
Some clients feel more comfortable having their SKFs measured by a technician of the same gender. If this
is not feasible, you could ask your clients if they would like another person of the same gender to observe the
test.
Educate your clients about the SKF test by talking about the purpose and use of the measurements,
pointing to the SKF sites on your body, and demonstrating on yourself how the SKF is measured.
Limit your verbal and facial reactions while collecting SKF data.
Based on Habash 2002.
However, if you are preparing to take an ACSM certification examination, you will need to
modify this standardized procedure slightly by using the ACSM-recommended criterion for
duplicate SKF measurements. The ACSM (2018) also suggests taking at least two
measurements at each site in rotational order; however, these two measurements at a given
site need to be within 2 mm of each other. If you take more than two measurements to meet
this criterion, average the two trials that are within ± 2 mm of each other, and use this value
in the prediction equation to estimate Db and %BF. On the other hand, some researchers
suggest taking three SKF measurements at each site and using the median (middle score)
instead of the mean (average) (Ward and Anderson 1998).
What types of SKF calipers are available, and how do they differ?
A variety of high-quality metal and plastic calipers are offered for measuring SKF thickness
(see figure 8.9). When choosing a caliper, you need to consider factors such as cost,
durability, accuracy, and precision as well as which type of caliper was used for developing a
specific SKF equation. Table 8.4 and figure 8.10 compare some of the basic characteristics of
selected SKF calipers.
412
FIGURE 8.9 Skinfold calipers.
413
High-quality metal calipers are accurate and precise throughout the range of measurement.
The Harpenden, Lange, Holtain, and Lafayette calipers exert constant pressure (~7-8 g·mm
) over their range (0-60 mm). Calipers should not have tension that varies by more than 2.0
−2
414
g·mm throughout the range of measurement or exceeds 15 g·mm (Edwards et al. 1955).
−2 −2
Excessive tension and pressure cause client discomfort (pinching sensation) and significantly
reduce the SKF measurement (Gruber et al. 1990). High-quality calipers also have excellent
scale precision (e.g., 0.2 and 1.0 mm, respectively, for Harpenden and Lange).
Although the Harpenden and Lange SKF calipers have similar pressure characteristics, a
number of researchers reported that SKFs measured with Harpenden calipers are significantly
smaller than those measured with Lange calipers (Gruber et al. 1990; Lohman et al. 1984;
Schmidt and Carter 1990). This difference translates into a systematic underestimation
(~1.5% BF) of average %BF by the Harpenden calipers (Gruber et al. 1990). Even though the
pressure is similar for the Lange (8.37 g·mm ) and Harpenden (8.25 g·mm ) calipers
−2 −2
(Schmidt and Carter 1990), researchers noted that opening the jaws of the Harpenden caliper
requires three times more force. Therefore, it is likely that the Harpenden compresses adipose
tissue to a greater extent, resulting in SKF measurements smaller than those that the Lange
caliper yields. The Cescorf skinfold caliper has a configuration and pressure characteristics
similar to those of the Harpenden. As expected, skinfold thickness measurements at the nine
sites assessed were significantly lower with a Cescorf caliper compared with a Lange caliper.
This difference translates into an underestimation (5.2%-6.9% BF) of average %BF by the
Cescorf calipers (Cyrino et al. 2003).
Are plastic SKF calipers as accurate as high-quality metal calipers?
Compared with high-quality calipers, some plastic calipers do not exert constant tension
throughout the range of measurement and have less scale precision (~2 mm) and a smaller
range of measurement (0-40 mm). Despite these differences, some plastic calipers compare
well (see table 8.4) with more expensive high-quality metal calipers (Cataldo and Heyward
2000). Given that the type of caliper is a potential source of measurement error, follow these
suggestions to minimize error:
Use the same caliper when monitoring changes in a client’s SKF thicknesses.
Use the same type of caliper as was used in the development of the specific SKF
prediction equation you have selected. If the same type of caliper is not available,
use one that gives similar readings (see figure 8.10).
Periodically check the accuracy of your caliper and calibrate if needed.
415
FIGURE 8.10 Relative ranking of values measured by various types of skinfold calipers. Calipers in italics give similar
skinfold readings.
How accurate are the digital SKF calipers?

In an attempt to reduce, if not eliminate, the human errors introduced by an inexperienced or
inattentive skinfold technician (e.g., failing to read the gauge after the proper time interval,
misreading the gauge, miscalculating body density and %BF), efforts are under way to
automate the skinfold thickness reading and calculation processes. A digital modification
utilizing miniaturized sensor technology and transmission of a wireless signal to a computer
running Liposoft 2008 software (Adipsmeter V1.0) is commercially available for Harpenden
calipers (LipoTool). The standard skinfold thickness assessment protocol is programmed into
the software application, thereby eliminating the technician’s need to read the gauge or
perform mathematical calculations. Amaral and colleagues (2011) investigated the validity
and reliability of the LipoTool by comparing skinfold thickness results with those obtained
using a standard Harpenden caliper. The mean difference between the two calipers was 0.3
mm, the associated limits of agreement ranged from −3.1 to 3.4 mm, and the correlation
coefficients for the skinfold sites were all strong (r > .91). To learn more about this digital
416
skinfold caliper system, see the work of Amaral and colleagues (2011).
Will my clients’ hydration level affect the SKF measurements?
Skinfold measurements may also be affected by compressibility of the adipose tissue and
hydration levels of your clients (Ward, Rempel, and Anderson 1999). Martin, Drinkwater,
and Clarys (1992) reported that variation in SKF compressibility may be an important
limitation of the SKF method. In addition, an accumulation of extracellular water (edema) in
the subcutaneous tissue—caused by factors such as peripheral vasodilation or certain diseases
—may increase SKF thicknesses (Keys and Brozek 1953). This suggests that you should not
measure SKFs immediately after exercise, especially in hot environments. Grossman and
Deitrick (2015) measured SKF thicknesses at four sites immediately before and 2 hr
following a resistance training workout about 60 min in duration. Although the postexercise
SKF measurements were approximately 3 mm higher compared with baseline, no significant
differences in ∑SKF or FFM were found. Also, most of the weight gain experienced by some
women during their menstrual cycles is caused by water retention (Bunt et al. 1989). This
theoretically could increase SKF thicknesses, particularly on the trunk and abdomen, but
there continues to be no empirical data to support or refute this hypothesis.
Should SKFs be measured on the right or left side of the body?
Only small differences (up to 2 mm) between SKF thicknesses on the right and left sides of
the body occur for the typical individual. The standard practice in the United States, as well
as in European and developing countries, however, is to take SKF measurements on the right
side of the body, as recommended in the Anthropometric Standardization Reference Manual
(Lohman, Roche, and Martorell 1988) and by the International Society for the Advancement
of Kinanthropometry (Norton et al. 2000) and ACSM (2018).
Should I use SKFs to measure the body fat of obese clients?
It is difficult, even for highly skilled SKF technicians, to accurately measure the SKF
thickness of extremely obese individuals. Sometimes a client’s SKF thickness exceeds the
maximum aperture of the caliper, and the jaws of the caliper may slip off the fold during the
measurement, resulting in a potentially embarrassing and awkward situation for you and your
client. Therefore, avoid using the SKF method to measure body fat of extremely obese
clients.
ULTRASOUND
Ultrasound technology is gaining ground in the body composition literature as a portable,
417
noninvasive alternative to the skinfold thickness assessment technique. It may also be used for
clinical purposes given its ability to assess adipose tissue thicknesses deep within the body
(Bazzocchi et al. 2016). Wagner (2013) has published an informative review of ultrasound
technology and its use in body composition assessment.
Ultrasound technology uses a handheld wand, or probe (see figure 8.11), with bidirectional
(sending and receiving) sound transducers integrated with a computerized application. The
pulsatile frequency of the ultrasound signals transmitted through the skin is generated by
piezoelectric crystals within the transducer itself. The signal frequencies generated exceed
what can be heard by the human ear and tend to range between 1 and 18 MHz. The signals
are reflected back to the transducer when they encounter a tissue interface. Depending on the
mode of ultrasound being used, the reflected signals are presented as a line drawing of peaks
with different amplitudes or as a series of dots forming horizontal bands of differing
brightness.
FIGURE 8.11 Handheld ultrasound probe.

Dale R. Wagner.
418
A-Mode Ultrasound
A-mode (amplitude mode) ultrasound devices use a single transducer to emit a narrow signal
beam and produce an image of amplitude-related spikes. The amplitude of the peaks reflects
the tissue depths (see figure 8.12). The subcutaneous adipose tissue interface with skeletal
muscle is defined as the midpoint of the highest peak (Smith-Ryan et al. 2014).
00:00 / 00:00
Video 8.3
FIGURE 8.12 Tissue depth measured using an A-mode ultrasound device.

Dale R. Wagner.
As an alternative to SKF measurements, the A-mode ultrasound uses the same sites as the
SKF method. Reviewing the Using the Skinfold Method section of this chapter will refresh
your familiarity with the skinfold sites and help you understand the direction of wand
movement at each site. The conversions of site-specific subcutaneous fat thicknesses into
%BF use proprietary conversions and prediction equations that mimic the traditional SKF
prediction equations (Baranauskas et al. 2017).
419
B-Mode Ultrasound
B-mode (brightness modulation) ultrasound uses a linear array of transducers to create a two-
dimensional image. The transducer interprets the reflected signals and presents those
processed signals as dots on the graphical display on the computer screen. The brighter the
dots, the stronger the signal reflections. The dots are depicted in an x- and y-axis format,
with the point of axes intersection (lower left of the graphic) being the highest value (mm).
The uppermost portion of the screen (smallest mm value) represents the contact between the
transducer and skin. A bright line or band of dots appears a bit below that and depicts the
first tissue interface (subcutaneous fat with skeletal muscle). The distance (depth in mm)
between the head of the probe and the tissue interface gives an indication of the time it takes
the signal to leave and return to the transducer. The average thickness (mm) is calculated by
the proprietary application and approximates one-half of the skinfold thickness at that site
(Wagner 2013).
According to Müller and associates (2016), the skinfold sites defined by the International
Society for the Advancement of Kinanthropometry are inappropriate for quantifying
subcutaneous fat with B-mode ultrasound. As a result, they standardized a B-mode
ultrasound technique that uses 8 sites and specific positioning of the client during the
identification of measurement sites (see B-Mode Ultrasound Technique). All measurements
are made with the client in a supine or side-lying position.
As explained by Wagner (2013), interpreting the results of an ultrasound scan is more
difficult than performing the scan in the first place. Accurate identification of the interfaces is
crucial. As the probe slides back and forth along the skin, a line of dots will be displayed on
the computer screen. The various tissue interfaces will be identifiable as continuous brightly
colored bands. With practice the technician should become proficient in identifying the
borders of the various interfaces. Another skill required for interpreting ultrasound images is
the accurate measurement of the tissue thicknesses, which depends on identification of the
interface borders and the proper placement of the software’s electronic calipers.

The accuracy and precision of ultrasound measurements and the ultrasound method are
directly related to the technician’s skill, mode of ultrasound, signal frequency, and sound
speed. The following questions and responses address these sources of measurement error.
What is the standardized technique to use with A-mode ultrasound?
Unlike for B-mode, there is not yet a standardized technique. The sites of measurement are
420
the same as for skinfold thickness assessment. The computer software will alert the technician
when repeat measurements are needed.
How much pressure should be applied to the skin with the probe?
Toomey and colleagues (2011) compared A-mode ultrasound measurements at several sites
(triceps, abdomen, anterior thigh) using three different amounts of skin and subcutaneous
adipose tissue compression. When the skin and underlying fat were compressed as much as
possible, the subcutaneous adipose tissue result was lower (36%, 37%, and 25%, respectively)
at each site tested as compared to when minimal compressive pressure was applied. Reducing
the maximal amount of pressure by one-half resulted in thickness differences from minimal
compression that were similar to those attained when the tissues were maximally compressed
(Toomey et al. 2011). Toomey and associates (2011) suggest applying just enough pressure so
that the image begins to appear on the computer monitor.
Standardized Procedures for B-Mode Ultrasound Technique
Standardized procedures for the B-mode ultrasound (Müller et al. 2016) are as follows.
1. Connect the ultrasound wand to the computer and open the software application.
2. Have your client void and change into loose-fitting shorts and a T-shirt; women
should wear a sports bra.
3. Measure your client’s standing height and weight while barefoot.
4. Enter the demographic information about your client into the proper fields on the
computer screen.
5. Position your client as recommended, and then meticulously identify, measure, and
mark the sites of interest on the right side of the body.
6. Center a dime-sized amount of conductive gel on the head of the probe. Störchle
and associates (2017) suggest using a thick layer of conductive gel to help prevent
overapplication of pressure; the layer of gel will appear as a dark band at the top of
the images.
7. Position the head of the probe lightly on the skin at the marked site, and hold the
wand perpendicular to the skin. Be sure the entire surface of the probe head is in
contact with the skin.
8. Have the client interrupt (stop) his breathing cycle at midpoint for all
measurements on the torso.
421
9. Slide the probe linearly about 5 mm in each direction while following the long axis
of the underlying muscle at that site.
10. Novice technicians are advised to save the image for subsequent analysis.
11. Repeat measurements at each site are recommended so that the average of two
within ±1 mm is attained and recorded.
How do signal frequency and sound speed affect measurement accuracy?
The frequency emitted by the transducer may be more important than the mode in that there
is an inverse relationship between signal frequency and depth of penetration (Wagner 2013).
However, with A-mode ultrasound, higher frequencies increase the resolution of the images
(Smith-Ryan et al. 2014).
With B-mode ultrasound, image resolution nears 0.1 mm at 18 MHz; whereas at 6MHz
resolution is approximately 0.3 mm (Störchle et al. 2017). Sound speed is also of utmost
importance; the wrong speed introduces an error of approximately 6% BF (Müller et al.
2016). For thick layers of subcutaneous adipose tissue, use a slow sound speed (1,450 m·s ).
−1
Otherwise, a sound speed of 1,540 m·s is appropriate.

−1
How accurate is the ultrasound method in comparison to other reference methods?
Research comparing the results of ultrasound assessments against reference methods is

limited but growing. Technician training, technology, and algorithms may need a bit more
development.
When using A-mode ultrasound to assess subcutaneous thicknesses for subsequent
conversion via equation into %BF, researchers report that the proprietary equations within
the devices underestimate %BF from DXA in 12 to 17 yr olds (Ripka et al. 2016) and in
comparison to a 4C model reference (Kendall et al. 2017). The study by the Smith-Ryan
team (2014) recruited overweight and obese adults and compared A-mode ultrasound device
derivations of %BF and FM to multicomponent model references. Siri’s (1961) 3C model
values were significantly underestimated by the seven-site assessment using a frequency of 2.5
MHz (Smith-Ryan et al. 2014).
O’Neill and colleagues (2016) used B-mode ultrasound to compare %BF results against
those from an iDXA for a sample of elite male rowers. The B-mode underestimated the
iDXA reference values for %BF while overestimating iDXA FFM. O’Neill’s group improved
the predictive accuracy of the B-mode ultrasound device by identifying significant
422
measurement sites via multiple linear regression analyses and creating a new prediction
equation. When this new equation [%BF = 0.476 × (∑ triceps, biceps, supraspinale, and
anterior thigh)] was applied to the athletes in that study, ultrasound %BF (16.9%) was
identical to the reference value, correlation was strong (r = .94), SEE acceptable (1.9%), and
limits of agreement were good (3.7%). O’Neill indicated that the ultrasound method worked
better for the athletes with less than 15% BF.
Smith-Ryan and colleagues (2016) again recruited overweight and obese adults to compare
B-mode ultrasound %BF and FM values against a 4C reference model. Sex-specific
comparisons indicated that the ultrasound method worked better for the men than the
women in this study. No mean difference was found for the men, but ultrasound (frequency:
5-13 MHz) overestimated the reference %BF by 9.2% on average for the women. As noted
by the authors, the men were leaner than the women.
Both A- and B-mode ultrasound devices demonstrate excellent reliability and moderately
strong to strong correlations with multicomponent reference models while resulting in large
limits of agreement (O’Neill et al. 2016; Ripka et al. 2016; Smith-Ryan et al. 2014, 2016).
Comments by the Smith-Ryan teams indicate that ultrasound technology is worth
considering for tracking body composition changes over time. The methodological
differences between ultrasound and a 4C reference are smaller than those of other laboratory
methods that take only a single measurement (e.g., ADP) (Smith-Ryan et al. 2014, 2016).
How does the ultrasound method compare to the results of SKFs and other 2C models?
The A-mode ultrasound results from the study by Wagner, Cain, and Clark (2016) showed
strong correlations (r > .68) and small minimal differences (<2.0% BF) at each of the three
sites (Jackson and Pollock 1978; Jackson, Pollock, and Ward 1980) measured. In what is
apparently the first published intertester reliability study using ultrasound (Wagner, Cain,
and Clark 2016), the two technicians attained better test-retest results with the A-mode
ultrasound (ICC = .99; 95% confidence interval 0.98-0.99 mm) than with SKF (ICC = .97;
95% confidence interval 0.33-0.99 mm). The intertester differences varied by the athletes’ sex
and assessment method. Ultrasound differences were approximately 0.2% BF for both sexes,
whereas SKF differences were close to 1.9% BF for the men and 3.3% BF for the women
(Wagner, Cain, and Clark 2016).
Wagner’s research group (2016) recruited NCAA Division I athletes for their study, in
which they reported a strong correlation (r > .92), good SEE (2.6% BF), and fair TE (4.4%
BF) but large limits of agreement when comparing A-mode ultrasound with ADP. A-mode
423
ultrasound overestimated %BF from ADP in only 5 of the 45 participating athletes (22 men
and 23 women). When comparing sex-specific results, no differences were found for the men.
Their body fat percentages differed minimally (± 1.5% BF), and TE improved to 2.4%.
Conversely, for the women, ultrasound overestimated %BF from ADP by approximately
5.0%, and TE increased to 5.5% BF (Wagner, Cain, and Clark 2016). Kendall and associates
(2017) reported no differences in FFM estimations when comparing A-mode ultrasound
(76.8 ± 9.1 kg) results to ADP (76.7 ± 9.5 kg) and to SKF (76.7 ± 9.0 kg) using the Jackson
and Pollock (1978) three-site equation. Comparisons of A-mode ultrasound measurements
and skinfold thicknesses acquired at the sex-specific sites for men (chest, abdomen, thigh)
and women (triceps, suprailiac, thigh) (Jackson and Pollock 1978; Jackson, Pollock, and
Ward 1980) resulted in similar group means (ultrasound: 18.2 ± 8.3% BF; SKF: 13.3 ± 7.1%
BF; SEE: 7.5%; TE: >4.0%) for a small, predominantly Caucasian male sample of young
adults (Loenneke et al. 2014).
Smith-Ryan and colleagues (2016) used the sex-specific seven-site assessments (Jackson
and Pollock 1978; Jackson, Pollock, and Ward 1980) for a sample of overweight and obese
adults (31.6 ± 5.2 kg·m ). There were nonsignificant differences for both the men (26.8 ±
−2
4.1% vs. 29.8 ± 6.0% BF) and women (47.4 ± 6.9% vs. 39.8 ± 4.4% BF) with B-mode
ultrasonography and SKF, respectively.
BIOELECTRICAL IMPEDANCE METHOD

Bioelectrical impedance analysis (BIA) is a rapid, noninvasive, and relatively inexpensive
method for evaluating body composition in field settings. With this method, a low-level
electrical current is passed through the client’s body, and the impedance (Z), or opposition to
the flow of current, is measured with a BIA analyzer. You can estimate the individual’s total
body water (TBW) from the impedance measurement because the electrolytes in the body’s
water are excellent conductors of electrical current. When the volume of TBW is large, the
current flows more easily through the body, with less resistance (R). The resistance to current
flow is greater in individuals with large amounts of body fat, since adipose tissue, with its
relatively low water content, is a poor conductor of electrical current. Because the water
content of the FFB component is relatively large (~73% water), fat-free mass (FFM) can be
predicted from TBW estimates. Individuals with large FFM and TBW have less resistance to
current flowing through their bodies than those with a smaller FFM.
Bioelectrical impedance indirectly estimates FFM and TBW. Therefore, the following
assumptions are made about the geometric shape of the body and the relationship of
424
impedance to the length and volume of the conductor.
• The human body is shaped like a perfect cylinder with a uniform length and cross-sectional
area. Of course, this assumption is not entirely true. Because the body segments are not
uniform in length or cross-sectional area, resistance to the flow of current through these body
segments will differ.
• Assuming the body is a perfect cylinder, at a fixed signal frequency (e.g., 50 kHz) the
impedance (Z) to current flow through the body is directly related to the length (L) of the conductor
(height) and inversely related to its cross-sectional area (A) [Z = ρ(L / A), where ρ is the specific
resistivity of the body’s tissues and is assumed to be constant]. To express this relationship in
terms of Z and the body’s volume, instead of its cross-sectional area, the equation is
multiplied by L / L: Z = ρ(L / A)(L / L). A × L is equal to volume (V), so rearranging this
equation yields V = ρL / Z. Thus, the volume of the FFM or TBW of the body is directly
2
related to L , or height squared (ht ), and indirectly related to Z.

2 2
• Biological tissues act as conductors or insulators, and the flow of current through the body will
follow the path of least resistance. Because the FFM contains large amounts of water (~73%)
and electrolytes, it is a better conductor of electrical current than is fat. Fat is anhydrous and a
poor conductor of electrical current. The total body impedance, measured at the constant
frequency of 50 kHz, primarily reflects the volumes of the water and muscle compartments
composing the FFM and the extracellular water volume (Kushner 1992).
• Impedance is a function of resistance and reactance, where Z = √(R2 + X ). Resistance (R) is a
c2
measure of pure opposition to current flow through the body; reactance (X ) is the opposition
c
to current flow caused by capacitance produced by the cell membrane (Kushner 1992). R is
much larger than X (at a 50 kHz frequency) when whole-body impedance is measured;
c
therefore, R is a better predictor of FFM and TBW than Z (Lohman 1989). For these
reasons, the resistance index (ht / R), instead of ht / Z, is often used in BIA models to
2 2
predict FFM or TBW.
Using the Bioelectrical Impedance Analysis Method

The traditional BIA method measures whole-body resistance using a tetrapolar wrist-to-
ankle electrode configuration at a single frequency for estimating TBW or FFM (figure 8.13).
However, technological advances and changes in theoretical modeling have led to a number
of variations in the traditional BIA method. These variations use sophisticated models to
assess segmental body composition and fluid subcompartments, thereby improving the
clinical usefulness of BIA. Also, user-friendly BIA analyzers designed for home use and
425
individual monitoring of health and fitness use upper or lower body impedance measures to
estimate body composition (figure 8.14).
FIGURE 8.13 Bioelectrical impedance analysis electrode placement and client positioning.
Whole-body bioimpedance measures (Z, R, and X ) are used in BIA prediction equations
c
to estimate TBW and FFM. These prediction equations are based on either population-
specific or generalized models. A population-specific equation is valid for only those
individuals whose physical characteristics match the sample from which the equation was
derived. Researchers have developed equations specific to age (Deurenberg et al. 1990;
Lohman 1992), ethnicity (Hastuti et al. 2016; Stolarczyk et al. 1994), body fatness (Gray et
al. 1989; Segal et al. 1988), and level of physical activity (Houtkooper et al. 1989).
Alternatively, generalized BIA equations have been developed for heterogeneous populations
varying in age, gender, and body fatness (Deurenberg et al. 1990; Gray et al. 1989; Kyle et al.
2001; Kushner and Schoeller 1986; Lukaski and Bolonchuk 1988; Van Loan and Mayclin
1987).
00:00 / 00:00
Video 8.4
Inexpensive lower body (foot-to-foot) and upper body (hand-to-hand) BIA devices are
available and have been marketed for home use. Two manufacturers of these devices are
Omron Healthcare and Tanita Corporation. The Tanita analyzers measure lower body
impedance between the right and left legs as the individual stands on the analyzer’s electrode
426
plates (see figure 8.14a). The Omron Body Logic analyzer, which is handheld, measures
upper body impedance between the right and left arms (see figure 8.14b). The Tanita and
Omron analyzers estimate %BF and FFM using proprietary equations developed by the
manufacturers. Typically, it is not possible to obtain impedance (resistance and reactance)
data from these analyzers. However, they do provide the general public with an inexpensive,
simple, and reasonably accurate means of self-assessing body fat.
00:00 / 00:00
Video 8.5
00:00 / 00:00
Video 8.6
FIGURE 8.14 (a) Tanita and (b) Omron bioelectrical impedance analyzers.
Table 8.5 presents commonly used population-specific and generalized BIA equations.
427
With these equations, you can accurately estimate the FFM of your clients within the
recommended values, ±2.8 kg for women and ±3.5 kg for men (Lohman 1992). To use these
equations, obtain R and X directly from your BIA analyzer. Estimate the %BF of your client
c
by determining the fat mass (FM) (FM = BM − FFM) and dividing FM by the client’s body
mass [%BF = (FM / BM) × 100].
BIA PRETESTING CLIENT GUIDELINES
No eating or drinking within 4 hr of the test.
No moderate or vigorous exercise within 12 hr of the test.
Void completely within 30 min of the test.
Abstain from alcohol consumption within 48 hr of the test.
Do not ingest diuretics, including caffeine, before the assessment unless they are prescribed by a physician.
If you are in a stage of your menstrual cycle during which you perceive you are retaining water, postpone
testing (female clients).
Experts recommend not using the FFM and %BF estimates obtained directly from your
BIA analyzer (e.g., BMR, Holtain, RJL, or Valhalla) unless you know for sure which
428
equations are programmed in the analyzer’s computer software, obtain information from the
manufacturer regarding the validity and accuracy of these equations, and determine that these
equations are applicable to your clients.
Although the relative predictive accuracy of the BIA method is similar to that of the SKF
method, BIA may be preferable in some settings for the following reasons:
It does not require a high degree of technician skill.

It is generally more comfortable and does not intrude as much on the client’s
privacy.
It can be used to estimate body composition of obese individuals.
Bioelectrical Impedance Analysis Technique

Bioelectrical impedance analysis accuracy highly depends on controlling the factors that may
increase measurement error. Regardless of the BIA method being used (whole, upper, or
lower body), your client must adhere to guidelines that control fluctuations in hydration
status (see BIA Pretesting Client Guidelines).
Use standardized testing procedures to minimize error in the BIA method (see
Standardized Procedures for the Whole-Body BIA Method).

The accuracy and precision of the BIA method are affected by instrumentation, client factors,
technician skill, environmental factors, and the prediction equation used to estimate FFM.
The following questions address sources of BIA measurement error.
Can different types of whole-body BIA analyzers be used interchangeably?
Research demonstrates significant differences in whole-body resistance when different brands

of single-frequency analyzers are used (Graves et al. 1989; Smye, Sutcliffe, and Pitt 1993).
For example, Smye, Sutcliffe, and Pitt (1993) reported lower resistances (6% or 32-36 Ω) for
the Holtain device compared with the Bodystat, RJL, and EZcomp analyzers. Graves and
colleagues (1989) noted that the correlation between resistance values measured with the
Valhalla and Bioelectrical Sciences (BES) analyzers was only r = .59; the average %BF
estimated for men from one BIA equation differed by 6.3% using the resistances from these
two instruments. Although there is a high correlation (r = .99) between resistances measured
with the Valhalla and RJL analyzers, the Valhalla analyzer produces significantly higher
429
resistances for men (~16 Ω) and women (~19 Ω), corresponding to a systematic
underestimation of FFM in men (~1.3 kg) and women (~1.0 kg) (Graves et al. 1989). Also,
differences may exist within a given model of analyzer. The Z values from three RJL (model
101) analyzers differed by 7 to 16 Ω, causing a difference in FFM of 2.1 kg for some
individuals (Deurenberg, van der Kooy, and Leenan 1989).
Do upper and lower body BIA analyzers accurately estimate body composition?
The Tanita Corporation now markets more than 30 different adult models of body
composition analyzers that vary in weight capacity, software, memory, and data output. The
majority of these are lower body analyzers, but some offer whole-body and regional
(segmental) analysis. Compared with two-component model estimates of FFM obtained
from underwater weighing, Tanita analyzer estimates of the average FFM of heterogeneous
adult samples are reasonably good (SEE = 3.5-3.7 kg) (Cable et al. 2001; Utter et al. 1999).
Estimates by Tanita analyzers also agree well with SKF estimates of %BF in collegiate
wrestlers (Utter et al. 2001) and with DXA estimates of FFM in children (Sung et al. 2001;
Tyrrel et al. 2001). On the other hand, compared with DXA, Tanita underestimated %BF
and FFM (kg) for a sample of college-age women (del Consuelo Velazquez-Alva et al. 2014).
Compared with UWW estimates of the FFM of high school wrestlers, the prediction error
for the Tanita analyzer (TBF-300WA) was larger than that of the SKF method (3.64 kg vs.
1.97 kg). Utter and colleagues (2005), therefore, recommend using the leg-to-leg Tanita
analyzer only when trained SKF technicians are not available.
The Tanita BC 532 lower body analyzer utilizes two frequencies (50 kHz and 200 μA) to
estimate %BF, FFM, and visceral fat. In a sample of 200 Chinese men and women ranging in
age from 18 to 80 yr, Wang and colleagues (2013) investigated the predictive accuracy of the
BC 532 as compared with reference measures from DXA (%BF and %FFM) and MRI
(visceral fat). Even though the correlations were strong, the underestimation of %BF DXA was
significant for both the men (10.7%, r = .84) and women (6.1%, r = .86), as was the
underestimation of %FFM (men: 1.4%, r = .84; women: 2.5%, r = .86). The reference visceral
fat percentage from MRI was significantly overestimated (20.4%) in the men and
underestimated (18.0%) in the women. The gender-specific visceral fat correlations were .81
and .86 for the men and women, respectively. The strength of correlations and magnitude of
mean differences reported by Wang and associates (2013) may differ for other population
subgroups using the BC 532.
In the late 1990s, Omron Healthcare developed a low-cost, hand-to-hand BIA analyzer
430
for home use. Omron’s proprietary equation was developed and cross-validated on a large
heterogeneous sample from three laboratories using HW to obtain two-component model
reference measures of %BF and FFM (Loy et al. 1998). The group predictive accuracy (SEE)
for estimating FFM was 3.9 kg for men and 2.9 kg for women. In an independent cross-
validation of the Omron analyzer, Gibson and colleagues (2000) reported slightly smaller
prediction errors (SEE = 2.9 kg for men and 2.2 kg for women). Loy and colleagues (1998)
noted that the average FFM estimates from the Omron device are similar to values obtained
with whole-body (RJL and Valhalla) analyzers. Last, in a study of Japanese men, the accuracy
of upper body (Omron, HBF-300), lower body (Tanita, TBF-102), and whole-body (Selco,
SIF-891) analyzers was compared against two-component model reference measures of %BF
obtained from HW. The average difference between reference and predicted %BF values was
slightly smaller for the Omron (2.2% BF) than for the whole-body (3.3% BF) and lower body
(3.2% BF) analyzers (Demura et al. 2002). However, estimation errors from the Omron and
Tanita devices tended to be greater at the lower and upper extremes of the %BF distribution.
Omron also developed BIA prediction equations to estimate the body composition of
physically active adults. These equations are programmed in the HBF-306 Omron analyzer
along with prediction equations for nonactive adults and children. The predictor variables in
the manufacturer’s equation for this unit are upper body impedance, age, gender, height,
weight, and level of physical activity (i.e., athlete or nonathlete). The prediction errors for
athletes (SEE = 3.8% and 3.6% BF for male and female athletes, respectively) were somewhat
less than those for nonathletes (SEE = 4.5% BF) (K. Yamanoto, personal communication).
Compared with a DXA reference method, the Omron HBF 300 model had good correlation
for %BF (r = .74; SEE = 3.6% BF) and FFM (r = .84; SEE = 2.45 kg) for a sample of
collegiate female athletes from three sports (Esco et al. 2011).
The Omron (HBF-306) model has been tested on ethnically diverse samples of European
and Asian populations. Generally the group predictive accuracy is good for these subgroups,
but individual prediction errors can be high (Deurenberg-Yap et al. 2001; Deurenberg and
Deurenberg-Yap 2002). Deurenberg-Yap and colleagues (2001) noted that Omron data
misclassified (gave false negatives) for 24% of the obese females and 44% of the obese males
in their study. When the Omron estimates of %BF were compared with those of a
multicomponent model, the SEE was 4.5% BF; the error in estimating %BF using the
Omron analyzer was related to the age, body fatness, and ratio of arm span to height of the
subjects (Deurenberg and Deurenberg-Yap 2002).
431
STANDARDIZED PROCEDURES FOR THE WHOLE-BODY BIA METHOD
1. Take bioimpedance measures on the right side of the body with the client lying supine on a nonconductive
surface in a room with normal ambient temperature (~25 °C).
2. Clean the skin at the electrode sites with an alcohol pad.
3. Place the sensor (proximal) electrodes on (a) the dorsal surface of the wrist so that the upper border of the
electrode bisects the head of the ulna and (b) the dorsal surface of the ankle so that the upper border of the
electrode bisects the medial and lateral malleoli (see figure 8.13). You can use a measuring tape and surgical
marking pen to mark these points for electrode placement.
4. Place the source (distal) electrodes at the bases of the second or third metacarpophalangeal joints of the
hand and foot (see figure 8.13). Make certain there is at least 5 cm (~2 in.) between the proximal and distal
electrodes.
5. Attach the lead wires to the appropriate electrodes. Red leads are attached to the wrist and ankle, and black
leads are attached to the hand and foot.
6. Make certain the client’s legs and arms are comfortably abducted, at about a 30° to 45° angle from the trunk.
Ensure there is no contact between the arms and trunk and between the thighs, as contact will short-circuit
the electrical path, dramatically affecting the impedance value.
Esco and colleagues (2011) urged caution when using the Omron HBF 300 to estimate the
body composition of athletic young women, given the magnitude of differences in estimating
%BF (5% BF underestimation) and FFM (3.4 kg overestimation) compared with DXA. In
comparing the Omron HBF 300 and Tanita TF 400 FS with each other as well as DXA for a
sample of Caucasian women representing the entire spectrum of BMI values, Vĕtrovska and
coauthors (2014) reported that both BIA devices follow the same pattern of underestimating
the DXA %BF values for the lower BMI ranges while overestimating the reference for those
with BMI values >30 kg·m . The Omron had the narrowest limits of agreement with DXA
−2
as long as the BMI values were <40 kg·m . The Tanita, conversely, performed better in
−2
relation to the DXA when BMI exceeded 30 kg·m , at which point DXA significantly
−2
overestimated the Omron and Tanita results. Below 30 kg·m there were no significant
−2
differences compared with the reference method.
Compared with handheld and leg-to-leg BIA analyzers, does octapolar bioimpedance
spectroscopy (BIS) provide a better estimate of body composition?
Bioimpedance spectroscopy (BIS) analyzers combine upper body, lower body, and whole-
body bioimpedance to estimate FFM and %BF. The tactile eight-point system (four pairs of
electrodes) from the line of InBody (manufactured by Biospace Co. Ltd.) and other vertical
432
analyzer manufacturers has electrodes embedded into the analyzers’ handles (thumb and arm)
and floor scale (ball of foot and heel). Comparing estimates from the InBody720 and
InBody320 BIS analyzers to multicomponent model estimates of %BF, Gibson and
colleagues (2008) reported large prediction errors for samples of Hispanic, black, and white
men (SEE = 5.2% BF) and women (SEE = 4.8% BF). The average %BF of the women was
significantly overestimated by 2.5% to 3.0% BF for both BIS analyzers. Compared with a
Lunar Prodigy Primo DXA, the InBody720 overestimated FFM (p < .05) and
underestimated FM (p < .05) for a sample of healthy postmenopausal women. The
methodological difference in FM estimation is more apparent at the upper ends of the Bland
and Altman distribution (Gába et al. 2015).
Esco’s research team (2015) compared whole-body and segmental results from an
InBody720 and Lunar Prodigy DXA for a sample of 45 female collegiate athletes. They
reported significantly lower (3.3%) %BF and higher (~2.1 kg) FFM results from the InBody
compared with the DXA, and the limits of agreement were large. Interestingly, the between-
device comparisons of segmental lean soft tissue (FFM minus bone) were not significant for
arms, legs, or torso. Thus, the authors commented that the InBody720 is a viable alternative
to DXA for quantifying segmental lean soft tissue in athletic women.
Using DXA for the reference method, Anderson, Erceg, and Schroeder (2012) also
investigated the ability of two eight-electrode InBody BIS analyzers (models 520 and 720) to
assess total and regional body composition for 25 men and 25 women. Fat mass (FM)
correlations were significant for both sexes (r = .90-.98), as were lean body mass (LBM)
correlations (r = .83-.97). Overall, the prediction errors (SEEs) ranged from 2.5 to 2.8 kg for
FM and 2.4 to 2.8 kg for FFM. The limits of agreement for FM were larger for the men.
Anderson, Erceg, and Schroeder (2012) concluded that both of the InBody analyzers provide
reliable and valid estimations of LBM and FM and can, therefore, be used interchangeably
with DXA for assessing these body composition components.
Do vertical whole-body BIA analyzers accurately estimate body composition?
Tanita, Omron, and Biospace have models that combine upper and lower body BIA
technology to provide varying levels of whole-body assessments. The Omron HBF 359 is an
eight-electrode multiple-frequency analyzer (50 kHz and 500 μA) that estimates skeletal
muscle mass and visceral fat in addition to %BF. Wang and colleagues (2013) investigated
the predictive accuracy of the Omron HBF 359 compared with DXA for %BF and with MRI
for skeletal muscle mass and visceral fat in a sample of Chinese adults (100 men and 100
433
women). The correlations between the two methods of estimating %BF ranged from r = .80
(men) to r = .86 (women). On average, Omron %BF significantly underestimated the DXA
reference measure by 5.8% BF (men) and 9.6% BF (women) (Wang et al. 2013). MRI
skeletal muscle percentage was significantly overestimated by 1.9% for the men but not for
the women, although the correlations (r = .72 and .71, respectively) were significant. MRI
visceral fat was overestimated by 13.3% for the men (r = .82) but underestimated by 8.5% for
the women (r = .85). Consequently, Wang and associates (2013) concluded that, for their
sample of Chinese adults, the HBF 359 accurately and reliably estimated skeletal muscle
percentage, but there was room for improvement in estimating %BF and visceral fat.
Additional research on other racial population subgroups is recommended.
The Tanita MC-180MA is a multifrequency body composition analyzer. It was compared
against a Lunar iDXA in order to determine how closely the two methods estimated the total
body and regional fat mass of more than 400 Irish adults ranging in age from 18 to 29 yr
(Leahy et al. 2012). The Tanita underestimated overall %BF from DXA, with the difference
between methods being most notable for the women and as levels of body fatness increased.
Fat in the trunk region was significantly overestimated by the Tanita for the men, but the
methods produced similar trunk region values for the women. Leahy and colleagues reported
that the Tanita accurately estimates FFM in the arms and legs compared with the iDXA.
Although they reported that the two methods were interchangeable for men with body fat
levels lower than 25%, they did not support using the Tanita to estimate body composition in
the trunk region.
The Tanita BC-418 is a single-frequency (50 kHz) analyzer. Compared with the Lunar
Prodigy Primo DXA reference, it underestimates FM (kg) while overestimating FFM (kg) of
postmenopausal women (Gába et al. 2015). Similar to the results of Leahy and colleagues
(2012), as the women’s body fat levels increase, so do the FM differences between methods.
Hurst and colleagues (2016) compared the dual-frequency InBody230 to both DXA
(QDR Discovery A) and ADP for adults spanning the age and BMI ranges. For the sample,
%BF from the InBody was significantly lower (~2% BF) compared with both DXA and
ADP, but more closely aligned with the DXA %BF. Limits of agreement between the
InBody and both references were large. The authors suggested the underlying cause of the
large limits of agreement with the InBody and DXA is most likely the DXA results at the
extremes of the distribution and not the InBody (Hurst et al. 2016).
How does my client’s hydration level affect the accuracy of bioimpedance measures?
434
A major source of error with the BIA method is intraindividual variability in whole-body
resistance due to factors that alter the client’s hydration. Between 3.1% and 3.9% of the
variance in resistance may be attributed to day-to-day fluctuations in body water (Jackson et
al. 1988). Factors such as eating, drinking, dehydrating, and exercising alter the hydration
state, thereby affecting total body resistance and the estimate of FFM. Measuring resistance 2
to 4 hr after a meal decreases R as much as 17 Ω and likely overestimates the FFM of your
client by almost 1.5 kg (Deurenberg et al. 1988). Likewise, Gallagher, Walker, and O’Dea
(1998) found a significant decrease in impedance (Z) 2 hr following breakfast, and this effect
lasted for 5 hr after consumption. In contrast to these studies, at only 1 hr postmeal, there
appear to be greater individual variability and smaller changes in R (Fogelholm et al. 1993).
Kushner, Gudivaka, and Schoeller (1996) concluded that eating or drinking minimally
influences whole-body Z within 1 hr following consumption but is likely to decrease Z (<3%)
at 2 to 4 hr. Dehydration has the opposite effect: R increases (~40 Ω), leading to a 5.0 kg
underestimation of FFM (Lukaski 1986).
Androutsos and associates (2015) investigated the influence of a drink- or food-based
intervention (2 days each) on impedance as monitored through foot-to-foot bioimpedance
analysis (Tanita TBF-300). The drink intervention group consumed 750 ml of mineral water
one day and an equal volume of a sport electrolyte drink the second day. The food-based
group consumed a high-carbohydrate meal and a high-fat meal on days 1 and 2, respectively.
Preingestion and immediate postingestion BIA (time 0) assessments were performed.
Additional assessments at 30 min intervals for a 2 hr postingestion follow-up were
undertaken. Impedance (Z) significantly increased at all time points for the two drinks as
compared with baseline. However, %BF increased with respect to baseline only for the time 0
water consumers. Ingesting the sport electrolyte beverage caused increases in %BF relative to
baseline at every time point postingestion.
The two meal interventions increased Z every 30 min beginning with the 30 min
postingestion assessment, also as compared with baseline. The high-fat meal also increased Z
at time 0. The increase in %BF with meal ingestion followed the same pattern as did the
change in Z. Beginning at the 90 min mark, the relative changes in Z and %BF of the food-
based group were significantly different from those of the water consumers at those time
points. Consequently it is logical to expect that body composition assessments with
bioelectrical impedance will be affected if beverages or food are consumed within the 2 hr
preceding the assessment (Androutsos et al. 2015). That effect may last even beyond that
demarcation.
435
How does exercise affect bioimpedance measures?
Kushner and colleagues (1996) suggested three ways in which exercise may influence BIA
measurements:
Increased blood flow and warming of skeletal muscle tissue reduce Z and the
specific resistivity (ρ) of muscle.
Increased cutaneous blood flow, skin temperature, and sweating lower Z.
Fluid loss due to exercise increases Z.
The effect of aerobic exercise on resistance measurements partially depends on exercise

intensity and duration. Jogging and cycling at moderate intensities (~70% V̇O max) for 90 to
2
120 min substantially decreased R (by 50-70 Ω), resulting in a large overestimation of FFM
(~12 kg) (Khaled et al. 1988; Lukaski 1986). In contrast, cycling at lower intensities (100 and
175 W) for 90 min had a much smaller effect on R (1-9 Ω) (Deurenberg et al. 1988). Liang
and Norris (1993) reported a decrease in R of about 3% immediately after 30 min of
moderate-intensity exercise, but R returned to normal 1 hr postexercise with water ad libitum.
The decrease in R following strenuous exercise most likely reflects a relatively greater loss of
body water in the sweat and expired air than the loss of electrolytes. This difference leads to a
higher electrolyte concentration in the body’s fluids, thereby lowering R (Deurenberg et al.
1988).
The BIA method was found to adequately predict changes in TBW after heat-induced
dehydration and glycerol-induced hyperhydration but not after exercise-induced dehydration;
thus, factors other than just total fluid volume affect BIA measures following exercise
(Koulmann et al. 2000). Researchers hypothesized that the redistribution of body fluids to
active muscles during exercise, which relatively increases hydration in these segments (legs),
might partially conceal the decreased fluid volumes in less active segments (trunk and arms).
Can I measure bioimpedance at any time during a client’s menstrual cycle?

Although the menstrual cycle alters TBW, the ratio of extracellular to intracellular water, and
body weight, researchers found only small differences in bioimpedance measures (Z and R)
between the follicular and premenstrual stages (~5-8 Ω) and between menses and the
follicular stage (~7 Ω) (Deurenberg et al. 1988; Gleichauf and Rose 1989). However, the
average body weight of the women studied was stable (<0.2 kg difference) during the
menstrual cycle. In women experiencing relatively large body weight gains (2-4 kg or 4.4-8.8
436
lb) during the menstrual cycle, a large part of the gain is due to an increase (1.5 kg or 3.3 lb
on average) in TBW (Bunt et al. 1989). Until there are more conclusive data on this issue,
you should take BIA measurements at a time during the menstrual cycle when the client
perceives she is not experiencing a large weight gain. This practice should minimize error and
more accurately estimate FFM for your clients.
Is there high agreement between bioimpedance values measured by two different

technicians?
Technician skill is not a major source of BIA measurement error. There is virtually no
difference in R measurements taken by different technicians, provided that standardized
procedures for electrode placement and client positioning are closely followed (Jackson et al.
1988). The proximal electrodes in particular need to be correctly positioned at the wrist and
ankle, as a 1 cm (0.4 in.) displacement may result in a 2% error in R (Elsen et al. 1987).
Lukaski (1986) reported a 16% increase in R (~79 Ω) due to improper electrode placement.
How does body position affect bioimpedance measures?
Proper positioning of the client is important for an accurate measurement. As a standard

practice, whole-body resistance is measured with the client lying in a supine position.
Changes in body position alter Z values as much as 12% (Lozano, Rosell, and Pallas-Areny
1995); moving from a standing to supine position immediately increases Z by about 3%
because of fluid shifts (Kushner et al. 1996). Also, the amount of time the client lies supine
before Z is recorded needs to be standardized; in the supine position, Z gradually increases
over several hours (Kushner et al. 1996). Experts recommend having your client lie supine for
at least 10 min before BIA measurement (Ellis et al. 1999). Subsequent analyses are equivocal
in their recommendations regarding fluid equilibration time. One research team indicates
that 5 min in a supine position is sufficient to allow TBW to stabilize; ICW and ECW need
more time (Gibson et al. 2015). A more conservative TBW and ECW equilibration of
“within a 15 min time period” is recommended by Thurlow’s group (2017). In addition, make
sure the client’s arms are abducted (30°-45°) from the trunk and that the thighs are not
touching each other. Crossing the limbs short-circuits the electrical path, dramatically
affecting bioimpedance values. For bioimpedance analysis with the client in an upright
position, have the client stand for 5 min before taking the measurement (Gibson et al. 2015).
Should I measure whole-body bioimped-ance on the right or left side of the body?
The standard practice is to measure whole-body bioimpedance on the right side of the body.
437
The differences between R measurements using ipsilateral (right arm–right leg or left arm–
left leg) and contralateral (right arm–left leg or left arm–right leg) electrode placements are
generally small (Graves et al. 1989; Lukaski et al. 1985).
How does temperature affect bioimpedance?

Bioimpedance measurements should be made with the client lying supine on a nonconductive
surface (e.g., stretcher bed or mat) in a room at normal ambient temperature (25 °C [77 °F]).
Researchers have demonstrated that ambient temperature affects skin temperature, and R
varies inversely with skin temperature (Caton et al. 1988; Gudivaka, Schoeller, and Kushner
1996; Liang, Su, and Lee 2000). Cool ambient temperatures (~14 °C) drop skin temperature
(24 °C compared with 33 °C under normal conditions), significantly increasing total body R
(by 35 Ω, on average) and decreasing estimated FFM (by ~2.2 kg) (Caton et al. 1988). Liang
and colleagues (2000) reported a slightly greater difference in R (46 Ω) between cold (17 °C
ambient temperature and 28.7 °C skin temperature) and hot (35 °C ambient and 35.8 °C skin
temperature) conditions.
OTHER ANTHROPOMETRIC METHODS

Anthropometry refers to the measurement of the size and proportion of the human body.
Body weight and stature (standing height) are measures of body size, whereas ratios of weight
to height represent body proportion. Circumferences, SKF thicknesses, skeletal diameters,
and segment lengths may be used to assess the sizes and proportions of body segments. A
circumference (C) is a measure of the girth of a body segment such as the arm, thigh, waist,
or hip. A skeletal diameter (D) is a measure of bony width or breadth (e.g., of the knee,
ankle, or wrist).
ANTHROPOMETRIC ASSESSMENTS CONDUCTED WITH A MOBILE APP
LeanScreen, an Apple mobile app, uses three photos (two frontal views and one sagittal view) taken in accordance with
app guidelines; sex and height must be key-entered. The user digitally identifies widths and depths on the photos. This
requires some knowledge of anatomical landmarks, as the demarcations are interpreted by the app as being
circumferences of the neck, waist, abdomen, and hips. The software does the rest of the work to estimate %BF.
To date, two research teams have investigated the app in relation to known methods. For a small sample of adults,
there were no significant differences in %BF estimations between LeanScreen, SKF, and BIA (Shaw, Robinson, and
Peart 2017). Compared with DXA and manually measured circumferences for a sizeable sample of weight-stable adults,
there were significant underestimations of %BF. Less than 45% of the %BF estimates were within ±4% of those
measured by DXA (MacDonald et al. 2017). Both research teams reported that the reliability of the app is high (r > .97).
However, the large intraindividual variability between the app and other methods currently limits its use for purposes
beyond general knowledge.
438
Anthropometric measures such as circumferences, SKFs, and skeletal diameters have been
used to assess total and regional body composition. Also, anthropometric indices such as body
mass index (BMI), waist-to-hip circumference ratio (WHR), waist circumference, and
sagittal abdominal diameter (SAD) are used to identify individuals at risk for disease.
Compared with SKFs, other anthropometric measures are relatively simple and inexpensive,
and they do not require a high degree of technical skill and training. They are well suited for
large epidemiological surveys and for clinical purposes.
The basic principles underlying the use of anthropometric measures such as circumference,
skeletal diameter, and BMI to estimate body composition are as follows:
• Circumferences are affected by fat mass, muscle mass, and skeletal size; therefore, they are
related to fat mass and lean body mass. Jackson and Pollock (1976) reported that circumference
and bony diameter are markers of lean body mass (muscle mass and skeletal size); however,
some circumferences are also highly associated with body fat. These findings confirm that
circumferences reflect both fat and fat-free components of body composition.
• Skeletal size directly relates to lean body mass. Behnke (1961) proposed that lean body mass
could be accurately estimated from skeletal diameters and developed equations for doing so.
Cross-validation of these equations yielded a moderately high (r = .80) relationship and
closely estimated average lean body mass values obtained from hydrodensitometry (Wilmore
and Behnke 1969, 1970). Behnke’s hypothesis was also supported by the observation that
skeletal diameters, along with circumferences, are strong markers of lean body mass (Jackson
and Pollock 1976).
• To estimate total body fat from weight-to-height indices, the index should be highly related to
body fat but independent of height. On the basis of data from two large-scale epidemiological
surveys (National Health and Nutrition Examination Surveys I and II), Micozzi and
colleagues (1986) reported that BMI (body weight divided by height squared) is not
significantly related to the height of men (r = −.06) and women (r = −.16). However, BMI is
not totally independent of height, especially in younger children (<15 yr). When the analysis
is controlled for age and adiposity, BMI is independent of height (Heymsfield et al. 2016).
Although BMI was directly related to SKF thickness and the estimated fat area of the arm (r
= .72-.80) (Micozzi et al. 1986), the relationship of BMI to body fat varies with age, gender,
and ethnicity (Deurenberg and Deurenberg-Yap 2001; Deurenberg, Yap, and van Staveren
1998; Gallagher et al. 1996; Rush et al. 1997; Wang et al. 1994).
Using the Anthropometric Method to Estimate Body Composition
439
Although some anthropometric prediction models use SKFs, circumferences, and skeletal
diameters to estimate body composition, only those equations using circumferences and
diameters are addressed in this chapter, for the following reasons:
The predictive accuracy of anthropometric (circumference and diameter) equations

is not greatly improved by the addition of SKF measures.
Anthropometric equations using only circumferences estimate the body fatness of
obese individuals more accurately than SKF prediction equations (Seip and
Weltman 1991).
Compared with SKFs, circumferences and skeletal diameters can be measured with
less error (Bray and Gray 1988a).
Some practitioners may not have access to SKF calipers.
Anthropometric prediction equations estimate total body density (Db), relative body fat
(%BF), and fat-free mass (FFM) from combinations of body weight, height, skeletal
diameters, and circumferences. Generally, equations using only skeletal measures have larger
prediction errors than those using both circumferences and bony diameters. Like SKF and
BIA equations, anthropometric equations are based on either population-specific or
generalized models.
Population-specific anthropometric equations are valid only for individuals whose physical
characteristics (age, gender, ethnicity, and level of body fatness) are similar to those of the
specific population. For example, anthropometric equations developed to estimate the body
composition of obese individuals (Weltman et al. 1988; Weltman et al. 1987) should not be
applied to nonobese individuals.
On the other hand, generalized equations, applicable to individuals of various age and body
fatness, have been developed for heterogeneous populations of women (15-79 yr; 13%-63%
BF) and men (20-78 yr; 2%-49% BF) (Tran and Weltman 1988, 1989). The predictive
accuracy of these generalized equations for estimating the %BF of obese men and women was
similar to that of fatness-specific (obese) equations (Seip and Weltman 1991). Typically,
generalized equations include body weight or height, along with two or three circumferences,
as predictors of Db or %BF. As in generalized SKF models, the relationship between some
circumference measures and Db is curvilinear (Tran and Weltman 1988, 1989). Also, age has
been shown to be an independent predictor of Db for women (Tran and Weltman 1989).
440
Table 8.6 provides anthropometric prediction equations for various population subgroups.
Table 8.6 Circumference Prediction Equations
Population subgroup Equation Reference

White women, 15-79 yr
Db (g·cc −1)a = 1.168297 − 0.002824(abdom Cb) + 0.0000122098(abdom Cb)2 − 0.000733128(hip C) + 0.000510477(ht) − 0.00021616(age) Tran and Weltman (1989)
White men, 15-78 yr b
%BF = −47.371817 + 0.57914807(abdom C ) + 0.25189114(hip C) + 0.21366088(iliac C) − 0.35595404(BM) Tran and Weltman (1988)
White, obese women, 20-60 yr b

%BF = 0.11077(abdom C ) − 0.17666(ht) + 0.14354(BM) + 51.033
Weltman et al. (1988)
White, obese men, 24-68 yr b

%BF = 0.31457(abdom C ) − 0.10969(BM) + 10.834 Weltman et al. (1987)
Db = body density; %BF = percent body fat; BM = body mass (kg); ht = height (cm).
aUse population-specific conversion formula to calculate %BF from Db.
bAbdom C (cm) is the average abdominal circumference measured at two sites: (1) anteriorly midway between the xiphoid process of sternum and the umbilicus and laterally between the lower end of the rib cage and iliac crests;
(2) at the umbilicus level.
Using Anthropometric Indices to Classify Disease Risk

Anthropometric measures have other uses besides estimating body composition. In large-
scale epidemiological surveys and clinical settings, indirect anthropometric indices such as
BMI, WHR, waist circumference, and SAD are used to assess regional fat distribution
(upper and lower body fat) and to identify at-risk individuals.
Body Mass Index

The body mass index (BMI) is used to classify individuals as obese, overweight, and
underweight; to identify individuals at risk for obesity-related diseases; and to monitor
changes in the body fatness of clinical populations (Roger et al. 2012; U.S. Department of
Health and Human Services 2000b; World Health Organization 1998). Body mass index is a
significant predictor of body fat levels, cardiovascular disease, and type 2 diabetes
(Ehrampoush et al. 2016; Freedman et al. 2012). Low BMI values have also been associated
with poor outcome and survival in clinical clients prone to protein-energy wasting (Leal et al.
2012). Because of this association and the fact that BMI is easily calculated (BMI = body
weight / height squared), BMI is widely used in population-based and prospective studies to
identify at-risk individuals.
Body mass index, however, is limited as an index of obesity (body fatness) because it does
not account for the whole-body or regional composition of and contribution to body weight.
In addition, factors such as age, ethnicity, body build, physical activity level, and frame size
affect the relationship between BMI and %BF. Thus, using BMI as an index of obesity may
result in misclassifications of underweight, overweight, and obese. Also, because BMI is a
better measure of nonabdominal and abdominal subcutaneous fat than of visceral fat (Camhi
441
et al. 2011), other anthropometric indices need to be used to assess fat distribution.
Body mass index (BMI) is the ratio of body weight to height squared: BMI (in kg·m ) = wt −2
(in kilograms) / ht (in meters). To calculate BMI, measure the body weight in kilograms and
2
convert the height from centimeters to meters (m = cm / 100). Alternatively, you can use a
nomogram (see figure 8.15) to calculate a client’s BMI (Bray 1978). To use this nomogram,
plot the client’s height and body weight in the appropriate columns, and connect the two
points with a ruler. Read the corresponding BMI at the point where the connecting line
intersects the BMI column.
FIGURE 8.15 Nomogram for body mass index.

Reprinted by permission from Macmillan Publishers Ltd. International Journal of Obesity, G.A. Bray, “Definition, Measurement, and Classification of the Syndromes of Obesity, 2 no. 2 (1978): 99-112. Copyright 1978.
Table 8.7 describes current standards for classifying BMI values. The World Health
Organization (1998) defines obesity as a BMI of 30 kg·m or more, overweight as a BMI −2
between 25 and 29.9 kg·m , and underweight as a BMI of less than 18.5 kg·m . These
−2 −2
suggested cutoffs are based on the relationship between BMI and morbidity and mortality
reported in observational studies in Europe and the United States. There is a J-shaped curve
442
associated with years of life lost and BMI at ages 30, 50, and 70 that indicates a level of
minimal likelihood of early mortality for those with BMIs in the normal weight range. For a
given BMI greater than 30 kg·m , the expected number of years of life lost is greatest for the
−2
30 and 50 yr olds. When the BMI is in the overweight category men are at an increasingly
,
higher risk of early death than are women (Ashwell et al. 2014).
Table 8.7 Classification of Overweight and Obesity Based on Body Mass Index (BMI)
Classification BMI value

Underweight <18.5
Normal weight 18.5-24.9
Overweight 25.0-29.9
Obesity
Class I 30.0-34.9
Class II 35.0-39.9
Class III ≥40.0
Data from World Health Organization 1998.
The use of BMI in health risk appraisals assumes that people who are disproportionately
heavy are so because of excess fat mass. However, controversy exists concerning the most
appropriate cutoff for designating obesity (Deurenberg 2001) and whether or not the cutoff
points used for Western and European populations are appropriate for use with Asian
populations (Wang et al. 2012). Caution is also urged when using BMI values to identify
employees for whom health insurance costs will rise for failure to meet a specific BMI target.
An evaluation of adult data in the NHANES 2005-2012 database and BMI’s ability to
correctly identify their cardiometabolic profile indicates that approximately 75 million
American adults will be misclassified based on BMI (Tomiyama et al. 2016).
The relationship between BMI and %BF is affected by age, gender, ethnicity, and body
build (Camhi et al. 2011; Deurenberg et al. 1998; Snijder, Kuyf, and Deurenberg 1999). For
a given BMI value, older individuals have a greater %BF compared with their younger
counterparts, and young adult males have a lesser %BF than young adult females. Also, for a
given %BF, age- and gender-matched whites have a higher BMI (1.3-4.6 kg·m ) compared−2
with other ethnic groups (e.g., African-Americans, Chinese, Indonesians, Ethiopians, and
Polynesians) (Camhi et al. 2011; Deurenberg et al. 1998). These findings suggest that using a
universal BMI cutoff to define obesity (≥30 kg·m ) may not be appropriate. Ethnic-specific
−2
cutoff values need to be established that account for the relationship between BMI and %BF
and for the morbidity and mortality risks in relation to BMI for specific ethnic groups
(Antoine-Jonville, Sinnapah, and Hue 2012; Deurenberg 2001; Wang et al. 2012). On the
other hand, some researchers believe new ethnic-specific BMI cut points are not needed.
443
Rather, they suggest that more effort be focused on understanding how the body shape,
composition, and segmental proportions underlying BMI relate to health risks and outcomes
as well as significant clinical conditions (Heymsfield et al. 2016).
Waist Circumference
Waist circumference is recognized as a useful measure of regional adiposity (i.e., abdominal
obesity) and as a predictor of obesity-related cardiometabolic disease (Ehrampoush et al.
2016; Moore 2009; Yoon and Oh 2014). It also appears useful in estimating health-related
differences in cardiorespiratory fitness in adults 20 to 85 yr of age (Drystad et al. 2017). Waist
circumference as a proxy for abdominal fat is reported to have a greater impact on overall
adiposity for women compared with men (Ehrampoush et al. 2016). Coupled with BMI,
waist circumference predicts musculoskeletal injury risk (Nye et al. 2014) and health risk
better than BMI alone (Ardern, Katzmarzyk, and Ross 2003; Zhu et al. 2004). Freedman
and Ford (2015) suggest that trends in obesity might be underestimated, especially for
women, if obesity is defined by BMI alone. However, waist circumference and its use in
various anthropometric indices are considered to be more universally informative than BMI
for some racial and ethnic minorities (Tarleton et al. 2015).
Yoon and Oh (2014) identified waist circumference cutoff values (men: 85 cm or 33.5 in.;
women: 80 cm or 31.5 in.) that may be beneficial for predicting a variety of chronic
conditions associated with abdominal obesity in a sample of Korean adults. The National
Cholesterol Education Program (NCEP; 2001) recommends using waist circumference
cutoffs of >102 cm (40 in.) for men and >88 cm (34.6 in.) for women to evaluate obesity as a
risk factor for cardiovascular and metabolic diseases. The International Diabetes Foundation
(2006) identified more conservative waist circumference values for Europids (people from
North America and Europe); they also identified maximal values for South Asian, Chinese,
Japanese, Ethnic South and Central American, Sub-Saharan African, and Eastern
Mediterranean and Middle Eastern populations.
Selection of the most appropriate waist circumference cut-points is complex given that age,
sex, race, ethnicity, and BMI influence these values; optimum waist circumference cut-points
vary according to health outcomes and the population studied (Klein et al. 2007). For
example, Kim and associates (2011) identified waist circumference cutoff values for Korean
men and women, respectively, that are more conservative than those proposed by Zhu and
colleagues (2005) and the NCEP (2001). Likewise, the cutoff points were identified as being
87 cm (34 in.) and 85 cm (33.5 in.), respectively, for elderly (≥65 yr) Korean men and women
444
(Lim et al. 2012). Furthermore, waist circumference measured midway between the iliac crest
and the lowest ribs is reported to be superior to waist circumference measured at the superior
border of the iliac crest in a sample of Taiwanese adults; the midpoint waist circumference is
more highly correlated to visceral fat area, blood pressure, HbA1C, blood glucose,
triglycerides, HDL-C, and C-reactive protein compared with the waist circumference
measured at the iliac crest for that population subgroup (Ma et al. 2012).
Waist-to-Hip Ratio
The waist-to-hip ratio (WHR) is an indirect measure of lower and upper body fat
distribution. Upper body obesity, or central adiposity, measured by the WHR moderately
relates (r = .48-.61) to risk factors associated with cardiovascular and metabolic diseases in
men and women (Ohrvall, Berglund, and Vessby 2000). Young adults with WHR values in
excess of 0.94 for men and 0.82 for women are at high risk for adverse health consequences
(Bray and Gray 1988b). The optimal WHR for Korean men and women (30-80 yr) is <0.90.
A WHR above 0.90 has good sensitivity (men: 82.9%; women 65.3%) and specificity (men:
55.6%; women: 70.9%) for detecting two or more factors (beyond waist circumference)
associated with metabolic syndrome in this population subgroup (Kim et al. 2011).
Although the WHR has been used as an anthropometric measure of central adiposity and
visceral fat, it has certain limitations:
The WHR of women is affected by menopausal status (Kim et al. 2011; Svendsen
et al. 1992; Weits et al. 1988). Postmenopausal women show more of a male
pattern of fat distribution than do premenopausal women (Ferland et al. 1989).
The WHR is not valid for evaluating fat distribution in prepubertal children
(Peters et al. 1992).
The accuracy of the WHR in assessing visceral fat decreases with increasing fatness.
Hip circumference is influenced only by subcutaneous fat deposition; waist
circumference is affected by both visceral fat and subcutaneous fat depositions.
Thus, the WHR may not accurately detect changes in visceral fat accumulation
(Goran, Allison, and Poehlman 1995; van der Kooy et al. 1993). Large WHR
values have been associated with an increased risk of first stroke (Oliveira, Avezum,
and Roever 2015).
To calculate the waist-to-hip ratio (WHR), divide waist circumference (in centimeters) by
445
hip circumference (in centimeters). The measurement site for waist circumference, however,
has not been universally standardized. The World Health Organization (1998) recommends
measuring waist circumference midway between the lower rib margin and the iliac crest and
measuring hip circumference at the widest point over the greater trochanters. In contrast, the
Anthropometric Standardization Reference Manual (Callaway et al. 1988) recommends
measuring the waist circumference at the narrowest part of the torso and the hip
circumference at the level of the maximum extension of the buttocks. The WHR norms
(table 8.8) were established using the measurement procedures described in the
Anthropometric Standardization Reference Manual. Instead of calculating the WHR by hand,
you can use the WHR nomogram (figure 8.16) to obtain values for your clients. Plot the
client’s waist and hip circumferences in the corresponding columns of the nomogram and
connect these points with a straight line. Read the WHR at the point where this line
intersects the WHR column.
446
FIGURE 8.16 Nomogram for waist-to-hip ratio (WHR).
Reprinted from G.A. Bray and D.S. Gray, “Obesity: Part I - Pathogenesis,” The Western Journal of Medicine 149 (1988): 429-441, by permission of BMJ Publishing Group.
Waist-to-Height Ratio
The waist-to-height ratio (WHTR) (i.e., waist circumference / standing height) has been
suggested as a better indicator of adiposity and health risks than waist circumference alone
(Ashwell, Gunn and Gibson 2011; Ashwell and Hsieh 2005; Hsieh, Yoshinaga, and Muto
2003). To minimize years of life lost because of obesity-related factors and maximize life
expectancy, an optimal WHTR of 0.50 (men) and 0.46 (women) in conjunction with sex-
specific adult BMIs of 24 and 26 kg·m , respectively, is recommended (Ashwell et al. 2014). -2
As a rule, waist circumference should be less than half the height. The risk of premature
mortality increases for both men and women, even in their younger years, if their WHTR is
in the “consider action” category (WHTR = 0.5 to 0.6). This risk increases dramatically if the
WHTR exceeds 0.6, as that places them in the “take action” category. To see the J-shaped
relationship between years of life lost and WHTR for 30, 50, and 70 yr olds, see the work of
Ashwell and associates (2014).
Flegal and colleagues (2009) reported that WHTR, waist circumference, and BMI were
447
highly related (r = .85-.97) across age groups and genders. Although all three of these
anthropometric indices performed similarly as indicators of body fatness, the relationship of
WHTR with %BF was slightly higher (r = .66-.87). WHTR was found to be consistently
superior to both BMI and waist circumference in terms of serving as an indicator of disease
outcome or risk factors for cardiovascular diseases, diabetes mellitus, metabolic syndrome,
hypertension, and dyslipidemia (Ashwell, Gunn, and Gibson 2011).
The Ashwell Body Shape Chart can be used to identify a client’s health risk based on body
shape (see appendix D.6). To use this chart, measure the client’s standing height and waist
circumference at the umbilical level. Find the point corresponding to the height (y-axis of
chart) and waist circumference (x-axis). This chart is applicable to adults from all racial and
ethnic groups, as well as children 5 yr of age or older.
Sagittal Abdominal Diameter

The sagittal abdominal diameter (SAD) is a measure of the anteroposterior thickness of the
abdomen at the umbilical level. It is also a simple indicator of the amount of dysfunctional
visceral adipose tissue in the body, known to lead to aberrations in blood glucose level, insulin
resistance, and increased risk for developing type 2 diabetes mellitus (Kahn et al. 2014). The
SAD is strongly related to visceral adipose tissue in men (r =.82) and women (r = .76), even
after adjusting for BMI (r = .66 and .63, respectively, for men and women) (Zamboni et al.
1998). However, this relationship is stronger in lean or moderately overweight individuals
than in obese individuals. Compared with waist circumference, WHR, and BMI, SAD is
more strongly related to risk factors for cardiovascular and metabolic diseases in women and
men (Ohrvall et al. 2000). In support of Ohrvall’s findings, Kahn and colleagues (2014)
found a greater association between SAD and disease-causing blood glucose abnormalities
independent of BMI, age, and waist circumference. At age 60, men’s SAD value is strongly
correlated to waist circumference, BMI, and WHR; whereas, for women, the only strong
correlations are between SAD with waist circumference and BMI. Sex-specific SAD cutoff
values of 22 cm and 20 cm, respectively, have been identified for men and women. Beyond
these values, cardiometabolic risk scores increase (Risérus et al. 2010).
According to an 8 yr longitudinal study of 5,168 Finnish adults, the relative risk of
developing diabetes between the lowest and highest quartiles was similar for SAD (14.7) and
BMI (15.0); the relative risk from waist circumference (11.4) and WHR (12.5) were lower.
The combination of high SAD and high BMI formed the most powerful predictor of
incident diabetes (Pajunen et al. 2013). The SAD is also associated with cardiovascular
448
disease risk factors in older women (67-78 yr) (Turcato et al. 2000); likewise, SAD was
directly correlated with triglyceride and blood glucose levels and inversely related with HDL-
C in a sample of overweight Brazilian adults with an average age and BMI of 54 yr and 30.5
kg·m , respectively (Pimentel et al. 2011).
−2
The procedures for measuring SAD have not been standardized. In most studies, SAD was
measured while the client was lying supine, legs extended, on an examination table. A
sliding-beam anthropometer is used to measure the vertical distance (to the nearest 0.1 cm)
between the top of the table and the abdomen at the level of the umbilicus or iliac crests. In
some studies, SAD was measured with the hips and legs flexed or with the client standing
instead of lying supine.
Using Anthropometric Measures to Classify Frame Size

Skeletal diameters are used to classify frame size in order to improve the validity of height-
weight tables for evaluating body weight. The rationale for including frame size is that
skeletal breadths are important estimators of the bone and muscle components of fat-free
mass. Estimating frame size allows you to differentiate between those who weigh more
because of a large musculoskeletal mass and those who weigh more because of a large fat mass
(Himes and Frisancho 1988). Since there are health implications for individuals who are
overweight, a critical evaluation of body weight is important. You can approximate frame size
using reference data for elbow width. For men, elbow width ≤6.7 cm is small and ≥8.1 cm is
large. Values ≤5.7 cm and ≥7.2 cm are classified, respectively, as small and large frame size for
women (Frisancho 1984). The anatomical landmarks for measurement are described in
appendix D.5, Standardized Sites for Bony Breadth Measurements.
Anthropometric Techniques
You must practice in order to become proficient in measuring skeletal diameters and
circumferences. Following the standardized procedures (see Standardized Procedures for
Anthropometric Measurements) will increase the accuracy and reliability of your
measurements (Callaway et al. 1988; Wilmore et al. 1988).

The accuracy and reliability of anthropometric measures are potentially affected by
equipment, technician skill, and client factors (Bray 1978; Callaway et al. 1988). The
following questions and responses concern these sources of measurement error.
What equipment will I need to measure bony widths?
449
Use skeletal anthropometers and sliding or spreading calipers to measure bony widths and
body breadths (see figure 8.17). The precision characteristics (0.05-0.50 cm) and range of
measurement (0-210 cm) depend on the type of skeletal anthropometer or caliper you are
using (Wilmore et al. 1988). The instruments must be carefully maintained and must be
calibrated periodically so that their accuracy can be checked and restored.
FIGURE 8.17 Skeletal anthropometers and anthropometric tape measure.
Can I use any type of tape measure to measure body circumferences?
Use an anthropometric tape measure to measure circumferences (see figure 8.17). The tape
measure should be made from a flexible material that does not stretch with use. You can use a
plastic-coated tape measure if an anthropometric tape measure is not available. Some
anthropometric tapes have a spring-loaded handle (i.e., Gulick handle) that allows a constant
tension to be applied to the end of the tape during the measurement.
Standardized Procedures for Anthropometric Measurements
1. Take all circumference and bony diameter measurements of the limbs on the right
side of the body.
2. Carefully identify and measure the anthropometric site. Be meticulous about
locating anatomical landmarks used to identify the measurement site (see appendix
D.4, Standardized Sites for Circumference Measurements, and appendix D.5,
Standardized Sites for Bony Breadth Measurements), and instruct your clients to
relax their muscles during the measurement.
3. Take a minimum of three measurements at each site in rotational order.
450
4. To measure the breadth of smaller segments, like the elbow or wrist, use small
sliding calipers (range of 30 cm or 11.8 in.) with greater scale precision instead of
larger skeletal anthropometers (range of 60-80 cm or 23.6-31.5 in.).
5. Hold the skeletal anthropometer or caliper in both hands so the tips of the index
fingers are adjacent to the tips of the caliper.
6. Place the caliper on the bony landmarks and apply firm pressure to compress the
underlying muscle, fat, and skin. Apply pressure to a point where the measurement
no longer continues to decrease.
7. Use an anthropometric tape to measure circumferences. Hold the zero end of the
tape in your left hand, positioned below the other part of the tape that is held in
your right hand.
8. Apply tension to the tape so that it fits snugly around the body part but does not
indent the skin or compress the subcutaneous tissue.
9. For some circumferences (e.g., waist, hip, and thigh), you should align the tape in a
horizontal plane, parallel to the floor.
How much skill and practice are required to ensure accurate circumference and skeletal
diameter measurements?
Technician skill is not a major source of measurement error for these methods compared with
the SKF and ultrasound methods. However, you need to practice in order to perfect the
identification of the measurement sites and your measurement technique. Experts
recommend practicing on at least 50 people and taking a minimum of three measurements
for each site in rotational order (Callaway et al. 1988). Closely follow standardized testing
procedures for locating measurement sites, positioning the anthropometer or tape measure,
and applying tension during the measurement. Appendix D.4 (Standardized Sites for
Circumference Measurements) and appendix D.5 (Standardized Sites for Bony Breadth
Measurements) describe some of the most commonly used circumference and skeletal
diameter sites.
Is there good agreement in circumference and skeletal diameter values when the
measurements are taken by two different technicians?
Variability in circumference measurements taken by different technicians is relatively small

(0.2-1.0 cm), with some sites differing more than others (Callaway et al. 1988). Skilled
451
technicians can obtain similar values even when measuring circumferences of obese
individuals (Bray and Gray 1988a).
Are the circumferences of obese clients more easily measured than SKFs?
As with the SKF method, it is more difficult to obtain consistent measurements of

circumference for obese compared with lean individuals (Bray and Gray 1988a). However,
circumferences are preferable to SKFs for measuring obese clients, for several reasons:
You can measure circumferences of obese individuals regardless of their size,

whereas the maximum aperture of the SKF caliper may not be large enough to
allow measurement.
Measurement of circumferences requires less technician skill.
Differences between technicians are smaller for circumferences than for SKF
measurements (Bray and Gray 1988a).
Is it possible to accurately measure bony widths of heavily muscled and obese clients?
Accurate measurement of bony diameters in heavily muscled or obese individuals may be
difficult because the underlying muscle and fat tissues must be firmly compressed. It may be
difficult to identify and palpate bony anatomical landmarks, leading to error in locating the
measurement site.
Key Points
Body composition is a key component of health and physical fitness; total body fat and fat distribution are
related to disease risk.
Standards for percent body fat can be used to classify body composition.
Average %BF and standards for obesity vary according to age, gender, and physical activity levels.
Hydrostatic weighing is a valid and reliable reference method for assessing body composition.
Air displacement plethysmography is an alternative to hydrostatic weighing for measuring body volume and
deriving body density.
Dual-energy X-ray absorptiometry is increasingly recognized as a reference method for assessing body
composition.
The absolute values of body composition variables determined by different DXA scanners are difficult to
compare because of differences in manufacturers and software versions.
452
Population-specific conversion formulas, based on multicomponent models of body composition, should be
used to convert Db into percent body fat.
The SKF method is widely used in field and clinical settings.
Generalized SKF equations for the prediction of Db are reliable and valid for a wide range of individuals.
The ultrasound method is gaining recognition as a viable noninvasive alternative to the SKF method.
Bioelectrical impedance analysis is a viable alternative for assessing body composition of diverse population
subgroups.
Circumferences and skeletal diameters can be used to estimate body composition.
Body mass index is a crude index of total body fatness.
Waist-to-hip ratio, waist circumference, WHTR, and SAD are acceptable indices for identifying at-risk
clients.
Key Terms
Learn the definition for each of the following key terms. Definitions of terms can be found in the glossary.
air displacement plethysmography (ADP)

anthropometry
Archimedes’ principle
attenuation
bioelectrical impedance analysis (BIA)
bioimpedance spectroscopy (BIS)
body density (Db)
body surface area
body volume (BV)
Boyle’s law
circumference (C)
damping technique
densitometry
dual-energy X-ray absorptiometry (DXA)
fat-free body (FFB)
fat-free mass (FFM)
fat mass (FM)
hemiscan procedure
hydrodensitometry
hydrostatic weighing (HW)
impedance (Z)
multicomponent model
453
percent body fat (%BF)
reactance (Xc)
relative body fat (%BF)

residual volume (RV)
resistance (R)
resistance index (ht2 / R)

sagittal abdominal diameter (SAD)
skeletal diameter (D)
skinfold (SKF)
tare weight
thoracic gas volume (TGV)
two-component model
ultrasound
underwater weight (UWW)
waist-to-height ratio (WHTR)
waist-to-hip ratio (WHR)
Review Questions
1. Why is it important to assess the body composition of your clients?
2. What are the standards for classifying obesity and minimal levels of body fat for men and women?
3. Explain why many researchers are calling for BMI and anthropometric cutoff points that are specific for the
client’s ethnicity.
4. Explain musculoskeletal changes and differences associated with gender, age, and physical activity that
make BMI better than adiposity for explaining body shape.
5. What are the assumptions of the two-component model of body composition? Identify two commonly used
two-component model equations for converting Db into %BF.
6. Explain how gender, ethnicity, and age affect FFB density and, therefore, two-component model estimates
of %BF.
7. Name three methods you can use to obtain reference measures of body composition. Which method is best?
Explain your choice.
8. Identify two ways to measure (not estimate) a client’s Db.
9. Distinguish between total Db and FFB density.
454
10. Describe how the HW method could be modified to test clients who are unable to be weighed under water at
RV.
11. Identify potential sources of measurement error for the SKF and ultrasound methods.
12. Explain the differences between A- and B-mode ultrasound result displays.
13. In lay terms, explain the basic theory underlying the use of BIA.
14. To obtain accurate estimates of body composition using the BIA method, your client must adhere to
pretesting guidelines. Identify these client guidelines.
15. Explain how BMI, WHR, WHTR, and waist circumference may be used to identify clients at risk due to
obesity.
16. Identify suitable field methods and prediction equations (i.e., SKF, BIA, or other anthropometric methods)
to estimate body composition for each of the following subgroups of the population: older adults, children,
obese individuals, and athletes.
455
CHAPTER 9
Designing Weight Management and Body

Composition Programs
KEY QUESTIONS
What is obesity and how prevalent is it worldwide?
What are the health risks associated with having high or low levels of body fat?
What are the primary causes of overweight and obesity?
How is healthy body weight determined?
What steps should I follow in planning a weight management program?
What are the recommended guidelines for weight loss and weight gain programs?
Why is exercise important for weight management?
What types of exercise are best for weight loss?
Does exercising without dieting improve body composition?
Health and longevity are threatened when a person is either overweight or underweight.
Overweight and obesity increase the risk of developing serious cardiovascular, pulmonary, and
metabolic diseases and disorders. Likewise, individuals who are underweight may have a
higher risk than others of cardiac, musculoskeletal, and reproductive disorders. Thus, healthy
weight is key to a healthy and longer life.
As a health and fitness professional, you have an enormous challenge and responsibility to
help determine a healthy body weight for your clients and to provide scientifically sound
weight management programs for them. This chapter presents guidelines and techniques for
determining healthy body weight. You will learn about weight control principles and
practices, as well as guidelines for designing exercise programs for weight loss, weight gain,
and body composition change.
OBESITY, OVERWEIGHT, AND UNDERWEIGHT: DEFINITIONS

AND TRENDS
456
Individuals with body fat levels falling at or near the extremes of the body fat continuum are
likely to have serious health problems that reduce life expectancy and threaten their quality of
life. Obese individuals have a higher relative risk of cardiovascular ischemic heart disease
(2.0×), stroke (1.55×), dyslipidemia, hypertension, glucose intolerance, insulin resistance,
diabetes mellitus (6.0×), obstructive pulmonary disease, gallbladder disease, osteoarthritis, and
cancers of the colon (1.2×), esophagus (2.3×), gallbladder (1.5×), and endometrium (2.5×)
(U.S. Department of Health and Human Services 2000b; International Association for the
Study of Obesity 2012). The prevalence of diabetes is highest among obese (18.5%) followed
by overweight (8.2%) and normal weight (5.4%) individuals, and this same pattern exists for
hypertension (35.7%, 26.4%, and 19.8%, respectively) and dyslipidemia (49.7%, 44.2%, and
28.6%, respectively) (Saydah et al. 2014). Obesity is independently associated with coronary
heart disease (CHD), heart failure, cardiac arrhythmia, stroke, and menstrual irregularities
(Pi-Sunyer 1999).
At the opposite extreme, underweight individuals with too little body fat tend to be
malnourished. These people have a relatively higher risk of fluid-electrolyte imbalances,
osteoporosis and osteopenia, bone fractures, muscle wasting, cardiac arrhythmias and sudden
death, peripheral edema, and renal and reproductive disorders (Fohlin 1977; Mazess, Barden,
and Ohlrich 1990; Vaisman, Corey, et al. 1988). One disease associated with extremely low
body fat levels is anorexia nervosa. Anorexia nervosa, an eating disorder found primarily in
females, is characterized by excessive weight loss. It afflicts approximately 1% of the female
population in the United States (Hudson et al. 2007). Compared with normal-weight
women, those with anorexia have extremely low body fat (8%-13% body fat), signs of muscle
wasting, and less bone mineral content and bone density (Mazess, Barden, and Ohlrich 1990;
Vaisman, Rossi, et al. 1988).
DEFINITIONS OF OBESITY, OVERWEIGHT, AND

UNDERWEIGHT
Obesity is an excessive amount of body fat relative to body weight. The term is not
synonymous with overweight. In many epidemiological studies, overweight is defined as a
body mass index (BMI) between 25 and 29.9 kg/m , obesity is defined as a BMI of 30 kg/m
2 2
or more, and underweight is defined by a BMI of less than 18.5 kg/m (U.S. Department of
2
Health and Human Services 2000b). To identify children and adolescents who are
overweight, the 85th and 95th percentile cutoffs for age and sex developed from the Centers
for Disease Control and Prevention growth charts are commonly used in the United States.
457
Children with a BMI greater than or equal to the 95th percentile for their age and sex are
categorized as obese; those with BMI values between the 85th and 94th percentiles are
categorized as overweight. However, these definitions are not universally accepted. Pooled
international data for BMI have been used to develop international standards for evaluating
childhood overweight and obesity. These standards are based on growth curves that relate the
cutoff points for BMI of different age-gender groups (2-18 yr) to the adult categories for
overweight (BMI ≥25 kg/m ) and obesity (BMI ≥30 kg/m ) (see Cole et al. 2000).
2 2
Because these criteria do not take into account the composition of an individual’s body
weight, they are limited as indexes of obesity and may result in misclassifications of
underweight, overweight, and obesity. Considerable variability in body composition exists for
any given BMI. Some individuals with low BMIs may have as much relative body fat as those
with higher BMIs. Older people have more relative body fat at any given BMI than younger
people (Baumgartner, Heymsfield, and Roche 1995). Thus, the prevalence of obesity could
be worse than currently thought.
TRENDS IN OVERWEIGHT AND OBESITY

Globally, the prevalence of overweight and obesity has reached pandemic proportions. The
prevalence of obesity in the world more than doubled between 1980 and 2014 (World Health
Organization 2016c). Worldwide, more than 1.9 billion adults are overweight; of these, over
600 million are obese (World Health Organization 2016c) when using the previously stated
BMI criteria. Thus, approximately 13% of the world’s population is obese. Some researchers
have projected that the prevalence of obesity will continue upward, with 20% of the world’s
population reaching obese status by the year 2030 (Smith and Smith 2016). However, some
studies suggest that the increases in prevalence of obesity for adults and children in some
countries may be slowing or leveling off (Flegal et al. 2012; Ogden et al. 2014; Rokholm,
Baker, and Sorensen 2010; Townsend, Rutter and Foster 2012). For example, in the United
States, 36.5% of adults are obese (BMI >30 kg/m ), and 17.0% of youth are obese (BMI
2
≥95th percentile) (Ogden et al. 2015). Obesity prevalence has not changed significantly in
either of these groups since 2003-2004; however, the increase in obesity is significant dating
back to 1999-2000. These data further document that the rate at which obesity is increasing
in the United States might be slowing, but it is not declining. Additionally, the proportion of
Americans who are severely obese (≥40 kg/m ) continues to increase, from 3.9% in 2000 to
2
6.6% in 2010. The trend of severe (morbid) obesity varies by gender and ethnicity; the
prevalence among women is about 50% higher than among men, and about twice as high
458
among blacks when compared with Hispanics or whites (Sturm and Hattori 2012).
The prevalence of overweight and obesity in adults varies among countries, depending in
part on the nation’s level of industrialization. For example, the United States, Canada,
Australia, and nearly all of Europe have an overweight prevalence >60%. In contrast, the
prevalence of overweight in India, Afghanistan, Indonesia, and most of the countries in
Africa is <20%. For an interactive world map of individual country statistics on BMI and
prevalence of overweight and obesity, search the Global Health Observatory data of the
World Health Organization (WHO) (www.who.int/gho/ncd/risk_factors/overweight/en).
Over the past 30 yr, the prevalence of overweight and obesity in children has increased
substantially. Globally, 170 million children (2-18 yr) are estimated to be overweight (World
Health Organization 2012a). Ahluwalia and colleagues (2015) reported that overweight
prevalence in 11, 13, and 15 yr olds did not change from 2002 to 2010 in the majority of
countries included in their study, but it did increase in many Eastern European countries.
The prevalence of children and adolescents (6-19 yr) who are overweight or obese (BMI
≥85th percentile) in the United States is about 34% (Ogden et al. 2014). The combined
prevalence of overweight and obesity is also 34% in Greek children (Kotanidou et al. 2013).
In comparison, researchers recently reported that the prevalence of overweight and obese
children in Australia varied from 12.4% to 30.2% depending on the territory (Ho et al. 2017).
Prevalence values ranged from 18.6% to 29.9% and 14.7% to 19.0% for South Korean boys
and girls (10-19 yr), respectively, depending on the criteria used to define overweight (Bahk
and Khang 2016).
Collectively, there appears to have been an increase in overweight prevalence among youth
worldwide up to about 2003, with a stabilization occurring over the next decade (Bahk and
Khang 2016; Ho et al. 2017; Kotanidou et al. 2013; Ogden et al. 2014). Also, it is alarming
that there are an estimated 41 million children under the age of 5 yr who are overweight or
obese (World Health Organization 2016c). According to this WHO report, the problem of
infant obesity, once considered unique to high-income nations, is an increasing problem in
low- and middle-income countries. However, in the United States, there was actually a
significant decrease in obesity among preschool children (2-5 yr); prevalence dropped from
13.9% in 2003-2004 to 8.4% in 2011-2012 (Ogden et al. 2014).
Because of the health risks and medical costs associated with obesity, the goal of the U.S.
surgeon general is to reduce the prevalence of overweight in children and obesity in adults to
no more than 14.6% and 30.6%, respectively, by the year 2020 (U.S. Department of Health
and Human Services 2012).
459
OBESITY: TYPES AND CAUSES
Combating obesity is not an easy task. Many overweight and obese individuals have
incorporated patterns of overeating and physical inactivity into their lifestyles, while others
have developed eating disorders, exercise addictions, or both. In an effort to lose weight
quickly and to prevent weight gain, many are lured by fad diets and exercise gimmicks; some
resort to extreme behaviors, such as avoiding food, bingeing and purging, and exercising
compulsively. Nicklas and colleagues (2012) reported that obese adults who lost at least 5% of
body weight achieved meaningful weight loss if they ate less fat, exercised more, used
prescription weight loss medications, or participated in commercial weight loss programs.
In a survey of weight control practices of adults in the United States, Weiss and colleagues
(2006) reported that 48% of women and 34% of men were trying to lose weight by means of
such practices as eating less food, eating less fat, choosing low-calorie foods, and exercising.
Less common practices included drinking water, skipping meals, eating diet foods, taking
special supplements or diet pills, joining weight loss programs, taking prescription diet pills,
and taking laxatives. Only one-third of those trying to lose weight reported using the
recommended method of restricting caloric intake and increasing physical activity to at least
150 min/wk; less than 25% combined caloric restriction with higher levels of physical activity
(>300 min/wk).
In a report on leisure-time physical activity among overweight adults in the United States
(Prevalence of Leisure-Time Physical Activity 2000), two-thirds of overweight adults
reported that they engaged in physical activity to try to lose weight; however, only 20%
exercised at least 30 min a day at a moderate intensity on most days of the week. Although
most of these individuals exercised 30 min or longer per session, only a minority exercised at
least five times per week. Similarly, Kruger, Yore, and Kohl (2007) reported in their study of
leisure-time physical activity patterns that fewer than half of people trying to lose or maintain
weight were regularly active. Therefore, low frequency of physical activity was the main
reason the physical activity recommendation was not met.
TYPES OF OBESITY
How fat is distributed in the body may be more important than total body fat for determining
one’s risk of disease. The waist-to-hip ratio (WHR) is strongly associated with visceral
adipose tissue (VAT), and the impact of regional fat distribution on health is related to the
amount of VAT located in the abdominal cavity. Abdominal fat is strongly associated with
diseases such as CHD, diabetes, hypertension, and hyperlipidemia (Bjorntorp 1988; Blair et
460
al. 1984; Ducimetier, Richard, and Cambien 1989).
The terms android obesity and gynoid obesity refer to the localization of excess body fat
mainly in the upper body (android) or lower body (gynoid). Android obesity (apple shaped) is
more typical of males; gynoid obesity (pear shaped) is more characteristic of females.
However, some men may have gynoid obesity, and some women may have android obesity.
Other terms are also used to describe types of obesity and regional fat distribution. Android
obesity is frequently simply called upper body obesity, and gynoid obesity is often described
as lower body obesity.
In field settings, you can assess regional fat distribution using the WHR. Chapter 8
presents measurement procedures (see Waist-to-Hip Ratio) and WHR norms (see table 8.8).
Generally, young adults with WHR values in excess of 0.94 for men and 0.82 for women are
at very high risk for adverse health consequences (Bray and Gray 1988b).
CAUSES OF OVERWEIGHT AND OBESITY

Many questions may arise in regard to overweight and obesity. This section addresses
common questions relating to their causes.
Why do people gain or lose weight?
An energy imbalance in the body results in a weight gain or loss. Energy is balanced when the
caloric intake equals the caloric expenditure. A positive energy balance is created when the
input (food intake) exceeds the expenditure (resting metabolism plus activity level). For every
3,500 kcal of excess energy accumulated, 1 lb (0.45 kg) of fat is stored in the body. A negative
energy balance is produced when the energy expenditure exceeds the energy input. People
can accomplish this by reducing the food intake or increasing the physical activity level. A
caloric deficit of approximately 3,500 kcal produces a loss of 1 lb of fat.
How are energy needs and energy expenditure measured?
Energy need and expenditure are measured in kilocalories (kcal). A kilocalorie is defined as
the amount of heat needed to raise the temperature of 1 kg (2.2 lb) of water 1 °C. Direct
calorimetry is used to measure the energy yield and caloric equivalent of various foods. These
foods are burned in a closed chamber in the presence of oxygen, and the amount of heat
liberated is measured precisely in kilocalories. Table 9.1 gives the energy yield and caloric
equivalents for carbohydrate, protein, and fat.
Table 9.1 Energy Yield and Caloric Equivalents for Macronutrients
461
Nutrient Energy yield (kcal·g−1) Caloric equivalents (kcal·L−1 O2)
Carbohydrate 4.1 5.1
Protein 4.3 4.4
Fat 9.3 4.7
The energy, or caloric, need is a function of an individual’s metabolic rate and physical
activity level. The basal metabolic rate (BMR) is a measure of the minimal amount of energy
(kcal) needed to maintain basic and essential physiological functions such as breathing, blood
circulation, and temperature regulation. Basal metabolic rate varies according to age, gender,
body size, and body composition. For assessment of BMR, the individual needs to be rested
and fasted and should be in a controlled environment. Since this is not always practical, we
use the term resting metabolic rate (RMR), or resting energy expenditure (REE), to indicate
the energy required to maintain essential physiological processes in a relaxed, awake, and
reclined state. The RMR is approximately 10% higher than the BMR.
Total energy expenditure (TEE) is the sum of the energy expended for BMR or RMR,
dietary thermogenesis (i.e., energy needed for digesting, absorbing, transporting, and
metabolizing foods), and physical activity. Some experts have further divided the physical
activity portion into exercise activity thermogenesis (EAT) and non-exercise activity
thermogenesis (NEAT) (i.e., energy expenditure of occupation, leisure, activities of daily
living, and unconscious or spontaneous motion such as fidgeting) (Aragon et al. 2017). It is
estimated that BMR makes up 60% to 70% of TEE; dietary thermogenesis is 8% to 15%, and
EAT and NEAT are 15% to 30% and 15% to 50%, respectively (Aragon et al. 2017). The
gold standard for measuring TEE is the doubly labeled water (with deuterium and oxygen-
18) method. This method is expensive and requires considerable expertise as well as
specialized equipment. Therefore, age- and gender-specific prediction equations have been
developed to estimate TEE (see table 9.2 and Steps for Estimating TEE later in the chapter).
Table 9.2 Prediction Equations for Estimating TEE (kcal·day−1) of Children and Adults
Gender and age Equation Physical activity coefficient (PA)

TEE = 88.5 − (61.9 × age) + PA [(26.7 × wt) + (903 × ht)] 1.00, if PAL is ≥1.0 and <1.4 (sedentary)
1.13, if PAL is ≥1.4 and <1.6 (low)

Male 3-18 yr
1.26, if PAL is ≥1.6 and <1.9 (active)
1.42, if PAL is ≥1.9 and <2.5 (very active)
TEE = 662 − (9.53 × age) + PA [(15.9 × wt) + (540 × ht)] 1.00, if PAL is ≥1.0 and <1.4 (sedentary)
1.11, if PAL is ≥1.4 and <1.6 (low)

Male ≥19 yr
TEE = 135.3 − (30.8 × age) + PA [(10.0 × wt) + (934 × ht)] 1.00, if PAL is ≥1.0 and <1.4 (sedentary)
1.16, if PAL is ≥1.4 and <1.6 (low)

Female 3-18 yr
462
TEE = 354 − (6.91 × age) + PA [(9.36 × wt) + (726 × ht)] 1.00, if PAL is ≥1.0 and <1.4 (sedentary)
1.12, if PAL is ≥1.4 and <1.6 (low)

Female ≥19 yr
TEE = total energy expenditure in kcal·day−1; PA = physical activity coefficient; wt = body weight in kilograms; ht = height in meters; PAL = physical activity level.
From Institute of Medicine 2002/2005.
Alternatively, energy expenditure during basal, resting, or activity states can be measured in
laboratory settings through indirect calorimetry. In this case, the body’s energy expenditure is
estimated from oxygen utilization. Every liter of oxygen consumed per minute yields
approximately 5 kcal (see table 9.1). For specific physical activities, energy expenditure is
typically expressed in METs (see chapter 4 and appendix E.3) as a multiple of the RMR.
One MET equals the assumed relative rate of oxygen consumption of 3.5 ml·min for each −1
kilogram of body weight (3.5 ml·kg ·min ) or the relative rate of energy expenditure of 1 −1 −1
kcal·hr for each kilogram of body weight (1 kcal·hr ·kg ).

−1 −1 −1
ACTIVITY TRACKERS: ARE THEY ACCURATE FOR ESTIMATING ENERGY

EXPENDITURE?
Being able to track energy (caloric) expenditure is of great importance for those pursuing weight loss and weight
maintenance goals. But the technology to track energy expenditure (EE) with a high degree of accuracy is still lacking.
Research results are mixed on the ability of activity trackers to accurately compute TEE in controlled laboratory settings,
during semi-structured activities, and in free-living environments. Although the correlation between activity tracking
accelerometers is reported to be moderate to strong, significant underestimations of the reference values are common.
Accelerometers, categorized as research-grade (ActiGraph GT3X+, BodyMedia Core, Body Media SenseWear),
underestimate energy expenditure in comparison to the gold standard, indirect calorimetry (Bai et al. 2016; Ferguson et
al. 2015; Imboden et al. 2017; Kim and Welk 2015).
Consumer-targeted devices worn during a variety of activities produce large differences and variable estimates of EE and
tend to underestimate reference values of EE (Bai et al 2016; Ferguson et al. 2015; Imboden et al. 2017; Kim and Welk
2015; Price et al. 2017; Sasaki et al. 2015). Typically, the proprietary algorithms developed by the manufacturers account
for differences between devices. Although some accelerometers perform better during moderate- to fast-paced activities
(ActiGraph GT3X+, BodyMedia SenseWear, Core Armband), others perform better during slow-paced activities
(activPAL). Lyden and associates (2017) reported that the activPAL accurately categorizes sedentary behaviors as well as
light-intensity and MVPA exercise compared with direct observation. Triaxial and multisensory devices tend to provide
more accurate estimates of TEE than uniaxial devices (Van Remoortel et al. 2012).
How is RMR regulated?

Thyroxine is extremely important in regulating RMR. Thyroid tumors or lack of iodine in
the diet can result in inadequate production of this hormone. Underproduction of thyroxine
can reduce RMR 30% to 50%. If energy input and expenditure are not adjusted accordingly,
the positive energy balance that is created results in a weight gain.
463
Growth hormone, epinephrine, norepinephrine, and various sex hormones may elevate
RMR as much as 20%. These hormones increase during exercise and may be responsible for
the elevation in RMR after cessation of exercise.
Does weight gain increase both the number and size of fat cells?
Obesity is associated with increases in both the number (hyperplasia) and size (hypertrophy)
of fat cells. A normal-weight individual has 25 to 30 billion fat cells, whereas an obese person
may have as many as 42 to 106 billion fat cells. Also, the adipose cell size of obese individuals
is on the average 40% larger than that of nonobese persons (Hirsh 1971). Caloric restriction
and exercise are effective in reducing fat cell size but not the number of fat cells in adults
(Hirsh 1971; Spalding et al. 2008).
Traditional thought is that the number of fat cells is set during childhood and adolescence,
and this number remains constant in both lean and obese adults, even if substantial weight
changes occur (Spalding et al. 2008). In other words, adipocytes experience hypertrophy and
atrophy with weight gain and loss, respectively, but the number of cells does not change
throughout adulthood. Epidemiological studies suggest that weight gain in the first 6 mo of
life is primarily a gain in fat and that this time period is critical for development of obesity
and cardiometabolic problems in adulthood (Gillman 2008). This has led some to speculate
that the key to preventing obesity is to closely monitor dietary intake and energy expenditure
during the adolescent growth spurt and puberty, as this could potentially retard the
development of new fat cells. However, this traditional thought has been challenged.
Tchoukalova and colleagues (2010) induced a body fat gain of about 4 kg in normal-weight
adult men and women. They observed that adipocytes experienced hypertrophy in the upper
body but hyperplasia in the lower body. They theorized that forming new fat cells in the
lower body might actually be a protective mechanism against accumulating more fat in the
upper body in response to overfeeding. Whatever the reason, it appears that differences in
preadipocyte cell dynamics may allow for regional hyperplasia in response to overfeeding. In a
recent review, Cuthbertson and colleagues (2017) seem to confirm this new theory. They
claim that subcutaneous adipose tissue (SAT) remodels itself to adapt to overfeeding, and
this remodeling can occur by either hypertrophy or hyperplasia.
What is the relative importance of genetics and environment in developing obesity?
Scientists have debated the relative contributions of genetics and environment to obesity.
Mayer (1968) observed that only 10% of children who had normal-weight parents were
obese. Overweight adolescents have a 70% chance of becoming overweight adults; this
464
probability increases to 80% if one parent or both parents are overweight or obese (U.S.
Department of Health and Human Services 2007). Deficiencies in a few genes, most notably
melanocortin-4 receptor and leptin, have been associated with obesity (Selassie and Sinha
2011). Although these data suggest a genetic influence, they do not rule out environmental
influences such as individual choices about eating and exercising. Bray (2004) described a
useful way to think about the relationship of genes to obesity when he wrote “the genetic
background loads the gun, but the environment pulls the trigger.”
In a controlled study of long-term (100 days) overfeeding in identical twins, Bouchard and
colleagues (1990) observed large individual differences in the tendency toward obesity and
distribution of body fat, even within each pair of twins. Changes in body weight due to
overfeeding of the twins were moderately correlated (r = .55). Overall, increases in body
weight, fat mass, trunk fat, and VAT were three times greater in high weight gainers
compared with low weight gainers. These data suggest that genotype explains some, but not
all, of a person’s adaptation to a sustained energy surplus. Approximately 25% of the
variability among individuals in absolute and relative body fat is attributed to genetic factors,
and 30% is associated with cultural (environmental) factors (Bouchard et al. 1988). Although
the identification of genes related to obesity is highly researched, studies to identify common
nucleotide polymorphisms associated with BMI have not been able to explain more than 2%
of the variability in BMI. Much of the interindividual variability in body weight is
attributable to the interactions between genes and environment or genes and behavior
(Bouchard 2008).
Recent research indicates that genes influence not only weight gain but also how and
where excess body fat is deposited, and this can affect one’s health. For example, some
individuals have a normal BMI yet they have a metabolically obese phenotype, characterized
by increased VAT relative to SAT, putting them at risk for metabolic and cardiovascular
disease. In other words, when these individuals overeat, they have a tendency to store excess
fat viscerally rather than subcutaneously. The opposite is also true; some people have a higher
BMI yet a lower risk for metabolic disease because they have a higher SAT:VAT ratio, or a
higher SAT capacity (Cuthbertson et al. 2017). Thus, with overfeeding, genetic and
epigenetic factors are at play to determine the distribution of adipose tissue.
Hill and Melanson (1999) suggested that the major cause of obesity in the United States is
environment. Over the past 30 yr, the U.S. population has been exposed to an environment
that strongly promotes the consumption of high-fat, energy-dense foods (increased energy
intake) as well as reliance on technology that discourages physical activity and reduces the
465
amount of physical activity (decreased energy expenditure) needed for daily living. Similarly,
Swinburn and associates (2011) and Selassie and Sinha (2011) addressed the interactions
between environmental and individual factors, including genetic makeup. Selassie and Sinha
(2011) identified increased portion sizes and consumption of high-fructose corn syrup and
sweetened beverages along with more sedentary careers and more leisure time spent on
computers rather than in physical activity as contributing behavioral factors to obesity.
Swinburn and colleagues (2011) concluded that changes in the food supply and marketing
environments that promote high energy intake, along with increased mechanization and the
subsequent reduction in physical activity, are key factors associated with the obesity epidemic.
WEIGHT MANAGEMENT PRINCIPLES AND PRACTICES

Proper nutrition (eating a well-balanced diet) and daily physical activity are key components
of a weight management program. In weight management programs, most clients are
interested in losing body weight and body fat, but some need to gain body weight. The basic
principle underlying safe and effective weight loss programs is that weight can be lost only
through a negative energy balance, which is produced when the caloric expenditure exceeds
the caloric intake. The most effective way of creating a caloric deficit is through a
combination of diet (restricting caloric intake) and exercise (increasing caloric expenditure).
On the other hand, for weight gain programs, the caloric intake must exceed the caloric
expenditure in order to create a positive energy balance. Weight Management Principles
summarizes principles and practices underlying the design of weight management programs.
People can win the battle of controlling body weight and obesity not only by understanding
why they eat and monitoring their food intake closely but also by incorporating more physical
activity into their lifestyles. The physically active lifestyle is characterized by
daily aerobic exercise;

strength and flexibility exercises;
increased participation in recreational activities such as bowling, golf, tennis, and
dancing; and
increased physical activity in the daily routine at home and work through restricting
use of labor-saving devices such as escalators, power tools, automobiles, and home
and garden appliances.
466
DESIGNING WEIGHT MANAGEMENT PROGRAMS:
PRELIMINARY STEPS
In designing weight management programs for weight loss or weight gain, you need to set
body weight goals and assess the calorie intake and expenditure for your clients.
SETTING BODY WEIGHT GOALS

To set healthy body weight goals for your clients, you must first assess their present body
weight, BMI, or body fat levels. You can easily measure a client’s body weight by using a
calibrated bathroom or physician’s scale. Clients should wear indoor clothing but not shoes.
TELEMONITORING SCALES: ELIMINATING SELF-REPORTED WEIGHT

ERRORS
Self-reported weight is often inaccurate, and the errors are more common in overweight individuals (Rowland 1990).
Telemonitoring scales allow for the immediate transmission of a client’s weight to a remote site, thereby eliminating any
self-report inaccuracies. These scales have been used in several weight loss intervention studies. In a study by
VanWormer and colleagues (2009), obese participants’ weights were automatically transmitted to counselors who could
provide customized feedback. They found that study participants who self-weighed at least once per week were 11 times
more likely to lose at least 5% of their prestudy body weight after 6 mo. Bluetooth-connected weighing scales allowing
study participants to self-weigh at home and have their data automatically transmitted to investigators at remote sites are
being used in studies of people who have type 2 diabetes (Wild et al. 2013) and weight gain during pregnancy (Bogaerts et
al 2017).
WEIGHT MANAGEMENT PRINCIPLES
Weight loss Weight gain Exercise
The major cause of obesity is lack of physical activity, not
overeating.
A well-balanced diet for good nutrition contains carbohydrate,

For fat-weight loss, aerobic exercise should be performed
protein, fat, vitamins, minerals, and water.

daily or twice daily.
The weight loss should be gradual—no more than 2 lb (1 kg) a

The dietary protein intake should be increased to 1.2-1.6 Resistance exercise training is excellent for maintaining fat-
week.
g·kg−1 body weight. free mass (for weight loss) and increasing FFM (for weight
The caloric intake should be at least 1,200 kcal·day −1, and gain).
The weight gain should be gradual—no more than 2 lb a
the caloric deficit should not exceed 1,000 kcal·day −1.

week. For weight loss, exercise helps create a caloric deficit by
increasing caloric expenditure.

A caloric deficit of 3,500 kcal is needed to lose 1 lb (0.5 kg) of
The daily caloric intake should exceed caloric needs by 400-
fat.
467
fat. 500 kcal·day −1. Exercise is better than dieting for maximizing fat loss and
minimizing lean tissue loss.

Weight loss should be due to fat loss rather than lean body A positive energy balance of 2,800-3,500 kcal is needed to
tissue. gain 1 lb of muscle tissue. Compared with fat, muscle tissue is more metabolically
active and uses more calories at rest.

On the same diet, a taller, heavier person will lose weight at a Weight gain should be due to increased fat-free mass (FFM)
faster rate than a shorter, lighter person because of a higher rather than fat mass. Low-intensity, longer-duration exercise maximizes total
RMR. energy expenditure better than high-intensity, shorter-

The individual should eat three meals and two or three
duration exercise.
Weight loss rate decreases over time because the difference healthy snacks per day (e.g., dried fruits, nuts, seeds, and
between the caloric intake and caloric needs gets smaller as one some liquid meals). RMR remains elevated 30 min or longer after vigorous
loses weight. exercise.

Protein powders are no more effective than natural protein
Men lose weight faster than women because of a higher RMR. sources (e.g., lean meats, skim milk, and egg whites). At a given heart rate, the more physically fit individual
expends calories at a faster rate than the less fit individual.

The individual should eat at least three meals a day. Amino acid supplements may promote muscle growth if
taken immediately before or after exercise. Exercise does not increase appetite.
Quick weight loss diets, diet pills, and appetite suppressants
should be avoided. Vitamin B12, boron, and chromium supplements do not Passive exercise devices (e.g., vibrators and sauna belts) do
increase fat-free mass. not massage away excess fat.

Carnitine supplementation does not promote body fat loss.
Spot reduction exercises do not preferentially mobilize

Compulsive eating behaviors should be identified and
subcutaneous fat stored near the exercising muscles.

modified.
To increase caloric expenditure, avoid using labor-saving
devices at home and work.
When you are evaluating a client’s body weight, you should not use height-weight tables
established by insurance companies. These tables are limited for two reasons:
The values represent height and weight with shoes and clothing. Whether
individuals were measured with shoes and clothing was not standardized.
Data were obtained from individuals who could afford life insurance; the data
represent predominantly young to middle-aged white males and females and
therefore are not representative of other population groups.
Individuals with a BMI from 18.5 up to 25 are considered to be at a healthy body weight
by many health standards. However, determining a healthy body weight from either BMI or
468
body fatness and health risk. These methods do not take into account the body composition
of the individual. For example, with the use of BMI or height-weight tables, many
mesomorphs having a large fat-free mass are classified as overweight, yet their body fat
content may be lower than average. Similarly, individuals may be overfat or obese even
though they are underweight according to the BMI and height-weight tables. Therefore, you
should use the body composition technique to estimate a healthy body weight and body fat
level for your clients.
When you use the body composition technique for estimating healthy body weight and
body fat levels, assess the fat-free mass (FFM) and percent fat (%BF) using one of the
methods described in chapter 8. A healthy body weight is based on the client’s present FFM
and %BF goal. Because some fat is needed for good health and nutrition, individuals should
attempt to achieve a %BF somewhere between the low and upper values recommended in
table 8.1. Remember, minimal %BF depends on age and is estimated to be 5% to 10% for
males and 12% to 15% for females. Cutoff values for obesity are also age dependent, ranging
from >22% to >31% BF for males and >35% to >38% BF for females. For an example of how
to calculate healthy body weight using the body composition technique, see Sample
Calculation of Healthy Body Weight.
With aging, there is a tendency to accumulate body weight and excess fat. Typically, adults
may expect to gain 15 lb (9 kg) of fat weight and lose 5 lb (2.3 kg) of lean body mass per
decade of life (Evans and Rosenberg 1992; Forbes 1976). This weight gain is primarily
characterized by an increase in body fat and a decrease in muscle mass and is associated with
declining physical activity levels with age. Each individual should attempt to maintain body
weight and fatness at healthy levels.
SAMPLE CALCULATION OF HEALTHY BODY WEIGHT
Demographic Data
Client: 31 yr old male
Current body composition:
Body weight = 185 lb (84.1 kg)
Body fat = 20% BF
Fat-free mass (FFM) = 148 lb (67.3 kg)
Goals: 12% BF and 88% FFM
Steps
469
1. Determine the client’s present %BF using one of the body composition methods (see chapter 8).
2. Calculate the client’s present FFM (in pounds): 185 lb × 0.80 (current %FFM) = 148 lb (67.3 kg).
3. Set reasonable body composition goals for client: 12% BF and 88% FFM.
4. Divide the present FFM (in pounds) by the %FFM goal to obtain target body weight: 148 lb / 0.88 = 168 lb
(76.4 kg).
5. Calculate weight loss by subtracting target body weight from present body weight: 185 − 168 = 17 lb (7.7 kg).
Assuming that FFM is maintained, this client must lose 17 lb of fat to achieve his target body weight and
body fat level.
ASSESSING CALORIE INTAKE AND ENERGY EXPENDITURE

The second step in planning weight management programs is to assess the client’s energy
(calorie) intake and expenditure. You will use these baseline data to estimate the rate of
weight loss or weight gain and the amount of time needed to achieve long-term goals of body
composition and body weight.
Energy Intake
A food record (see appendix E.1, Food Record and RDA Profile) is used to determine an
individual’s daily caloric intake. The client keeps a record of the type and quantity of foods
eaten each day for 3 to 7 days. Make certain the client records all foods consumed;
underreporting of food intake ranges from 10% to 45%. Use computer software to assess the
average daily caloric intake and to compare average nutrient intakes against recommended
amounts for each nutrient. Several dietary analysis programs are available online. The food
record can also help you analyze dietary patterns such as types of foods consumed, frequency
of eating, and the caloric content of each meal. For a ranking of smartphone apps for tracking
food intake, see Patel and colleagues (2015).
Energy Expenditure
You can use either the factorial method or the TEE method to assess the energy needs of
your clients. For the factorial method, RMR or REE and the additional calories expended
during work, household chores, personal daily activities, and exercise are estimated. Various
methods used to estimate RMR and the additional energy requirements for occupational and
physical activities are presented in this section. Although the factorial approach may
reasonably estimate your clients’ energy expenditure, it is limited in that the equations used to
estimate RMR have prediction errors. Also, it is neither feasible nor practical to measure the
470
wide range of activities performed throughout a normal day. Therefore, the TEE method for
estimating total energy expenditure has been endorsed by the Institute of Medicine (2005).
For the total energy expenditure (TEE) method, the individual’s TEE is predicted using
equations derived from doubly labeled water measures of TEE in free-living individuals (see
table 9.2).
METHODS OF ESTIMATING RESTING METABOLIC RATE
Method Equation
I. BODY SURFACE AREA (BSA)a

Men
RMR = BSA × 38 kcal·hr −1 × 24 hr
Women
RMR = BSA × 35 kcal·hr −1 × 24 hr
II.A. HARRIS-BENEDICT EQUATIONSb

Men RMR = 66.473 + 13.751(BM) + 5.0033(ht) − 6.755(age)
Women RMR = 655.0955 + 9.463(BM) + 1.8496(ht) − 4.6756(age)
II.B. MIFFLIN ET AL. EQUATIONSb

Men RMR = 9.99(BM) + 6.25(ht) − 4.92(age) + 5.0
Women RMR = 9.99(BM) + 6.25(ht) − 4.92(age) − 161
II.C. MOLNAR ET AL. (1995) EQUATION

Boys (10-16 yr) RMR = 50.9 (BM) + 25.3 (ht) − 50.3 (age) + 26.9
Girls (10-16 yr) RMR = 51.2 (BM) + 24.5 (ht) − 207.5 (age) + 1629.8
III. FAT-FREE MASS (FFM)

Men and women RMR = 500 + 22 (FFM in kg)
IV. QUICK ESTIMATE (FROM BODY MASS)

Men RMR = BM (in lb) × 11 kcal·lb −1
RMR = BM (in kg) × 24.2 kcal·kg −1

Women
RMR = BM (in lb) × 10 kcal·lb −1
RMR = BM (in kg) × 22.0 kcal·kg −1
aAdjust RMR for age. RMR decreases 2% to 5% per decade after age 40.
bBM in kilograms; ht in centimeters; age in years.
Factorial Method: Estimation of Resting Energy Expenditure

Indirect calorimetry can be used to obtain reference measures of RMR or REE. Prediction
equations are an inexpensive alternative to indirect calorimetry (see Methods of Estimating
Resting Metabolic Rate). You can estimate body surface area (BSA) from height and weight
using the nomogram in figure 9.1.
471
FIGURE 9.1 Nomogram to predict body surface area.
Reprinted by permission from W.E. Collins, Clinical Spirometry (Braintree, MA: Warren E. Collins, 1967), 33. Copyright Warren E. Collins.
The average male or female between 20 and 40 yr of age burns 38 kcal·hr and 35 kcal·hr , −1 −1
respectively, for each square meter of BSA. For example, according to method I for
estimating RMR, a 5 ft 2 in. (157.5 cm), 120 lb (54.5 kg) female has a BSA of 1.54 m and a 2
daily resting metabolic need of 1,294 kcal (1.54 m × 35 kcal·hr × 24 hr). 2 −1
You can obtain a quicker but less accurate estimate of REE by multiplying the body weight
(BW) by a factor of 10 (for BW measured in pounds) or 22 (for BW measured in kilograms)
for women and by a factor of 11 (for BW in pounds) or 24.2 (for BW in kilograms) for men
(see method IV). With this method, the REE for the woman in our example is 1,200 kcal
(120 lb × 10).
Resting energy expenditure gradually decreases with age because the number of
metabolically active cells is reduced. The REE declines 2% to 5% during each decade of life
after age 25 (Sharkey and Gaskill 2007). To prevent gradual weight gain with aging, people
must reduce caloric intake or increase physical activity level. In the past, the Harris-Benedict
(1919) equations (method II.A) were widely used to estimate REE of adults. However, the
American Dietetic Association (2003) recommends using the equations of Mifflin and
472
colleagues (1990) to estimate the REE of healthy individuals (see method II.B). Both
equations (Harris-Benedict and Mifflin) are gender specific and take into account not only
height and weight but also age. Roza and Shizgal (1984) cross-validated the original Harris-
Benedict equations, developed new equations using data from a large number of subjects, and
concluded that the original equations published in 1919 yielded identical estimates of REE.
Also, the Harris-Benedict equations accurately estimated the REE of a large sample (N =
2,528) of normal-weight, overweight, and obese individuals, but these equations tended to
overestimate REE in underweight persons (Muller et al. 2004; O’Riordan et al. 2010). In
contrast, the American Dietetic Association (2003) reported that the Harris-Benedict
equations generally overestimated REE but that the equations of Mifflin and colleagues
accurately estimated (within ±10%) the REE for 80% of their sample. The Mifflin equation is
also the most accurate for adults with BMIs of 25 to 40 kg/m (Weijs 2008).
2
Indirect calorimetry is the method of choice for measuring REE, especially in obese
populations where prediction equations tend to systematically underestimate REE of these
clients (Wilms et al. 2010). The Harris-Benedict equations and the equations of Mifflin and
colleagues, however, may provide a practical alternative for estimating REE when planning
weight management programs for overweight or obese individuals. For children (10-16 yr),
the gender-specific prediction equations (method II.C) provide a reasonably accurate estimate
of REE (Hofsteenge et al. 2010; Molnar et al. 1995).
In addition to body size and age, REE is influenced by body composition. Muscular
individuals have a higher REE than fatter individuals of the same body weight because fat
tissue is less metabolically active than muscle tissue. The RMRs of women are 5% to 10%
lower than those of men (McArdle, Katch, and Katch 1996). This lower rate may be
attributable to a greater relative fat content and lower FFM for women. To use method III
(see Methods of Estimating Resting Metabolic Rate), you must measure the FFM of your
client using one of the body composition methods suggested in chapter 8.
Factorial Method: Estimation of Additional Caloric Requirements

Resting energy expenditure accounts for 50% to 70% of total daily caloric needs, but this
value depends on the activity level and occupation of the person. The percentage is greater for
less active individuals, who require fewer calories above the resting level. For example, if a
sedentary male office worker has a resting metabolic need of 1,680 kcal, the additional caloric
need as a consequence of the nature of his work is approximately 40% above resting level, or
672 kcal. Provided he performs no additional physical activities, his total daily caloric need is
473
2,352 kcal. In this case, REE accounts for 71% of his total daily caloric requirements. Table
9.3 presents additional caloric requirements for selected occupational activity levels.
Table 9.3 Additional Energy Requirements for Selected Activity Levels
PERCENTAGE ABOVE BASAL METABOLISM
Occupational activity level* Men Women

Sedentary 15 15
Lightly active 40 35
Moderately active 50 45
Very active 85 70
Exceptionally active 110 100
*Examples for each occupational activity level are as follows:
Sedentary = inactive.
Lightly active = most professionals, office workers, shop workers, teachers, homemakers.
Moderately active = workers in light industry, most farm workers, active students, department store workers, soldiers not in active service, commercial fishing workers.
Very active = full-time athletes and dancers, unskilled laborers, forestry workers, military recruits and soldiers in active service, mine workers, steel workers.
Exceptionally active = lumberjacks, blacksmiths, female construction workers.
After determining the daily energy needs of the client from his REE and occupation, you
can estimate his additional calorie expenditure due to physical activity and exercise by using a
physical activity log (appendix E.2, Physical Activity Log). The individual records every
activity performed and the total amount of time spent in each activity. The estimated energy
expenditure for a variety of activities is listed in appendix E.3, Gross Energy Expenditure for
Conditioning Exercises, Sports, and Recreational Activities. You can calculate the total
caloric expenditure for each activity by converting the METs to kcal·kg ·hr (1 MET = 1 −1 −1
kcal·kg ·hr ) and multiplying this value by the client’s body weight (kg). This yields the total
−1 −1
number of kilocalories the client expends per hour of that activity. You can determine the
kcal·min expenditure by dividing the kcal·hr by 60 min. Calculate the total caloric energy
−1 −1
expenditure of the activity by multiplying the kcal·min by the duration of the activity. −1
Keeping a physical activity log is a very time-consuming process for both you and your
clients, and it may not increase the accuracy of your estimate of additional caloric expenditure
because many clients tend to overestimate the actual duration of their physical activity. It may
be best to just ask your clients to list the frequency, intensity, and average time for the
physical activities and sports they perform on a regular basis; you can then determine their
calorie expenditure for each activity as just described. Add these values to the daily caloric
need estimated for their RMR and occupation, and advise clients that on days they are active
they can increase their calorie intake accordingly.
Total Energy Expenditure Method

For this method, the age- and gender-specific equations in table 9.2 are used to estimate your
474
clients’ TEE. These equations predict TEE from age, body weight, height, and physical
activity coefficient. The physical activity coefficient depends on your clients’ physical activity
level (PAL); given that energy expenditure is highly dependent on physical activity, PAL is
commonly described as the ratio of TEE to BMR (PAL = TEE / BMR). The PAL
categories were developed from doubly labeled water measures of TEE and BMR in normal,
healthy individuals. Data from elite athletes and extremely active individuals (i.e., military
personnel and astronauts) were not included (Brooks et al. 2004). Physical activity levels are
classified as sedentary (1.0 to <1.4), low (1.4 to <1.6), active (1.6 to <1.9), and very active (1.9
to <2.5). To obtain a fairly good estimate of your clients’ PAL, you can use various tools such
as self-reported physical activity questionnaires, physical activity diaries, pedometers,
accelerometers, heart rate monitors, and other wearable technology (Keim, Blanton, and
Kretsch 2004). For information about the validity and reliability of pedometers and
accelerometers for monitoring physical activity levels, see chapter 3. Steps for Estimating
TEE illustrates how you can use the TEE equations to estimate your clients’ daily energy
expenditure.
DESIGNING WEIGHT LOSS PROGRAMS

When the caloric expenditure exceeds the caloric intake, a negative energy balance, or caloric
deficit, is created. The most effective way of producing this deficit is to use a combination of
caloric restriction and exercise. Because a deficit of 3,500 kcal is needed to lose 1 lb (0.45 kg)
of fat, you can easily calculate the daily caloric deficit that is needed to result in the target
weekly weight loss you set for your client. An average deficit of 500 kcal will produce a
weekly weight loss of approximately 1 lb (0.45 kg), given that 500 kcal × 7 days = 3,500
calories. An average deficit of 1,000 will produce a weight loss of 2 lb (0.90 kg) a week (1,000
kcal × 7 days, or 2 lb). The daily caloric deficit should not exceed 1,000 kcal per day.
It is important to note that the relationship of 3,500 kcal to 1 lb (0.45 kg) of fat is
mathematical; however, in reality this weight loss rule is overly simplistic (Cuthbertson et al.
2017; Hall et al. 2011). This relationship assumes that body weight changes reflect changes in
adipose tissue without regard to changes in FFM. An increase in lean mass as a result of
resistance training, for example, would increase RMR and alter this relationship. Conversely,
losses of FFM reduce RMR, making it more difficult to lose weight. Hall and colleagues
(2011) at the National Institutes of Health (NIH) considered all these variables in creating a
web-based simulator for predicting weight change over time by using a mathematical
modeling approach. The Body Weight Planner is available on the NIH website at
475
www.niddk.nih.gov/health-information/weight-management/body-weight-planner.
Additionally, the Body Weight Planner is linked to the U.S. Department of Agriculture’s
SuperTracker (www.supertracker.usda.gov), another web-based program that provides a
personalized meal plan based on a person’s goals and results from the Body Weight Planner.
To ensure that the weight loss is a result of the loss of body fat rather than lean body tissue,
you should do the following:
Use the body composition method to estimate the client’s healthy body weight and
fat loss.
Encourage daily participation in aerobic exercise and resistance training programs
to enhance the loss of fat and to conserve FFM.
Work with a nutritionist to plan a diet that restricts calorie intake but contains
adequate amounts of good sources of carbohydrate, protein, and fat. The diet
should contain at least 130 g of carbohydrate per day and 0.8 g of protein per
kilogram of body weight per day.
When you design the weight loss program of diet and exercise, use descriptive data to help
set reasonable goals for your clients. These data include age, gender, height, body weight,
relative body fat (%BF), %BF goal, average calorie intake, cardiorespiratory fitness level, and
occupation. See Steps for Designing a Weight Loss Program.
STEPS FOR ESTIMATING TEE
To estimate a client’s total energy expenditure (TEE) from age- and gender-specific TEE equations, follow these steps:
Step 1: Determine the client’s gender and age (50 yr old male).
Step 2: Measure the client’s body weight and height (BW = 180 lb; ht = 70 in.). Convert body weight in
pounds to body weight in kilograms: 180 lb / 2.204 = 81.7 kg. Convert height in inches to height in meters:
70 in × 0.0254 = 1.78 m.
Step 3: Estimate your client’s PAL (1.70, or active, from physical activity log).
Step 4: Select the appropriate age- and gender-specific TEE prediction equation from table 9.2: for males
≥19 yr.
TEE (kcal·day−1) = 662 − (9.53 × age) + PA [(15.9 × wt) + (540 × ht)]
Step 5: Determine the physical activity coefficient corresponding to your client’s PAL (1.25 for PAL = 1.70).
476
Step 6: Substitute the values for age, body weight, physical activity, and height into the equation:
TEE (kcal·day−1) = 662 − (9.53 × 50 yr) + 1.25 [(15.9 × 81.7 kg) + (540 × 1.78 m)]
Step 7: Calculate the estimated TEE (kcal·day−1):
TEE (kcal·day−1) = 662 − (9.53 × 50 yr) + 1.25 [(15.9 × 81.7 kg) + (540 × 1.78 m)]
= 662 − (476.5) + [1.25 × (1299 + 961)]
= 185.5 + 2,260
TEE = 2,445.7, or 2,446 kcal·day−1
STEPS FOR DESIGNING A WEIGHT LOSS PROGRAM
Summary of Client’s Demographic Data
1. Client’s age and gender: 35 yr old female
2. Height: 62 in. or 157.5 cm
3. Body weight: 131 lb or 59.55 kg
4. Percent fat: 26% BF; relative FFM: 74%
5. Percent fat goal: 20% BF; relative FFM goal: 80%
6. Average daily calorie intake: 2,000 kcal
7. Cardiorespiratory fitness level: Below average
8. Occupation: Secretary
Steps
1. Assess the body weight and body composition of the client.
2. Assess the daily calorie intake of the subject (use 3- or 7-day food records).
3. Estimate a healthy target body weight based on the client’s percent fat goal.
Present FFM = 96.9 lb (131 lb × 0.74) (relative FFM)

Target body weight = 121 lb (96.9 lb / 0.80) (relative FFM goal)
4. Calculate the weight loss and total calorie deficit needed to achieve that weight loss.
1. Weight loss = 10 lb (131 lb − 121 lb)
477
2. Caloric deficit = 35,000 kcal (10 lb × 3,500 kcal·lb−1)
5. Estimate the daily energy expenditure of the client from the following equation: energy expenditure = RMR
+ daily activity level.
1. RMR = 655.0955 + 9.463(59.55 kg) + 1.8496(157.5 cm) − 4.6756(35 yr) = 1,346 kcal
2. Daily occupational activity level: lightly active 35% above basal level (see table 9.3).
Additional kcal = 1,346 × 0.35 = 471 kcal
3. Total energy expenditure = 1,346 + 471 = 1,817 kcal
6. Plan to produce a calorie deficit of 700 to 800 kcal per day by reducing the calorie intake by 500 kcal per day
and increasing the calorie expenditure by 200 to 300 kcal per day through exercise. To calculate caloric
expenditure during exercise, refer to appendix E.3. Multiply the calories burned per minute per kilogram of
body weight by the duration of the activity and the client’s body weight. Continue this program until the
total calorie deficit of 35,000 kcal is reached. In a little over 7 wk the client will lose approximately 10 lb (4.5
kg). This is a gradual average weight loss of 1 1/2 lb (0.7 kg) per week. Reassess the body composition to see
if the percent fat goal was reached.
7. Put the client on a maintenance diet and exercise program.
1. Calculate the total energy expenditure using an estimate of RMR based on the new body weight.
RMR + activity level + exercise = total energy expenditure, where
RMR = 1,303 kcal (use Harris-Benedict formula substituting a body weight of 55 kg)
Occupational activity level = 456 kcal (1,303 × 0.35)
Exercise = 300 kcal
Total energy expenditure = 1,303 + 456 + 300 = 2,059 kcal
2. Advise the client that if she continues to exercise daily, expending approximately 300 kcal per
workout, she may increase her calorie intake to 2,060 kcal per day. However, for days when she
cannot exercise, the calorie intake must be restricted to 1,760 kcal.
EXERCISE PRESCRIPTION FOR WEIGHT LOSS
478
Exercise alone—without dieting—has only a modest effect on weight loss. The most
successful weight loss programs, therefore, use a combination of dieting and exercising to
optimize the energy deficit and to maintain weight loss. The options for weight loss diet
plans can be overwhelming. For a comprehensive review of major diet archetypes and their
effects on body composition, see the recent position stand by the International Society of
Sports Nutrition (Aragon et al. 2017). We suggest that you work closely with a nutrition
professional when designing weight management programs for your clients. This is
particularly important if a client has any metabolic complications (e.g., diabetes, dyslipidemia,
hyper- or hypothyroidism). In the same way anyone can dispense personal training
information without the appropriate educational training or certification (see the preface),
anyone can give nutrition advice. However, there are restrictions on who can use the title
dietitian or nutritionist. Table 9.4 is a summary of the statutory provision regarding
professional regulation of dietitians and nutritionists in each state of the United States.
479
The amount of physical activity and exercise needed to benefit health, prevent overweight
and obesity, or maintain weight loss differs (see table 9.5). For health benefits, the ACSM
and AHA recommend at least 30 min of moderate-intensity (3-6 METs) physical activity on
a minimum of 5 days/wk or 20 min of vigorous-intensity (>6.0 METs) activity on a
minimum of 3 days/wk (American College of Sports Medicine 2009). Likewise, the Physical
Activity Guidelines for Americans (Howley 2008) recommend 150 to 300 min/wk of
moderate intensity (3-6 METs) or 75 to 150 min/wk of vigorous intensity or both (≥6.0
480
METs).
To prevent weight gain, the ACSM (2009) recommends moderate-intensity physical
activity between 150 and 250 min/wk. However, the International Association for the Study
of Obesity (IASO) consensus statement suggests that 30 min of daily physical activity (210
min/wk) may be insufficient for preventing weight gain or regain after weight loss (Saris et al.
2003). To maintain weight and to prevent unhealthy weight gain and transition to
overweight or obesity in adults, 45 to 60 min of moderate-to-vigorous activity (PAL = 1.7)
on most, preferably all, days is recommended (Institute of Medicine 2005; U.S. Department
of Health and Human Services 2005; Saris et al. 2003). For children and adolescents, at least
60 min of moderate to vigorous physical activity daily is recommended to maintain healthy
body weight as well as good health and fitness (U.S. Department of Health and Human
Services 2007).
The optimal physical activity level (PAL) for preventing weight gain differs from that for
creating a negative energy balance for weight loss and maintenance of weight loss. For a
modest weight loss (i.e., 2-3 kg or 4.4-6.6 lb), the ACSM (2009) recommends moderate-
intensity physical activity between 150 and 250 min/wk; however, there is a dose effect for
physical activity and weight loss, with >250 min/wk of physical activity associated with
clinically significant (3% or greater) weight loss (American College of Sports Medicine 2009).
The ACSM (2009) acknowledges that physical activity is necessary to prevent regaining
weight after weight loss. Although the specific amount of physical activity needed to prevent
weight regain is uncertain at this time, some studies suggest that weight maintenance after
weight loss is improved by engaging in more than 250 min/wk of physical activity. The
ACSM (2009) noted that 60 min/day of walking at a moderate intensity is associated with
weight maintenance. To maintain weight loss and to prevent weight regain in formerly obese
adults, the IASO consensus statement (see Saris et al. 2003) recommends a minimum of 60
min, but preferably 80 to 90 min, of moderate-intensity (2.8-4.3 METs) physical activity and
exercise (e.g., walking or cycling) per day. This intensity and duration of physical activity
approximately equals 35 min of vigorous activity (6-10 METs or PAL = 1.9-2.5).
Table 9.5 summarizes physical activity recommendations for health benefits, healthy
weight loss, and weight management. The exercise prescription for weight loss and weight
management will differ depending on the client’s goal. You can use the information in table
9.5 to develop exercise prescriptions for weight loss, weight maintenance, and prevention of
weight gain or regain.
481
Benefits of Exercise
The section highlights some common questions about the benefits of exercise in a weight loss
program.
Why is physical activity an essential part of weight loss programs?

It is important that you present realistic expectations to your clients and let them know that
exercise alone—without diet modification—results in only modest weight loss. Individual
weight loss from exercise alone is highly variable, but when performed at recommended levels
for general health benefits (i.e., 150 min/wk), losses often amount to only 2 kg or less in
studies lasting 4 to 6 mo (Swift et al. 2014). With high-volume exercise (>500 kcal per
session), substantial weight loss occurs (7%-8% of mass in 12-14 wk), but this volume of
training is not sustainable or practical for many people (Swift et al. 2014). So why prescribe
exercise for weight loss? Even without weight loss, exercise confers numerous health benefits
(see chapter 1). For example, in a review of studies specific to children and adolescents, both
diet only and diet plus exercise resulted in similar weight loss; however, the addition of
exercise led to greater improvements in HDL, fasting glucose, and fasting insulin levels (Ho
et al. 2013). Furthermore, exercise improves body composition even in the absence of weight
loss. Exercise maintains or slows down the loss of FFM that occurs with dieting only and is
important for maintaining weight loss after dieting.
482
A recent study by Drenowatz and colleagues (2017) highlights the importance of physical
activity for maintaining body weight over time. These researchers tracked 195 young adults
who had no intention for weight change over a 2 yr period. Body composition and energy
expenditure measurements were collected every 3 mo. After 2 yr, 57% of the participants had
maintained their weight (<5% weight change), while 14% lost weight (−6.9 kg) and 29%
gained weight (7.1 kg). The average total daily energy expenditure and total daily energy
intake remained stable in all three groups. However, moderate to vigorous physical activity
increased by about 35 min/day in the participants who lost weight and decreased by the same
amount in the participants who gained weight. Further demonstrating the importance of
exercise for maintaining a healthy body weight, Catenacci and colleagues (2011) used triaxial
accelerometers to track the moderate to vigorous physical activity of successful weight loss
maintainers and overweight participants. The weight loss maintainers engaged in about 290
min/wk of sustained physical activity compared with just 134 min/wk for the overweight
group.
Several studies demonstrate that body composition (and consequently long-term health)
improves by adding exercise to the weight loss program even if exercise does not substantially
increase the amount of weight lost. Maintaining FFM is particularly important in older
adults at risk for sarcopenia. Weinheimer, Sands, and Campbellnure (2010) reviewed weight
loss studies of middle-aged and older adults. When diet alone was the strategy for weight
loss, a substantial amount of the weight lost (≥15%) was in the form of FFM in 81% of the
study groups. In contrast, when exercise was included only 39% of the study groups
demonstrated substantial losses in FFM.
Pavlou and colleagues (1985) studied the contribution of exercise to the preservation of
FFM in mildly obese males on a rapid weight loss diet. The exercise group dieted and
participated in an 8 wk walking-jogging program, 3 days/wk. The nonexercising group dieted
only. Although the total weight loss of the exercise group (−11.8 kg) and nonexercise group
(−9.2 kg) was similar, the composition of the weight loss differed significantly. The exercise
group maintained FFM (−0.6 kg), while the nonexercise group lost a significant amount of
FFM (−3.3 kg). Also, the exercise group lost more fat (11.2 kg) than the nonexercise group
(5.9 kg). In other words, for the nonexercising subjects, only 64% of the total weight loss was
fat weight compared with 95% for the exercising subjects. The researchers concluded that the
addition of aerobic exercise to the dietary regimen preserves existing FFM and increases fat
utilization for energy production, and it is more effective in reducing fat stores than diet
alone.
483
Similarly, Kraemer, Volek, and colleagues (1999) compared the effects of a weight loss
dietary regimen with and without exercise in overweight men. The diet-only group did not
exercise; the exercise groups participated in either an aerobic exercise program or a combined
aerobic and resistance training exercise program, 3 days/wk for 12 wk. By the end of the
program, all three groups lost a similar amount of body weight (~9-10 kg), but the
composition of the weight loss differed significantly. For the diet-only group, only 69% of the
total weight loss was fat weight compared with 78% for the diet plus aerobic exercise group
and 97% for the diet and exercise (aerobic + resistance training) group. These results suggest
that using a combination of aerobic and resistance training exercises in conjunction with
dieting is more effective than dieting alone for preserving FFM and maximizing fat loss.
How does exercise promote fat loss and the preservation of lean body mass?
In response to aerobic and resistance training exercise, levels of growth hormone,

epinephrine, and norepinephrine increase. These hormones stimulate the mobilization of fat
from storage and activate the enzyme lipase, which breaks down triglycerides into free fatty
acids. Free fatty acids are then metabolized, and they serve as an important energy source,
especially during aerobic exercise. Heavy resistance training exercise also stimulates the
release of anabolic hormones such as testosterone and growth hormone, resulting in increased
protein synthesis, muscle growth, and FFM (Kraemer et al. 1991).
How does improved cardiorespiratory fitness help control body weight?
As an individual’s cardiorespiratory fitness level increases through training, the amount of

work the person can accomplish at a given submaximal heart rate increases. Thus, the more-
fit individual expends calories faster than the less-fit individual at a given exercise heart rate.
For example, at a heart rate of 150 bpm, the rate of energy expenditure is approximately 10
and 15 kcal·min for fair and superior fitness levels, respectively.
−1
During high-intensity aerobic exercise, lactate production increases and inhibits fatty acid
metabolism. However, endurance training increases the lactate threshold (point at which
lactate accumulates significantly in the blood). In aerobically trained individuals, the
percentage of the energy derived from the oxidation of free fatty acids during submaximal
exercise is greater than that derived from glucose oxidation (Coyle 1995; Mole, Oscai, and
Holloszy 1971). The reduction in muscle glycogen utilization is also associated with a greater
rate of oxidation of intramuscular triglyceride (Coyle 1995).
To expend the amount of energy recommended to prevent weight regain after weight loss,
cardiorespiratory fitness (V̇O max) needs to increase. Therefore, weight reduction programs
2
484
should increase cardiorespiratory fitness so that participants are able to reach this physical
activity goal within a reasonable amount of time (Saris et al. 2003).
What effect does exercise have on the REE?
Another reason for including exercise in the weight loss program is its positive effect on
REE. Research indicates that exercise may counter the reduction in RMR that usually occurs
as a result of dieting (Thompson, Manore, and Thomas 1996). It is well known that the rate
of weight loss declines in the later stages of dieting because of a decrease in REE. The
lowered REE is an energy-conserving metabolic adaptation to prolonged periods of caloric
restriction (Donahue et al. 1984). In a study of 12 overweight females, Donahue and
colleagues (1984) reported that diet alone caused a 4.4% reduction in the relative REE (REE
/ BW). After the addition of 8 wk of aerobic exercise to the program, the relative REE
increased by 5%. The net effect of exercise was to offset the diet-induced metabolic
adaptation and return the REE to the normal, prediet level.
Exercise may also facilitate weight loss by causing an increase in postexercise REE.
Moderate- to high-intensity aerobic exercise increases the postexercise REE by 5% to 16%,
and the elevated REE may persist for 12 to 39 hr postexercise (Bahr et al. 1987; Bielinski,
Schultz, and Jequier 1985; Sjodin et al. 1996). The postexercise elevation in REE appears to
be related to the exercise intensity and duration (Brehm 1988). Cycling at 70% V̇O max for
2
20 min produced a 5% to 14% elevation in REE for 12 hr in young, healthy men (Bahr et al.
1987). Although it is tempting to apply these findings to clients who are elderly or obese, it is
not known whether the postexercise metabolic response of these individuals is similar to that
of young men.
Are wearable activity monitors helpful for losing weight?
Self-monitoring increases awareness of energy balance. Real-time continuous feedback might

motivate some clients to make better dietary and physical activity choices. In a recent
systematic review, Goode and colleagues (2017) concluded that wearable monitors such as
accelerometers have a small positive effect on physical activity and weight loss. They noted
that these effects are diminished when compared against other robust weight loss
interventions rather than just inactive controls. Also, the duration of most of the studies
reviewed was 24 wk or less, making it difficult to assess long-term effectiveness. They
suggested that future studies investigate integrating wearables with other weight loss
strategies. That is what Shuger and colleagues (2011) did. They placed 197 overweight or
obese adults who were under the 150 min/wk recommendation of moderate to vigorous
485
physical activity into one of four groups: self-directed weight loss, group-based behavioral
weight loss program, wearing an activity monitor, or the group-based program plus the
activity monitor. Those in the group-based program who were wearing the activity monitors
lost the most weight at the end of the 9 mo intervention. All groups reduced waist
circumference, but no group lost significantly more girth than others.
Recently, Jakicic and colleagues (2016) conducted a randomized controlled trial that
spanned 2 yr with 471 adults. For the first 6 mo, both groups received a behavioral weight
loss intervention (low-calorie diet, physical activity, and group counseling), after which
participants were randomized into a standard intervention group that self-monitored diet and
physical activity using a website and an enhanced intervention group that used a wearable
tracker with web interface to monitor diet and activity. At the end of 24 mo, the standard
intervention group actually lost more weight (−5.9 kg) than the group that used wearable
technology (−3.5 kg). Both groups had similar improvements in body composition, fitness,
physical activity, and diet. Taken together, these studies suggest that wearable technology
might provide a modest improvement in physical activity and weight loss in the short term,
but these devices do not seem to offer any benefit over traditional weight loss interventions
over the long term.
Types of Exercise
This section addresses common concerns regarding the types of exercise suitable for weight
loss programs.
Is aerobic exercise better than resistance exercise for weight loss?

Most weight loss programs recommend energy restriction (diet) and increased physical
activity to create an energy deficit. Evidence suggests a dose-response relationship for weight
loss; individuals performing the greatest amount of physical activity achieve greater weight
loss. Aerobic exercise (e.g., walking) is effective for weight loss, fat loss, and long-term weight
control (Gordon-Larsen et al. 2009; Nelson and Folta 2009).
Although resistance training increases muscle mass and REE, this mode of training does
not produce a clinically significant weight loss (~3% of body weight) and does not increase
weight loss when combined with calorie restriction. Resistance training, however, may
increase the loss of fat mass when combined with aerobic exercise (American College of
Sports Medicine 2009). Also, the combination of aerobic and resistance training has been
shown to have a positive effect on preventing weight gain and on regaining harmful VAT
following a diet-induced weight loss (Hunter et al. 2010).
486
Although aerobic exercise is more effective than resistance training for reducing body
weight and fat mass, resistance training plays an important role in preserving FFM and
increasing REE, especially for overweight older adults on a weight loss diet (Avila et al.
2010). Combining these two modes of training may be the most effective way to maximize
fat loss while maintaining metabolically active FFM.
Is high-intensity exercise better than light- to moderate-intensity exercise for weight loss?
An important reason for including exercise as part of a weight loss program is to maximize
energy expenditure, thereby creating a larger negative energy balance. Weight loss and loss of
fat mass are positively related to weekly energy expenditure (Ross and Janssen 2001). When
the same amount of energy is expended, total fat oxidation is higher during low-intensity
exercise than during high-intensity exercise. Close examination of energy expenditure during
selected physical activities (appendix E.3, Gross Energy Expenditure for Conditioning
Exercises, Sports, and Recreational Activities) reveals that increases in speed (intensity) of
exercise produce only small increases in the rate of energy expenditure (METs).
For example, if a 123 lb (56 kg) woman increases the speed of running from a slow speed
(5.0 mph or 12 min·mi ) to a faster speed (7.0 mph or 8.5 min·mi ), the rate of expenditure
−1 −1
increases only 3.2 kcal·min . At the 8.5 min·mi pace, the woman expends 11.5 METs (11.5
−1 −1
kcal·kg ·min or 10.7 kcal·min ) and is able to run a maximum distance of 3 mi (4.8 km).
−1 −1 −1
The duration of the workout is 25.5 min (8.5 min·mi × 3 mi), and the total caloric
−1
expenditure is 274 kcal (25.5 min × 10.7 kcal·min ). When she reduces the exercise intensity
−1
by decreasing her speed to a pace of 12 min·mi , her relative energy expenditure decreases (8
−1
METs or 8 kcal·kg ·min or 7.5 kcal·min ), but she is able to run a distance of 4 mi (6.4 km).
−1 −1 −1
The duration of the workout increases to 48 min (12 min·mi × 4 mi), and the total caloric
−1
expenditure is increased (48 min × 7.5 kcal·min = 360 kcal). Thus, the duration of the
−1
exercise and total distance may be somewhat more important than the speed (intensity) of
exercise for maximizing the energy expenditure.
In 2009, Nicklas and colleagues reported that vigorous aerobic exercise (70%-75% heart
rate reserve [HRR]) and moderate-intensity aerobic exercise (45%-50% HRR), combined
with caloric restriction, produced similar amounts of weight loss and abdominal fat loss in
overweight and obese women. Given that most obese individuals prefer to exercise at a slower
pace and at low to moderate intensity, it probably is not necessary to prescribe vigorous-
intensity exercise as part of a weight loss program.
What aerobic exercise mode is best to maximize fat loss?
487
In a meta-analysis of 53 studies dealing with the effects of exercise on body weight and
composition, Ballor and Keesey (1991) reported that fat loss for males participating in aerobic
exercise training was, on average, 1.9 kg for cycling (0.11 kg·wk ) and 1.6 kg for running and
−1
walking (0.12 kg·wk ). For resistance training, body weight increased an average of 1.2 kg,
−1
but fat mass was reduced by 1.0 kg. For females, fat mass decreased significantly (1.3 kg) for
running and walking but not cycling. These studies suggest that in terms of fat loss, aerobic
exercise modes are equally effective for men, but running and walking may be better than
cycling for women.
Are spot-reduction exercises effective for decreasing body fat in localized regions of the
body?
Specific spot-reduction exercises are no more effective than general aerobic exercise for
changing limb and body girth measurements or for altering total body composition (Carns et
al. 1960; Noland and Kearney 1978; Roby 1962; Schade et al. 1962). Katch and colleagues
(1984) assessed changes in the diameter of adipose cells from the abdomen and gluteal and
subscapular sites resulting from a 27-day training program in which each subject performed
5,004 sit-ups. Although the training significantly reduced fat cell diameter, the effect was
similar at all three sites: abdomen, −6.4%; gluteal, −5.0%; and subscapular, −3.7%. It appears
that a sit-up exercise program does not preferentially reduce the fat in the abdominal region.
Despres and colleagues (1985) reported that a 20 wk cycling program significantly reduced
%BF and body weight. Cycling affected trunk skinfolds (SKFs) (−22%) more than extremity
SKFs (−12.5%). If fat was mobilized preferentially from subcutaneous stores near the
exercising muscle mass, one would expect the lower extremity SKFs to be more affected by
cycling than the trunk SKFs. Yet Despres and colleagues (1985) noted an 18% reduction in
the suprailiac SKF and a 13% reduction in the thigh SKF. This suggests that SAT in the
abdomen is more sensitive to the lipolytic effect of catecholamines than SAT in the thighs
(Smith et al. 1979).
The enzyme lipoprotein-lipase is responsible for lipid accumulation. In women,
lipoprotein-lipase activity is higher in the gluteofemoral region than in the abdominal region
(Litchell and Boberg 1978). Estrogen and progesterone appear to enhance lipoprotein-lipase
activity in women. Also, the lipolytic response to catecholamines is lower in the femoral than
in the abdominal depots for both men and women (Rebuffe-Scrive 1985).
Thus, the regional distribution and mobilization of adipose tissue appear to follow a
biologically selective pattern regardless of type of exercise. Even with weight reduction, the
488
relative fat distribution remains stable as measured by the WHR; however, the waist-to-thigh
ratio decreases, suggesting that the thigh region is slightly more resistant to fat mobilization
in women (Ashwell et al. 1985).
In addition, upper body resistance training does not appear to preferentially reduce SAT in
the upper arm. Kostek and colleagues (2007) reported that subcutaneous fat changes,
measured by magnetic resonance imaging (MRI), in trained and untrained arms did not
differ significantly following 12 wk of resistance training. These findings suggest that
resistance training exercise does not result in spot reduction.
DESIGNING WEIGHT GAIN PROGRAMS

Because genetics plays an important role in weight gain, some clients may have difficulty
gaining weight, especially if they have inherited a high RMR. Before prescribing weight gain
programs, you should rule out the possibility that diseases and psychological disorders
associated with malnutrition (e.g., anorexia nervosa) are not causing your client to be
underweight. For athletes who are competing in weight classes, Macedonio and Dunford
(2009) provide detailed information and suggestions for making weight.
The number of additional calories needed in order for a person to gain 1 lb (0.45 kg) of
muscle tissue has not yet been firmly established. However, research suggests that an excess of
2,800 to 3,500 kcal is required. As with weight loss, the 3,500 kcal rule is theoretical. For a
more accurate and individualized estimate of number of additional calories needed to gain a
specific amount of weight over a certain time period, use the NIH’s Body Weight Planner
(see Designing Weight Loss Programs section). Adding 500 to 1,000 kcal·day to the diet
−1
promotes weight gain; however, only 30% to 50% of the weight gain is muscle. Given that
the remaining amount of the weight gain is fat, the International Society of Sports Nutrition
does not recommend ingesting a high-calorie diet to build muscle mass (Kreider et al. 2010).
As with weight loss programs, the diet portion of the plan is just as important as the
exercise portion for gaining weight. The emphasis should be on increasing lean mass rather
than body fat. The International Society of Sports Nutrition recommends a daily protein
intake of 1.4 to 2.0 g·kg of body weight, in doses of 0.25 g·kg , evenly distributed every 3 to
−1 −1
4 hr throughout the day for building muscle mass (Jäger et al. 2017). Again, it is highly
recommended that you consult a trained nutrition professional when planning weight gain
diets. When comparing your clients’ typical nutrient intakes against recommended dietary
intakes, you should focus on the same questions as outlined for weight loss programs (see the
Exercise Prescription for Weight Loss section). Specifically,
489
use the body composition method to estimate a healthy target weight and the
amount of FFM to be gained;
work closely with a nutrition professional to make sure your clients consume an
adequate amount of high-quality protein;
prescribe resistance training as outlined in the next section;
track changes in FM and FFM throughout the weight gain program using the body
composition assessment methods described in chapter 8.
Guidelines for Exercise Prescription for Weight Gain
Mode: Resistance training

Intensity: 70% to 75% 1-RM or 10-RM to 12-RM
Sets: Three for novices; more than three for advanced weightlifters
Number of exercises: One or two per muscle group for novices; three or four per
muscle group for advanced weightlifters
Duration: 60 min or longer
Frequency: 3 days/wk for novices; 5 or 6 days/wk for advanced weightlifters
Length of program: Dependent on desired weight gain
Rest: 2 min between sets
EXERCISE PRESCRIPTION FOR WEIGHT GAIN

As part of the weight gain program, you should prescribe resistance training to increase
muscle size. A high-volume resistance training program is the best approach for maximizing
the development of muscle size. Because some clients may not be able to tolerate this volume
of training at first, novice weightlifters should start slowly by performing only three sets of
each exercise at the prescribed intensity and by reducing the number of exercises for each
muscle group. Depending on the client’s goal, this may be sufficient to increase FFM. For
some clients, however, you may need to progressively increase the training volume in order to
elicit further improvements in muscle size and FFM. See Guidelines for Exercise Prescription
for Weight Gain for resistance training recommendations.
490
DESIGNING PROGRAMS TO IMPROVE BODY COMPOSITION
Some clients may wish to improve their body composition without changing their body
weight. For these individuals, you can design exercise programs to decrease body fat, increase
FFM, or both. Research has shown that regular participation in an exercise program may
alter an individual’s body composition. Aerobic exercise and resistance training are effective
modes for decreasing SKF thicknesses, fat weight, and %BF of both women and men.
QUESTIONS ABOUT EXERCISE AND BODY COMPOSITION

CHANGES
This section addresses commonly asked questions about what role exercise plays in altering
body composition.
What is the effect of aerobic exercise training on body fat?
Numerous studies have been conducted to determine the effect of aerobic exercise training on
body composition. The modes of exercise include cycling, walking, jogging, running, and
swimming. Wilmore and colleagues (1970) reported that a 10 wk jogging program (3
days/wk) produced a significant increase in body density of sedentary men. Because total
body weight decreased and FFM remained stable, the increase in body density was attributed
almost entirely to fat loss. Pollock and colleagues (1971) also noted that a 20 wk walking
program (4 days/wk) produced a decrease in %BF and total body weight of men.
In addition to total fat loss, aerobic exercise is critical for inducing loss of VAT. As
mentioned previously, VAT is of greater importance than total fat with regard to adverse
health effects. It was determined through meta-analysis that aerobic exercise is effective at
reducing VAT, but resistance exercise is not (Ismail et al. 2012).
Which aerobic exercise mode is best for maximizing fat loss?
One study compared cycling, running, and walking of equal frequency, duration, and
intensity (Pollock, Miller, et al. 1975). All three programs produced significant reductions in
%BF and body weight. Also, Despres and colleagues (1985) reported that a 20 wk cycling
program (4 or 5 days/wk) resulted in significant reductions in body weight, %BF, and fat cell
weight in a group of sedentary men. In contrast, a major finding in a recent meta-analysis of
high-intensity interval training (HIT) was that running was effective at reducing the fat mass
of overweight and obese people but cycling was not (Wewege et al. 2017). The authors had
no clear explanation for why the different exercise modalities produced different results, but
they theorized that more muscle recruitment during running for a given submaximal
491
workload could lead to greater energy expenditure. Nevertheless, the high impact of running
might produce more injuries, and that should also be a consideration for your overweight and
obese clients.
How many times a week should I exercise to maximize the loss of body fat?
The frequency of the training program may affect the magnitude of the changes in body
composition. Pollock, Miller, and colleagues (1975) compared aerobic exercise programs
consisting of 2, 3, or 4 days/wk. Even though the total mileage and caloric expenditure were
the same, exercising 2 days/wk was not sufficient to produce significant alterations in body
composition. The authors concluded that a program of 3 or 4 days/wk produces significant
body composition changes, with 4 days/wk being superior to 3 days/wk.
Does the intensity of aerobic exercise affect body composition changes?
Irving and colleagues (2008) compared the effects of low-intensity (RPE ~10-11) and high-
intensity (RPE ~12-15) exercise training on VAT and body composition of obese women
with metabolic syndrome. Using computerized technology, they noted that high-intensity
training produced significantly larger reductions in SAT and VAT in the abdomen compared
with low-intensity training. Similarly, a large effect size (.7) was reported for decreased
android fat mass in a group that performed cycle ergometry intervals (1:2 min ratio of 90%
and 30% V̇O peak), but there was no effect for a group that maintained 50% V̇O peak;
2 2
training groups were matched for total energy expenditure (Wallman et al. 2009). Also,
reductions in total fat, subcutaneous leg and trunk fat, and insulin resistance in young women
were greater for a group that performed HIT 3 days/wk for 15 wk compared with the same
frequency for a steady-state exercise group (Trapp et al. 2008). In a recent review and meta-
analysis of this topic, Wewege and colleagues (2017) determined that both HIT and
moderate-intensity continuous training are effective at reducing FM and waist circumference,
but neither is more effective than the other. However, HIT requires about 40% less training
time.
What effect does resistance training have on body fat and FFM?
Although resistance training may increase body weight, it positively affects fat mass, %BF,
and FFM (Ballor and Keesey 1991). Cullinen and Caldwell (1998) found that normal-weight
women (19-44 yr) participating in a moderate-intensity resistance training program (2
days/wk for 12 wk) significantly increased FFM (~4.5%) and decreased %BF (~8.7%). In
Wilmore’s study (1974), subjects trained 2 days/wk for 10 wk. At each training session, they
492
performed two sets of 7-RM to 9-RM for eight different weight training exercises. Men and
women exhibited similar alterations in body composition. Although the total body weight
remained stable, the FFM increased significantly for both sexes. As a result of resistance
training, the relative body fat decreased 9.6% and 10.0% for women and men, respectively.
Velthuis and colleagues (2009) reported that a 12 mo program of moderate to vigorous
exercise combining aerobic and resistance training did not produce significant changes in
body weight of sedentary postmenopausal women. However, body composition was affected
positively, with the exercise group showing significant improvements in FM, %BF, FFM,
and waist circumference. The loss of lean mass can be attenuated during periods of energy
deficit with resistance training and a high protein intake (1.8-3.0 g·kg ·day ) (Churchward-
−1 −1
Venne et al. 2013).

Conventional thought is that resistance training improves FFM, but aerobic exercise is
necessary for loss of FM. However, the results of several recent studies suggest that adding
resistance training to aerobic exercise augments the FM loss. In a randomized intervention
study, sedentary overweight and obese adults were placed into either an aerobic exercise group
or a combined aerobic-resistance exercise group (Sanal, Ardic, and Kirac 2013). Not
surprisingly, the FFM increase was greater in the combined exercise group than the aerobic-
only exercise group after 12 wk. But adding resistance exercise to aerobic exercise also
enhanced the reduction of FM in the legs for women. Drenowatz and colleagues (2015)
tracked 348 young adults with BMIs between 20 and 35 kg/m over a year. In addition to
2
monitoring TEE with a wearable device, participants self-reported the type of activity they
were doing and had their body composition measured every 3 mo. Similar to the results of
Sanal, Ardic, and Kirac (2013), resistance training had a positive effect on both lean mass and
FM, whereas aerobic exercise affected FM only. Furthermore, in the subset of overweight
and obese participants, resistance exercise actually had a greater effect on FM than did
aerobic exercise.
How does exercise promote body composition changes?
The significant loss of fat weight and %BF with aerobic exercise and resistance training is a
function of hormonal responses to the exercise. Exercise increases the circulatory levels of
growth hormone (GH), and the levels remain elevated for 1 to 2 hr after exercise (Hartley et
al. 1972; Hartley 1975). Exercise also stimulates the release of catecholamines from the
adrenal medulla. Both GH and catecholamines increase the mobilization of free fatty acids
from storage (Hartley 1975). Eventually, the muscle may metabolize these free fatty acids
493
during rest and low-intensity exercise.
The increase in FFM with resistance training may be due to muscle hypertrophy, increased
protein content in the muscle, or increased bone density. Muscle hypertrophy and increased
protein are mediated by changes in serum testosterone and GH levels in response to
weightlifting. Immediately following heavy resistance weightlifting, serum testosterone levels
are significantly elevated for men but not for women (Fahey et al. 1976; Weiss, Cureton, and
Thompson 1983). Growth hormone levels in men are increased significantly for 15 min
following a 21 min bout of high-intensity (85% of 1-RM) leg press exercises. However, low-
intensity, high-repetition (28% of 1-RM, 21 reps per set) leg presses produced no significant
change in GH even though the total amount of work and duration of exercise were equal.
Thus, the intensity and number of repetitions play a role in GH release in response to
weightlifting exercise (Vanhelder, Radomski, and Goode 1984).
In addition, resistance training has an effect on the hormonal profiles of younger (30 yr)
and older (62 yr) men (Kraemer, Häkkinen, et al. 1999). Following a 10 wk periodized
strength-power training program, young men had significant increases in free testosterone at
rest and in response to weightlifting exercise. Younger men also showed increases in resting
levels of IGF (insulin-like growth factor) binding protein-3 after training. For the older men,
training produced a significant increase in total testosterone in response to weightlifting
exercise, as well as a significant reduction in resting cortisol levels.
Are exercise-induced changes in body composition influenced by genotype?
Raue and colleagues (2012) identified and compared gene sets responsible for eliciting a
growth response to resistance training in young (24 yr) and old (84 yr) adults. They noted
that age affects the genetic response of skeletal muscle to resistance exercise and that these
genes are correlated with gains in muscle size and strength. These findings may explain, in
part, interindividual variations in muscle hypertrophy and changes in FFM in response to
Research has demonstrated that genotype and alleles are associated with risk of obesity. An
allele is defined as one member of a pair or series of genes that occupy a specific position on a
specific chromosome. Individuals with the obesity-associated genotype (FTO) and the risk
allele (A/A) are 1.67 times more at risk for obesity than those without the A/A allele.
Rankinen and colleagues (2010) reported that the FTO genotype is related to body fat
responses to aerobic exercise training. Individuals with the obesity risk allele (A/A) were more
resistant to changes in adiposity; the loss of FM and %BF was three times less than that of
494
individuals with the C allele. This finding may represent one mechanism by which the FTO
allele promotes overweight and obesity. Recently, Leonska-Duniec and colleagues (2017)
confirmed the relationship between FTO and increased BMI. However, despite changes in
BMI, BMR, FM, FFM, HDL, and glucose following a 12 wk training program, there was
no interaction between the FTO gene with the risk allele and physical activity on any of these
variables. Contrary to the research of Rankinen and colleagues (2010), this finding suggests
that even in those with a genetic predisposition to obesity, physical activity is still effective at
modifying body composition and obesity-related traits.
EXERCISE PRESCRIPTION FOR BODY COMPOSITION CHANGE

It is clear that both aerobic exercise and resistance training contribute to body composition
change. Combining these two types of exercise may be the most effective way to alter body
composition of nondieting individuals (Dolezal and Potteiger 1998). When designing
exercise programs to promote changes in body composition, adhere to the guidelines for
exercise prescription for fat loss and for fat-free mass gain. Prescribe aerobic exercise to
reduce body fat and resistance training exercise to increase FFM. See Guidelines for Exercise
Prescription for Fat Loss and Guidelines for Exercise Prescription for Fat-Free Mass Gain.
Guidelines for Exercise Prescription for Fat Loss
Goal: Fat loss

Mode: Type A and B aerobic activities (see Classification of Aerobic Exercise
Modalities in chapter 5)
Intensity: Moderate to high (RPE 10-15) or HIT
Duration: 30 to 45 min
Frequency: Minimum of 3 days/wk
Length: Minimum of 8 wk
Guidelines for Exercise Prescription for Fat-Free Mass Gain
Goal: Increase FFM and reduce body fat

Mode: Dynamic resistance training
Intensity: 70% to 85% 1-RM
Repetitions: 6 to 12
495
Sets: Three
Length: Minimum of 8 wk
Key Points
Obesity is an excess of body fat that increases health risks.
Two types of obesity are upper body (android) and lower body (gynoid) obesity.
The number of fat cells in the body is determined primarily during childhood and adolescence; however,
recent research suggests that adults can experience hyperplasia in certain areas of the body with overfeeding.
Physical inactivity contributes to the cause of obesity.
The body composition method provides a useful estimate of a healthy body weight.
Fitness professionals should consult with nutrition professionals when designing weight loss and weight
gain plans.
Effective weight loss programs create a negative energy balance by restricting caloric intake and increasing
physical activity and exercise; weight gain programs create a positive energy balance by increasing caloric
intake.
For weight loss programs, the combined daily caloric deficit due to calorie restriction and extra exercise
should not exceed 1,000 kcal; for weight gain programs, the daily caloric intake should exceed the energy
need by no more than 500 kcal.
Adding a combination of aerobic and resistance training exercises to the dieting regimen is an effective way
to maximize fat loss and preserve FFM during weight loss.
The optimal amount of physical activity for preventing weight gain differs from that needed to create a
negative energy balance for weight loss and for maintenance of weight loss.
For weight gain programs, resistance training will ensure that most of the weight gain is due to increases in
lean body tissues.
Aerobic exercise and resistance training are effective ways to improve body composition without changing
body weight.
Key Terms
allele
android obesity
anorexia nervosa
basal metabolic rate (BMR)
496
dietary thermogenesis
exercise activity thermogenesis (EAT)
factorial method
gynoid obesity
healthy body weight
hyperplasia
hypertrophy
kilocalorie (kcal)
lower body obesity
negative energy balance
non-exercise activity thermogenesis (NEAT)
obesity
overweight
physical activity level (PAL)
positive energy balance
resting energy expenditure (REE)
resting metabolic rate (RMR)
subcutaneous adipose tissue (SAT)
total energy expenditure (TEE)
total energy expenditure (TEE) method
underweight
upper body obesity
visceral adipose tissue (VAT)
Review Questions
1. Using BMI, what are the cutoff values for classification of obesity, overweight, healthy body weight, and
underweight?
2. Describe how you can determine a healthy body weight for a client.
3. For typical weight loss programs, identify the minimal caloric intake per day and maximal caloric deficit
(i.e., negative energy balance) per day. What is the best way to create this daily caloric deficit?
4. Explain why a taller, heavier person will lose weight at a faster rate than a shorter, lighter person when the
two individuals are on the same diet.
5. Explain why exercise is an important component of weight loss and weight gain programs.
6. Describe two methods you can use to estimate the energy needs of your clients.
497
7. Describe the optimal amount of physical activity (intensity, duration, and frequency) for health benefits,
weight loss, weight maintenance, and prevention of weight regain.
8. Estimate the daily caloric intake for a 50 yr old, 150 lb (68 kg) female professor who is 5 ft 5 in. and who
bikes a total of 60 min, 5 days/wk, to and from the university.
9. Describe the basic exercise prescriptions for weight loss and weight gain programs.
498
CHAPTER 10
Assessing Flexibility
KEY QUESTIONS
What is the difference between static and dynamic flexibility?
What factors affect flexibility? How is flexibility assessed?
Are indirect measures of flexibility valid and reliable?
What are the general guidelines for flexibility testing?
What test can I use to assess the flexibility of older adults?
How can I test the lumbar stability of my clients?
Flexibility is an important, yet often neglected, component of health-related fitness.

Adequate levels of flexibility are needed for maintenance of functional independence and
performance of activities of daily living (ADLs) such as bending to pick up a newspaper or
getting out of the backseat of a two-door car. Over the years, flexibility tests have been
included in most health-related fitness test batteries, since it has been long thought that lack
of flexibility is associated with musculoskeletal injuries and low back pain. However,
compared with research on other physical fitness components, not many studies substantiate
the importance of flexibility to health-related fitness.
Research suggests that individuals with too little flexibility (ankylosis) or too much
(hypermobility) are at higher risk than others for musculoskeletal injuries (Jones and Knapik
1999), but there is limited evidence that a greater than normal amount of flexibility actually
decreases injury risk (Knudson, Magnusson, and McHugh 2000). Also, research fails to
support an association between lumbar or hamstring flexibility and the occurrence of low
back pain (Jackson et al. 1998; Kuukkanen and Malkia 2000; Stutchfield and Coleman 2006).
Still, flexibility should be included in health-related fitness test batteries to identify
individuals at the extremes who may have a higher risk of musculotendinous injury.
This chapter describes direct and indirect methods for assessing flexibility. It presents
guidelines for flexibility testing as well as norms for commonly used flexibility tests.
499
BASICS OF FLEXIBILITY
Flexibility and joint stability are highly dependent on the joint structure, as well as on the
strength and number of ligaments and muscles spanning the joint. To fully appreciate the
complexity of flexibility, you should review the anatomy of joints and muscles. This section
deals with the definitions and nature of flexibility and also presents factors influencing joint
mobility.
DEFINITIONS AND NATURE OF FLEXIBILITY

Flexibility is the ability of a joint, or series of joints, to move through a full range of motion
(ROM) without injury. Static flexibility is a measure of the total ROM at the joint; it is
limited by the extensibility of the musculotendinous unit. Dynamic flexibility is a measure of
the rate of torque or resistance developed during stretching throughout the ROM. Although
dynamic flexibility accounts for 44% to 66% of the variance in static flexibility (Magnusson et
al. 1997; McHugh et al. 1998), more research is needed to firmly establish the relationship
between static and dynamic flexibility and to determine whether these two types of flexibility
are distinct entities or two aspects of the same flexibility component (Knudson et al. 2000).
The ROM is highly specific to the joint (i.e., specificity principle) and depends on
morphological factors such as the joint geometry and the joint capsule, ligaments, tendons,
and muscles spanning the joint. The joint structure determines the planes of motion and may
limit the ROM at a given joint. Triaxial joints (e.g., ball-and-socket joints of the hip and
shoulder) afford a greater degree of movement in more directions than nonaxial, uniaxial, or
biaxial joints (see table 10.1).
The tightness of soft tissue structures such as muscle, tendons, and ligaments is a major
limitation to both static and dynamic flexibility. Johns and Wright (1962) determined the
500
relative contribution of soft tissues to the total resistance encountered by the joint during
movement:
Joint capsule—47%
Muscle and its fascia—41%
Tendons and ligaments—10%
Skin—2%
The joint capsule and ligaments consist predominantly of collagen, a nonelastic connective
tissue. The muscle and its fascia, however, have elastic connective tissue; therefore, they are
the most important structures in terms of reducing resistance to movement and increasing
dynamic flexibility.
The tension within the muscle-tendon unit affects both static flexibility (ROM) and
dynamic flexibility (stiffness or resistance to movement). The tension within this unit is
attributed to the viscoelastic properties of connective tissues, as well as to the degree of
muscular contraction resulting from the stretch reflex (McHugh et al. 1992). Individuals with
less flexibility and tighter muscles and tendons have a greater contractile response during
stretching exercises and a greater resistance to stretching. The elastic deformation of the
muscle-tendon unit during stretching is proportional to the load or tension applied, whereas
the viscous deformation is proportional to the speed at which the tension is applied. When
the muscle and tendon are stretched and held at a fixed length (e.g., during static stretching),
the tension within the unit, or tensile stress, decreases over time (McHugh et al. 1992). This
is called stress relaxation. A single static stretch sustained for 90 sec produces a 30% increase
in viscoelastic stress relaxation and decreases muscle stiffness for up to 1 hr (Magnusson
1998).
Studies examining the viscoelastic effects of stretching have clearly demonstrated that
increases in joint ROM are associated with decreases in passive resistance to stretch
(McHugh and Cosgrave 2010). The immediate and prolonged effects of static stretching,
however, depend on total duration of stretching. A stretch duration of 2 min or less has no
prolonged effect on muscle stiffness; 50% of the effect of a 4 min stretch duration on passive
resistance is lost in 10 min; 50% of the effect of an 8 min stretch is lost within 30 min
(McHugh and Cosgrave 2010). Nakamura and associates (2011) reported that 5 min of static
stretching decreased stiffness of the muscle and the muscle-tendon unit, and this effect lasted
for at least 10 min following static stretching. Herda and colleagues (2011) noted that the
501
type of static stretching affects muscle-tendon stiffness; constant-tension static stretching is
more effective than constant-angle static stretching in decreasing muscle-tendon stiffness.
Both forms of static stretching, however, produced similar improvements in ROM and
similar decrements in strength.
To date, a handful of studies have considered the acute effects of dynamic stretching on
passive muscle stiffness (Chen et al. 2015; Herda et al. 2013; Mizuno and Umemura 2016;
Samukawa et al. 2011). All found that ROM increased after dynamic stretching, but there is
some debate as to how this increase in ROM occurs. Samukawa and colleagues (2011)
reported that dynamic stretching lengthened the displacement of the musculotendinous
junction. In contrast, Mizuno and Umemura (2016) reported no displacement of the
musculotendinous junction with increased ankle ROM, and the passive mechanical properties
(such as stiffness) of the musculotendinous unit remained unchanged. They theorized that
the increased ROM that accompanies dynamic stretching might be due to enhanced stretch
tolerance. However, Herda and colleagues (2013) reported a decrease in passive resistive
torque and passive stiffness following 2 min of dynamic stretching of the knee flexors. Chen
and associates (2015) also reported that muscle stiffness of the hamstrings decreased
significantly more in a dynamic stretching group compared with static stretching and control
groups in their sample of young men with limited passive straight leg elevation.
Additionally, Mahieu and associates (2007) reported that 6 wk static and ballistic
stretching programs had different effects on passive resistive torque and tendon stiffness.
Both forms of stretching increased ankle dorsiflexion ROM. Static stretching significantly
reduced passive resistive torque of the calf muscles but had no effect on Achilles tendon
stiffness, whereas ballistic stretching had the reverse effect—Achilles tendon stiffness
decreased, but passive resistive torque of the plantar flexors was unchanged.
FACTORS AFFECTING FLEXIBILITY

Flexibility is related to body type, age, gender, and physical activity level. This section
addresses some commonly asked questions about flexibility.
Does body type limit flexibility?

Individuals with large hypertrophied muscles or excessive amounts of subcutaneous fat may
score poorly on ROM tests because adjacent body segments in these people contact each
other sooner than in those with smaller limb and trunk girths. However, this does not
necessarily mean that all heavily muscled or obese individuals have poor flexibility. Many
bodybuilders and obese individuals who routinely stretch their muscles have adequate levels of
502
flexibility.
Why do older individuals tend to be less flexible than younger people?
Inflexible and older individuals have increased muscle stiffness and a lower stretch tolerance
compared with younger individuals with normal flexibility (Magnusson 1998). As muscle
stiffness increases, static flexibility progressively decreases with aging (Brown and Miller
1998; Gajdosik, Vander Linden, and Williams 1999). A decline in physical activity and
development of arthritic conditions, rather than a specific effect of aging, are the primary
causes for the loss of flexibility as one grows older. Still, flexibility training can help
counteract age-related decreases in ROM. Girouard and Hurley (1995) reported significant
improvements in shoulder and hip ROM of older men (50-69 yr) following 10 wk of
flexibility training. Thus, older persons can benefit from flexibility training and should be
encouraged to perform stretching exercises at least two times a week to counteract age-related
decreases in ROM (Garber et al. 2011; Tremblay et al. 2011).
Are females more flexible than males?
Some evidence suggests that females are generally more flexible than males at all ages (Alter
2004; Payne et al. 2000). The greater flexibility of women is usually attributed to gender
differences in pelvic structure and hormones that may affect connective tissue laxity (Alter
2004). Allison and colleagues (2015) recently compared musculoskeletal, biomechanical, and
physiological gender differences in United States military personnel. Female soldiers
demonstrated significantly greater ROM for shoulder extension, abduction, and external
rotation, as well as hip extension and knee flexion, than their male counterparts. It was also
noted that the females had better hamstring flexibility and less posterior shoulder tightness
than the males. There were no significant gender differences for shoulder flexion and internal
rotation, hip flexion, torso rotation, and calf flexibility.
How do physical activity and inactivity affect flexibility?
Habitual movement patterns and physical activity levels apparently are more important
determinants of flexibility than gender, age, and body type (Harris 1969; Kirby et al. 1981).
Lack of physical activity is a major cause of inflexibility. It is well documented that inactive
persons tend to be less flexible than active persons (McCue 1953) and that exercise increases
flexibility (Chapman, deVries, and Swezey 1972; deVries 1962; Hartley-O’Brien 1980).
Disuse, due to lack of physical activity or immobilization, produces shortening of the muscles
(i.e., contracture) and connective tissues, which in turn restricts joint mobility.
503
Moving the joints and muscles in a repetitive pattern or maintaining habitual body
postures may restrict ROM because of the tightening and shortening of the muscle tissue.
For example, joggers and people who sit behind a desk for long periods need to stretch the
hamstrings and low back muscles to counteract the tautness developed in these muscle
groups.
Does warming up affect flexibility?

Although active warm-up exercises such as walking, jogging, and stair climbing increase
muscle temperature and decrease muscle stiffness, warming up alone does not increase ROM
(deWeijer, Gorniak, and Shamus 2003; Shrier and Gossal 2000). Female taekwondo athletes
who underwent active and passive warm-up protocols did not increase ROM of the plantar
flexors, but those in the static stretching group did (Nuri, Ghotbi, and Faghihzadeh 2013).
The effect of combining stretching and active warm-up on passive resistance to stretch has
not been studied extensively. Some studies, however, have shown that active warm-up
combined with either static or dynamic stretching is more effective than stretching alone in
increasing the length of the hamstring muscles (deWeijer, Gorniak, and Shamus 2003), in
improving ROM (Murphy et al. 2010; Perrier, Pavol, and Hoffman 2011; Shrier and Gossal
2000), and in decreasing passive resistance (Magnusson et al. 2000). In fact, Murphy and
associates (2010) reported that 5 min of running before and after static stretching resulted in
greater increases in hip flexor ROM than did static stretching alone or 10 min of running
prior to static stretching. The improvement in ROM lasted for 30 min.
Can you develop too much flexibility?

It is important to recognize that excessive amounts of stretching and flexibility training may
result in hypermobility, or an increased ROM of joints beyond normal, acceptable values.
Hypermobility leads to joint laxity (looseness or instability) and may increase one’s risk of
musculoskeletal injuries. For example, it is not uncommon for gymnasts and swimmers to
experience shoulder dislocations because of joint laxity and hypermobility. As an exercise
specialist, you need to be able to accurately assess ROM and to design stretching programs
that improve your clients’ flexibility without compromising joint stability.
ASSESSMENT OF FLEXIBILITY
Field and clinical tests are available for assessing static flexibility. Although ROM data are
important, measures of dynamic flexibility (i.e., joint stiffness and resistance to movement)
may be more meaningful in terms of physical performance. Dynamic flexibility tests measure
504
the increase in resistance during muscle elongation; several studies have shown that less stiff
muscles use elastic energy more effectively during movements involving the stretch-
shortening cycle (Kubo et al. 2000; Kubo, Kawakami, and Fukunaga 1999). However,
dynamic flexibility testing is limited to the research setting because the equipment is
expensive. Typically, static flexibility is assessed in field and clinical settings by direct or
indirect measurement of the ROM.
DIRECT METHODS OF MEASURING STATIC FLEXIBILITY

To assess static flexibility directly, measure the amount of joint rotation in degrees using a
goniometer, electrogoniometer, flexometer, or inclinometer. The following sections describe
the procedures for these tests.
General Guidelines for Flexibility Testing
To assess a client’s flexibility, you should select a number of test items because of the highly
specific nature of flexibility (Dickinson 1968; Harris 1969). Direct tests that measure the
range of joint rotation in degrees are usually more useful than indirect tests that measure
static flexibility in linear units. When administering these tests,
have the client perform a general warm-up followed by static stretching prior to the
test, avoiding fast, jerky movements and stretching beyond the pain-free range of
joint motion;
administer three trials of each test item;
compare the client’s best score against norms in order to obtain a flexibility rating
for each test item; and
use the test results to identify joints and muscle groups in need of improvement.
Universal Goniometer and Digital Goniometer Test Procedures

The universal goniometer is a protractor-like device with two steel or plastic arms that
measure the joint angle at the extremes of the ROM (see figure 10.1). The stationary arm of
the goniometer is attached at the zero line of the protractor, and the other arm is movable.
To use the goniometer, place the center of the instrument so it coincides with the fulcrum, or
axis of rotation, of the joint. Align the arms of the goniometer with bony landmarks along the
longitudinal axis of each moving body segment. Measure the ROM as the difference between
505
the joint angles (degrees) at the extremes of the movement.
FIGURE 10.1 Measuring range of motion at the knee joint using a universal goniometer.
Inexpensive digital goniometers are now readily available (see figure 10.2). The testing
procedure is identical to that of the universal goniometer, but the device provides a digital
ROM value rather than requiring the practitioner to read the result from the protractor-like
dial. No significant differences were found between universal and digital goniometers when
five therapists measured five joint motions on each of six patient models (Carey et al. 2010).
They concluded that digital goniometers have acceptable criterion-related validity for
measuring joint ROM, and the inter- and intrarater reliability is equivalent to the universal
goniometer. Furthermore, the digital display might reduce reading errors.
506
FIGURE 10.2 Measuring range of motion at the elbow joint using a digital goniometer.
Dale R. Wagner.
Table 10.2 summarizes procedures for measuring ROM for various joints using a universal
goniometer. The American College of Sports Medicine (ACSM; 2018) recommends using
goniometers to obtain precise measurement of joint ROM. For more detailed descriptions of
these procedures, see Greene and Heckman 1994 and Norkin and White 1995. Table 10.3
presents average ROM values for healthy adults.
507
00:00 / 00:00
Video 10.1
00:00 / 00:00
Video 10.2
508
00:00 / 00:00
Video 10.3
509
Electrogoniometer Test Procedures
An electrogoniometer consists of one or two flexible potentiometers, or strain gauges,
between two end-blocks. One block is placed on the stationary body segment while the other
is designed for the movable segment. The blocks are affixed to the skin with double-sided
tape. The center of the flexible cable housing the potentiometers should be placed over the
center of the joint (see figure 10.3). Once in place, the client simply moves the limb, and the
voltage output from the potentiometer varies depending on the joint angle.
Electrogoniometers are available in different sizes to accommodate the size of the person and
joint to be tested.
510
FIGURE 10.3 Measuring range of motion at the elbow joint using an electrogoniometer.
Dale R. Wagner.
Electrogoniometers offer several advantages over universal goniometers and digital

goniometers. First, they are flexible, not rigid like universal and digital devices. Second, they
are much easier to use because the technician’s hands are free. Third, with a two-channel
electrogoniometer, measurements can be made in two planes simultaneously. For example,
wrist flexion and extension can be measured at the same time as radial and ulnar deviation.
Several researchers have evaluated electrogoniometers against motion analysis and have
concluded that they provide a high degree of reliability and validity for measuring joint ROM
(Bronner, Agraharasamakulam, and Ojofeitimi 2010; Urwin et al. 2013).
Researchers are experimenting with wearable goniometers so that joint angles can be
measured in real time while clients are performing daily tasks. Tognetti and colleagues (2014,
2015) described a textile-based goniometer with an electrically insulated layer sandwiched
between two layers of knitted piezoresistive fabrics. The resistance difference between the two
layers is measured. When the sensor is flat, the resistance difference is zero, but when the
sensor is flexed the resistance difference is proportional to the angle it is being bent. Output
from the textile goniometer and a triaxial accelerometer are fused to provide ambulatory
ROM data (Tognetti et al. 2015).
Flexometer Test Procedures

Another tool you can use to measure ROM is the Leighton flexometer (see figure 10.4). This
device consists of a weighted 360° dial and weighted pointer. The ROM is measured in
relation to the downward pull of gravity on the dial and pointer. To use this device, strap the
instrument to the body segment, and lock the dial at 0° at one extreme of the ROM. After
511
the client executes the movement, lock the pointer at the other extreme of the ROM. The
degree of arc through which the movement takes place is read directly from the dial. Tests
have been devised to measure the ROM at the neck, trunk, shoulder, elbow, radioulnar, wrist,
hip, knee, and ankle joints using the Leighton flexometer (Hubley-Kozey 1991; Leighton
1955).
FIGURE 10.4 Measuring range of motion at the elbow joint using a Leighton flexometer.
Inclinometer Test Procedures

The inclinometer is another type of gravity-dependent goniometer (see figure 10.5). To use
this device, hold it on the distal end of the body segment. The inclinometer measures the
angle between the long axis of the moving segment and the line of gravity. This device is
easier to use than the flexometer and universal goniometer because it is held by hand on the
moving body segment during the measurement and does not have to be aligned with specific
bony landmarks. The American Medical Association (1988) recommends the double-
inclinometer technique, using two inclinometers, to measure spinal mobility (see figure 10.5).
00:00 / 00:00
Video 10.4
512
FIGURE 10.5 Measuring lumbosacral flexion using the double-inclinometer technique.
As is the case with goniometers, digital inclinometers are now commonly available and
might reduce reading error.
Validity and Reliability of Direct Measures

The validity and reliability of these devices for directly measuring ROM are highly dependent
on the joint being measured and technician skill. Radiography is considered to be the best
reference method for establishing validity of goniometric measurements. Research shows high
agreement between ROM measured by radiographs and universal goniometers for the hip
and knee joints (Ahlback and Lindahl 1964; Enwemeka 1986). Mayer, Tencer, and
Kristoferson (1984) reported no difference between radiography and the double-inclinometer
technique for assessing spinal ROM of patients with low back pain.
The intratester and intertester reliabilities of goniometric measurements are affected by
difficulty in identifying the axis of rotation and palpating bony landmarks. Measurements of
upper extremity joints are generally more reliable than ROM measurements of the lower
extremity joints (Norkin and White 1995). Generally, the inclinometer reliably measures
ROM at most joints; however, the intertester reliability of inclinometer measurements is
variable and joint specific. Studies have reported reliability coefficients ranging from .48 for
lumbar extension (Williams, Binkley, et al. 1993) to .96 for subtalar joint position (Sell et al.
1994). Also, the intrarater reliabilities for inclinometer measurements of the flexibility of the
iliotibial band (hip adduction) and for ROM measurements of the lumbar spine and lordosis
generally exceed .90 (Ng et al. 2001; Reese and Bandy 2003). To obtain accurate and reliable
ROM measurements, you need a thorough knowledge of anatomy and of standardized
testing procedures, as well as training and practice to develop your measurement techniques.
INDIRECT METHODS OF MEASURING STATIC FLEXIBILITY

Because of the belief that lack of flexibility is associated with low back pain and
musculoskeletal injuries, most health-related fitness test batteries include a sit-and-reach test
513
to evaluate the static flexibility of the lower back and hamstring muscles (Payne et al. 2000).
The sit-and-reach test provides an indirect linear measurement of the ROM. Several sit-and-
reach protocols have been developed using either a yardstick (meter stick) or a box, or both,
to measure flexibility in inches or centimeters.
Although some fitness professionals assume the sit-and-reach is a valid measure of low
back and hamstring flexibility, research has shown these tests to be moderately related to
hamstring flexibility (r = .39-.89) but poorly related to low back flexibility (r = .10-.59) in
children (Patterson at al. 1996), adults (Hui et al. 1999; Hui and Yuen 2000; Jackson and
Langford 1989; Martin et al. 1998; Minkler and Patterson 1994), and older adults (Jones et
al. 1998). Moreover, in a prospective study of adults, Jackson and colleagues (1998) reported
that the sit-and-reach test has poor criterion-related validity and is unrelated to self-reported
low back pain. Likewise, Grenier, Russell, and McGill (2003) noted that sit-and-reach test
scores do not relate to a history of low back pain or discomfort in industrial workers.
Although sit-and-reach scores were moderately related (r = .42) to lumbar ROM in the
sagittal plane, the sit-and-reach test could not distinguish between workers who had low back
discomfort and workers who did not. The researchers concluded that standard fitness test
batteries should include measures of lumbar ROM instead of the sit-and-reach test to assess
low back fitness. Lumbar ROM in the sagittal plane can be measured directly with an
inclinometer (double-inclinometer technique, see figure 10.5) or indirectly with the skin
distraction test. (See the Skin Distraction Test section later in this chapter.)
SMARTPHONE AS AN INCLINOMETER
Several smartphone applications (apps) are available, free or for a nominal charge, allowing the phone to be used as an
inclinometer. Many of the apps used to measure ROM use the smartphone’s built-in accelerometers, but some also use
magnetometers and others are photographic based (Milani et al. 2014). Several research teams have investigated the
reliability and validity of these apps, with promising results (Charlton et al. 2015; Vohralik et al. 2015; Wellmon et al.
2016). Wellmon and colleagues (2016) demonstrated that the inherent measurement error (independent of client factors)
due to the smartphone, installed apps, and examiner skill is <2° of measurement variability. Vohralik and associates
(2015) compared an app to a digital inclinometer and found it to be a reliable and valid measure of ankle dorsiflexion.
Charlton and colleagues (2015) created a custom app and evaluated it against a bubble inclinometer and a nine-camera
3D motion analysis system. The reliability and validity of the smartphone app was comparable to the bubble
inclinometer, and the validity compared to the motion analysis was excellent (ICCs >.88) for six of seven hip movements.
The consensus is that bubble and digital inclinometers are preferable if available, but if not, smartphone apps provide a
reliable and valid method of assessing ROM. Search for “goniometer” or “inclinometer” on iTunes or Google Play for an
app that meets your needs.
Although research affirms that the sit-and-reach test does not validly measure low back
514
flexibility, it may still be used to provide an indirect measure of hamstring length. Davis and
colleagues (2008) reported that sit-and-reach scores were moderately related to other
measures of hamstring length such as sacral angle (r = .65), knee extension angle (r = .57),
and straight leg raise (r = .65). However, in a study of 141 young male athletes, Muyor and
colleagues (2014) found that the sit-and-reach test had only low to moderate validity as a
measure of hamstring flexibility. In fact, pelvic tilt and lumbar flexibility explained the
greatest amount of variability in sit-and-reach scores, and hamstring extensibility had very
little influence. Sit-and-reach tests should be limited to identifying individuals at the
extremes who may have a higher risk of muscle injury because of hypermobility or lack of
flexibility in the hamstring muscles.
The following sections describe the protocols for various types of sit-and-reach tests as well
as the skin distraction test. Before clients take any of these tests, have them perform a general
warm-up to increase muscle temperature, as well as stretching exercises for the muscle groups
to be tested. When monitoring your clients’ progress using these tests, be certain to record
and to standardize the time of testing. Time of day may affect modified sit-and-reach test
performance, with higher scores achieved later in the day (Guariglia et al. 2011). Unless
otherwise stated, have your clients remove their shoes for all sit-and-reach test protocols.
Standard Sit-and-Reach Test

The ACSM (2018) and the Canadian Society for Exercise Physiology (2013) recommend
using the standard sit-and-reach test to assess hip and hamstring flexibility. This test uses a
sit-and-reach box with a zero point at 26 cm. Have clients sit on the floor with their knees
extended and the soles of their feet against the edge of the box. The inner edges of the soles
of the feet must be 6 in. (15.2 cm) apart. Instruct clients to keep their knees fully extended,
arms evenly stretched, and hands parallel with the palms down (fingertips may overlap) as
they slowly reach forward as far as possible along the top of the box. Have clients hold this
position for ~2 sec. Advise your clients that lowering the head maximizes the distance
reached. The client’s score is the most distant point along the top of the box that the
fingertips contact. If the client’s knees are flexed or motion is jerky or bouncing, do not count
the score. Administer two trials and record the maximum score to the nearest 0.5 cm. Table
10.4 presents age-gender norms for this test.
515
V Sit-and-Reach Test
The V sit-and-reach, also known as the YMCA sit-and-reach test, uses a yardstick instead of
a box. Secure the yardstick to the floor by placing tape (12 in. long) at a right angle to the 15
in. (38 cm) mark on the yardstick. The client sits, straddling the yardstick, with the knees
extended (but not locked) and feet spread 12 in. (30.5 cm) apart. The heels of the feet touch
the tape at the 15 in. mark. Instruct the client to reach forward slowly and as far as possible
along the yardstick while keeping the two hands parallel (fingertips may overlap) and to hold
this position momentarily (~2 sec). Make certain the knees do not flex and that the client
does not lead with one hand. The score (in centimeters or inches) is the most distant point on
the yardstick contacted by the fingertips. Table 10.5 presents percentile ranks for the V sit-
and-reach test.
516
Modified Sit-and-Reach Test
To account for a potential bias due to limb-length differences (i.e., individuals who have
short legs relative to the trunk and arms may have an advantage when performing the
standard sit-and-reach test), Hoeger (1989) developed a modified sit-and-reach test that
takes into account the distance between the end of the fingers and the sit-and-reach box and
uses the finger-to-box distance as the relative zero point. This test uses a 12 in. (30.5 cm) sit-
and-reach box (see figure 10.6). The client sits on the floor with buttocks, shoulders, and
head in contact with the wall; extends the knees; and places the soles of the feet against the
box. A yardstick is placed on top of the box with the zero end toward the client. Keeping the
head and shoulders in contact with the wall, the client reaches forward with one hand on top
of the other, and the yardstick is positioned so that it touches the fingertips. This procedure
establishes the relative zero point for each client. As you firmly hold the yardstick in place,
the client reaches forward slowly, sliding the fingers along the top of the yardstick. The score
(in inches) is the most distant point on the yardstick contacted by the fingertips. Table 10.6
provides age-gender percentile norms for the modified sit-and-reach test.
00:00 / 00:00
517
Video 10.5
FIGURE 10.6 Modified sit-and-reach test.
Research comparing the standard and modified sit-and-reach test scores indicated that
individuals with proportionally longer arms than legs (lower finger-to-box distance) had
significantly better scores on the standard sit-and-reach test than those with moderate or
high finger-to-box distances; in contrast, the modified sit-and-reach test scores did not differ
significantly among the three groups (Hoeger et al. 1990; Hoeger and Hopkins 1992).
Similar to the traditional sit-and-reach test, the modified sit-and-reach test was only
moderately related to criterion measures of hamstring flexibility and poorly related to low
back flexibility (Hui et al. 1999; Minkler and Patterson 1994). Consequently, it appears that
the validity of the modified sit-and-reach test is no better than that of the standard sit-and-
reach test for assessing flexibility of the low back and hamstring muscle groups.
Back-Saver Sit-and-Reach Test

The standard, modified, and V sit-and-reach tests require the client to stretch the hamstring
518
muscles of both legs simultaneously, causing some discomfort when the anterior portions of
the vertebrae are compressed during the stretch. The back-saver sit-and-reach test was
devised to relieve some of this discomfort by measuring the flexibility of the hamstring
muscles one leg at a time. Instruct the client to place the sole of the foot of the extended
(tested) leg against the edge of the sit-and-reach box and to flex the untested leg, placing the
sole of that foot flat on the floor 2 to 3 in. (5-8 cm) to the side of the extended (tested) knee
(see figure 10.7). Then follow the instructions for the standard sit-and-reach test to
determine the client’s flexibility score for each leg.
FIGURE 10.7 Back-saver sit-and-reach test.
Research suggests that the validity of this test (r = .39-.71) is similar to that of the standard
sit-and-reach test (r = .46-.74) for assessing hamstring flexibility of men and women (Hui
and Yuen 2000; Jones et al. 1998). Chillon and colleagues (2010) noted, however, that hip
angle explained 42% of the variance in back-saver sit-and-reach test scores of adolescents.
Lumbar angle and thoracic angle explained an additional 30% and 4% of the variance,
respectively. These findings suggest that, in adolescents, the back-saver sit-and-reach test
may provide a valid measure of hip and low back flexibility. Norms for this test are available
elsewhere (see Cooper Institute for Aerobics Research 1992).
Modified Back-Saver Sit-and-Reach Test

While performing the back-saver sit-and-reach test, some participants may complain about
the uncomfortable position of the untested leg. Hui and Yuen (2000), therefore, modified
this test by having the client perform a single-leg sit-and-reach on a 12 in. (30.5 cm) bench
(see figure 10.8). Instruct the client to place the untested leg on the floor with the knee flexed
at a 90° angle. Align the sole of the foot of the tested leg with the 50 cm mark on the meter
rule. Then follow the instructions for the standard sit-and-reach test to determine the client’s
519
hamstring flexibility for each leg. Hui and Yuen (2000) reported that the validity of this test
(r = .50-.67) for assessing hamstring flexibility was similar to that of the standard (r = .46-.53)
and V (r = .44-.63) sit-and-reach tests. The modified back-saver test, however, was rated as
the most comfortable compared with the other test protocols. Norms for this test have not yet
been established.
00:00 / 00:00
Video 10.6
FIGURE 10.8 Modified back-saver sit-and-reach test.
Skin Distraction Test

The modified Schober test (MacRae and Wright 1969) and the simplified skin distraction
test (Van Adrichem and van der Korst 1973) are useful in assessing low back flexibility.
These field tests are reliable and have good agreement with radiographic measurements of
spinal flexion and extension (Williams, Binkley, et al. 1993). For the simplified skin
distraction test, place a 0 cm mark on the midline of the lumbar spine at the intersection of a
horizontal line connecting the left and right posterior superior iliac spines while the client is
standing erect. Place a second mark 15 cm (5.9 in.) superior to the 0 cm mark (see figure
520
10.9). As the client flexes the lumbar spine, these marks move away from each other; use an
anthropometric tape measure to measure the new distance between the two marks. The
lumbar flexion score is the difference between this measurement and the initial length
between the skin markings (15 cm). In a group of 15 to 18 yr old subjects, the simplified skin
distraction scores averaged 6.7 ± 1.0 cm in males and 5.8 ± 0.9 cm in females. However,
normal values for other age groups are not yet available. You can also use this technique to
measure lumbar spinal extension (simplified skin attraction test) by having the client extend
backward and measuring the difference between the initial length and the new distance
between the superior and inferior skin markings.
FIGURE 10.9 Measuring lumbosacral flexion using the simplified skin distraction test.
LUMBAR STABILITY TESTS

Lumbar instability increases the risk of developing low back pain. The primary muscle groups
responsible for stabilizing the lumbar spine are the trunk extensors (erector spinae), trunk
flexors (rectus abdominis and abdominal oblique muscles), and lateral flexors (quadratus
lumborum). Research indicates that muscle endurance is more protective than muscle
strength for reducing low back injury (McGill 2001). To evaluate the balance in the isometric
endurance capabilities of these muscle groups in healthy individuals, McGill, Childs, and
Liebenson (1999) used three tests: trunk extension, trunk flexion, and side bridge.
To measure the isometric endurance of the trunk extensors, have your client lie prone with
the lower body secured (use straps) to the test bed at the ankles, knees, and hips and with the
upper body extended over the edge of the bed. The test bed should be approximately 25 cm
(10 in.) above the surface of the floor. During the test, the client holds the upper arms across
521
the chest, with the hands resting on the opposite shoulders. Instruct your client to assume
and maintain a horizontal position above the floor for as long as possible. Use a stopwatch to
record in seconds the time from which the client assumes the horizontal position until the
upper body contacts the floor.
To measure the isometric endurance of the trunk flexors, have your client sit on a test
bench with a movable back support set at a 60° angle. The client flexes the knees and hips to
90° and folds the arms across the chest. Use toe straps to secure the client’s feet to the test
bench. Instruct your client to maintain this body position for as long as possible after you
lower or remove the back support. End the test when the client’s trunk falls below the 60°
angle. Use a stopwatch to record in seconds the elapsed time.
To measure the isometric endurance of the lateral flexors, use the side bridge. Ask your
client to assume a side-lying position on a mat, with the legs extended. The top foot should
be placed in front of the lower foot for support. Instruct your client to lift the hips off the mat
while supporting the body in a straight line on one elbow and the feet for as long as possible.
The client should hold the uninvolved arm across the chest. End the test when the client’s
hips return to the mat. Use a stopwatch to record in seconds the elapsed time. Administer
this test for both the right and left sides of the body.
Refer to McGill, Childs, and Lieberson (1999) for gender-specific means for the isometric
endurance of the trunk extensor, trunk flexor, and lateral flexor muscle groups. Additionally,
refer to table 6.3 for normative data for forearm planks. You can use these reference values to
evaluate lumbar stability and to set training goals for your clients.
FLEXIBILITY TESTING OF OLDER ADULTS

Flexibility is an important component of the functional fitness of older individuals. Older
adults need to safely perform activities of daily living (ADLs) in order to maintain their
functional independence as they age. Flexibility facilitates ADLs such as getting in and out of
a car or chair, dressing, and bathing. The Senior Fitness Test, developed by Rikli and Jones
(2013), includes two measures of flexibility for older adults—the chair sit-and-reach and the
back scratch tests.
CHAIR SIT-AND-REACH TEST

Many older individuals have difficulty performing sit-and-reach tests because functional
limitations (e.g., low back pain and poor ROM) prevent them from getting down to and up
from the floor. Jones and colleagues (1998) devised a chair sit-and-reach test that is similar to
522
the back-saver protocol (see figure 10.8) in that it tests only one leg, thereby reducing stress
on the spine and lower back. Compared with standard (r = .71-.74) and back-saver (r =
.70-.71) sit-and-reach protocols, the chair test yielded similar criterion-related validity
coefficients (r = .76-.81) as a measure of hamstring flexibility in older (>60 yr) men and
women. Table 10.7 presents age-gender norms for the chair sit-and-reach test.
Purpose: Assess lower body (hamstring) flexibility.
Application: A measure of the ability to perform ADLs such as climbing stairs and
getting in and out of a car, chair, or bathtub.
Equipment: You will need a folding chair that has a seat height of 17 in. (43 cm) and
that will not tip forward, as well as an 18 in. (46 cm or half a yardstick) ruler.
Test procedures: Place the folding chair against a wall for stability, and have your client sit
on the front edge of the seat. The client extends the leg being tested in front of the
hip, with the heel on the floor and the ankle dorsiflexed approximately 90°. The client
flexes the untested leg so that the sole of the foot is flat on the floor about 6 to 12 in.
(15-30.5 cm) to the side of the body’s midline. With the extended leg as straight as
possible and the hands on top of each other (palms down), the client slowly bends
forward at the hip joint, keeping the spine as straight as possible and the head in
normal alignment (not tucked) with the spine (see figure 10.10). The client reaches
down the extended leg, trying to touch the toes, and holds this position for 2 sec.
Place the ruler parallel to the client’s lower leg, and administer two practice trials
followed by two test trials.
Scoring: The middle of the big toe (medial aspect) at the end of the shoe represents a
zero score. Reaches short of the toes are recorded as minus scores; reaches beyond the
toes are recorded as plus scores. Record the best score to the nearest half inch and
compare it against the norms in table 10.7.
523
FIGURE 10.10 Chair sit-and-reach test.
524
BACK SCRATCH TEST
Limited ROM in the upper body, especially in the shoulder joints, may cause painful
movement and increase the chance of injury during performance of common tasks such as
putting on and taking off clothes. The back scratch test appears to have good construct
validity, as evidenced by its ability to detect declines in shoulder flexibility across age groups
(60-90 yr) (Rikli and Jones 1999). Table 10.8 presents age-gender norms for the back scratch
test.
Purpose: Assess upper body (shoulder joint) flexibility.
Application: A measure of the ability to perform ADLs such as combing hair, dressing,
and reaching for a seat belt.
Equipment: You will need an 18 in. (46 cm) ruler.
Test procedures: Ask your client to reach, with the preferred hand (palm down and fingers
extended), over the shoulder and down the back while reaching around and up the
middle of the back with the other hand (palm up and fingers extended) (see figure
525
10.11). Allow the client to choose the best, or preferred, hand through trial and error.
Administer two practice trials followed by two test trials.
Scoring: Use the ruler to measure the overlap (plus score) or gap (minus score) between
the middle fingers of each hand. If the fingers just touch each other, record a zero.
Record the best score to the nearest half inch, and compare this value against the
norms in table 10.8.
526
FIGURE 10.11 Back scratch test.
Key Points
Static flexibility is a measure of the total ROM at the joint.
Dynamic flexibility is a measure of the rate of torque or resistance developed during movement through the
ROM.
Flexibility is highly joint specific, and the ROM depends, in part, on the structure of the joint.
Lack of physical activity is a major cause of inflexibility.
A universal goniometer, electrogoniometer, flexometer, or inclinometer can be used to obtain direct

measures of ROM.
A yardstick and anthropometric tape measure can be used to obtain indirect measures of ROM.
Sit-and-reach tests are only moderately related to hamstring flexibility and poorly related to low back
flexibility.
The chair sit-and-reach and the back scratch tests can be used to assess flexibility of older adults.
Lumbar instability increases risk of developing low back pain.
527
Muscle endurance is more protective than muscle strength for reducing low back injury.
Key Terms
ankylosis
biaxial joint
contracture
dynamic flexibility
elastic deformation
electrogoniometer
flexibility
flexometer
goniometer
hypermobility
inclinometer
joint laxity
nonaxial joint
range of motion (ROM)
static flexibility
static stretching
stress relaxation
triaxial joint
uniaxial joint
viscoelastic properties
viscous deformation
Review Questions
1. Why are flexibility tests included in most health-related fitness test batteries?
2. Identify and explain how morphological factors affect range of joint motion.
3. How do age, gender, and physical activity (or lack thereof) affect flexibility?
4. Identify and briefly describe three direct methods for measuring static flexibility.
5. Do sit-and-reach tests yield valid measures of hamstring and low back flexibility? Explain.
6. Is the modified sit-and-reach test more valid than the standard sit-and-reach test for assessing hamstring
528
and low back flexibility?
7. Describe three tests that can be used to evaluate lumbar stability.
8. Describe two tests that indirectly measure the flexibility of older adults.
529
CHAPTER 11
Designing Programs for Flexibility and Low Back

Care
KEY QUESTIONS
How do training principles apply to the design of flexibility training programs?
Are all methods of stretching safe and effective for improving flexibility?
What are the recommended guidelines for designing a stretching program?
How do you individualize flexibility programs to meet the goals and abilities of each client?
How often does a client need to exercise to improve flexibility?
Is there an optimal combination of stretch duration and repetitions for improving range of motion?
Can low back syndrome be prevented?
What exercises are recommended for low back care?
Flexibility training is a systematic program of stretching exercises that progressively increases

the range of motion (ROM) of joints over time. It is well documented that stretching
improves flexibility and ROM. Generic exercise prescriptions for improving flexibility are not
recommended; flexibility programs should be individualized to address the needs, abilities,
and physical activity interests of each client. Your clients’ flexibility assessments (see chapter
10) can help you focus on the joints and muscle groups needing improvement. Lifestyle
assessments (see appendix A.5) can help identify muscle groups and body parts with limited
joint mobility caused by habitual body postures (e.g., sitting at a desk for long times at work)
or repetitive movement patterns during exercise (e.g., jogging).
This chapter presents guidelines for designing flexibility programs. Basic training
principles are applied to developing flexibility programs. The chapter compares various
methods of stretching and addresses questions about the flexibility exercise prescription. In
addition, it presents approaches and recommendations for designing low back care programs.
TRAINING PRINCIPLES
530
The principles of overload, specificity, progression, and interindividual variability (see the
Basic Principles for Exercise Program Design section in chapter 3) apply to flexibility
programs. Flexibility is joint specific (Cotten 1972; Harris 1969; Munroe and Romance
1975); to increase the ROM of a particular joint, select exercises that stretch the appropriate
muscle groups (i.e., apply the specificity principle). Review your anatomy and kinesiology,
particularly muscle origins and insertions, joint structures and functions, and agonist-
antagonist muscle pairs. For excellent anatomical illustrations of muscles stretched during the
performance of a variety of flexibility exercises, see Nelson and Kokkonen (2014). To improve
ROM at a joint, your clients must overload the muscle group by stretching the muscles
beyond their normal resting length but not beyond the pain-free ROM. The pain-free ROM
varies among individuals (interindividual variability principle), depending on their stretch
tolerance (the amount of resistive force to stretch within target muscles that a person can
tolerate before experiencing pain) and their perception of stretch and pain (Magnusson 1998;
Shrier and Gossal 2000). Periodically your clients will need to increase the total time of
stretching by increasing the duration or number of repetitions of each stretch in order to
ensure the overload required for further ROM improvements (progression principle).
STRETCHING METHODS
Traditionally, four stretching methods have been used to improve ROM: ballistic, slow static,
dynamic, and proprioceptive neuromuscular facilitation. Ballistic stretching uses jerky,
bouncing movements to lengthen the target muscles, whereas static stretching uses slow,
sustained muscle lengthening to increase ROM. Dynamic stretching is typically performed
with slow movements that are repeated several times, producing an increased range of joint
motion. Commonly used proprioceptive neuromuscular facilitation (PNF) stretching
techniques involve maximal or submaximal contractions (isometric or dynamic) of target
(agonist) and opposing (antagonist) muscle groups followed by passive stretching of the target
muscles (Chalmers 2004).
Stretching techniques are classified as active, passive, or active-assisted. In active
stretching, the client moves the body part without external assistance (i.e., voluntarily
contracts the muscle). In passive stretching, the client relaxes the target muscle group as the
body part is moved by an assistant (e.g., partner, personal trainer, physical therapist, or
athletic trainer). In active-assisted stretching, the client moves the body part to the end of its
active ROM and the assistant then moves the body part beyond its active ROM. Table 11.1
summarizes the advantages and disadvantages of stretching methods. The following questions
531
address issues you should consider when selecting a stretching method for a client’s flexibility
program.
Which method of stretching is best for improving ROM?
All four stretching methods (ballistic, dynamic, slow static, and PNF) produce acute and
chronic gains in flexibility and ROM at the knee, hip, trunk, shoulder, and ankle joints
(Thacker et al. 2004; Mahieu et al. 2007). Although slow static stretching is considered safer
than ballistic or PNF stretching and is easier to perform because it does not require special
equipment or an assistant, each stretching method has its proponents. Proprioceptive
neuromuscular facilitation stretching is frequently used in sport and rehabilitation settings,
while ballistic and dynamic stretching are often preferred by those engaging in explosive
activities.
Konrad, Stafilidis, and Tilp (2016) compared dorsiflexion ROM before and after four
repetitions of 30 sec of either static, ballistic, or PNF stretching. All stretching groups
increased ROM and decreased passive resistive torque, with no clinically relevant difference
between stretching groups. Likewise, Maddigan, Peach, and Behm (2012) noted that static
stretching, traditional assisted PNF, and unassisted PNF (using a stretch strap) produce
similar gains in static (active and passive) ROM and dynamic ROM of the hip flexors. In an
extensive review of static, dynamic, and PNF stretching research, Behm et al. (2016)
concluded it was not possible to rank one stretching method over another for increasing
ROM. Furthermore, each stretching method has distinct loading characteristics that likely
influence the specific mechanisms responsible for increasing ROM. These findings suggest
that all types of stretching should be considered for training and rehabilitation programs.
Therefore, choose a method that meets each client’s specific abilities (e.g., stretch tolerance
and pain threshold), needs, and long-term goals.
532
What are some of the commonly used PNF stretching techniques, and how are they
performed?
Various PNF techniques use different combinations of dynamic (concentric and eccentric)
and isometric contraction of target and opposing muscle groups. The contract-relax (CR)
and contract-relax agonist contract (CRAC) techniques are common PNF procedures. In
the CR technique, the client first isometrically contracts the target muscle group; this is
immediately followed by slow, passive stretching of the target muscle group. The first two
steps of the CRAC and CR techniques are identical except the client assists the CRAC
stretching phase by actively contracting the opposing muscle group. For example, to stretch
the pectoral muscles, the client sits on the floor and extends the arms horizontally. The client
isometrically contracts the pectoral muscles as the partner offers resistance to horizontal
flexion. Following the isometric contraction, the partner slowly stretches the pectorals as the
client actively contracts the horizontal extensors in the upper back (see figure 11.1). For
detailed explanations and illustrations of PNF and facilitated stretching techniques, see Alter
(2004) and McAtee and Charland (2007).
00:00 / 00:00
Video 11.1
533
FIGURE 11.1 Contract-relax agonist contract (CRAC) proprioceptive neuromuscular facilitation stretching technique
for the shoulder horizontal flexors.
What are the general recommendations for performing PNF stretches?

The following steps are recommended for performing PNF stretches to increase ROM:
Stretch the target muscle group by moving the joint to the end of its ROM.
Isometrically contract the stretched muscle group against an immovable resistance
(such as a partner or wall) for 5 to 10 sec.
Relax the target muscle group as you stretch it actively or passively (with a partner)
to a new point of limitation.
For the CRAC technique, contract the opposing muscle group submaximally for 5
or 6 sec to facilitate further stretching of the target muscle group.
Which PNF technique is best?
Several researchers have reported that the CRAC technique is superior to the CR technique
534
for increasing ROM (Alter 2004; Ferber, Osternig, and Gravelle 2002; Moore and Hutton
1980; Osternig et al. 1990). However, these investigators also noted that CRAC produced
larger gains in electromyographic activity, meaning that the muscle was under more tension.
Despite an increase in joint ROM, this technique may not induce muscular relaxation, and
Ferber, Osternig, and Gravelle (2002) noted that extra care should be taken when applying
this technique to older adults. Therefore, you need to consider the client’s stretch tolerance
when selecting a PNF stretching technique.
Kay, Dods, and Blazevich (2016) recently introduced a modified CR technique that
reduces the pain and risk of PNF. Instead of performing the contraction while the muscle was
stretched, the study participants returned to anatomical position for the contraction; thus, the
contraction was performed “off stretch,” resulting in a stretch-return-contract (SRC)
sequence. Compared with CR, the SRC technique was equally effective at increasing
dorsiflexion ROM and reducing both muscle and tendon stiffness, but the tensile loading of
the tendon was 10.6% less during SRC. The practical application is a reduced risk of muscle
damage and less pain with the SRC technique, yet it is equally effective as CR.
Are there any disadvantages to PNF stretching?
A major disadvantage of the PNF technique is that most of the exercises cannot be performed
alone. An assistant is needed to resist movement during the isometric contraction phase and
to apply external force during the stretching phase. Overstretching may cause injury,
especially if the assistant has not been carefully trained in the correct PNF procedures.
Assisted stretching procedures such as PNF should be carefully performed by trained clients
or exercise professionals who understand the correct procedures and the risks of incorrect
stretching (Knudson, Magnusson, and McHugh 2000).
Why is slow static stretching safer than ballistic stretching?
Some exercise professionals recommend slow static stretching over ballistic stretching because
there is less chance of injury and muscle soreness resulting from jerky, rapid movements.
Mahieu and associates (2007) observed that a 6 wk slow static stretching program produced a
significant increase in ankle dorsiflexion ROM resulting from a significant decrease in passive
resistive torque of the calf muscles. Ballistic stretching, however, had no effect on passive
resistance but produced a significant decrease in the stiffness of the Achilles tendon.
Ballistic stretching uses relatively fast bouncing motions to produce stretch. The
momentum of the moving body segment rather than external force pushes the joint beyond
its present ROM. This technique appears counterproductive for increasing muscle relaxation
535
and stretch. During the movement, the muscle spindles signal changes in both muscle length
and contraction speed. Because of a lower threshold, the spindle responds more to the speed
of the movement than to the length or position of the muscle. In fact, muscle spindle activity
is directly proportional to the speed of movement. Thus, ballistic stretching evokes the stretch
reflex, producing more contraction and resistance to stretch in the target muscle group. Also,
the muscle has viscous properties. The viscous material resists elongation more when the
stretch is applied rapidly (Taylor et al. 1990). Therefore, ballistic stretching places greater
strain on the muscle and may cause microscopic tearing of muscle fibers and connective
tissues.
In slow static stretching, the client stretches the target muscle group when the joint is at
the end of its ROM. While maintaining this lengthened position, the client slowly applies
torque to the target muscle group to stretch it further. Because the dynamic portion of the
muscle spindle rapidly adapts to the lengthened position, spindle discharge decreases. This
decrease lessens the reflex contraction of the target muscle group and allows the muscle to
relax and be stretched even further. The force needed to lengthen a muscle is affected by the
rate of stretching and by the duration over which the target muscle group is held at a specific
length (Taylor et al. 1990). Resistance to elongation is greater for rapid (e.g., ballistic)
stretching than for slow static stretching. Also, the resistance produced by the viscous
properties of the muscle decreases over time as the target muscle is held at its stretched
length. The resulting stress relaxation allows further elongation of the target muscle group
(Chalmers 2004).
How do constant-angle and constant-torque static stretching produce gains in flexibility?
The term static stretching implies that the joint angle does not change during the stretching
exercise. With constant-angle stretching, the resistance to stretch in the muscle-tendon unit
decreases as the joint is held at a constant angle. As mentioned earlier, this is referred to as
the stress relaxation response. With constant-torque stretching, the torque applied to the
muscle decreases muscle stiffness, thereby improving ROM. During constant-torque
stretching, technically the joint is not static; the joint angle increases because of the constant
pressure applied to the muscle-tendon unit, causing it to elongate. This is known as
viscoelastic creep.
In a study comparing the effects of constant-angle and constant-tension static stretching
on passive torque, passive ROM, and muscle-tendon stiffness, Herda and coworkers (2011)
reported that both forms of static stretching improve ROM; however, only constant-torque
536
stretching decreases muscle-tendon stiffness. They concluded that constant-torque stretching
changes both the viscosity and elasticity of the muscle-tendon unit, whereas constant-angle
stretching affects only the viscosity. These findings suggest that constant-torque stretching
may be better suited than constant-angle stretching for reducing risk of muscle strain when
treating injuries such as Achilles tendonitis and plantar fasciitis. More research is warranted
to fully understand the mechanisms underlying changes in muscle-tendon stiffness with
different forms of stretching.
What are the physiological mechanisms underlying flexibility gains when stretching
actively or passively?
The mechanisms responsible for flexibility gains differ for passive and active stretching. With
passive stretching, the targeted muscle does not contract. Riley and Van Dyke (2012)
discussed evidence suggesting that passive stretching produces transient reductions in muscle
stiffness due to viscoelastic relaxation lasting about 24 hr. Passively stretching the hamstrings
1 min/day for 10 consecutive days using a Biodex dynamometer produced a progressive
reduction in stiffness. To be effective, however, passive stretching needs to be performed daily
because reversion to prestretch levels begins in 24 hr. Lengthening the muscle tissue during
passive stretching affects fibroblasts in the mysium coverings wrapped around muscle fibers,
the muscle fasciculi, tendons, and connective tissues surrounding nerve fibers. The fibroblasts
respond to the mechanical stimuli of the stretch, producing changes in these connective
tissues, thereby reducing muscle-tendon stiffness.
With active stretching, the lengthened muscle contracts during the stretching exercise. The
actual length of the muscle may increase provided the muscle is stretched beyond its optimal
length needed for maximum overlap of contractile filaments and crossbridge formation. This
point in the range of joint motion at which optimal muscle length is achieved is limited by
the individual’s pain tolerance, and it depends on the optimal length of each targeted muscle
(Riley and Van Dyke 2012). Active tension (stretching plus contractile activity) is needed to
stimulate production of sarcomeres in series within the muscle fiber, thereby increasing the
actual length of the targeted muscle. This process depends on calcium-dependent pathways
(i.e., calcium from the sarcoplasmic reticulum) that regulate the number of series sarcomeres
produced in response to stretching combined with muscle contractile activity.
These findings potentially affect exercise prescriptions for flexibility programs. When the
goal of the program is to improve flexibility of clients with limited ROM due to injury,
immobilization, reduced mobility, or habitual body postures, active stretching may be more
537
beneficial than passive stretching to restore their muscle length and ROM. Flexibility
programs using passive stretching exercises should be performed daily because muscle-tendon
stiffness continuously adapts and adjusts day to day to the experienced ROM (Riley and Van
Dyke 2012).
What are the physiological mechanisms underlying the increased ROM produced by the
PNF method?
In reviews by Sharman, Cresswell, and Riek (2006) and Hindle and colleagues (2012), four
theoretical mechanisms responsible for gains in ROM from PNF stretching were identified.
These theories include autogenic inhibition, reciprocal inhibition, viscoelastic stress
relaxation, and gate control theory of pain modulation. Unfortunately, there is little empirical
evidence to support any of these mechanisms.
Autogenic inhibition refers to a reduction in the excitability of the targeted muscle because
of inhibitory signals sent from the Golgi tendon organ, or GTO, during isometric
contraction (i.e., greater GTO activation leads to reflex relaxation). Also, voluntary
contraction of opposing muscle groups during CRAC stretching was simply explained by
reciprocal inhibition (as the opposing muscle group is voluntarily contracted, the target
muscle group is reflexively inhibited). Traditionally, increases in ROM from PNF stretching
were attributed to autogenic or reciprocal inhibition or both; however, these simple
explanations are likely inadequate to explain the complex mechanisms underlying muscle
stretch.
In contrast to the neurophysiological hypotheses of autogenic and reciprocal inhibition,
more contemporary views of how PNF stretching improves ROM include the viscoelastic
properties of stretched muscle (stress relaxation hypothesis) and the ability to tolerate
stretching (gate control theory). Muscles and tendons have both elastic and viscous
properties. The amount of tension needed to elongate the musculotendinous unit (MTU) is
dictated by the elastic property of the MTU, while the viscous property resists the elongation
of a rapid stretch. However, the viscous material loses its ability to resist the elongation force
over time (stress relaxation), and the MTU will elongate if the tension is sustained. Although
this seems like a plausible theory, the change in passive torque within the muscle that
accompanies stress relaxation is short lived, lasting less than an hour after PNF stretching
(Magnusson et al. 1996).
The gate control theory, explaining what occurs when two stimuli such as pain and stretch
activate their respective receptors simultaneously, may help explain longer-term adaptations
538
from PNF stretching. Stretching a muscle beyond its natural ROM and contracting it is seen
as potentially damaging, resulting in activation of the GTOs to prevent injury. However, with
a consistent and repeated PNF protocol, the GTOs adapt, becoming more accustomed to
increased stretch and force applied to the muscle, and cause less inhibition (Hindle et al.
2012).
What is the recommended duration and intensity of the static contraction phase during
PNF stretching to maximize long-term gains in ROM?
In a review of PNF stretching studies, Sharman, Cresswell, and Riek (2006) reported that the
static contraction duration of the targeted muscle was between 3 and 15 sec. Although there
is some evidence for a correlation between longer static contraction duration and increased
ROM (Rowlands, Marginson, and Lee 2003), the ROM gains reported in the majority of
reviewed studies were independent of the duration of the static contraction. Sharman,
Cresswell, and Riek (2006) recommended the shortest duration of 3 sec. There is some
disagreement regarding the optimal intensity of the static contraction during PNF to
optimize ROM gains. Feland and Marin (2004) found no significant difference in ROM
improvements using 20%, 60%, and 100% of maximum voluntary contraction (MVC), so
Sharman, Cresswell, and Riek (2006) recommended a low intensity of 20% MVC to
minimize risk. Subsequent to this recommendation, other researchers have reported that
static contractions of 60% to 65% MVC during PNF stretching produce the greatest
improvement in ROM (Kwak and Ryu 2015; Sheard and Paine 2010). Regardless, all agree
that 100% MVC is not necessary for increasing ROM with PNF stretching. The American
College of Sports Medicine (ACSM) recommends 3 to 6 sec of 20% to 75% MVC followed
by a 10 to 30 sec assisted stretch (2018).
DESIGNING FLEXIBILITY PROGRAMS: EXERCISE

PRESCRIPTION
After assessing a client’s flexibility, you must identify the joints and muscle groups needing
improvement and select an appropriate stretching method and the specific exercises for the
exercise prescription. Appendix F.1 illustrates flexibility exercises for various regions of the
body. For additional stretching exercises, see Alter (2004), Frederick and Frederick (2017),
McAtee and Charland (2014), and Nelson and Kokkonen (2014). Follow the guidelines (see
Guidelines for Designing Flexibility Programs and Client Guidelines for Stretching
Programs later in this chapter), and be sure to address the following questions regarding the
client’s exercise prescription.
539
How many exercises should be included in a flexibility program?
A well-rounded program includes at least one exercise for each of the major muscle groups of
the body, including the neck, shoulders, upper and lower back, pelvis, hips, and legs. It is
especially important to select exercises for problem areas such as the lower back, hips, and
posterior thighs and legs. Use the results of the flexibility tests to identify specific muscle
groups with relatively poor flexibility, and include more than one exercise for these muscle
groups. The workout should take 15 to 30 min depending on the number of exercises to be
performed.
Are some stretching exercises safer than others?
Some stretching exercises are not recommended for flexibility programs because they create
excessive stress, thereby increasing the client’s chance of musculoskeletal injuries—especially
to the knee joints and low back region. Appendix F.2, Exercise Dos and Don’ts, illustrates
exercises that are contraindicated for flexibility programs and suggests alternative exercises
you can prescribe to increase the flexibility of specific muscle groups. For detailed analysis of
risk factors and options for minimizing risk for certain stretching exercises, see Alter (2004).
What is a safe intensity for stretching exercises?
The intensity of slow static stretching and PNF stretching exercises should always be below
the pain threshold of the individual. Some mild discomfort will occur, especially during PNF
exercises when the target muscle is contracted isometrically. However, as stated previously,
this contraction can be less than MVC (Kwak and Ryu 2015; Rowlands, Marginson, and Lee
2003; Sheard and Paine 2010), and the joint should not be stretched beyond its pain-free
ROM (American College of Sports Medicine 2018).
How long does each stretch need to be held?
The ACSM (2018) recommends holding a stretched position for 10 to 30 sec for most adults
and 30 to 60 sec for older adults. After a 5 wk program of static hamstring stretches for either
30, 60, 90, or 120 sec, similar benefits in passive knee extension ROM were achieved
regardless of stretch duration (Ford, Mazzone, and Taylor 2005). This suggests that holding
a stretch for 30 sec is just as effective as longer stretches at improving ROM. Depending on
the objective, it may be important to consider the intensity of the stretch in conjunction with
the duration. Freitas and colleagues (2015) had participants hold stretches for 90 sec at
maximal tolerable intensity, 135 sec at 75% intensity, and 180 sec at 50%. They concluded
that the higher-intensity stretch was best for achieving ROM gains, but the longer stretch
540
duration potentiated passive torque decrease.
Research suggests that the total stretching time in a workout may be more important than
the duration of each stretch (Cipriani, Abel, and Pirrwitz 2003; Johnson et al. 2014; Roberts
and Wilson 1999; Zakas et al. 2005). This seems to be the case regardless of whether study
participants had good ROM (Cipriani, Abel, and Pirrwitz 2003), had tight hamstrings
(Johnson et al. 2014), were young athletes (Roberts and Wilson 1999), or were older adults
(Zakas et al. 2005). Johnson and colleagues (2014) had participants perform either 10 sec
stretches for nine repetitions or 30 sec stretches for three repetitions for a total stretching
time of 90 sec, 6 days/wk for 6 wk. Both groups showed similar improvements in knee
extension ROM. Zakas and associates (2005) compared a single stretch of 60 sec against two
stretches of 30 sec and four stretches of 15 sec. The other research teams used different
stretching durations (e.g., 5 sec × 9 reps vs. 15 sec × 3 reps for a total stretching time of 45
sec, or 10 sec × 6 reps vs. 30 sec × 2 reps for a total stretching time of 60 sec). All came to the
same conclusion that similar ROM gains are achieved with multiple shorter-duration
stretches or fewer longer-duration stretches.
The findings from these studies have implications for designing flexibility programs. For
clients with a low stretch tolerance, you can prescribe shorter stretch duration (e.g., 10 sec)
and more repetitions; for those who can tolerate longer stretch durations (30 sec or more),
you can prescribe fewer repetitions.
In light of these findings, you should consider having your clients perform each stretching
exercise for a total of 45 sec to 2 min. The combination of duration and repetitions used to
reach this recommended total should be individualized to each client’s tolerance for the
sensation of stretching. For short durations, the stretch should be sustained at least 10 sec. As
flexibility improves, you can progressively overload the target muscle groups by changing
either the stretch duration (10-30 sec) or the number of repetitions so that the total time the
stretched position is held gradually increases. As your clients’ stretch tolerance improves,
consider increasing the duration and decreasing the number of repetitions of each stretch.
Remember that you must gradually increase the total stretching time for each exercise in
order to ensure overload and further improvements in ROM.
How many repetitions of each exercise should be performed?

The ACSM (2018) recommends performing two to four repetitions of each stretching
exercise and accumulating a stretching duration of 60 sec for each exercise. As flexibility
improves during the training program, the number of repetitions of each flexibility exercise
541
may be gradually increased to five or six to progressively overload the muscle group. However,
recent research indicates that a single 30 sec static stretch to maximal tolerated discomfort is
sufficient to reduce fascicle stiffness, and additional repetitions do not further affect
mechanical properties of the muscle (Opplert, Gentry, and Babault 2016). The ACSM
recommendation of 60 sec is within the 45 sec to 2 min range given in the previous
paragraph. The Opplert et al. 2016 study contradicts this recommendation, but it is
important to include this contradictory finding.
How often should flexibility exercises be performed?

The Physical Activity Guidelines for Americans states that all adults should stretch to
maintain flexibility for physical activity and performance of activities for daily living (U.S.
Department of Health and Human Services 2008). Flexibility exercises should be performed
a minimum of 2 days/wk (American College of Sports Medicine 2018), but preferably daily
(Knudson et al. 2000). Flexibility exercises should be performed after moderate or vigorous
physical activity, and they are often an integral part of the cool-down segments of aerobic
exercise and resistance training workouts.
Does stretching prior to physical activity prevent injury?
For years clinicians, coaches, and exercise practitioners have recommended stretching as part
of the warm-up. Because an active warm-up prevents injury and because stretching is
commonly included in the warm-up, one could easily, but mistakenly, conclude that
stretching prevents injury. However, the evidence as to whether stretching before exercise can
prevent injury is equivocal.
In a systematic review, Behm and colleagues (2016) identified 12 studies; 8 showed some
effectiveness of stretching. However, they noted that it is difficult to confidently attribute
injury reduction to preactivity stretching because the studies varied in design, stretch
duration, type of activity, stretching with and without warm-up, and definition of injury. The
type of activity and the joint studied might also influence the results. For example, a
stretching program to improve shoulder rotation in high school pitchers resulted in
significantly fewer shoulder injuries (as defined as inability to play for more than a week
because of shoulder symptoms) for the stretching group compared with a control group
(Shitara et al. 2017). In contrast, a review of the impact of stretching on injury prevention in
endurance runners concluded that stretching has no clinical benefit on the risk reduction of
chronic injuries in this population (Baxter et al. 2017). For more explosive activities such as
sprinting, there may be an injury prevention benefit. Stojanovic and Ostojic (2011) surmised
542
that increased flexibility likely decreases incidence of muscle strain injury in soccer players,
but they cautioned that this assumption is based on indirect evidence. Randomized controlled
investigations that support the argument that preactivity stretching prevents injury are
lacking.
There is some evidence supporting the use of preparticipation stretching for reducing risk
of muscle strains during or after performance (Chen et al. 2015; Chen et al. 2011; McHugh
and Cosgrave 2010). Chen and colleagues (2015) reported that 15 sec of static stretching
repeated six times was more effective than dynamic stretching or no stretching at reducing
muscle soreness and biomarkers of muscle damage (creatine kinase and myoglobin) following
maximal eccentric contractions. However, in several systematic reviews and meta-analyses of
interventions to minimize exercise-induced muscle soreness, the authors concluded that
stretching either before, after, or before and after exercise does not result in any clinically
relevant reductions in delayed-onset muscle soreness (DOMS) (Herbert, de Noronha, and
Kamper 2011; Torres et al. 2012). For more information about DOMS, refer to chapter 7.
Does stretching affect maximal muscle performance?
In the most extensive systematic review of this topic to date, Behm and colleagues (2016)
evaluated 125 static stretching studies, 48 dynamic stretching studies, and 11 PNF stretching
studies. Their analysis considered dose-response relationship, the performance task (power-
speed or strength), and contraction type (concentric, eccentric, or static). Overall, static
stretching and PNF stretching reduced performance by 3.7% and 4.4%, respectively, but
performance improved by 1.3% when dynamic stretching was performed before the activity.
When the static stretch totaled ≥60 sec, the decrements in performance were greater (−4.6%)
than when the total stretch duration was <60 sec (−1.1%), but there was no dose-response
relationship for PNF or dynamic stretching. However, the authors noted that the lag time
between stretching and the performance measurement was often short (3-5 min). A more
realistic time gap between stretching and athletic performance is likely ≥10 min, and the
influence stretching had on performance was trivial in studies with this lag time.
The Canadian Society for Exercise Physiology (www.csep.ca) created a position stand
based on the review by Behm and colleagues (2016). They do not recommend prolonged
(≥60 sec) static stretching if the activity will take place within 5 min of the stretch. However,
they also claim there is likely a greater benefit than cost (in terms of performance, ROM, and
injury prevention) when stretching is incorporated into an optimal preevent warm-up that
includes aerobic and task-specific dynamic activities.
543
Can vibration-aided static stretching increase flexibility?
A review of the literature (Cochrane 2013) and a meta-analysis (Osawa and Oguma 2013)
have been carried out on this topic. It is clear that adding vibration to the stretching exercise
can enhance ROM beyond what is obtainable with static stretching alone, both acutely and
over several sessions or weeks of training. The additive effects of vibration might be small but
are nevertheless significant (Osawa and Oguma 2013). Furthermore, there does not appear to
be any detrimental effect on muscle power, offering an added advantage over static stretching
without vibration (Cochrane 2013). However, vibration training has little impact on sprint
performance (Cochrane 2013).
There are still several unanswered questions regarding vibration training. First, the
mechanisms by which vibration enhances flexibility are not fully understood. There are
numerous theories, including increased stretch tolerance due to a decrease in pain sensation;
increased blood flow, which increases muscle temperature; inhibition of the antagonist;
decreased musculoskeletal stiffness; and suppression of the central nervous system (Cochrane
2013; Osawa and Oguma 2013). Second, researchers have used a variety of combinations of
vibration devices, frequencies, amplitudes, volumes, and durations. Thus, the optimal
vibration strategy to combine with stretching for increasing ROM is still unknown.
Does the flexibility exercise prescription need to be adapted for older individuals?
Range of motion decreases with age because of disuse, changes in tissue viscoelasticity, and
diseases such as arthritis. There is little doubt that stretching improves ROM in older adults
(Stathokostas et al. 2012). However, the results are mixed as to whether flexibility training
improves functional outcomes that might help older adults maintain independence. In an
extensive systematic review of this topic, Stathokostas and colleagues (2012) concluded that a
relationship between improved flexibility and functional outcomes could not yet be
established. Additionally, the results were too varied to make any clear exercise prescription
recommendations for older adults regarding the optimal type of stretch, duration, and
number of repetitions for improving flexibility or functional outcomes.
At this time, it is not possible to firmly recommend how to change program guidelines
when designing flexibility programs for older adults. Regardless of the stretching method you
use with older adults, take care not to exceed the stretch tolerance of your clients. Age-related
changes in the viscoelastic properties of muscle and connective tissue reduce the stretch
tolerance of older adults. Additionally, ordering prescribed stretches so that all floor-based
stretches are grouped together, rather than placing them between standing stretches, is
544
advisable in this population.
You can use the general guidelines presented in this section as a starting point for
designing flexibility programs. You should individualize programs to take into account client
factors such as tolerance to stretching and pain, needs, and long-range goals. For example,
shorter-duration–higher-repetition static stretching may be more appropriate for clients with
low stretch tolerance, whereas longer-duration PNF stretching may be more suitable for
athletes or for clients in injury rehabilitation programs. Also, the optimal duration, frequency,
and total time of stretching may vary among muscle groups because their viscoelastic
properties and response to the stretch stimulus may differ (Shrier and Gossal 2000). See
Sample Flexibility Program later in this chapter for a program designed for a 35 yr old
woman who wants to improve her overall flexibility. Note that this program includes more
than one exercise for muscle groups with poor to fair flexibility ratings.
Instruct clients who are engaging in stretching programs to adhere to the recommended
guidelines (see Client Guidelines for Stretching Programs).
DESIGNING LOW BACK CARE EXERCISE PROGRAMS

Low back pain frequently causes activity restrictions for middle-aged and older adults,
disabling 3 to 4 million people each year. Chronic low back pain is the number one cause of
disability in the working population (Carpenter and Nelson 1999), and the lifetime
prevalence might be as high as 84% (Almoallim et al. 2014). The safest and most effective
way to prevent and rehabilitate low back injuries remains controversial. However, in a recent
systematic review and meta-analysis, Steffens and colleagues (2016) concluded that exercise
alone or in combination with education is effective for preventing low back pain, whereas
other strategies such as shoe insoles, back belts, and education without exercise were not
effective prevention methods. This section describes two exercise approaches for low back
care programs as well as information about core stability and proper posture. The approach
you select depends on the client’s needs, health and fitness status, and training objective (e.g.,
reducing low back pain, lowering the risk of low back injury, or maximizing athletic
performance).
Husu and Suni (2012) examined the relationship between health-related fitness (i.e.,
balance, flexibility, muscle endurance, and body mass index [BMI]) and low back pain in
older adults. They reported that balance (measured by single-leg stand) and trunk flexibility
(side-bending ROM) were the most powerful predictors of low back pain. Poor fitness scores
545
for dynamic trunk extension and BMI (≥27 kg/m ) increased the risk of disability due to low
2
back pain. Tests of trunk flexibility, trunk muscle endurance, balance, and BMI may be useful
screening tools for identifying older clients with increased risk of low back pain and disorders.
Guidelines for Designing Flexibility Programs
Mode: Static, dynamic, or PNF stretching for most clients; ballistic stretching may
be useful for clients engaging in sports that involve ballistic movements
Number of exercises: 10 to 12
Frequency: Minimum of 2 days/wk, preferably daily
Intensity: Slowly stretch the muscle to a position of mild discomfort
Duration of stretch: 10 to 30 sec for static or dynamic stretching; 3 to 6 sec
contraction followed by 10 to 30 sec of assisted stretching for PNF
Repetitions: Two to four for each exercise so that the total duration of each
stretching exercise is at least 60 sec
Volume: 60 sec total stretching time for each flexibility exercise
Time: 15 to 30 min per session
Based on Kravitz and Heyward 1995; American College of Sports Medicine 2018.
Client Guidelines for Stretching Programs
Perform a general warm-up before stretching to increase body temperature and to

warm the muscles to be stretched.
Stretch all major muscle groups as well as opposing muscle groups.
Focus on the target muscle involved in the stretch, relax the target muscle, and
minimize the movement of other body parts.
Hold the stretch for 10 to 30 sec; older individuals should hold the stretch 30 to 60
sec.
Stretch to the limit (end point) of the movement, not to the point of pain.
Keep breathing slowly and rhythmically while holding the stretch.
Stretch the target muscle groups in different planes to improve overall ROM at the
joint.
Although stretching may not prevent injury or reduce muscle soreness, it is a
546
reasonable practice to include stretching exercises after the active warm-up and
cool-down phases of your exercise program (American College of Sports Medicine
2018).
TRADITIONAL APPROACH
Traditionally, low back care programs have been designed to correct improper alignment and
support of the spinal column and pelvis. Generally, a combination of stretching and
strengthening exercises is prescribed to increase (a) the ROM of the hip flexors, hamstrings,
and low back extensor muscles and (b) the strength of the abdominal muscles.
Exercise professionals have focused primarily on strengthening the abdominal muscles in
order to prevent low back pain and injury, giving little or no attention to the low back
muscles. Research, however, suggests that low back strengthening programs are effective for
relieving and preventing low back pain and injury (Carpenter and Nelson 1999). A current
practice in some low back care programs is to include exercises to increase the strength and
endurance of both the abdominal and low back extensor muscles.
SAMPLE FLEXIBILITY PROGRAM
Client Data
Age: 35 yr
Gender: Female
Program goal: Improve overall flexibility
Number of exercises: 12
Method: Static stretching
Intensity: Just below pain threshold
Duration of stretch: 10 sec
Repetitions: 4-6 per exercise
Total stretch time: 50-120 sec per exercise
Frequency: Daily
Overload: Gradually increase stretch duration or repetitions up to a maximum of 2 min per exercise
547
To strengthen the low back (lumbar extensor) muscles, pelvic stabilization is a key
requirement. If the pelvis is not stabilized during extension of the trunk, the hip extensor
548
muscles rotate the pelvis (~110°), and the lumbar vertebrae maintain their relative position to
each other (do not extend). On the other hand, when the pelvis is immobilized, the lumbar
vertebrae extend (~72°) as the low back extensor muscles contract (Carpenter and Nelson
1999). Most calisthenic-type floor exercises do not isolate the low back muscles because the
pelvis is free to move. Using a lumbar extension machine, with thigh and femur restraints to
stabilize the pelvis, prevents hip extension and isolates the low back muscles during the
movement. Exercising on a lumbar extension machine with a minimal training volume (one
set of 8-15 reps of lumbar extension exercise to fatigue per week) significantly improves
lumbar muscle strength and bone mineral density (Graves et al. 1994; Pollock, Garzarella,
and Graves 1992) and reduces the incidence of back injuries (Mooney et al. 1995).
Individuals with chronic low back pain who participate in this type of low back strengthening
program can expect significant improvements in joint mobility and muscular strength and
endurance as well as relief from pain (Carpenter and Nelson 1999).
To strengthen the abdominal muscles, select exercises that maximize the activation of the
abdominal muscles but minimize the compression (load) of the lumbar vertebrae (i.e., a high
challenge-to-compression ratio). Since the psoas muscle (prime mover for hip flexion) is a
major source of spinal loading, choose exercises that minimize the activation of this muscle,
such as bent-knee curl-ups (feet free or anchored), dynamic cross-knee curl-ups (curl-ups
with a twist), isometric side support (side bridge), and dynamic sideward curl exercises (Axler
and McGill 1997; Juker et al. 1998; Knudson 1999). The bent-knee curl-up exercise
emphasizes the rectus abdominis, while the isometric side support emphasizes the abdominal
oblique and quadratus lumborum muscles. Because of their low challenge-to-compression
ratios, the following abdominal exercises are not recommended: straight leg or bent-knee sit-
ups, supine straight leg raises, and hanging bent-knee raises (Axler and McGill 1997).
Using the traditional approach, the following exercises are recommended for low back care.
Some of these exercises are described and illustrated in appendix F.3.
Pelvic tilt (supine-lying position) to stretch the abdominal muscles

Knee-to-chest (supine-lying position) to stretch the hamstring, buttock, and low
back muscles
Trunk flex (on hands and knees) to stretch the back, abdominal, and hamstring
muscles
Lumbar extension exercises with pelvic stabilization (on machine) to strengthen the
low back extensors
549
Curl-ups, dynamic cross-knee curl-ups, and isometric side-support exercises to
strengthen the abdominal and quadratus lumborum muscles
Single-leg extension (prone-lying position) to strengthen the hamstring and
buttock muscles and to stretch the hip flexor muscles
ALTERNATIVE APPROACH
Studies suggest that the major cause of low back injury during exercise or performance of
activities of daily living is lumbar instability rather than improper alignment of the spinal
column and pelvis per se (McGill 2001). Research also indicates that muscle endurance is
more protective than muscle strength for reducing low back injury, and that greater lumbar
mobility (ROM) actually increases one’s risk of low back injury (McGill 2001, 2016). Thus,
sufficient stability of the lumbar spine (i.e., lumbar stabilization) is the major emphasis of
this approach to low back care. To measure lumbar stability, see the Lumbar Stability Tests
section in chapter 10. For detailed discussion and suggestions for applying the concept of
lumbar stabilization to low back care programs, see Bracko (2004) and Norris (2000).
To develop and maintain lumbar stability, experts (McGill 2001) recommend the
following:
Bracing the lumbar spine during activity by isometrically cocontracting the

abdominal wall and low back muscles
Maintaining a neutral spine (i.e., the natural lordotic curve in the lumbar spine
while standing upright) during activity
Avoiding end ROM positions (fully flexed or extended) of the trunk while lifting or
exercising
Performing exercises that emphasize the development of muscle endurance rather
than strength
The following sequence of exercises is specifically recommended for beginners who are
starting a low back care program. These exercises are illustrated in appendix F.3.
Cat-camel exercise to slowly and dynamically move through the full range of spinal
flexion and extension, with emphasis on spinal mobility rather than pressing and
holding the trunk position at the ends of the ROM (usually five or six cycles of this
550
exercise are sufficient)
Stretching exercises to increase mobility at the hip and knee joints
Curl-ups with one leg flexed and hands placed underneath the lumbar spine to help
in maintaining a neutral spine
Isometric side-support (side bridge) exercises for the quadratus lumborum and
abdominal oblique muscles
Single-leg extension holds (modified bird dog exercises) while on hands and knees
for the low back and hip extensor muscles
Isometric stabilization exercises requiring simultaneous contraction of the
abdominal muscles to generate an abdominal brace during performance of other
exercises
Dynamic hollowing, or drawing of the navel toward the spine, for the deeper
abdominal wall muscles (i.e., transverse abdominis and internal obliques)
The North American Spine Society (2009) recommends stretching, core strengthening,
and resistance training exercises to prevent back pain and to maintain a healthy back. To view
images for each of these exercises, go online to www.spine.org/knowyourback and select the
Prevention tab.
Neck, inner thigh, and hamstring stretches

Shoulder rolls and frontal core stretches
Backward bending
Standing thread the needle
Doorway chest stretch
Wall wash
Transverse and sagittal core strengthening
Abdominal crunch and other abdominal exercises
Neck press
Side bridge
Prone bridge or plank
CORE STABILITY TESTING AND TRAINING FOR LOW BACK
551
PAIN
There is no consensus on the definition of core and the measurement of core stability let alone
whether or not it is an effective treatment or preventive strategy for low back pain. Willson
and colleagues (2005) narrowly defined the core as the lumbopelvic hip complex. Akuthota
and associates (2008) described the core as a muscular box bordered anteriorly by the
abdominals, posteriorly by the paraspinals and gluteals, superiorly by the diaphragm, and
inferiorly by the pelvic floor and hip girdle. Behm and colleagues (2010a, 2010b) used an
even broader definition to include the entire axial skeleton and all soft tissue with a proximal
attachment to the axial skeleton. The muscles that contribute to core stability are listed in
Core Stability Muscles and Function in chapter 7. Additionally, core stability programs
should consider the sensory and motor components related to these soft tissues (Akuthota et
al. 2008). Kibler, Press, and Sciascia (2006) defined core stability as the “ability to control the
position and motion of the trunk over the pelvis to allow optimum production, transfer and
control of force and motion to the terminal segment in integrated athletic activities” (p. 189).
The Sahrmann Core Stability Test (Sahrmann 2002) described in chapter 6 is a commonly
used assessment tool. Akuthota and colleagues (2008) suggested 10 exercises that place the
body in all three planes to measure core stability, and Tidstrand and Horneij (2009) reported
good interrater reliability for the single-leg stance and sitting on a balance ball with one leg
lifted. However, all these assessments require some subjective clinical judgement. Kahraman
and colleagues (2016) recently developed a core stability assessment battery that includes
more objective measurements. They put 38 patients with nonspecific low back pain through
33 tests that might relate to core stability. The tests were categorized into five components of
core stability: strength, endurance, flexibility, motor control, and function. They selected the
tests with the highest interrater reliability in each category to create a reliable core stability
test battery (ICC ≥.90). The test battery proposed by Kahraman and colleagues (2016)
includes the following:
Partial curl-up test (strength)

Side bridge (endurance)
Trunk flexor test (endurance)
Sit-and-reach test (flexibility)
Single-leg hop (function)
Lateral step-down (function)
552
Unilateral stance test with eyes open (motor control)
Training strategies for developing core stability are as varied as the definition of the core.
Behm and colleagues (2010a, 2010b) recommended using ground-based free weight lifts
(e.g., squats, dead lifts) for training the core musculature but noted the benefit of resistance
exercises on unstable surfaces and that this type of training might decrease the incidence of
low back pain. Akuthota and colleagues (2008) proposed a core stability program that
includes abdominal bracing from a variety of postures to isolate the transversus abdominis;
quadruped arm and leg lifts (bird dog) for the paraspinals; side planks for the quadratus
lumborum and obliques; and trunk curls for the rectus abdominis.
Although there is a strong theoretical basis that core stability training will aid in the
prevention or treatment of low back pain, does it really work? After a systematic review of the
literature, Stuber and colleagues (2014) were critical of the quantity and quality of the
literature addressing this question. They noted there were very few randomized controlled
trials, precluding any conclusions about whether core stability training was better than
conventional treatment or training for reducing low back pain. Likewise, Davin and
Callaghan (2016) determined that evidence is inconclusive that stabilization exercises are
more effective than other forms of exercise for treating low back pain. In a meta-analysis with
limited studies, Wang, Zheng, and associates (2012) concluded that core stability exercise is
more effective than general exercise for reducing pain and disability in patients with chronic
low back pain in the short term, but no longer-term (>6 mo) differences in reducing pain
were observed between exercise strategies.
PILATES FOR LOW BACK PAIN

Pilates is a popular exercise method, particularly among women, that emphasizes precision of
movement. Introduced in the 1920s by Joseph Pilates, it is a blend of Greek and Roman
gymnastics, yoga, Zen meditation, and martial arts (Kloubec 2011). Pilates exercises
emphasize stabilizing the core musculature and controlling breathing before going through a
controlled ROM. The exercises can be performed on a mat or on a specialized sliding
platform with springs and pulleys called a reformer.
There is strong evidence that Pilates develops abdominal muscular endurance and posterior
trunk flexibility (Kibar et al. 2016; Sekendiz et al. 2007); thus, it is logical that Pilates
training might be considered beneficial for treating low back pain. Considerable research
exists on Pilates but of variable quality. Recently, Lin and associates (2016) conducted a
553
systematic review of the effects of Pilates on patients with chronic nonspecific low back pain.
They limited their review to only randomized controlled trials of high quality. They
determined that 6 to 12 wk of Pilates training offers significant improvement in pain relief
and functional capacity compared with usual or routine health care. However, other exercise
treatment that includes waist or torso movement was just as effective as Pilates. Yamato and
colleagues (2016) concurred, stating Pilates is more effective than minimal intervention for
treating chronic low back pain but likely no better than other forms of exercise. In a recent
study, men (40-55 yr) with chronic low back pain were randomly assigned to Pilates training,
McKenzie back exercises, or a control group. Pain decreased and general health improved in
both treatment groups, but there was no difference between Pilates and McKenzie exercises
(Hasanpour-Dehkordi, Dehghani, and Solati 2017).
Key Points
The specificity, overload, progression, and interindividual variability principles should be applied to
designing flexibility programs.
Four methods of stretching are static, dynamic, ballistic, and PNF.
Some consider PNF superior to other stretching techniques for increasing ROM, but all methods (PNF,
ballistic, dynamic, and static) effectively increase ROM.
The contract-relax (CR) and contract-relax agonist contract (CRAC) are common PNF stretching
techniques.
Ballistic stretching is not generally recommended because of its high risk for injury and muscle soreness.
For static stretching programs, gains in ROM are related to the total time the stretch is sustained; total time
of stretching is a function of stretch duration and the number of repetitions of the exercise.
A well-rounded flexibility program includes at least one exercise for each major muscle group.
Muscle groups should not be stretched beyond the pain-free ROM.
Typically, the duration of the stretch should be 10 to 15 sec for beginners and no more than 60 sec for more
advanced clients.
Beginners should start with two to four repetitions of each exercise.
Flexibility exercises should be performed a minimum of 2 days/wk but preferably daily.
To progressively overload the target muscle group, gradually increase the total time of the stretch (60-120
sec) by increasing the duration of stretch (10-60 sec) and the number of repetitions (4-6 reps).
Stretching does not prevent overuse injuries or improve physical performance, but it may reduce the risk of
muscle strains.
Short-duration (<45 sec) static stretching is not detrimental to strength, power, and speed performances,
554
and it may be included as part of the preparticipation warm-up routine.
Lumbar instability is a major cause of low back problems.
Exercises that develop and maintain lumbar stability are recommended for low back care programs.
Exercises developing muscle endurance may be more effective than exercises developing muscle strength for
the prevention and treatment of low back injuries.
The Sahrmann Core Stability Test (Sahrmann 2002) is popular among clinicians, and the test battery by
Kahraman and colleagues (2016) has excellent interrater reliability.
It is questionable whether core stability training and Pilates are any more effective than conventional
exercise training that includes the torso for reducing low back pain.
Key Terms
Learn the definition of each of the following key terms. Definition of terms can be found in the glossary.
active-assisted stretching
active stretching
autogenic inhibition
ballistic stretching
contract-relax agonist contract (CRAC) technique
contract-relax (CR) technique
core stability
dynamic stretching
flexibility training
lumbar stabilization
passive stretching
pelvic stabilization
Pilates
proprioceptive neuromuscular facilitation (PNF)
reciprocal inhibition
static stretching
stress relaxation
stretch tolerance
viscoelastic creep
Review Questions
1. Explain why ballistic stretching is not usually recommended for flexibility programs.
555
2. Identify two sensory receptors of the musculotendinous unit, and explain how each receptor is affected by
slow static stretching.
3. What are the physiological mechanisms responsible for gains in ROM from PNF stretching?
4. Identify three high-risk flexibility exercises and suggest safe alternatives.
5. What are the advantages and disadvantages of slow static, dynamic, and PNF stretching?
6. Describe the basic guidelines for designing flexibility programs. Explain how the specificity and overload
training principles apply.
7. Explain why stretching does not prevent injury.
8. Describe three abdominal exercises that have high challenge-to-compression ratios.
9. What are the similarities and differences between the traditional and alternative approaches to low back
care programs?
10. Describe the recommended sequence of exercises for starting a low back care program.
556
CHAPTER 12
Assessing Balance and Designing Balance Programs

KEY QUESTIONS
What are static and dynamic balance?
What factors affect balance?
How is balance assessed?
What are general guidelines for balance testing?
What is balance training?
What types of exercise are best suited for improving balance?
What are the general recommendations for designing balance training programs?
Although balance is not generally included in health-related physical fitness test batteries, it
is gaining recognition as a key component of functional fitness. In the past, balance was
viewed primarily as a performance-based measure, with balance training geared toward
improving sport performance. In a worldwide survey of fitness trends for 2018, fitness
programs for older adults and functional fitness ranked number 9 and 10, respectively
(Thompson 2017).
Balance is an especially important component of functional fitness for older adults in terms
of preventing falls, performing activities of daily living (ADLs), and maintaining functional
independence. In the United States, more than one in four older adults (65 yr or older) fall
each year; falling is the leading cause of injury deaths among older adults (Bergen, Stevens,
and Burns 2016). Over the past decade, the rate of fall-related deaths rose steadily in this
population (www.cdc.gov/injury/wisqars). To reduce the risk of falling, older adults are
encouraged to exercise regularly and to engage in physical activity modes that improve
strength, power, and balance. The most recent Physical Activity Guidelines for Americans
(U.S. Department of Health and Human Services 2008) and the Canadian Physical Activity
Guidelines (Tremblay et al. 2011) recommend that older (≥65 yr) adults with poor mobility
participate in balance activities. Also, the American College of Sports Medicine position
557
statements on exercise for older adults (Chodzko-Zajko et al. 2009) and the quality and
quantity of exercise for developing and maintaining fitness (Garber et al. 2011) recommend
balance and neuromotor training for older individuals with poor mobility or at risk for falls.
Neuromotor training includes exercises to improve balance, agility, gait, coordination, and
proprioception (American College of Sports Medicine 2018; Bushman 2011, 2012). This
type of training is especially beneficial as part of comprehensive exercise programs for older
adults.
This chapter presents definitions and theoretical frameworks for balance and describes
tools and tests for its assessment. Guidelines for balance testing are presented along with
norms for selected balance tests. Suggestions for designing training programs to improve
balance are also provided.
DEFINITIONS AND NATURE OF BALANCE

Balance is the ability to keep the body’s center of gravity within the base of support when one
is maintaining a static position, performing voluntary movements, or reacting to external
disturbances. Postural stability and equilibrium are terms often used to refer to the construct
of balance. Clinically, balance is commonly thought of as static or dynamic. Static balance is
the ability to maintain the center of gravity within the supporting base while standing or
sitting, whereas dynamic balance refers to maintaining an upright position while the center of
gravity and base of support are moving and the center of gravity is moving outside of the
supporting base (e.g., walking). Reactive balance is the ability to compensate and recover
from perturbations while standing or walking (e.g., maintaining balance after tripping over an
obstacle). Functional balance refers to the ability to perform daily movement tasks requiring
balance such as picking up an object from the floor, dressing, and turning to look at
something behind you.
Balance is a complex construct involving multiple biomechanical, neurological, and
environmental systems. Over the years, the theoretical frameworks dealing with balance have
moved from reflex and hierarchical perspectives to a dynamic systems model that describes
how these systems function and interact to achieve balance and postural control. The reflex
model assumes that sensory input controls motor output; the hierarchical model is based on
control of movement by higher brain centers (e.g., cortex and midbrain). The dynamic
systems model describes balance control as adaptive and functional, providing multiple
solutions for accomplishing a movement goal. In this model, the higher brain centers work in
conjunction with lower centers rather than control them. The visual, somatosensory
558
(proprioception), and vestibular (inner ear) systems interact to maintain balance. The visual
system provides information about the body’s location relative to its environment; the
somatosensory system discerns position and movements of body parts; the vestibular system
provides information about head position in relation to gravity and senses how fast and in
what direction the head is accelerating. In addition, internal factors such as muscle tone,
strength, and range of motion, as well as environmental factors, contribute to balance.
FACTORS AFFECTING BALANCE AND RISK OF FALLING

Balance is related to age, gender, body size, and physical fitness level. This section addresses
some commonly asked questions about balance and the risk of falling.
How does body size affect balance?
The height of the body’s center of gravity relative to the supporting base affects balance. The
higher the center of gravity is from the base of support, the lower the stability. Shorter
individuals have a lower center of gravity and therefore potentially greater stability compared
with taller individuals. Both height and body weight are predictors of postural sway.
Does foot size affect balance?
There is a direct relationship between the size of the supporting base and stability; the larger
the supporting base, the greater the stability. A larger base of support allows the vertical
projection of the body’s center of gravity (i.e., line of gravity) to move a greater distance
before falling outside of the supporting base and losing balance. This explains why it is more
difficult to maintain your balance while standing on the tips of your toes than when standing
on both feet. Foot size (length and width) may affect balance especially when one is
performing tasks that require standing on one leg.
Do women have better static balance than men?
Gender differences in skeletal structure (e.g., shape of pelvis) and body shape (apple vs. pear
shaped) affect the location of the center of gravity within the body. Typically in women, the
relative height of the center of gravity from the supporting base during standing tends to be
lower than that of men (~55% and 57% of standing height, respectively) because of the wider
pelvic structure of women and their tendency to be pear shaped. Therefore, one might
hypothesize that static balance ability of women may be somewhat better than that of men.
However, research does not support this. There were no gender differences in unipedal (one-
leg) stance performance with eyes open and closed for adults 18 to 99 yr of age (Springer et
al. 2007) or for adolescent track and field athletes (Knight et al. 2016).
559
How does preactivity stretching affect balance performance?
Some research suggests that preactivity stretching impairs physical performance by decreasing
muscular strength and power (see chapter 11). Since strength and power are related to
balance, one might speculate that stretching may negatively affect balance performance. In
fact, a couple of studies reported that acute static stretching (stretch duration of 45 sec)
decreases balance (Hoshang Bakhtiary, Aminian-Far, and Hedayati 2013; Behm et al. 2004).
Both anterior-posterior and medial-lateral dynamic balance decreased in females assigned to
three sets of 45 sec static stretches (hamstrings, quadriceps, and gastrocnemius), but there was
no balance deficit in a group that did only 15 sec stretches (Hoshang Bakhtiary, Aminian-
Far, and Hedayati 2013). On the other hand, some studies reported that shorter-duration
(≤15 sec) static stretching and PNF CRAC stretching (proprioceptive neuromuscular
facilitation; contract-relax with agonist contraction) significantly improve postural stability
and dynamic balance of healthy young women and men (Costa et al. 2009; Nelson et al.
2012; Ryan, Rossi, and Lopez 2010).
What factors contribute to the risk of falling?

In addition to extrinsic factors (e.g., poor lighting, loose rugs, and obstacles in walkways),
intrinsic factors contribute to the risk of falling. Factors such as muscle weakness and gait and
balance problems are the second most common cause for falls in older adults (Rubenstein and
Josephson 2002). Additionally, the ability to produce force quickly (i.e., muscle power)
declines more rapidly than strength with advancing age (Granacher, Gruber, and Gollhofer
2010). Muscle power may be more relevant than muscle strength for preventing falls. The
inability to compensate for unexpected disturbances in stance and gait (i.e., reactive balance),
as well as the inability to maintain balance while simultaneously performing cognitive or
other motor functions, may increase a person’s risk of falling.
To assess risk of falling, Granacher and colleagues (2012) recommend a test battery that
measures muscle power, dynamic steady-state balance under multitask conditions, and
reactive balance. Recently, Kim and associates (2017) found that the Short Physical
Performance Battery developed by Guralnik and colleagues in 1994 is a suitable screening
tool for risk of falls, as fallers had significantly lower scores on this test battery than nonfallers
in their study of 307 older adults aged 65 to 92 yr. This test battery measures lower limb
function. It involves an assessment of standing balance with progressively more challenging
foot positions, a 4 m walking test to assess gait speed, and five repetitions of standing as fast
as possible from a seated position. This test battery was more associated with fall history than
560
the timed up-and-go test or the Berg Balance Scale (both described later in this chapter).
Does regular exercise reduce the risk of falling?
Regular exercise may be one way to prevent falls and fall-related fractures. Carter and
associates (2001) reported that impairments in muscle and joint function, the vestibular
system, vision, proprioception, cognition, static and dynamic balance, and gait predispose
individuals to falls and fractures. Balance, resistance, and flexibility training programs were
more effective than endurance training for reducing risk of falling (Province et al. 1995). In a
position statement from Exercise and Sports Science Australia, the authors declared that
exercise is an effective method of preventing falls, but they stressed that the exercise
prescription should progressively challenge balance (Tiedemann et al. 2011). Similarly,
Shubert (2011) commented that an exercise program to prevent falls should focus on at least
two of the following three modes of balance exercises: (1) movement around the center of
mass, (2) use of a narrow base of support, and (3) minimal upper extremity support.
Furthermore, 50 hr of balance training is the minimal dose to offer a protective effect against
falling for older adults (Shubert 2011). General recommendations for exercises that challenge
balance and therefore reduce the risk of falling are presented later in this chapter.
If exercise is an effective means of preventing falls, it is logical that exercise would also reduce
the fear of falling in older adults. However, research suggests that this reduction is only small
to moderate immediately after the exercise intervention and may not last long term (Kendrick
et al. 2014). The investigators noted that the quality of research on this topic is poor, and
there is a need for well-designed randomized trials.
ASSESSMENT OF BALANCE
Direct measures of balance may be obtained using computerized force plate devices to assess
the adaptive functioning of the sensory, motor, and biomechanical components in accordance
with the dynamic systems model of balance. For those who do not have access to the
advanced technology necessary to obtain direct measures, field and clinical tests are available
for assessing static and dynamic balance, as are tests of functional balance using indirect
measures. For detailed descriptions and illustrations for static and dynamic balance field tests,
see Reiman and Manske (2009). Because balance is complex, most balance test batteries are
comprehensive and include multiple test items to assess both static and dynamic balance. A
single simple test such as the one-leg stance may be limited in that it measures only a few of
the components of balance.
561
ASSESSING STATIC AND DYNAMIC BALANCE WITH DIRECT
MEASURES
The application of technology to balance assessment has produced a number of excellent
computerized systems to assess static and dynamic balance. Generally, the relatively high cost
(some >$100,000 U.S.) of these computerized systems, however, precludes their usefulness in
most field and clinical settings. These systems consist of a computerized force plate with
three or more force transducers that quantify vertical pressures applied to the support
platform. These vertical pressures are used to derive the anteroposterior and mediolateral
coordinates of the center of pressure. The systems provide data about postural sway and
steadiness while the client remains motionless, with weight distribution between the feet, the
ability to move the center of vertical force (center of pressure) to maintain balance, and
automatic motor responses to platform disturbances (Guskiewicz and Perrin 1996). Force
platform balance tests provide valid information about postural control that can be used to
predict risk of falling among older people with and without a history of balance problems or
falling (Pajala et al. 2008).
Computerized dynamic posturography assesses the individual and composite functioning
of sensory, motor, and biomechanical components of balance (e.g., NeuroCom Equitest;
figure 12.1). The motor control tests provide data about the client’s responses to sudden
movements of the force plate that threaten balance (i.e., reactive balance). The sensory
organization tests examine the client’s ability to maintain an upright posture when visual and
proprioceptive sensory information is modified mechanically (Nashner 1997). This test has
moderate to good validity and reliability for assessing the dynamic postural stability of
physically active adults and older adults (Dickin and Clark 2007; Dickin 2010). The
NeuroCom Balance Master can be used to assess functional tasks such as walking, turning,
and changing posture (e.g., sitting to standing). It measures weight symmetry, weight shifts,
and limits of stability.
562
FIGURE 12.1 NeuroCom computerized dynamic posturography.
Dale R. Wagner.
USING NINTENDO WII TO ASSESS BALANCE
As a low-cost alternative to expensive force platforms and sophisticated, computerized posturography devices, the
Nintendo Wii balance board has been successfully used to assess static and dynamic balance. When compared against
laboratory tests, the Wii balance board yields valid and reliable measures of unidirectional and rotational displacement of
the center of pressure for younger and older adults (Clark et al. 2010; Kalisch et al. 2011). The Wii balance board test was
poorly correlated with functional mobility (timed up-and-go test and obstacle course) in a sample of seniors (Reed-Jones
et al. 2012). However, the authors discovered a relationship between the Wii balance score and visual processing speed,
suggesting that the Wii Fit balance test may provide supplemental information not obtained from standard functional
mobility and balance tests. For the technical specifications of the Wii balance board (e.g., drift, hysteresis) for measuring
center of pressure, see Weaver, Ma, and Laing (2017). For a review of the balance research that has been done using the
Wii Fit, see Goble, Cone, and Fling (2014).
The limits of stability test, a measure of the maximum excursion of the center of gravity,
assesses the degree to which the individual is able to lean in several directions while
maintaining balance over a fixed supporting base (Clark et al. 2005). Research shows this test
provides reliable scores and predicts risk of falling (Clark, Rose, and Fujimoto 1997;
563
Wallman 2001). The limits of stability in normal adults are 12° in the anteroposterior plane
and 16° in the mediolateral direction.
The Biodex Stability System may be used to evaluate and train neuromuscular control by
quantifying the ability to maintain dynamic postural stability on both stable and unstable
surfaces. This system provides ongoing visual feedback to the individual while attempting to
reproduce specified movement patterns of the center of gravity. Using the Biodex Stability
System, one calculates the stability index by dividing the client’s anteroposterior score
(measured in degrees) by the normal value (12°) and multiplying by 100. Similarly, to
calculate the mediolateral stability index, one divides the client’s score by the normal value
(16°) and multiplies by 100. Combined values less than 100% are indicative of balance
problems (de Bruin et al. 2009).
In fitness, athletic, and rehabilitation settings, less expensive balance systems can be used to
assess neuromuscular control, proprioception, and mechanoreceptor input (e.g., Biodex
Stability System and Kinesthetic Ability Trainer). These systems, however, do not have the
ability to quantify the vestibular and visual components of balance. Typically, they consist of a
multiaxial platform positioned on a U-joint with eight movable springs, and they are used for
balance training.
ASSESSING STATIC BALANCE WITH INDIRECT MEASURES

As early as the mid-1800s, Romberg developed tests to measure static balance with a
narrowed base of support during standing. Steady-state static balance while standing can be
assessed under single-task conditions (i.e., standing only) or multitask conditions (standing
while performing other motor or cognitive tasks). To assess risk of falling, reactive balance
should also be evaluated. This section describes the protocols for various types of static
balance tests and reactive balance tests that may be easily used in field or clinical settings.
Romberg Tests
The Romberg tests measure static balance during standing with eyes open and eyes closed.
For the original Romberg test, the client stands barefoot with arms folded across the chest
and the feet close together in the frontal plane (Romberg test = feet side by side). For the
modified Romberg test, also known as the sharpened Romberg test, the client stands barefoot
using a tandem stance (feet positioned heel to toe) with eyes open and eyes closed. These
tests are scored objectively; the number of seconds the client maintains a steady position
without swaying, up to a maximum of 60 sec, is recorded.
The side-by-side and tandem stance tests were primarily developed to discriminate
564
between poor and acceptable balance in elderly individuals. The test-retest reliability of the
tandem stance test ranges from .76 (eyes closed) to .91 (eyes open) (Franchignoni et al.
1998). Shubert and colleagues (2006) reported that the tandem stance test is moderately
related to walking speed (r = .50) and dynamic balance (r = .46) in older adults (65 yr and
older) who are able to walk independently. Likewise, Gras and colleagues (2017) reported a
moderate correlation between the modified Romberg test and a 10 m walk (r = .45) but
strong correlations to the Berg Balance Scale (r = .64) and timed up-and-go test (r = −.65).
Older adults who could maintain this tandem stance for 30 sec with eyes open scored better
on the Berg and up-and-go tests than those who could not complete 30 sec of the modified
Romberg test. Pajala and associates (2008) noted that the inability to complete the tandem
stance is a significant predictor of fall risk. However, other researchers observed that the side-
by-side and tandem stance tests have poor validity for predicting falls in older adults (Yim-
Chiplis and Talbot 2000) and do not discriminate between individuals on the lower and
higher ends of the balance spectrum (Curb et al. 2006).
Unipedal Stance Test

The unipedal stance test, or timed one-leg stance test, also provides a simple measure of static
balance performance. The validity of this test has been demonstrated by its relationship with
gait performance, risk of falling, and ability to perform activities of daily living (ADLs) for
older adults (Bohannon 2006a). The test-retest reliability of the one-leg stance ranges from
.74 (eyes closed) to .91 (eyes open) (Whitney, Poole, and Cass 1998). This widely used test
provides a reliable measure of static balance for children and adults (Emery 2003; Emery et
al. 2005; Muehlbauer et al. 2011). The one-leg stance test makes up the balance assessment
portion for a new field-based physical fitness test battery for preschool children (Ortega et al.
2015). At the opposite end of the age spectrum, poor one-leg stance performance is a
powerful predictor of low back pain in older (70-85 yr) adults (Husu and Suni 2012).
00:00 / 00:00
Video 12.1
565
For this test, the client stands on one leg with eyes open and closed. The test is scored as
the number of seconds the client is able to maintain balance on the dominant leg. In a meta-
analysis, Bohannon (2006b) reported that testing procedures for the one-leg stance test are
not standardized. The following are some of the testing procedures that varied:
Client was barefoot or wearing shoes.

Dominant, nondominant, or both legs were tested.
Maximum duration of the test varied between 5 and 60 sec.
Number of trials varied from one to five.
Dependent measure was either best score or average of the trials.
The test duration most frequently reported in the 22 studies that Bohannon reviewed was
30 sec. The average one-leg stance (eyes open) time for apparently healthy older adults
declined across age groups: 27.0 sec for 60 to 69 yr, 17.2 sec for 70 to 79 yr, and 8.5 sec for 80
to 99 yr. Springer and colleagues (2007) developed age-gender norms for the unipedal stance
test (eyes open and closed) for adults 18 yr to 99 yr (see table 12.1). To use these norms, be
certain to follow recommended testing procedures (see Test Procedures for Unipedal Stance
Test).
TEST PROCEDURES FOR UNIPEDAL STANCE TEST
1. Determine clients’ dominant leg by having them kick a ball.
2. Prior to raising one leg off the floor, clients fold their arms across the chest.
3. Clients stand barefoot on their dominant leg and raise the other foot near to but not touching the ankle of
566
the stance limb. Start the stopwatch as soon as clients lift their foot off the floor.
4. For the eyes-open test, clients focus on a spot on the wall at eye level throughout the test.
5. Terminate the test when a client does any of the following:
Uncrosses or uses arms to maintain balance
Moves the raised foot away from the standing limb or touches the floor with the raised foot
Moves the weight-bearing foot to maintain balance
Exceeds maximum duration of 45 sec
Opens eyes during the eyes-closed one-leg stance test
6. Administer three trials and use the best score.
Balance Error Scoring System (BESS)

The Balance Error Scoring System (BESS), first described by Riemann, Guskiewicz, and
Shields in 1999, is a field test of static balance that combines the Romberg tests and unipedal
stance test. The BESS involves three stances: (1) feet side by side (Romberg), (2) standing on
nondominant foot (unipedal) with contralateral limb in 20° to 30° of hip flexion and 40° to
50° of knee flexion, and (3) tandem with the heel of the dominant foot touching the toes of
the nondominant foot. These three stances are performed twice, once on a firm surface
(figure 12.2) and once on a medium-density foam pad (45 cm × 13 cm thick, density 60 2
kg/m ; figure 12.3). Instruct clients to place their hands on the iliac crest and close their eyes
3
for each trial. Each stance is held for 20 sec.
00:00 / 00:00
Video 12.2
567
FIGURE 12.2 BESS test on firm surface: (a) feet side by side; (b) balanced on nondominant foot; (c) heel of dominant
foot touching toes of nondominant foot.
FIGURE 12.3 BESS test on balance pad: (a) feet side by side; (b) balanced on nondominant foot; (c) heel of dominant
foot touching toes of nondominant foot.
The BESS is scored by summing 1 point for each error committed during each of the six
20-sec trials. If the client is unable to sustain the stance position for more than 5 sec, the trial
is considered incomplete and is assigned a maximal error score of 10. The following are the
errors to be scored in the BESS:
Lifting hands off iliac crest

Opening eyes
568
Stepping, stumbling, or falling
Moving hip into more than 30° of flexion or abduction
Lifting forefoot or heel
Remaining out of testing position for more than 5 sec
The BESS was originally validated against objective sway measures obtained by a
NeuroCom (Riemann, Guskiewicz, and Shields 1999). The intraclass correlation coefficients
for interrater reliability ranged from .78 to .96. In a review of the BESS, criterion-related
validity was reported as moderate to high, and reliability ranged from moderate to good (Bell
et al. 2011). Since very few errors occur during the double-leg stance on both firm and foam
surfaces, Hunt and colleagues (2009) suggested eliminating this portion of the test. With the
double-leg stance condition removed, intraclass reliability increased. Hunt and colleagues
(2009) suggested three trials of just four conditions (one-leg and tandem, both firm and
foam), and this has become known as the modified BESS. Given that the BESS is an
inexpensive, quick, and easy-to-administer field test, it is used in a variety of settings and
populations, most commonly as a sideline assessment in the management of sport-related
concussion (Guskiewicz 2011). The BESS can detect balance deficits in those who are
concussed or fatigued, and BESS scores increase with ankle instability and age (Bell et al.
2011).
Reactive Balance Tests

Reactive balance tests assess the individual’s ability to compensate and recover balance in
response to unexpected external forces or perturbations. Research suggests that tests of static,
dynamic, and functional balance might be related, but reactive postural control is an
independent domain of balance (Klein, Fiedler, and Rose 2011). The nudge test and postural
stress test can be used to subjectively assess the reactive balance of your clients. For the nudge
test, the examiner pushes against the sternum with light pressure while the client is standing
with feet together. The response to this perturbation is graded on a scale of 0 to 2: 0 = client
starts to fall and needs assistance to prevent falling; 1 = client maintains balance by moving
the feet; and 2 = client remains stable throughout the test (Wild, Nayak, and Isaacs 1981).
Given the subjective nature of this test, it may be best used as a quick and easy way to identify
clients in need of a more sophisticated assessment of balance. The postural stress test
measures the client’s ability to recover standing balance in response to varying degrees of
perturbations using a pulley weight system that displaces the center of gravity posteriorly.
569
Three intensities of force relative to the client’s body mass (i.e., 1.5%, 3%, and 4% of body
mass) are applied to the client’s waist. A 10-point scale is used to evaluate reactive balance,
with a score of 0 indicating failure to remain upright and a score of 9 representing an efficient
postural response to maintain a standing posture (Chandler, Duncan, and Studenski 1990).
ASSESSING DYNAMIC BALANCE WITH INDIRECT MEASURES

Dynamic balance is the ability to maintain postural stability while moving. Dynamic balance
involves completing functional tasks without compromising the base of support while
moving; it is important for preventing falls, especially in older adults and children, and for
preventing sport injuries in athletes and physically active individuals. This section describes
field tests and clinical protocols for tests of dynamic balance of children, older adults, and
athletes.
Functional Reach Tests

The functional reach test was developed to measure dynamic balance of adults by determining
the maximum distance an individual can reach beyond arm’s length without losing balance or
moving the feet (Duncan et al. 1990). The validity of this test is good, with concurrent
validity coefficients ranging between .64 (one-leg stance test) and .71 (center-of-pressure
excursion). The test-retest reliability was .86 to .88 (Franchignoni et al. 1998; Whitney,
Poole, and Cass 1998).
00:00 / 00:00
Video 12.3
For this test, a yardstick or meter stick is attached to the wall, parallel to the floor, at the
height of the client’s acromion process. The client stands with the lateral aspect of the
shoulder parallel to the wall, makes a fist with the right hand, and raises the right arm with
elbow extended until the fist is at the height of the yardstick. The initial measure is the point
along the measuring stick corresponding to the distal end of the third metacarpal. The client
is instructed to reach forward as far as possible without falling or taking a step, and the
570
farthest distance reached along the stick is recorded (figure 12.4). The functional reach score
is the difference between the two recorded distances, measured to the nearest 0.5 cm (0.25
in.). After one practice trial, three trials are administered, and scores are averaged. Scores on
the functional reach test are used to classify older individuals into fall risk categories: low risk
= >25.4 cm (>10 in.); moderate risk = 15.24 to 25.4 cm (6-10 in.); high risk = <15.24 cm (<6
in.); very high risk = unable to reach (Duncan et al. 1990, 1992).
FIGURE 12.4 The functional reach balance test.
The functional reach test has been used to assess the dynamic balance of children and
adolescents, 5 to 15 yr, with test-retest reliability coefficients ranging from .64 to .75
(Donahue, Turner, and Worrell 1994). In this study, gender, height, body weight, and arm
length did not predict functional reach performance. Conversely, Habib and Westcott (1998)
reported that 17% of the variance in functional reach scores of children is attributed to age,
and 15% of the variance may be attributed to height, body weight, and base of support (i.e.,
length of the feet). Norris and associates (2008) noted that only body weight (r = .34) was
significantly related to functional reach scores of children 3 to 5 yr.
The functional reach test has been modified to measure upward reach during standing,
with reach distance normalized for foot length and stature. Row and Cavanaugh (2007)
stated that the standing upward reach test posed a greater challenge to dynamic balance for
both younger and older individuals compared with the forward functional reach test. In
addition, the reach strategy (i.e., whether or not the heels were raised from the floor during
the test) accounted for differences in reach performances of older adults.
571
Thompson and Medley (2007) proposed a variation of the functional reach test to assess
the forward and lateral reach of adults 21 to 97 yr while sitting in a chair. This modification
of the functional reach test can be used to assess the dynamic balance and risk of falling for
individuals who use wheelchairs or frail older adults who cannot perform the standing
functional reach test. The researchers reported that sitting reach scores of older adults are
significantly less than those of younger and middle-aged adults. In addition, length of the
arms did not affect performance.
Timed Up-and-Go Tests

Timed up-and-go tests are used to assess dynamic balance and agility. These performance
attributes are related to functional abilities such as getting up from a seated position to answer
the phone or doorbell in a timely manner; therefore, this test is typically included in balance
test batteries for older adults. Podsiadlo and Richardson (1991) described the timed up-and-
go test as the amount of time needed to rise from an armchair, walk to a line 3 m (10 ft)
away, turn, and return to a seated position in the chair. The timed up-and-go test has
excellent test-retest reliability (r = .99), and test scores are related to gait speed, stair climbing,
risk of falling, postural stability, and cognitive ability (Bohannon 2006a; Herman, Giladi, and
Hausdorff 2011; Oh et al. 2011; Shumway-Cook, Brauer, and Woollacott 2000). Pondal and
del Ser (2008) reported that approximately 26% of the variance in timed up-and-go scores of
older adults (71-99 yr) without gait disturbances is explained by age, gender, body weight,
nutrition status, and cognitive impairment.
00:00 / 00:00
Video 12.4
The testing procedures used for the timed up-and-go test have varied. In some studies,
chairs differ in terms of seat height (40-50 cm) and style (armchair or armless chair).
Although almost all studies have the clients walking a distance of 10 ft (3 m), Rikli and Jones
(2013) provide performance norms for an 8 ft (2.44 m) up-and-go test for older adults. Also,
instructions for this test vary from walking at a normal pace to walking as quickly as possible.
572
Usually more than one trial is administered (Bohannon 2006a). Each of these factors affects
performance scores. Therefore, when using norms to evaluate your clients’ performance on
this test, make certain you administer the test in the same manner and with the same
instructions used to develop the test norms.
On the basis of a meta-analysis of 21 studies that included 4,395 older adults (60-99 yr),
Bohannon (2006a) concluded that timed up-and-go scores exceeding 9.0 sec for 60 to 69 yr,
10.2 sec for 70 to 79 yr, and 12.7 sec for 80 to 99 yr are considered to be worse than average
for these age groups. Table 12.2 presents age-gender norms for older (>70 yr) adults (Pondal
and del Ser 2008). For this variation of the timed up-and-go test, an armless chair with a 40
to 45 cm (about 16-18 in.) seat height was used. At the signal to go, subjects were instructed
to stand up, walk toward the marker (10 ft or 3 m distance), turn around, walk back to the
chair, and sit down again as quickly as possible.
As part of the Senior Fitness Test battery, Rikli and Jones (2013) suggest the 8 ft (2.44 m)
timed up-and-go test for assessing the balance and agility of older adults. This test has
excellent test-retest reliability (r = .95) and is able to categorize older adults according to
functional independence. Table 12.3 presents age-gender norms for the 8 ft up-and-go test.
573
Purpose: Assess dynamic balance and agility.
Application: A measure of the ability to perform ADLs such as getting up quickly to
answer the phone or go to the bathroom.
Equipment: You will need a folding chair that has a seat height of 17 in. (43 cm) and
that will not tip forward, as well as a measuring tape and cone marker.
Test procedures: Place the folding chair against a wall for stability, and have your client sit
in the middle of the chair, with hands on thighs, one leg slightly ahead of the other,
and body leaning slightly forward. On the signal “go,” have the client get up from the
chair, walk as quickly as possible around a cone placed 8 ft (2.44 m) away, and return
to the chair (figure 12.5). Administer one practice trial followed by two test trials.
Scoring: Start the stopwatch exactly on the signal to go, and stop it at the exact time the
client sits in the chair. Record the score to the nearest tenth of a second. Compare the
best score of the two trials against the norms in table 12.3.
574
FIGURE 12.5 The 8 ft timed up-and-go test.
Star Excursion Balance Test and Y Balance Test

The star excursion balance test is a measure of dynamic balance that provides a significant
challenge to athletes and physically active individuals. For this test, the individual must
maintain a base of support on one leg while maximally reaching in different directions in a
star pattern with the opposite leg. The goal of the test is to minimize displacement of the
center of pressure and to maximize reach distance while maintaining unilateral support. The
star excursion balance test is useful for screening deficits in dynamic postural control due to
musculoskeletal injuries (e.g., ankle instability) and during the rehabilitation of orthopedic
injuries in otherwise healthy, physically active adults (Olmsted et al. 2002; Hegedus et al.
2015). For example, side-to-side asymmetry in the anterior direction may help identify
athletes at risk for sustaining noncontact injuries to the lower limb (Stiffler et al. 2017). The
reliability of this test ranges between .85 and .96 (Hertel, Miller, and Deneger 2000).
Although height and leg length were significantly related to excursion distance, foot type
(pes planus, cavus, or rectus) and range of motion at the hip and ankle were not. Given that
leg length accounted for 23% of variance in excursion distance, Gribble and Hertel (2003)
575
recommend normalizing the test scores for leg length. To accomplish this, divide the
excursion distance by the client’s leg length and multiply by 100. Leg length is measured as
the distance from the anterior superior iliac spine to the center of the medial malleolus with
the client lying supine. Different foot alignments and hand positions also alter the reach
distances, and maintaining the hands on the hips while changing the toe/heel position has
been suggested for standardization (Cug 2017).
The star excursion balance test is performed with the client standing (preferably barefoot)
in the middle of a grid formed by eight lines extending from the center at 45° from each other
(see figure 12.6). The client is allowed six practice trials in each of the eight directions on
each leg. Clients begin reaching in the anterior direction and progress clockwise around the
grid. Three trials are administered in the eight directions for each limb. During the trials, the
client reaches to the farthest point possible on the line with the most distal part of the reach
foot, and the tester marks this point on the grid line. Between individual trials in each
direction, the client is given a 10 sec rest. For each direction, the distance from the point of
maximum excursion to the center of the grid is measured in centimeters using a standard tape
measure. The average of the three trials is used to quantify reach distance in each direction.
When both the dominant and nondominant limbs are tested, a 5 min rest is provided. Table
12.4 presents the average reach distances in the eight directions for young women and men
(Gribble and Hertel 2003).
FIGURE 12.6 Star excursion balance test.
Table 12.4 Average Normalized Distances (%) for Star Excursion Balance Test
Reach direction Male Female

Anterior 79.2 76.9
576
Anterolateral 73.8 74.7
Lateral 80.0 79.8
Posterolateral 90.4 85.5
Posterior 93.9 85.3
Posteromedial 95.6 89.1
Medial 97.7 90.7
Anteromedial 85.2 83.1
Note: Normalized score (%) = excursion distance / leg length × 100. Distance and leg length are measured in cm.
One limitation of the star excursion balance test is the amount of time needed to
administer 48 practice trials and 24 test trials for each leg. Robinson and Gribble (2008)
reported that the number of practice trials may be reduced from six to four for each direction.
Hertel and colleagues (2006) simplified the star excursion balance test using factor analysis.
Results from the analysis showed that the posteromedial reach score is highly representative
of all eight directions of the test. They recommend using just the anteromedial, medial, and
posteromedial reach tasks to test for functional deficits caused by chronic ankle instability for
young adults. Plisky and colleagues (2006; 2009) further modified the test by using only the
anterior, posteromedial, and posterolateral directions (forming a “Y”) in what has become
known as the Y balance test. The Y balance test takes less time than the star excursion
balance test and has superior test-retest and inter-rater reliability (Plisky et al. 2009). Despite
the similarities in concept between the star excursion balance test and the Y balance test,
differences exist. Reach distance is greater in the anterior direction during the star excursion
test compared with the Y test (Coughlan et al. 2012; Fullam et al. 2014). Researchers
theorized that different postural control strategies may be used for the two tests (Coughlan et
al. 2012), and more hip flexion is involved during the Y balance test (Fullam et al. 2014);
thus, the two tests are not interchangeable.
00:00 / 00:00
Video 12.5
Gait Velocity Test

Gait velocity provides an indirect measure of dynamic balance while walking, and it may be
useful for detecting mobility problems and assessing risk of falls for older adults. For gait
577
velocity, the amount of time to walk a set distance is measured. The average gait velocity
needed to safely cross a street during a green light is 122 cm·sec ; however, 96% of older (≥65
−1
yr) pedestrians walked with a gait velocity less than this value (Hoxie et al. 1994). Older
individuals with a history of falling have slower gait velocities compared with those with no
history of falls (Guimaraes and Isaacs 1980). Also, the risk of falls is significantly greater for
individuals who stop walking when talking (Lundin-Olsson, Nyberg, and Gustafson 1997).
However, adding a second task does not enhance the prediction of falls in older people; dual-
task tests of gait speed are no more predictive of falls than simple gait velocity tests (Menant
et al. 2014).
ASSESSING DYNAMIC BALANCE USING TEST BATTERIES

As mentioned earlier, balance is a complex construct. Most balance test batteries are
comprehensive and include multiple test items to assess both static and dynamic balance.
These test batteries require the client to perform a variety of functional tasks that mimic
ADLs. The tasks usually include maintaining a fixed sitting or standing posture, walking,
rising from a seated position, and transferring between chairs. This section presents
commonly used balance test batteries.
Tinetti Performance-Oriented Mobility Assessment

The Performance-Oriented Mobility Assessment (POMA) is a test battery developed to
assess balance and gait of older adults (Tinetti 1986). It contains 14 performance-based items
that evaluate gait maneuvers and position changes encountered in normal daily activities, such
as standing from a sitting position and stepping over an obstacle on an uneven surface. Each
item is scored on a scale ranging from 0 (cannot perform) to 2 (normal performance); the
maximum score is 28. This test battery provides a reliable and valid assessment of balance in
elderly, community-dwelling populations, and scores are associated with risk of falling (Berg
et al. 1992; Tinetti, Speechley, and Ginter 1988). Scores greater than 25 are indicative of
good balance and lower fall risk (Mayson et al. 2008). For detailed instructions about the
POMA, see Tinetti 1986 and Tinetti, Speechley, and Ginter 1988.
Berg Balance Scale

The Berg Balance Scale (BBS) is widely used to evaluate balance performance of nursing
home residents and community-dwelling older adults. Inter- and intrarater reliability for this
scale is extremely high (r = .98). The BBS has good concurrent validity: r = .91 with POMA
test scores and r = −.76 with timed up-and-go scores (Podsiadlo and Richardson 1991;
578
Tinetti 1986).
The BBS evaluates performance on 14 functional mobility tasks and takes about 15 min to
administer. Participants are scored on a 5-point scale, with a score of 0 indicating that task
could not be completed and a score of 4 indicating the task was performed independently
(Berg et al. 1992). The maximum score is 56; a score of 45 or less used to identify individuals
with greater risk for falls (Hawk et al. 2006). However, Muir and colleagues (2008) reported
that the cutoff value of 45 has poor sensitivity (25%-42%) for predicting one or more falls in a
community-dwelling older population. They concluded that multiple factors contribute to
risk of falls in older people and that balance impairment alone does not adequately predict
future risk. For complete instructions about scoring each item of the BBS, see Berg and
colleagues 1992.
Dynamic Gait Index

The Dynamic Gait Index (DGI) provides a valid and reliable composite measure of a person’s
ability to adapt gait during movement-related tasks. This test battery consists of eight items
that are scored on a scale of 0 (cannot perform) to 3 (normal performance), with a maximum
score of 24. For some of the items, the task is supposed to be completed as quickly as
possible. Also, some items require performing two tasks simultaneously, such as walking
while turning the head and looking up on command. Mayson and colleagues (2008) reported
a positive association between DGI scores and a measure of cognition. Performance on this
test is dependent on balance, mobility, and cognitive function (Mayson et al. 2008). Scores
>20 are associated with a lower incidence of falling (Riddle and Stratford 1999; Shumway-
Cook et al. 1997). For detailed instructions and information about the DGI, see Shumway-
Cook and Woollacott 1995.
DESIGNING BALANCE TRAINING PROGRAMS

The ACSM’s 2008 Physical Activity Guidelines for Americans suggests balance training at
least 3 days/wk for inactive and active older adults (≥65 yr). In a position statement on
exercise for older adults (Chodzko-Zajko et al. 2009), the ACSM also recommended that
older adults engage in balance activities at least twice a week. Subsequently, the ACSM
expanded its position statement to include not only balance but also additional neuromotor
skills—agility, gait, coordination, and proprioception (Garber et al. 2011). Neuromotor
exercise training is specifically recommended for older adults who have poor mobility or who
are at risk for falling. The ACSM (2018) makes no specific recommendations regarding the
frequency, intensity, or type of exercise for neuromotor training; however, they commented
579
that incorporating balance, agility, and proprioceptive exercise into a training program at least
2 days/wk is effective at reducing falls.
For older clients with balance and mobility disorders, participating in a balance training
program may be beneficial for increasing mobility, performing ADLs safely, and preventing
falls (Garber 2011). For younger and middle-aged adults, limited evidence supports definitive
recommendations with regard to the benefits of neuromotor training, but there is substantial
evidence that balance training and proprioceptive training reduce the risk of ankle sprains in
athletes. In a systematic review of well-controlled studies, Hübscher and colleagues (2010)
determined that balance training alone resulted in a significant 64% risk reduction of ankle
sprains. In another study over 6 yr, researchers observed a significant 81% reduction in ankle
sprains from the first 2 yr period to the third biennium in a team of professional basketball
players taking part in a proprioceptive training program (Riva et al. 2016). Additionally, 6 wk
of balance training after an acute ankle sprain substantially reduces the risk of a recurrent
sprain (McKeon and Hertel 2008). When designing a balance program for your clients,
follow the general recommendations of the ACSM (2018) (see Recommendations for
Balance Training Programs).
BALANCE TRAINING EXERCISE PRESCRIPTION

Compared with the other physical fitness components, there is a lack of research on balance
training for athletes, children, and middle-aged and older adults. It is difficult to compare
studies examining the effects of exercise on balance because of diversity in the populations
(e.g., young athletes to frail older adults), as well as the lack of standardization in balance
outcome measures and training regimens. Also, there is no gold standard measure of balance.
The following questions address issues you should consider when prescribing balance training
for your clients.
What types of physical activities may be used to improve balance?
Given that balance performance is affected by muscle strength, power, and flexibility,
resistance training and stretching programs may be useful for maintaining and improving
balance. In addition to increasing strength and range of motion, Pilates, yoga, tai chi, dance,
walking, and combinations of exercise modes may be suitable activities for improving balance.
Balance discs, foam pads and rollers, balance boards including Nintendo Wii, stability balls,
and computerized balance training systems are tools that may add variety and challenge to
balance training programs.
In a review of exercise interventions to improve balance, Howe and colleagues (2007)
580
analyzed results from 34 studies, with a total of 2,883 participants. Subsequently, this review
was updated and expanded to include 60 additional studies involving 7,000 more participants
(Howe et al. 2011). The researchers categorized the training interventions as follows: gait,
balance, coordination, and functional tasks training; resistance training for strength and
power; three-dimensional exercise including tai chi, yoga, dance, and qigong; general physical
activity (walking or cycling); and computerized balance training, vibration platform training,
and multimodal physical activity programs that used a combination of exercise modes. Table
12.5 summarizes the positive effects some of these interventions have on direct and indirect
measures of balance. There was insufficient evidence that computerized balance training or
vibration plate training improves balance.
Table 12.5 Positive Effects of Various Physical Activity Interventions on Balance
Type of training Direct measures Indirect measures

One-leg stance with eyes open
Static and dynamic stability with force platform Timed up-and-go
Gait, balance, coordination, functional tasks training
Limits of stability Berg balance test
Gait velocity
Functional reach
Timed up-and-go
Resistance training Omnidirectional tilt One-leg stance with eyes open and closed
Tandem stance
Gait velocity
Walking on balance beam

One-leg stance with eyes open
Tai chi, yoga, dance, qigong training
Timed up-and-go
Gait velocity
Tandem walking
Tandem stance
Functional reach
General physical activity (walking)
Walking on balance beam
Timed up-and-go
Walking speed
Functional reach
Tandem stance
Body sway Tandem walking
Multimodal training
Limits of stability Timed up-and-go
One-leg stance with eyes open and closed
Berg balance test
Note: Training significantly improved performance on the direct and indirect balance tests listed.
Recommendations for Balance Training Programs
Have clients engage in balance activities 2 or 3 days/wk.

Progressively increase the difficulty of the balance exercises by using a narrower
base of support such as two-leg stance, semitandem stance, tandem stance, and
one-leg stance.
Include dynamic movements that challenge the center of gravity such as tandem
walking and circle turns.
581
Displace the center of mass by stepping over obstacles and balancing on a rocker
platform.
Use exercises that stress postural muscles, such as heel stands and toe stands, and
exercises that reduce visual or sensory input, such as standing with eyes closed or
standing on a foam pad.
Prescribe multifaceted activities such as tai chi and yoga.
What types of balance training activities can be used with older adults?
Numerous investigations have demonstrated that balance in older adults can be improved
with exercise. However, Low, Walsh, and Arkesteijn (2017) recently set out to determine if
postural control, as measured by center of pressure, can be improved in older adults, thereby
identifying a mechanism to improve balance. They identified 22 randomized controlled trials
that measured center of pressure in people >60 yr of age and involved either balance,
resistance, or multicomponent exercise intervention. Balance exercise decreased total sway
with eyes open and eyes closed, but neither resistance nor multicomponent training affected
center-of-pressure measurements. Thus, postural control is improved by balance training
interventions but not resistance training or multicomponent training.
In contrast to postural control, studies that have evaluated balance in older adults
overwhelmingly support a multicomponent strategy combining a variety of exercise
modalities. In a systematic review of this topic, 7 of 10 trials reported that an exercise
intervention enhanced balance and reduced the incidence of falls in physically frail older
adults (Cadore et al. 2013). According to the authors, the best strategy seems to be training
multiple components, such as balance, strength, and endurance. Of course, a multicomponent
exercise plan has the added benefit of improving not only balance and functional performance
but also cardiorespiratory fitness and metabolic health (Bouaziz et al. 2016). Granacher and
colleagues (2013) suggested that core strength training, Pilates exercise, or both be included
as an adjunct or even substitution to traditional balance and resistance training programs for
older adults to improve balance and functional performance. In addition to traditional balance
and resistance training programs, research supports group exercise programs such as tai chi,
yoga, and Pilates (all discussed later in this chapter) for improving balance in the senior
population. Additionally, 8 of 9 studies in a review of dance interventions to improve the
health of older adults showed positive changes in balance; thus dance, regardless of style, is
thought to improve balance and functional fitness in seniors (Woei-Ni Hwang and Braun
582
2015).
As mentioned previously in this text, exergaming (or virtual reality training) is popular
among youth, but it is also being used to keep older adults active. In a recent meta-analysis,
Donath, Rossler, and Faude (2016) evaluated the effectiveness of exergaming interventions
on balance and functional mobility of people over 60 yr of age. Virtual reality training was
superior to control groups for both standing balance and functional mobility, but slightly less
effective than traditional balance training. The authors concluded that exergaming might
serve as an attractive complement to other traditional exercise methods for improving balance
and functional mobility for seniors.
Does resistance training improve balance?

Improved balance is often mentioned as one of the benefits of resistance training. Muscle
weakness is one intrinsic factor related to balance disorders and risk of falling. Reduced
muscle strength, especially in the lower extremities, affects a person’s ability to perform
ADLs such as stair climbing or standing from a seated position. Orr, Raymond, and Singh
(2008) published the first systematic review of studies that assessed the effect of progressive
resistance exercise training on balance of older adults. They noted that only a small
percentage of balance outcome measures were significantly improved by resistance training:
static balance (26%), dynamic balance (14%), functional balance (57%), and computerized
dynamic posturography (8%). Thus, resistance training as an isolated intervention does not
consistently improve balance of older adults. In many of these studies, the researchers selected
universal whole-body and lower body strength exercises for the resistance training programs
instead of identifying key muscles used for balance, which may explain some of the
discrepancy in results. Also, machine-based resistance training may not be as effective as free
weight training for improving balance.
Recent randomized controlled trials further highlight the inconsistency in results
dependent on the progressive resistance training program employed and the balance measure
evaluated. Joshua and colleagues (2014) randomly assigned older adults (≥65 yr) to one of
three groups: traditional balance training, progressive resistance training specific to the lower
extremities, or a combination that received both treatments alternately. Over a 6 mo duration,
all groups improved on the functional reach test; however, the resistance training group had
significantly better change scores than the balance training group. In contrast, older adults
who adhered to a multicomponent training protocol (aerobic, strength, and balance) for 16
wk improved their sit-to-stand and one-leg stance tests more than a resistance training group
583
(total body on resistance machines) (Ansai et al. 2016).
Research suggests that strength alone is not the major underlying mechanism for poor
balance. Muscle power (force × velocity) may also be a limiting factor in balance control. This
is especially the case for reactive balance in which the act of stepping to catch one’s balance
requires muscle power and proprioception (Klein, Fiedler, and Rose 2011). Age-related
decreases in neural processing may diminish the ability to develop force rapidly in response to
postural challenges (Orr et al. 2008). In fact, power declines more rapidly than strength with
advancing age (Granacher, Muehlbauer, and Gruber 2012). Mayson and colleagues (2008)
reported that leg press velocity was positively related to dynamic balance performance (i.e.,
Berg Balance Scale, POMA, and Dynamic Gait Index), whereas greater leg strength was
associated with better performance on static balance tests (e.g., unipedal stance test). In older
men, power training with low loads (20% 1-RM) induced larger improvements in balance
performance than did moderate (50% 1-RM) or heavy (80% 1-RM) loads (Orr et al. 2006).
More research is needed to determine the most effective resistance training loads, modes, and
volume for improving balance.
In future studies of progressive resistance training, it may be prudent to focus on the type
of balance to be developed (i.e., static, dynamic, or functional) as well as specific muscle
groups critical for balance such as the ankle dorsiflexors and plantar flexors, the knee
extensors and flexors, and the hip abductors and adductors. Hess and Woollacott (2005)
reported that a high-intensity strength training program targeting key lower extremity muscle
groups (i.e., knee flexors and extensors and ankle plantar flexors and dorsiflexors) significantly
improved postural control in balance-impaired older adults.
How effective is tai chi for improving balance and preventing falls in older adults?
Considerable research exists on the efficacy of tai chi for improving balance and preventing
falls in older people. In a review that included seven randomized controlled trials, Huang and
Liu (2015) reported that practicing tai chi significantly reduces the time to complete the up-
and-go test in older adults. The one-leg stance and BBS are also improved. In a study that
had older adults (60-80 yr) complete 15 min of tai chi, 15 min of balance exercises, 15 min of
strength training, and 10 min of stretching, 3 days/wk for 12 wk, there were significant
improvements over a control group (Zhuang et al. 2014). The improvements included 17.6%
for the timed up-and-go test, 54.7% for the 30-sec chair stand test, and significant
improvements in each direction of the star excursion balance test. In a review of 18
prospective studies, multiple studies reported improved static balance (posturography and
584
one-leg stance) as well as dynamic and functional balance (posturography, functional reach,
BBS, POMA, timed up-and-go) with tai chi training (Liu and Frank 2010). From this
review, Liu and Frank (2010) determined that tai chi was more effective for balance
improvement than routine daily activity but not significantly better than functional balance
training or resistance training.
Systematic reviews from 2008 (Harmer and Li) and 2010 (Logghe et al.) evaluating the
impact of tai chi on preventing falls were inconclusive. Despite fall reductions of 21% to 49%,
Logghe and colleagues (2010) concluded that the evidence for the efficacy of tai chi was
insufficient, and they thought a dose-effect relationship was likely. However, subsequent
systematic reviews all indicated that practicing tai chi improves balance and reduces both fear
of falling and the total number of falls (Lee and Ernst 2012; Liu and Frank 2010; Mat et al.
2015; Schleicher, Wedam, and Wu 2012). In a review of systematic reviews, Lee and Ernst
(2012) stated that many of the health benefits attributed to tai chi are unsubstantiated, but
the evidence is “convincingly positive” that tai chi is an effective fall prevention strategy. It is
a better fall prevention strategy than education alone (Liu and Frank 2010). In a review
limited to patients with osteoarthritis of the knee, Mat and colleagues (2015) concluded that
tai chi, as well as strength training and aerobic exercise, but not water-based exercise,
improved balance and fall risk in this population.
What is the optimal style of tai chi for improving balance?
Tai chi is practiced in a variety of styles (e.g., Yang, Wu, Chen, Sun, and Hao). The style and
forms used are sometimes not indicated in the research, making it difficult to determine if
one style is superior to another for improving balance. Schleicher, Wedam, and Wu (2012)
noted that the styles identified in their review were the Yang, Sun, and Chen. Each style has
its own movements and traditional length of practice. The most popular style practiced by
older adults is Yang (Liu and Frank 2010), characterized by slow, large, graceful movements
that blend from one pose to the next. A high stance position (knees bent <30°) and an upright
posture add to its appeal with the older population. In contrast, the Chen style is more
“martial” in appearance, with a lower stance plus stomping and explosive movements breaking
up the slow movements. The Wu style is slow, and the Hao style requires a high stance
position. In addition to the styles, there are a variety of forms (individual movements) within
each style. Liu and Frank (2010) noted that the 24-form Yang style was the most frequently
reported in their review of the literature, but shorter forms (e.g., 6-form Yang) are more
appropriate for frail clients or those at low functional levels.
585
How many tai chi sessions are needed to show improvement in balance?
The research clearly shows that the number of tai chi sessions makes a difference in terms of
training effects on balance. According to Schleicher, Wedam, and Wu (2012), in some cases
balance improvements were observed with as little as 16 hr total of practice; however, there
does appear to be a dose-effect relationship, with longer practice time leading to better
balance and fall risk reduction. Generally, 40 or more sessions are needed to show significant
improvements in balance performance. To reduce risk of falls, tai chi training programs
should last at least 15 wk. Because of age-related declines in physical abilities, the duration
and frequency of tai chi programs for older adults may need to be increased to derive the
degree of improvement seen in younger adults. As with all training programs, the exercise
prescription will vary depending on the client’s ability and goals. There is no universally
accepted exercise prescription for tai chi; however, Liu and Frank (2010) suggested the
following for older people who are practicing tai chi to overcome balance deficits:

Exercises: 12 or fewer forms
Session time: At least 45 min
Duration: At least 12 wk
Is yoga an effective exercise mode for improving balance?
Despite the popularity of yoga relative to tai chi, there are few yoga studies. In a systematic
review of yoga for balance improvement in healthy individuals ranging from youth to older
adults, 15 studies met the authors’ inclusion criteria, but only 5 were randomized controlled
trials (Jeter et al. 2014). As with tai chi, there are various styles of yoga, and the authors noted
that the studies in their review varied by style, frequency, and duration. Nevertheless, there
was at least one positive balance outcome in 11 of the 15 studies, indicating that yoga may
have a beneficial effect on balance. In a meta-analysis limited to people >60 yr, Youkhana and
colleagues (2016) determined that yoga practice leads to small improvements in balance and
medium improvements in physical mobility. Nick and associates (2016) placed older adults
(60-74 yr) who had poor balance scores (BBS <45) and a fear of falling (Modified Falls
Efficacy Scale [MFES] <8) into either a control group or yoga group. Following 8 wk of yoga
(two 1 hr sessions per week), the yoga group significantly improved their BBS and MFES
scores, with no change for the control group.
586
In an interesting randomized controlled trial that compared yoga with tai chi and standard
balance training, Ni and colleagues (2014) placed older adults with a history of falling into
one of these three treatment groups for 12 wk. They observed improvements in field
measures (8 ft up-and-go, one-leg stance, functional reach, and gait speed) as well as dynamic
posturography scores in all three groups, but there were no differences between the groups.
They suggest that yoga may be an alternative to tai chi and traditional balance training for
improving postural stability. Similarly, Gothe and McAuley (2016) reported that yoga,
performed 3 days/wk for 8 wk of a randomized controlled trial, was just as effective at
improving the functional fitness of previously sedentary 55 to 79 yr olds as a stretching-
strengthening program that followed the CDC and ACSM guidelines. These studies all
suggest that yoga is at least as effective as other modes of exercise for improving balance and
functional mobility.
Is Pilates an effective exercise mode for improving balance?
Much of the research on Pilates has focused on treating low back pain (see chapter 11);
however, Pilates is also lauded by its practitioners as an excellent mode of exercise for
improving balance. In a summary of systematic reviews based on randomized controlled trials,
Kamioka and colleagues (2016) evaluated the effectiveness of Pilates exercise. In addition to
providing short-term pain relief and functional improvement for chronic low back pain
sufferers (see chapter 11), there is evidence of improved dynamic balance, flexibility, and
muscular endurance in healthy people. After 16 Pilates sessions over 2 mo, older adults (61-
87 yr) from a senior center improved their timed up-and-go and functional reach tests by
1.39 sec and 1.13 in., respectively (Pata, Lord, and Lamb 2014). They also decreased their
fear of falling. In another study, participants significantly improved peak torque of the knee
flexors and extensors and postural stability (measured with a Biodex Stability System)
following 24 sessions of Pilates (1 hr sessions 3 days/wk for 8 wk) (Yu and Lee 2012).
In contrast to these positive results from studies with no control group or nonexercising
controls, recent randomized controlled trials comparing Pilates with other exercise modes
revealed no advantage of Pilates (Donath et al. 2016; Mesquita et al. 2015). Mesquita and
colleagues (2015) placed 58 older women into one of three groups: PNF stretching, Pilates,
or control. After 4 wk (three 50 min sessions per week), both treatment groups significantly
improved in the timed up-and-go and functional reach tests, but there was no difference
between PNF and Pilates. Donath and colleagues (2016) stratified 48 seniors to Pilates,
traditional balance training, or control. Following 8 wk (two sessions per week), the
587
traditional balance training group outperformed the Pilates group on the Y balance score and
one-leg stance. Taken together, these results suggest that although Pilates improves balance,
it is no more effective than other traditional exercise methods.
BALANCE TRAINING PROGRAMS

Balance training has been widely used in sports rehabilitation (injuries to ankle and knee
joints) and in fall prevention programs for geriatric populations. Traditionally, these programs
include static and dynamic exercises with eyes open and closed on stable and unstable
surfaces. Although the training intensity, frequency, and volume vary greatly among studies
using this approach, the results generally support traditional balance training for inducing
short- and long-term improvements in balance and postural control (Granacher et al. 2011).
Recently, perturbation-based balance training has been promoted to reduce risk of falling,
given that slipping and tripping account for 30% to 50% of falls in older adults. Using this
approach, perturbation exercises that mimic real-life conditions are included. Compared with
the traditional approach, perturbation-based training appears to be more beneficial for
improving reactive balance (i.e., recovery of equilibrium), demonstrating that the specificity
principle also applies to balance training (Granacher et al. 2011).
Two recent reviews set out to define the dose-response relationships of balance training in
young, healthy adults (Lesinski et al. 2015) and older adults (Granacher et al. 2017). The
dose-response relationship and recommendations were similar for both groups. Both studies
recommend a duration of 11 to 12 wk, with a frequency of three sessions a week, each
exercise maintained for 21 to 40 sec. The duration of each training session is only 11 to 15
min for young adults (Lesinski et al. 2015) but 31 to 45 min for older adults (Granacher et al.
2017). For young adults, the recommendation applies only to static balance; there is not yet
enough research to make a recommendation for reactive balance. In contrast, for older adults,
the effect size for this type of training was large on measures of reactive balance but only
small to medium on static balance.
In light of the complex nature of balance, the current ACSM guidelines (2018) do not
include a defined exercise prescription for balance. We recommend using the general
guidelines presented in this section as a starting point. Individualize programs to take into
account your clients’ needs, goals, age, and physical activity status. Keep in mind that balance
training is task specific. Task-specific exercises that target a single, specific balance or gait
impairment are more effective than generalized exercise for improving balance. Techniques
from a variety of exercise modalities can be combined to form a comprehensive balance
588
program that challenges your clients. The Sample Progressive Balance Program illustrates
how seated, standing, and movement balance activities may be adapted to increase the
exercise challenge for further improvements in balance. In the Sample Multimodal Program,
exercises from tai chi, Pilates, agility training, and lunges are used to improve balance and
mobility of an older adult.
SAMPLE PROGRESSIVE BALANCE PROGRAM
589
590
Many excellent resources are available for designing an individualized balance training
program for your clients. ABLE Bodies Balance Training (see Scott 2008) presents a 16 wk
exercise program that safely takes older adults through the exercise progressions for
improving balance and mobility, flexibility, posture and core stability, strength, and
cardiorespiratory endurance. Resources for developing safe exercise programs for improving
balance and functional fitness of older adults are available (see Rose 2010; Scott 2008), and
Granacher (2011) offers guidelines for designing programs for the older population (see
Guidelines for Designing Balance Training Programs for Older Adults). For ideas about
incorporating Pilates and yoga workouts into balance training programs, see Isacowitz (2014)
and Shaw (2016).
SAMPLE MULTIMODAL BALANCE TRAINING PROGRAM
Client Data
Age: 65 yr
Gender: Female
Body weight: 145 lb
Program goal: Improve static and dynamic balance; prevent falls
Method: Tai chi, Pilates, lunges, and agility training
Frequency: 3 or more days/wk
Duration: 45 to 60 min per session
591
Guidelines for Designing Balance Training Programs for Older Adults
Mode: Tai chi; Pilates; yoga; static, dynamic, and reactive balance training
exercises; resistance training for strength and power; or a combination of modes
Equipment: Balance discs, foam pads and rollers, balance boards, stability balls,
computerized balance systems, and force platforms to add variety and challenge to
the program
Sets and duration: 3-8 sets; 20-40 sec each set
Time: 20-30 min per session; 2 hr/wk
Length of program: 3 to 6 mo depending on exercise mode
592
Key Points
Balance is an important component of the functional fitness of older adults.
To reduce risk of falling, older adults are encouraged to engage in balance activities at least 2 days/wk.
Static balance is the ability to maintain the center of gravity within the supporting base during standing or
sitting.
Dynamic balance is the ability to maintain an upright position while the center of gravity and supporting
base are moving.
Reactive balance is the ability to compensate and recover from perturbations while standing or walking.
Functional balance is the ability to perform daily movement tasks requiring balance.
The dynamic systems model of balance describes balance control as adaptive and functional.
Visual, somatosensory, and vestibular systems interact to maintain balance.
Body size, foot size, gender, aging, and physical activity affect balance and risk of falling.
Indirect measures of balance are valid and reliable and useful in field and clinical settings.
Direct measures of balance can be used to assess static and dynamic balance and postural stability. Because
the testing equipment is costly, these tests may be more suitable for research settings.
Pilates, yoga, tai chi, dancing, walking, and resistance training are effective training modes for improving
balance.
Key Terms
balance
center of pressure
computerized dynamic posturography
dynamic balance
functional balance
gait velocity
limits of stability
line of gravity
neuromotor training
reactive balance
static balance
Review Questions
593
1. Why is balance testing included in functional fitness test batteries?
2. Define neuromotor training.
3. Balance is a complex construct. Identify the biomechanical, neurological, and environmental systems that
influence and control balance performance.
4. Define static, dynamic, and reactive balance, and give examples of tests that may be used to assess these
types of balance.
5. Identify indirect balance tests that are typically used to assess functional balance in older adults and those
that are typically used in clinical settings to assess balance in injured athletes.
6. Explain how the visual, proprioceptive, and vestibular systems interact to maintain and control balance.
7. Describe how aging affects balance.
8. Identify intrinsic factors that contribute to risk of falling in older adults.
9. Identify modes of exercise that can be used to improve balance.
10. What are the center of pressure and limits of stability, and how are these measures used to assess dynamic
balance?
11. Briefly describe the generic exercise prescription for improving balance of older adults.
594
APPENDIX A
Health and Fitness Appraisal
This appendix includes questionnaires and forms you can duplicate and use for the pretest
health screening of your clients. The PAR-Q+ (appendix A.1) is used to identify individuals
who need medical clearance from their physicians before taking any physical fitness tests or
starting an exercise program. The Medical History Questionnaire (appendix A.2) is used to
obtain a personal and family health history for your clients. As part of the pretest health
screening, ask your clients if they have any of the conditions or symptoms listed in Risk
Factors, Signs, and Symptoms of Disease (appendix A.3). The ePARmed-X+ (appendix A.4)
may be used by physicians to assess and convey medical clearance for physical activity
participation of your clients.
You can obtain a lifestyle profile for your clients by using either the Lifestyle Evaluation
form (appendix A.5) or the Fantastic Lifestyle Checklist (appendix A.6). Be sure that each
participant signs the Informed Consent (appendix A.7) before undergoing any physical
fitness tests or engaging in an exercise program. Appendix A.8 includes websites for selected
professional organizations and institutes.
595
Appendix A.1 Physical Activity Readiness Questionnaire for
Everyone (PAR-Q+)
596
597
598
Reprinted with permission from the PAR-Q+ Collaboration and the authors of the PAR-Q+ (Dr. Darren Warburton, Dr. Norman Gledhill, Dr. Veronica Jamnik, and Dr. Shannon Bredin).
599
600
Appendix A.2 Medical History Questionnaire
601
602
From A.L. Gibson, D.R. Wagner, and V.H. Heyward, Advanced Fitness Assessment and Exercise Prescription, 8th ed. (Champaign, IL: Human Kinetics, 2019).
603
604
Appendix A.3 Risk Factors, Signs, and Symptoms of Disease
Instructions: Ask your clients if they have any of the following conditions and risk factors. If
so, refer them to their physicians to obtain a signed medical clearance prior to any exercise
testing or participation. See the glossary for definitions of terms.
Client’s name: _________________________ Date: _________________________
605
606
607
608
Appendix A.4 Electronic Physical Activity Readiness Medical
Examination (ePARmed-X+)
609
610
611
612
Source: Electronic Physical Activity Readiness Medical Examination (ePARmed-X+). Available: http://eparmedx.com. Reprinted with permission of the PAR-Q+ Collaboration.
613
Appendix A.5 Lifestyle Evaluation
614
615
616
Appendix A.6 Fantastic Lifestyle Checklist
617
Used with permission of Doug M.C. Wilson.
618
619
Appendix A.7 Informed Consent
In order to assess cardiorespiratory function, body composition, and other physical fitness
components, the undersigned hereby voluntarily consents to engage in one or more of the
following tests (check the appropriate boxes):
❐ Graded exercise stress test

❐ Body composition tests
❐ Muscle fitness tests
❐ Flexibility tests
❐ Balance tests
Explanation of the Tests

The graded exercise test is performed on a cycle ergometer or motor-driven treadmill. The
workload is increased every few minutes until exhaustion or until other symptoms dictate that
we terminate the test. You may stop the test at any time because of fatigue or discomfort.
The underwater weighing procedure involves being completely submerged in a tank or tub
after fully exhaling the air from your lungs. You will be submerged for 3 to 5 seconds while
we measure your underwater weight. This test provides an accurate assessment of your body
composition.
For muscle fitness testing, you lift weights for a number of repetitions using barbells or
exercise machines. These tests assess the strength and endurance of the major muscle groups
in the body.
For evaluation of flexibility, you perform a number of tests. During these tests, we measure
the range of motion in your joints.
For balance tests, we will be measuring the amount of time you can maintain certain
stances or the distance you are able to reach without losing balance.
Risks and Discomforts

During the graded exercise test, certain changes may occur. These changes include abnormal
blood pressure responses, fainting, irregularities in heartbeat, and heart attack. Every effort is
made to minimize these occurrences. Emergency equipment and trained personnel are
available to deal with these situations if they occur.
You may experience some discomfort during the underwater weighing, especially after you
expire all the air from your lungs. However, this discomfort is momentary, lasting only 3 to 5
620
seconds. If this test causes you too much discomfort, an alternative procedure (e.g., skinfold
or bioelectrical impedance test) can be used to estimate your body composition.
There is a slight possibility of pulling a muscle or spraining a ligament during the muscle
fitness and flexibility testing. In addition, you may experience muscle soreness 24 or 48 hours
after testing. These risks can be minimized by performing warm-up exercises prior to taking
the tests. If muscle soreness occurs, appropriate stretching exercises to relieve this soreness
will be demonstrated.
Expected Benefits From Testing

These tests allow us to assess your physical working capacity and to appraise your physical
fitness status. The results are used to prescribe a safe, sound exercise program for you.
Records are kept strictly confidential unless you consent to release this information.
Inquiries
Questions about the procedures used in the physical fitness tests are encouraged. If you have
any questions or need additional information, please ask us to explain further.
Freedom of Consent
Your permission to perform these physical fitness tests is strictly voluntary. You are free to
stop the tests at any point, if you so desire.
_____________________________________________________
I have read this form carefully, and I fully understand the test procedures I will perform and
the risks and discomforts. Knowing these risks and having had the opportunity to ask
questions that have been answered to my satisfaction, I consent to participate in these tests.
621
622
Appendix A.8 Websites for Selected Professional Organizations
and Institutes
Name Website address
American Association of Cardiovascular and Pulmonary Rehabilitation (AACPR) www.aacvpr.org
American College of Sports Medicine (ACSM) www.acsm.org
American Council on Exercise (ACE) www.acefitness.org
American Fitness Professionals and Associates (AFPA) www.afpafitness.com
American Society of Exercise Physiologists (ASEP) www.asep.org
Athletics and Fitness Association of America (AFAA) www.afaa.com
Australian Association for Exercise and Sports Science (AAESS) www.aaess.com.au
Canadian Academy of Sport and Exercise Medicine (CASEM) www.casem-acmse.org
Canadian Society for Exercise Physiology (CSEP) www.csep.ca
Cooper Institute www.cooperinst.org
Gatorade Sport Science Institute (GSSI) www.gssiweb.com
IDEA Health and Fitness Association www.ideafit.com
Institute for Credentialing Excellence www.credentialingexcellence.org
International Association of Fitness Sciences www.iafscertification.com
International Federation of Sports Medicine (FIMS) www.fims.org
International Fitness Professionals Association (IFPA) www.ifpa-fitness.com
International Health, Racquet, & Sportsclub Association www.ihrsa.org
International Coalition for Aging and Physical Activity (ICAPA) www.humankinetics.com/icapa
National Athletic Trainers Association (NATA) www.nata.org
National Board of Fitness Examiners www.nbfe.org
National Commission for Certifying Agencies (NCCA) www.credentialingexcellence.org/ncca
National Strength and Conditioning Association (NSCA) www.nsca-lift.org
North American Society for Pediatric Exercise Medicine (NASPEM) www.naspem.org
SHAPE America www.shapeamerica.org
Sports Medicine Australia www.sma.org.au
Sports Medicine New Zealand www.sportsmedicine.co.nz
Note: Organizations and institutes dealing with exercise physiology, sports medicine, or physical fitness.
623
APPENDIX B
Cardiorespiratory Assessments
Appendix B.1 is a summary of GXT and cardiorespiratory field test protocols that are
presented in more detail in chapter 4. This appendix summarizes popular maximal and
submaximal protocols for treadmill, cycle ergometer, bench-stepping, stair-climbing,
recumbent stepper, rowing ergometer, and distance run/walk tests, as well as methods you
can use to obtain an estimate of your clients’ V̇O max for each protocol.
2
Appendix B.2, the Rockport Fitness Charts, provides age-gender norms for the Rockport
walking test. These charts may be used to classify your clients’ aerobic capacity.
Appendix B.3 presents a variety of step test protocols. Testing and scoring procedures are
included for each protocol. For some protocols, prediction equations are available to estimate
your clients’ V̇O max.
2
Appendix B.4 presents OMNI RPE and facial expression RPE scales for children and
adults engaging in running and walking, stepping, elliptical training, and resistance exercise.
Instructions for administering these scales are provided.
Appendix B.5 provides the answers to questions posed in the sample case study presented
in chapter 5.
624
Appendix B.1 Summary of Graded Exercise Test and
Cardiorespiratory Field Test Protocols
625
626
627
Appendix B.2 Rockport Fitness Charts
628
Reprinted with permission of The Rockport Company, Inc.
629
Appendix B.3 Step Test Protocols
Harvard Step Test (Brouha 1943)
Age and sex: Young men

Stepping rate: 30 steps·min –1
Bench height: 20 in.

Duration of exercise: 5 min
Scoring procedures: Sit down immediately after exercise. The pulse rate is counted in 1/2
min counts, from 1 to 1 1/2, 2 to 2 1/2, and 3 to 3 1/2 min after exercise. The three
1/2 min pulse counts are summed and used in the following equation to determine
physical efficiency index (PEI):
You can evaluate the performance of college-age males using the following PEI
classifications: <55 = poor, 55 to 64 = low average, 65 to 79 = average, 80 to 89 = good, and
≥90 = excellent.
Three-Minute Step Test (Hodgkins and Skubic 1963)

Age and sex: High school- and college-age women
Stepping rate: 24 steps·min –1

Scoring procedures: Sit down immediately after exercise. The pulse rate is counted for 30
sec after 1 min of rest (1 to 1 1/2 min after exercise). Use the recovery pulse count in
the following equation:
You can evaluate the performance of college-age women using the following classifications
for cardiovascular (CV) efficiency: 0 to 27 = very poor, 28 to 38 = poor, 39 to 48 = fair, 49 to
59 = good, 60 to 70 = very good, and 71 to 100 = excellent.
OSU Step Test (Kurucz, Fox, and Mathews 1969)
Age and sex: Men 19 to 56 yr

Stepping rate: 24 to 30 steps·min –1
630
Bench height: Split-level bench 15 and 20 in. high with an adjustable hand bar
Duration of exercise: 18 innings, 50 sec each
Phase I: 6 innings, 24 steps·min , 15 in. bench

–1
Phase II: 6 innings, 30 steps·min , 15 in. bench

–1
Phase III: 6 innings, 30 steps·min , 20 in. bench

–1
(Each inning consists of 30 sec of stepping and 20 sec of rest.)
Scoring procedures: Exactly 5 sec into each rest period, take a 10 sec pulse count.
Terminate the test when the heart rate reaches 150 bpm (25 counts × 6). The score is
the inning during which the heart rate reaches 150 bpm.
Eastern Michigan University Step Test (Witten 1973)
Age and sex: College-age women

Bench height: Tri-level bench 14 to 20 in.


–1

–1

–1
Phase IV: 5 innings, 30 steps·min , 20 in. bench

–1
Scoring procedures: Exactly 5 sec into each rest period, take a 10 sec pulse count.
Terminate the test when the heart rate reaches 168 bpm (28 counts × 6). The score is
the inning during which the heart rate reaches 168 bpm.
Cotten Revision of OSU Step Test (Cotten 1971)
Age and sex: High school- and college-age men



–1

–1

–1
631
Scoring procedures: As with the OSU step test, the score is the inning during which the
heart rate reaches 150 bpm (25 counts in 10 sec). V̇O max in ml·kg ·min can be
2 –1 –1
estimated using the following equation:
V̇O max = (1.69978 × step test score) – (0.06252 × body weight in lb) + 47.12525
2
Queens College Step Test (McArdle et al. 1972)

Age and sex: College-age women and men
Stepping rate: 22 steps·min for women; 24 steps·min for men
–1 –1
Bench height: 16 1/4 in.

Scoring procedures: Remain standing after exercise. Beginning 5 sec after the cessation of
exercise, take a 15 sec pulse count. Multiply the 15 sec count by 4 to express the score
in beats per minute (bpm). V̇O max in ml·kg ·min can be estimated using the
2 –1 –1
following equations:
Women: V̇O max = 65.81 − (0.1847 × HR)

2
Men: V̇O max = 111.33 − (0.42 × HR)

2
632
Appendix B.4 OMNI Rating of Perceived Exertion Scales
633
634
635
Reprinted, by permission, from R.J. Robertson, Perceived Exertion for Practitioners: Rating Effort With the OMNI Picture System (Champaign, IL: Human Kinetics, 2004), 141.
Reprinted from Journal of Science and Medicine in Sport 20(1), Y-L. Chen, W-K. Chiou, Y-T. Tzeng, C-Y. Lu, and S-C. Chen, “A rating of perceived exertion scale using facial expressions for conveying exercise intensity for
children and young adults,” pages 66-69, copyright 2017, with permission from Elsevier.
636
Appendix B.5 Analysis of Sample Case Study in Chapter 5
1. Need for Medical Clearance and CHD Risk Profile

This client has not participated in structured, moderate-intensity physical activity ≥30 min on
≥3 d·wk for the past 3 mo. Her health history does not reveal any cardiovascular, metabolic,
−1
or renal disease. However, the sensation of dizziness and light-headedness while in a resting
state may be considered a positive sign or symptom suggestive of one of the disease states of
concern. Given her failure to meet the requirement of regular exercise and a positive
symptomology, this client should obtain medical clearance prior to starting an exercise
program. Although likely due to job stress, the headaches and discomfort in her neck should
be brought to the physician’s attention during that appointment.
This client has risk factors for CHD. Her total cholesterol (TC; 220 mg·dl ) is borderline
–1
high (200-230 mg·dl ), and her blood pressure (140/82 mmHg) is categorized as stage 2
–1
hypertension (140-159 mm Hg). Also, her HDL-C (37 mg·dl ) and TC/HDL ratio (5.9)
–1
place her at higher risk (<40 mg·dl and >5.0, respectively). She quit smoking cigarettes (one
–1
pack a day) 3 yr ago, which is a step in the right direction. Following the National
Cholesterol Education Program’s recommendation, you should encourage this client to have
her LDL-C assessed to determine if she needs a cholesterol treatment program. Engaging in
an aerobic exercise program should lower her systolic blood pressure. Her triglycerides and
blood glucose levels are normal. Encourage her to dine out less frequently and to eat three
well-balanced meals a day. When dining out, she should select foods that are low in saturated
fat, cholesterol, and sodium. This may help lower her blood cholesterol and blood pressure.
The client is also at greater risk because of
the high stress associated with her job (police officer) and lifestyle (divorced parent
raising two children),
family history of cardiovascular disease, and
physical inactivity (she does not exercise regularly outside of work-related physical
activity).
2. Special Considerations
The client has not exercised aerobically for the past 6 yr, and she has gained 15 lb during that
time. It is likely that she will experience some discomfort when she starts her aerobic exercise
637
program. Thus, it is important to initially prescribe low-intensity exercise to minimize her
physical discomfort.
You also need to consider her busy schedule to find a convenient time for her to exercise.
She reports feeling dizzy after eating. Although it is not your diagnosis to make, the likely
reason is that she is eating only one meal a day, and the insulin surge after eating is lowering
her blood glucose level. It is important to convince this client to start eating at least three
meals a day to avoid this problem.
3. HR, BP, and RPE Responses to Graded Exercise Test

The client’s HR response to the graded exercise test was normal. The exercise HR increased
during each stage of the exercise test. The maximal heart rate (190 bpm) was very close to her
age-predicted maximal HR (220 – 28 = 192 bpm). The client’s BP response to the graded
exercise test was normal. The diastolic BP remained fairly constant (78-82 mmHg), and the
systolic BP increased with each stage of the exercise test. The RPEs were normal. The ratings
increased linearly with exercise intensity, culminating with an RPE = 18.
4. Functional Aerobic Capacity

Being cleared by the physician, the client performed a graded exercise test and voluntarily
terminated it because of fatigue. This was most likely a maximal-effort exercise test as
indicated by the RPE (18) and the exercise heart rate (190 bpm) during the last stage of the
graded exercise test. The treadmill speed and grade during the last stage of the protocol was
2.5 mph and 12%, respectively. This corresponds to a functional aerobic capacity of 7.0
METs or 24.5 ml·kg ·min . According to the norms, this client’s cardiorespiratory fitness
–1 –1
level is poor for her age.
5. & 6. Training HRs

The graph of the client’s HR and RPE responses to the graded exercise test is presented in
figure B.5.
Given the client’s poor cardiorespiratory fitness level and her lack of regular aerobic
exercise, the initial minimal training intensity will be 50% V̇O R (4.0 METs), gradually
2
increasing to a maximum intensity of 75% V̇O R (5.5 METs). The corresponding training
2
HRs, extrapolated from figure B.4, are 152 bpm (50% V̇O R or 4.0 METs) and 174 bpm
2
(75% V̇O R or 5.5 METs). The HRs and RPEs corresponding to the relative exercise
2
intensities in the following chart were extrapolated from the graph.
638
7. Speed Calculations (ACSM Formula for Walking on Level Course)
To calculate walking speed corresponding to 60% of the client’s V̇O R [0.60 × (7 – 1) + 1] = 2
4.6 METs):
a. Convert METs into ml·kg ·min . –1 –1
4.6 METs × 3.5 ml·kg ·min = 16.1 ml·kg ·min

–1 –1 –1 –1
b. Substitute into ACSM walking equation and solve for speed (m·min ). –1
V̇O = [speed × 0.1] + [1.8 × speed × grade] + resting V̇O

2 2
16.1 ml·kg ·min = [speed × 0.1] + [1.8 × speed × 0% grade] + 3.5 ml·kg ·min
–1 –1 –1 –1
12.6 ml·kg ·min = m·min × 0.1

–1 –1 –1
126 m·min = speed –1
c. Convert speed (m·min ) into miles per hour (26.8 m·min = 1 mph).
–1 –1
126 m·min /26.8 m·min = 4.7 mph

–1 –1
d. Convert miles per hour into minutes per mile walking pace.
60 min·hr / 4.7 mph = 12.8 min·mile , or 12:48 (12 min, 48 sec per mile)
–1 –1
Follow these same steps to calculate the walking speed corresponding to 70% V̇O R and 75% 2
V̇O R.
2
(Answers: 70% V̇O R = 5.5 mph; 75% V̇O R = 5.9 mph)

2 2
8. Lifestyle Modifications
Eat three well-balanced meals a day.

Avoid fried foods high in saturated fats, cholesterol, and sodium.
Dine out less frequently, and select restaurants offering healthy food choices (e.g.,
salad bar, grilled skinless chicken, or fish).
639
Exercise aerobically at least 3 days a week.
Try using relaxation techniques (e.g., stretching, progressive relaxation, mental
imagery) to relax in the evening instead of drinking wine.
B.5 Plotting heart rate versus METs for graded exercise test.
640
APPENDIX C
Muscular Fitness Exercises and Norms
Appendix C.1 describes standardized testing protocols for 11 muscle groups using digital
handheld dynamometry.
Appendix C.2 provides age-gender squat and bench press norms for untrained to elite
lifters.
Appendix C.3 describes and illustrates some sample basic isometric exercises for a variety
of muscle groups.
Appendix C.4 provides an extensive list of dynamic resistance training exercises. Exercises
for the upper and lower extremities are organized by body region (e.g., chest, upper arm,
thigh). For each exercise, equipment, body positions, joint actions, prime movers, and
exercise variations are presented.
641
Appendix C.1 Standardized Testing Protocols for Digital
Handheld Dynamometry
642
643
Appendix C.2 1-RM Squat and Bench Press Norms for Adults
644
645
Appendix C.3 Isometric Exercises
646
Exercise 1: Chest Push
Muscle groups: Shoulder and elbow flexors

Equipment: None
Description:
1. Lock hands together.

2. Keep forearms parallel to ground and hands close to chest.
3. Push hands together.
00:00 / 00:00
Video C3.1
647
648
Exercise 2: Shoulder Pull
Muscle groups: Shoulder and elbow extensors

Equipment: None
Description:
1. Using same position as in chest push, attempt to pull hands apart.
00:00 / 00:00
Video C3.2
649
650
Exercise 3: Triceps Extension
Muscle groups: Elbow extensors

Equipment: Towel or rope
Description:
1. Placing right hand over shoulder and left hand at small of back, grasp rope or towel
behind back.
2. Attempt to pull towel or rope upward with right hand.
3. Change position of hands.
00:00 / 00:00
Video C3.3
651
652
Exercise 4: Arm Curls
Muscle groups: Elbow flexors

Equipment: Towel or rope
Description:
1. Stand with knees flexed about 45°.

2. Place rope or towel behind thighs, and grasp each end with hands shoulder-width
apart.
3. Attempt to flex elbows.
653
Exercise 5: Ball Squeeze
Muscle groups: Wrist and finger flexors

Equipment: Tennis ball
Description:
1. Hold tennis ball firmly in hand and squeeze maximally.
654
Exercise 6: Leg and Thigh Extensions
Muscle groups: Hip and knee extensors

Equipment: Rope
Description:
1. Stand on rope with knees flexed.

2. Grasp rope firmly with hands at sides, elbows fully extended.
3. Keeping trunk erect, attempt to extend legs by lifting upward.
655
Exercise 7: Leg Press
Muscle groups: Hip and knee extensors

Equipment: Doorway
Description:
1. Sit in doorway facing side of door frame.

2. Grasp door frame behind feet.
3. Attempt to extend legs by pushing feet against door frame.
656
Exercise 8: Leg Curl
Muscle groups: Knee flexors

Equipment: Dresser or desk
Description:
1. Pull out lower dresser drawer slightly.

2. Lying prone, with knees flexed, hook heels under bottom of drawer.
3. Attempt to pull heels toward head.
657
Exercise 9: Knee Squeeze or Pull
Muscle groups: Hip adductors or abductors

Equipment: Chair
Description:
1. Sitting on chair with forearms crossed and hands on inside of knees, attempt to
squeeze knees together (adductors).
2. Same position but place hands on outside of knees; attempt to pull knees apart
(abductors).
658
Exercise 10: Pelvic Tilt
Muscle groups: Abdominals

Equipment: None
Description:
1. Lie supine with knees flexed and arms overhead.

2. Tighten abdominal muscles while pressing lower back into floor.
659
Exercise 11: Gluteal Squeeze
Muscle groups: Hip extensors and abductors

Equipment: None
Description:
1. Lie prone with legs together and fully extended.

2. Tighten and squeeze the buttocks together.
660
Appendix C.4: Dynamic Resistance Training Exercises
661
662
663
664
APPENDIX D
Body Composition Assessments
Appendix D.1 presents prediction equations for estimating residual lung volume. Use these
equations only when it is not possible to directly measure a client’s residual lung volume.
Appendix D.2 describes and illustrates the standardized sites for skinfold measurements,
and appendix D.3 describes the skinfold sites and measurement procedures for Jackson’s
generalized skinfold prediction equations for men and women.
Standardized sites for circumference (appendix D.4) and bony breadth (appendix D.5)
measurements are also provided. Follow these procedures to identify and measure various
sites.
Appendix D.6 contains the Ashwell Body Shape Chart. Use this chart to compare your
clients’ waist circumference to standing height.
665
Appendix D.1 Prediction Equations for Residual Volume
666
Appendix D.2 Standardized Sites for Skinfold Measurements
667
FIGURE D.2.1 (a) Site and (b) measurement of the chest skinfold.
00:00 / 00:00
Video D2.1
668
FIGURE D.2.2 (a) Site and (b) measurement of the subscapular skinfold.
00:00 / 00:00
Video D2.2
669
FIGURE D.2.3 (a) Site and (b) measurement of the midaxillary skinfold.
670
FIGURE D.2.4 (a) Site and (b) measurement of the suprailiac skinfold.
671
FIGURE D.2.5 (a) Site and (b) measurement of the abdominal skinfold.
00:00 / 00:00
Video D2.3
672
FIGURE D.2.6 (a) Site and (b) measurement of the triceps skinfold.
673
FIGURE D.2.7 (a) Site and (b) measurement of the biceps skinfold.
674
FIGURE D.2.8 (a) Site and (b) measurement of the thigh skinfold.
00:00 / 00:00
Video D2.4
675
FIGURE D.2.9 (a) Site and (b) measurement of the calf skinfold.
00:00 / 00:00
Video D2.5
Courtesy of Linda K. Gilkey. From A.L. Gibson, D. Wagner, and V.H. Heyward, Advanced Fitness and Exercise Prescription, 8th ed. (Champaign, IL: Human Kinetics, 2019).
676
Appendix D.3 Skinfold Sites for Jackson’s Generalized Skinfold
Equations
From A.L. Gibson, D. Wagner, and V.H. Heyward, Advanced Fitness and Exercise Prescription, 8th ed. (Champaign, IL: Human Kinetics, 2019).
677
Appendix D.4 Standardized Sites for Circumference
Measurements
678
00:00 / 00:00
Video D4.1
00:00 / 00:00
Video D4.2
679
Appendix D.5 Standardized Sites for Bony Breadth
Measurements
00:00 / 00:00
Video D5.1
680
00:00 / 00:00
Video D5.2
681
Appendix D.6 Ashwell Body Shape Chart
Adapted, by permission, from Ashwell Associates. © Dr. Margaret Ashwell OBE. From A.L. Gibson, D. Wagner, and V.H. Heyward, Advanced Fitness and Exercise Prescription, 8th ed. (Champaign, IL: Human Kinetics, 2019).
682
APPENDIX E
Energy Intake and Expenditure
You can use the Food Record and RDA Profile (appendix E.1) to obtain information about
your clients’ energy intake and daily energy needs.
Your clients may use the Physical Activity Log (appendix E.2) to record the type and
duration of physical activities they engage in daily. This provides an estimate of their daily
caloric expenditure due to activity. Appendix E.3 presents MET estimates of gross
expenditure for conditioning exercises, sports, and recreational activities. You can use these
estimates to calculate your clients’ energy expenditure (kcal·min ) for a variety of activities.
–1
683
Appendix E.1 Food Record and RDA Profile
684
Food code: This is generally for office use. If you have the food code list, however, use this
space to more precisely describe your food item.
Amount: You can use common measures (cup, slice, etc.) or weight for your foods.
Food description: Be specific. For example, bread choices include soft and firm textures;
vegetables may be raw or cooked fresh, frozen, or canned; meats should be lean only or lean
with some fat; fruit juices are fresh, frozen, or canned; and cheese might be cream or skim,
soft, hard, or cottage.
685
Most people engage in a variety of activities in a 24 hr period, and each activity can use a
different amount of energy. Thus, any table of activity levels must depend on averages.
Choose the level that represents your normal daily average.
1. Sedentary: Inactive, sometimes under someone else’s care. Energy level is for basal
metabolism plus about 15% for minimal activities.
2. Lightly active: Most professionals (lawyers, doctors, accountants, architects, etc.),

office workers, shop workers, teachers, homemakers with mechanical appliances,
unemployed persons.
3. Moderately active: Most persons in light industry, building workers (excluding heavy
laborers), many farm workers, active students, department store workers, soldiers not
in active service, people engaged in commercial fishing, homemakers without
mechanical household appliances.
4. Very active: Full-time athletes, dancers, unskilled laborers, some agricultural workers
(especially in peasant farming), forestry workers, army recruits, soldiers in active
service, mine workers, steel workers.
5. Exceptionally active: Lumberjacks, blacksmiths, female construction workers,

rickshaw pullers.
From ESHA Research, Salem, OR. From A.L. Gibson, D.R. Wagner, and V.H. Heyward, Advanced Fitness Assessment and Exercise Prescription, 8th ed. (Champaign, IL: Human Kinetics, 2019).
686
Appendix E.2 Physical Activity Log
Name:_______________________________ Date:_______________________________
687
Appendix E.3 Gross Energy Expenditure for Conditioning
Exercises, Sports, and Recreational Activities
688
689
690
691
APPENDIX F
Flexibility and Low Back Care Exercises
Appendix F.1 describes and illustrates selected static stretching exercises for flexibility. This
information is organized by body region and muscle groups. Appendix F.2 summarizes
Exercise Dos and Don’ts. For each contraindicated exercise, a safe alternative exercise is
presented.
Recommended exercises for low back care programs are illustrated in appendix F.3. This
appendix provides a description and identifies muscle groups involved for each exercise.
692
Appendix F.1 Selected Flexibility Exercises
ANTERIOR THIGH REGION
Muscle Groups: Quadriceps and Hip Flexors
Exercise 1
Description: From a standing position, raise one foot toward hips and grasp ankle. Pull leg
upward toward buttocks.
693
Exercise 2
Description: Lying on your side, flex the knee and grasp the ankle. Press the foot into the
hand and squeeze pelvis forward. Do not pull the foot.
694
Exercise 3
Description: In a prone position, flex the knee and grasp ankle or foot with both hands. Do
not pull on the foot. Keep knees on the floor and do not arch the back.
695
POSTERIOR THIGH REGION
Muscle Groups: Hamstrings and Hip Extensors
Exercise 1
Description: In a supine position, grasp knee and pull knee toward chest, then flex head to
knee.
00:00 / 00:00
Video F1.1
696
Exercise 2
Description: From a long-sitting position, grasp ankles and flex trunk to legs.
697
Exercise 3
Description: From a standing position, place your foot on a low step, keep the knee flexed
slightly, and bend from the hips until you feel the stretch.
698
Exercise 4
Description: From a sitting position, with one knee flexed, flex the trunk keeping the spine
extended until you feel tension.
699
Exercise 5
Description: From a lying position, with one leg extended and the other leg flexed, grasp leg
with both hands and flex thigh to trunk.
700
GROIN REGION (MEDIAL THIGH REGION)
Muscle Group: Hip Adductors
Exercise 1
Description: From a tailor-sitting position, with soles of feet together, place hands on inside of
knees and push downward slightly.
701
Exercise 2
Description: From a straddle-standing position, flex one knee and hip, lowering body closer to
floor.
702
Exercise 3
Description: Standing on one leg while supporting yourself against wall or chair abduct hip,
keeping leg straight. Have partner, if available, grasp ankle and passively stretch the muscle
further.
703
LATERAL THIGH-TRUNK REGION
Muscle Groups: Hip Abductors and Trunk Lateral Flexors
Exercise 1
Description: From standing position, with arms overhead, clasp hands together and laterally
flex trunk to side no more than 20°.
704
Exercise 2
Description: From a crossed-leg sitting position, rotate trunk to the right. Place hands on
right side of upper leg and pull. Repeat to opposite side.
705
POSTERIOR LEG REGION
Muscle Group: Plantar Flexors
Exercise 1
Description: Assume front-leaning position against wall or chair with one foot ahead of the
other. Flex hip, knee, and ankle to lower your body closer to the ground, keeping feet flat on
floor.
706
Exercise 2
Description: Standing with balls of feet on stairs, curb, or wood block, lower heels to floor.
707
ANTERIOR LEG REGION
Muscle Group: Dorsiflexors
Exercise 1
Description: Standing with ankle of the nonsupporting leg fully extended, stretch the
dorsiflexors by slowly flexing the knee of the supporting leg.
708
UPPER AND LOWER BACK REGIONS
Muscle Group: Trunk Extensors
Exercise 1
Description: Sit with legs crossed and arms relaxed. Tuck chin and curl forward, attempting to
touch forehead to knees.
709
Exercise 2
Description: In a supine position, with knees flexed, grasp thighs below the knee caps and
bring knees to chest. Flatten lower back to floor.
710
Exercise 3
Description: From a kneeling position, bring chin to chest. Contract abdomen and buttocks
muscles while rounding lower back.
711
ANTERIOR CHEST, SHOULDER, AND ABDOMINAL REGIONS
Muscle Groups: Shoulder Flexors and Adductors, Trunk Flexors
Exercise 1
Description: In a prone position, push up until elbows are extended. Keep pelvis and hips on
floor.
712
Exercise 2
Description: Grasp towel or rope with both hands. Rotate arms overhead behind trunk.
713
Exercise 3
Description: Clasp hands together behind trunk with elbows extended. Slowly raise arms
upward.
00:00 / 00:00
Video F1.2
714
715
Appendix F.2 Exercise Dos and Don’ts
716
717
718
719
Appendix F.3 Exercises for Low Back Care
Pelvic Tilt (stretches abdominal muscles)
Lie on your back with knees bent, feet flat on the floor, and arms at your sides. Flatten the
small of your back against the floor. (Your hips will tilt upward.) Hold.
720
Double Knee to Chest (stretches hip, buttock, and lower back muscles)
Lie on your back with knees bent, feet flat on the floor, and arms at your sides. Raise both
knees, one at a time, to your chest and hold both with your hands. Lower your legs, one at a
time, to the floor and rest briefly.
721
Trunk Flex (stretches back, abdominal, and leg muscles)
On your hands and knees, tuck in your chin and arch your back. Slowly sit back on your
heels, letting your shoulders drop toward the floor. Hold.
722
Cat and Camel (strengthens back and abdominal muscles)
On your hands and knees with your head parallel to the floor, arch your back and then let it
slowly sag toward the floor. Try to keep your arms straight.
723
Partial Sit-Up (strengthens abdominal muscles)
Lie on your back with knees bent, feet flat on the floor, and arms crossed over your chest.
Keeping your middle and lower back flat on the floor, raise your head and shoulders off the
floor, and hold. Gradually increase your holding time.
724
Single-Leg Extension (strengthens hip and buttock muscles; stretches abdominal and leg
muscles)
Lie on your abdomen with your arms folded under your chin. Slowly lift one leg—not too
high—without bending it, while keeping your pelvis flat on the floor. Slowly lower your leg
and repeat with the other leg.
725
Single-Leg Extension Hold (strengthens trunk extensors)
On your hands and knees with your head parallel to the floor, extend your thigh and leg and
hold this position. Raising the contralateral arm simultaneously is more difficult and increases
the extensor muscle activity and spinal compression.
726
Curl-Up With Leg Extended (strengthens abdominal muscles)
Lie on your back with one knee flexed (foot flat on floor) and the other knee extended. Place
your hands under the lumbar spine to preserve the neutral spine position. Slowly raise your
head and shoulders off the floor.
727
Isometric Side Support or Side Bridge (strengthens lateral muscles of trunk and abdomen)
Assume a side support position with body supported by the knee, thigh, and forearm (flexed
to 90°), and hold this position. Supporting the body with the feet, instead of the knee and
thigh, increases the muscle activity and spinal load.
728
Standing Cat and Camel (strengthens back and abdominal muscles)
Stand with feet shoulder-width apart and with hands on knees. Straighten back and hold this
position. Perform 10 to 20 repetitions.
729
Bent-Knee Curl-Up (strengthens abdominal muscles)
Lie on your back with one knee bent and with foot flat on the floor. Place arms across chest.
Lift shoulders off ground and hold this position momentarily. Perform 10 to 20 repetitions.
730
Modified Front Bridge (strengthens back and abdominal muscles)
Assume a front support position with the body supported by the forearms (elbows flexed to
90°), knees, and toes. Hold this position for 10 to 20 counts.
731
Modified Bird Dog (strengthens hip extensors)
Assume a front support position with the body supported by both hands (shoulder-width
apart and elbows extended), one knee, and one foot. Extend unsupported leg so that thigh is
parallel with the trunk. Hold this position momentarily. Perform 10 repetitions for each leg.
Support the body with one arm to increase the difficulty of this exercise.
732
Standing McKenzie Exercise (stretches abdominal muscles; strengthens back extensors)
Assume a standing position with feet shoulder-width apart and with hands placed on hips.
Extend the trunk and hold this position momentarily. Perform 10 repetitions.
733
List of Abbreviations
Terms
%BF Percent body fat
A-mode Amplitude modulation
ACSM American College of Sports Medicine
ADL Activities of daily living
ADP Air displacement plethysmography
AIT Aerobic interval training
APHRmax Age-predicted maximal heart rate
ATP Adenosine triphosphate
AV Atrioventricular
AVG Active video game
B-mode Brightness modulation
BBS Berg Balance Scale
BESS Behavioral Error Scoring System
BIA Bioelectrical impedance analysis
BIS Bioimpedance spectroscopy
BM Body mass
BMI Body mass index
BMR Basal metabolic rate
BP Blood pressure
BSA Body surface area
BV Body volume
BW Body weight
C Circumference
734
CAAHEP Commission on Accreditation of Allied Health Education Programs
CDC Centers for Disease Control and Prevention
CE Constant error
CHD Coronary heart disease
CMJ Countermovement jump
CR Contract-relax
CRAC Contract-relax with agonist contraction
CREP Coalition for the Registration of Exercise Professionals
CRF Cardiorespiratory fitness
CrP Creatine phosphate
CSA Cross-sectional area
CSEP Canadian Society for Exercise Physiology
CTT Counting talk test
CV Cardiovascular
CVD Cardiovascular disease
D Skeletal diameter
Db Body density
DBP Diastolic blood pressure
DDR Dance Dance Revolution
DOMS Delayed-onset muscle soreness
DXA Dual-energy X-ray absorptiometry
EAT Exercise activity thermogenesis
ECG Electrocardiogram
EDD Exercise deficit disorder
EIM Exercise is Medicine®
EIMD Exercise-induced muscle damage
EMG Electromyography
ePARmed-
electronic Physical Activity Readiness Medical Examination
735
X+ electronic Physical Activity Readiness Medical Examination
ESH-IP European Society of Hypertension International Protocol
FFB Fat-free body
FFM Fat-free mass
FITT-VP Frequency, intensity, time, type of exercise, volume, and progression
FM Fat mass
FRC Functional residual lung capacity
FSR Final stepping rate
GH Growth hormone
GI Glycemic index
GIS Geographical information system
GPS Global positioning system
GV Volume of air in gastrointestinal tract
GWAS Genome-wide associated study
GXT Graded exercise test
HDL High-density lipoprotein
HDL-C High-density lipoprotein cholesterol
HIT High-intensity interval training
HMB β-hydroxy-β-methylbutyrate
HR Heart rate
HRmax Maximal heart rate
HRrest Resting heart rate
HRR Heart rate reserve
HT Standing height
HT /R2 Resistance index
HW Hydrostatic weighing
IDF International Diabetes Federation
736
IOM Institute of Medicine
LDL Low-density lipoprotein
LDL-C Low-density lipoprotein cholesterol
LP Linear periodization
LTPA Leisure-time physical activity
MET Metabolic equivalent of task
MET·min MET minutes
MetS Metabolic syndrome
MIPA Moderate-intensity physical activity
MRI Magnetic resonance imaging
MVC Maximal voluntary contraction
MVPA Moderate- to vigorous-physical activity
N Sample size
NBFE National Boards of Fitness Examiners
NCCA National Commission for Certifying Agencies
NCD Noncommunicable disease
NCEP National Cholesterol Education Program
NCEP-
National Cholesterol Education Program – Adult Treatment Panel III
ATPIII
NEAT non-exercise activity thermogenesis
NHANES National Health and Nutrition Examination Survey
NIDDM Non-insulin-dependent diabetes mellitus
NIH National Institutes of Health
NIR Near-infrared interactance
P Power output
PAL Physical activity level
PAR-Q Physical Activity Readiness Questionnaire
PARmed-
737
PARmed- Physical Activity Readiness Medical Examination Questionnaire
X
PEI Physical efficiency index
PFA Perceived functional ability
PNF Proprioceptive neuromuscular facilitation
POMA Performance-Oriented Mobility Assessment
PRET Perceptually regulated exercise tests
Q̇ Cardiac output
ρ Specific resistivity
R Resistance for bioimpedance analysis
r Pearson product–moment correlation
RDA Recommended dietary allowance
REE Resting energy expenditure
REP Repetition
RER Respiratory exchange ratio
RLP Reverse linear periodization
RM Repetition maximum
Rmc Multiple correlation coefficient
RMR Resting metabolic rate
ROM Range of motion
RPE Rating of perceived exertion
RV Residual (lung) volume
SAD Sagittal abdominal diameter
SAT Subcutaneous adipose tissue
SBP Systolic blood pressure
SCENIHR Scientific Committee on Emerging and Newly Identified Health
SEE Standard error of estimate
Society of Health and Physical Educators; commonly referred to as SHAPE

SHAPE
738
SHAPE Education, Recreation, and Dance)
SIT Sprint interval training
SKF Skinfold
∑SKF Sum of skinfolds
SRP Steep ramp cycling protocol
SV Stroke volume
TBW Total body water
TC Total cholesterol
TC/HDL-
Ratio of total cholesterol to HDL-cholesterol
C
TE Total error
TEE Total energy expenditure
TGV Thoracic gas volume
TLC Total lung capacity
TLCNS Total lung capacity, head not submerged
TMS Transcranial magnetic stimulation
UWW Underwater weight
UP Undulating periodization
VAT Visceral adipose tissue
VIPA Vigorous-intensity physical activity
VLDL Very low-density lipoprotein
V̇O 2 Volume of oxygen consumed per minute
V̇O max
2 Maximal oxygen uptake
V̇O R2 Oxygen uptake reserve
VT Ventilatory threshold
WBAN Wireless body area network
WBV Whole-body vibration
739
WHR Waist-to-hip ratio
WHO World Health Organization
WHTR Waist-to-height ratio
Xc Reactance
YMCA Young Men’s Christian Association
YYIR1C Modified Yo-Yo Intermittent Recovery Level 1 test for children
Z Impedance
Units of Measure
bpm beats per minute
C Celsius
cc cubic centimeter
cm centimeter
dl deciliter
F Fahrenheit
ft-lb foot-pound
g gram
hr hour
Hz hertz
in. inch
kcal kilocalorie
kg kilogram
kgm kilogram-meter
kHz kilohertz
km kilometer
L liter
lb pound
m meter
meq milli-equivalent
740
meq milli-equivalent
mg milligram
mi mile
min minute
ml milliliter
mm millimeter
mmHg millimeters of mercury
mo month
mph miles per hour
N newton
Nm newton-meter
rpm revolutions per minute
sec second
W watt
wk week
yr year
μg microgram
μg RE retinol equivalent
Ω ohm
741
Glossary
absolute V̇O2—Measure of rate of oxygen consumption and energy cost of non-weight-bearing activities; measured in L·min
−1 or ml·min−1.
accelerometer—Device used to record body acceleration from minute to minute, providing detailed information about
frequency, duration, intensity, and patterns of movement.
accommodating-resistance exercise—Type of exercise in which fluctuations in muscle force throughout the range of motion
are matched by an equal counterforce as the speed of limb movement is kept at a constant velocity; isokinetic exercise.
acquired immune deficiency syndrome (AIDS)—Disease characterized as a deficiency in the body’s immune system, caused
by human immunodeficiency virus (HIV).
active-assisted stretching—Stretching technique that involves voluntarily moving a body part to the end of its active range of
motion, followed by assistance in moving the body part beyond its active range of motion.
active stretching—Stretching technique that involves moving a body part without external assistance; voluntary muscle
contraction.
activities of daily living (ADLs)—Normal everyday activities such as getting out of a chair or car, climbing stairs, shopping,
dressing, and bathing.
acute-onset muscle soreness—Soreness or pain occurring during or immediately after exercise; caused by ischemia and
accumulation of metabolic waste products in the muscle.
aerobic interval training (AIT)—Subclass of high-intensity interval training; consists of repeated combinations of near
maximal (80%-95% V̇O2R) 4 min bouts of exercise and rest or recovery periods of similar duration.
air displacement plethysmography (ADP)—Densitometric method to estimate body volume using air displacement and
pressure-volume relationships.
allele—One member of a pair or series of genes that occupy a specific position on a specific chromosome.
android obesity—Type of obesity in which excess body fat is localized in the upper body; upper body obesity; apple-shaped
body.
aneurysm—Dilation of a blood vessel wall causing a weakness in the vessel’s wall; usually caused by atherosclerosis and
hypertension.
angina pectoris—Chest pain.
ankylosis—Limited range of motion at a joint.
anorexia nervosa—Eating disorder characterized by excessive weight loss.
anthropometry—Measurement of body size and proportions including skinfold thicknesses, circumferences, bony widths
and lengths, stature, and body weight.
aortic stenosis—Narrowing of the aortic valve, obstructing blood flow from the left ventricle into the aorta.
Archimedes’ principle—Principle stating that weight loss underwater is directly proportional to the volume of water
displaced by the body’s volume.
arrhythmia—Abnormal heart rhythm.
arteriosclerosis—Hardening of the arteries, or thickening and loss of elasticity in the artery walls that obstruct blood flow;
caused by deposits of fat, cholesterol, and other substances.
742
asthma—Respiratory disorder characterized by difficulty in breathing and wheezing due to constricted bronchi.
ataxia—Impaired ability to coordinate movement characterized by staggering gait or postural imbalance.
atherosclerosis—Buildup and deposition of fat and fibrous plaque in the inner walls of the coronary arteries.
atrial fibrillation—Cardiac dysrhythmia in which the atria quiver instead of pumping in an organized fashion.
atrial flutter—Type of atrial tachycardia in which the atria contract at rates of 230 to 380 bpm.
atrophy—A wasting or decrease in size of a body part.
attenuation—Weakening of X-ray energy as it passes through fat, lean tissue, and bone.
augmented unipolar leads—Three ECG leads (aVF, aVL, aVR) that compare voltage across each limb lead to the average
voltage across the two opposite electrodes.
auscultation—Method used to measure heart rate or blood pressure by listening to heart and blood sounds.
autogenic inhibition—A theory to explain the effectiveness of PNF stretching by claiming a decrease in the excitability of
the targeted muscle due to inhibitory signals from the Golgi tendon organ during the static contraction portion of PNF.
autophagy—A cell-level recycling program that sequesters damaged organelles and misfolded proteins, breaks them down
into smaller pieces, and reuses those smaller components to support cellular viability.
auxotonic muscle action—Type of dynamic muscle action in which there is variable muscle tension as a result of changing
velocities and joint angles.
balance—Complex construct involving multiple biomechanical, neurological, and environmental systems.
ballistic stretching—Type of stretching exercise that uses a fast bouncing motion to produce stretch and increase range of
motion.
basal metabolic rate (BMR)—Measure of minimal amount of energy needed to maintain basic and essential physiological
functions.
behavior modification model—Psychological theory of change; clients become actively involved with the change process by
setting short- and long-term goals.
β-hydroxy-β-methylbutyrate (HMB)—Dietary supplement known to increase lean body mass and strength of individuals
engaging in resistance training.
bias—In regression analysis, a systematic over- or underestimation of actual scores caused by technical error or biological
variability between validation and cross-validation samples; constant error.
biaxial joint—Joint allowing movement in two planes; condyloid and saddle joints.
bioelectrical impedance analysis (BIA)—Field method for estimating the total body water or fat-free mass using measures of
impedance to current flowing through the body.
bioimpedance spectroscopy (BIS)—Type of bioimpedance analysis that combines upper body, lower body, and whole-body
bioimpedance to estimate FFM and %BF; utilizes a range of electrical frequencies and allows for determination of
extracellular water (low-level frequencies) and intracellular water (high-level frequencies).
Bland and Altman method—Statistical approach used to assess the degree of agreement between methods by calculating the
95% limits of agreement and confidence intervals; used to judge the accuracy of a prediction equation or method for
estimating measured values of individuals in a group.
body composition—A component of physical fitness; absolute and relative amounts of muscle, bone, and fat tissues
composing body mass.
body density (Db)—Overall density of fat, water, mineral, and protein components of the human body; total body mass
expressed relative to total body volume.
body mass (BM)—Measure of the size of the body; body weight.
743
body mass index (BMI)—Crude index of obesity; body mass (kg) divided by height squared (m2).
body surface area—Amount of surface area of the body estimated from the client’s height and body weight.
body volume (BV)—Measure of body size estimated by water or air displacement.
body weight (BW)—Mass or size of the body; body mass.
bone strength—Function of mineral content and density of bone tissue; related to risk of bone fracture.
Boyle’s law—Isothermal gas law stating that volume and pressure are inversely related.
bradycardia—Resting heart rate <60 bpm.
bronchitis—Acute or chronic inflammation of the bronchi of the lungs.
caloric threshold—Method to estimate duration of exercise based on the caloric cost of the exercise and to estimate the total
amount of exercise needed per week for health benefits.
cardiac arrest—Sudden loss of heart function usually caused by ventricular fibrillation.
cardiomyopathy—Any disease that affects the structure and function of the heart.
cardiorespiratory endurance—Ability of heart, lungs, and circulatory system to efficiently supply oxygen to working muscles.
cardiovascular disease (CVD)—Disease of the heart, blood vessels, or both; types of cardiovascular disease include
atherosclerosis, hypertension, coronary heart disease, congestive heart failure, and stroke.
center of pressure—Vertical force applied to the supporting base or a force platform during sitting or standing.
chest leads—Six ECG leads (V1 to V6) used to measure voltage across specific areas of the chest.
cholesterol—Waxy, fatlike substance found in all animal products (e.g., meats, dairy products, and eggs).
chylomicron—Type of lipoprotein derived from intestinal absorption of triglycerides.
circumference (C)—Measure of the girth of body segments.
cirrhosis—Chronic degenerative disease of the liver in which the lobes are covered with fibrous tissue; associated with
chronic alcohol abuse.
claudication—Cramp-like pain in the calves due to poor circulation in leg muscle.
compound sets—Advanced resistance training system in which two sets of exercises for the same muscle group are
performed consecutively, with little or no rest between sets.
computerized dynamic posturography—Computer system designed to assess the individual and composite functioning of
sensory, motor, and biomechanical components of balance.
concentric muscle action—Type of dynamic muscle contraction in which muscle shortens as it exerts tension.
congestive heart failure—Impaired cardiac pumping caused by myocardial infarction, ischemic heart disease, or
cardiomyopathy.
constant error (CE)—Average difference between measured and predicted values for cross-validation group; bias.
constant-resistance exercise—Type of exercise in which the external resistance remains the same throughout the range of
motion (e.g., lifting free weights or dumbbells).
continuous exercise test—Type of graded exercise test that is performed with no rest between workload increments.
continuous training—One continuous aerobic exercise bout performed at low to moderate intensity.
contract-relax agonist contract (CRAC) technique—Type of proprioceptive neuromuscular facilitation technique in which
the target muscle is isometrically contracted and then stretched; stretching is assisted by a submaximal contraction of the
agonistic muscle group.
contract-relax (CR) technique—Type of proprioceptive neuromuscular facilitation technique in which the target muscle is
isometrically contracted and then stretched.
contracture—Shortening of resting muscle length caused by disuse or immobilization.
744
core stability—Ability to maintain ideal alignment of neck, spine, scapulae, and pelvis while exercising.
core strengthening—Strengthening core muscle groups (erector spinae and abdominal movers and stabilizers) used for core
stability.
coronary heart disease (CHD)—Disease of the heart caused by a lack of blood flow to heart muscle, resulting from
atherosclerosis.
countermovement jump (CMJ)—Commonly employed jumping technique that includes a quick eccentric movement
(flexion of hips and knees and backward swing of arms) followed immediately by an explosive concentric movement to propel
the individual upward or forward.
counting talk test (CTT)—Method to monitor exercise intensity; measure of the client’s ability to comfortably count out
loud while exercising; based on the relationship between exercise intensity and pulmonary ventilation.
criterion method—Gold standard, or reference method; typically a direct measure of a component used to validate other
tests.
cross-training—Type of training in which the client participates in a variety of exercise modes to develop one or more
components of physical fitness.
cuff hypertension—Overestimation of blood pressure caused by use of a bladder that is too small for the arm circumference.
cyanosis—Bluish discoloration of skin caused by lack of oxygenated hemoglobin in the blood.
damping technique—Technique used to reduce the motion of the underwater weighing scale arm during the total body
submersion process.
decision-making theory—Theory stating that individuals decide whether or not to engage in a behavior by weighing the
perceived benefits and costs of that behavior.
delayed-onset muscle soreness (DOMS)—Soreness in the muscle occurring 24 to 48 hr after exercise.
densitometry—Measurement of body volume leading to calculation of total body density; hydrodensitometry and air
displacement plethysmography are densitometric methods.
diabetes—Complex disorder of carbohydrate, fat, and protein metabolism resulting from a lack of insulin secretion (type 1)
or defective insulin receptors (type 2).
diastolic blood pressure (DBP)—Lowest pressure in the artery during the cardiac cycle.
dietary thermogenesis—Energy needed for digesting, absorbing, transporting, and metabolizing foods.
diminishing returns principle—Training principle; as genetic ceiling is approached, rate of improvement slows or evens off.
discontinuous exercise test—Type of graded exercise test that is performed with 5 to 10 min of rest between increments in
workload.
discontinuous training—Several intermittent, low- to high-intensity aerobic exercise bouts interspersed with rest or relief
intervals.
dose-response relationship—The volume of physical activity is directly related to health benefits from that activity.
dual-energy X-ray absorptiometry (DXA)—Method used to measure total body bone mineral density and bone mineral
content as well as to estimate fat and lean soft tissue mass.
dynamic balance—Ability to maintain an upright position while the center of gravity and base of support are moving.
dynamic flexibility—Measure of the rate of torque or resistance developed during stretching throughout the range of joint
motion.
dynamic muscle action—Type of muscle contraction producing visible joint movement; concentric, eccentric, or isokinetic
contraction.
dynamic stretching—Type of stretching exercise that uses slow, controlled movements that are repeated several times to
produce stretch and increase range of motion.
745
dynapenia—Age-related loss in muscle strength.
dyslipidemia—Abnormal blood lipid profile.
dyspnea—Shortness of breath or difficulty breathing caused by certain heart conditions, anxiety, or strenuous exercise.
eccentric muscle action—Type of muscle contraction in which the muscle lengthens as it produces tension to resist gravity or
decelerate a moving body segment.
eccentric training—Resistance training strategy that places an emphasis on eccentric muscle actions, often using specialized
machines to administer an eccentric load.
edema—Accumulation of interstitial fluid in tissues such as the pericardial sac and joint capsules.
elastic deformation—Deformation of the muscle-tendon unit that is proportional to the load or force applied during
stretching.
electrocardiogram (ECG)—A composite record of the electrical events in the heart during the cardiac cycle.
electrogoniometer—Flexible strain gauges that cross the joint to measure range of motion; capable of measuring range of
motion in two planes simultaneously.
elevated blood pressure—Systolic blood pressure ranging from 120 to 129 mmHg and diastolic blood pressure lower than 80
mmHg.
embolism—Piece of tissue or thrombus that circulates in the blood until it lodges in a vessel.
emphysema—Pulmonary disease causing damage to alveoli and loss of lung elasticity.
exercise activity thermogenesis (EAT)—The portion of physical activity energy expenditure derived from exercise.
exercise deficit disorder (EDD)—Term associated with children who do not engage in at least 60 min/day of moderate- to
vigorous-intensity physical activity.
exercise-induced hypertrophy—Increase in size of muscle as a result of resistance training.
exercise-induced muscle damage (EIMD)—Skeletal muscle damage induced through exercise.
exergaming—Interactive digital games in which the player physically moves to score points.
factorial method—Method used to assess energy needs; the sum of the resting metabolic rate and the additional calories
expended during work, household chores, personal daily activities, and exercise.
false negative—An error in which individuals are incorrectly identified as having no risk factors when in fact they do have
risk factors.
false positive—An error in which individuals are incorrectly identified as having risk factors when they do not have risk
factors.
fat-free body (FFB)—All residual, lipid-free chemicals and tissues in the body, including muscle, water, bone, connective
tissue, and internal organs.
fat-free mass (FFM)—See fat-free body; weight or mass of the fat-free body.
fat mass (FM)—All extractable lipids from adipose and other tissues in the body.
FITT-VP principle (FITT-VP)—Describes six components of an exercise prescription: frequency, intensity, time, type,
volume, and progression of activity.
flexibility—Ability to move joints fluidly through complete range of motion without injury.
flexibility training—Systematic program of stretching exercises that progressively increases the range of motion of joints over
time.
flexometer—Device for measuring range of joint motion using a weighted 360° dial and pointer.
free-motion machines—Resistance exercise machines that have adjustable seats, lever arms, and cable pulleys for exercising
muscle groups in multiple planes.
746
functional balance—Ability to perform daily activities requiring balance (e.g., picking up an object from the floor).
functional fitness—Ability to perform everyday activities safely and independently without fatigue; requires aerobic
endurance, flexibility, balance, agility, and muscular strength.
functional training—System of exercise progressions for specific muscle groups using a stepwise approach that increases the
difficulty level (strength) and skill (balance and coordination) required for each exercise in the progression.
gait velocity—The speed of walking. Indirect measure of dynamic balance while walking used to detect mobility problems
and risk of falling.
generalized prediction equations—Prediction equations that are applicable to a diverse, heterogeneous group of individuals.
genome-wide assessment studies (GWAS)—An investigation across the human genome that compares DNA variants of
individuals presenting different phenotypes for a disease or trait; typically involves use of control group (disease or trait not
present) and affected group with the disease or trait.
global positioning system (GPS)—System that uses 24 satellites and ground stations to calculate geographic locations and
accurately track a specific activity.
glucose intolerance—Inability of the body to metabolize glucose.
goniometer—Protractor-like device used to measure joint angle at the extremes of the range of motion.
graded exercise test (GXT)—A multistage submaximal or maximal exercise test requiring the client to exercise at gradually
increasing workloads; may be continuous or discontinuous; used to estimate V̇O2max.
Graves’ disease—Disease associated with an overactive thyroid gland that secretes greater than normal amounts of thyroid
hormones; also known as hyperthyroidism or thyrotoxicosis.
gross V̇O2—Total rate of oxygen consumption, reflecting the caloric cost of both rest and exercise.
gynoid obesity—Type of obesity in which excess fat is localized in the lower body; lower body obesity; pear-shaped body.
HbA1c—An indicator of the average blood glucose over the previous 2 to 3 mo; glycosylated hemoglobin.
HDL-cholesterol (HDL-C)—Cholesterol transported in the blood by high-density lipoproteins.
health belief model—Model suggesting that individuals will change a behavior because they perceive a threat of disease if
they do not change.
healthy body weight—Body mass index from 18.5 to 25 kg/m2.
heart block—Interference in the conduction of electrical impulses that control normal contraction of the heart muscle; may
occur at sinoatrial node, atrioventricular node, bundle of His, or a combination of these sites.
heart rate monitor—Device used to assess heart rate and to monitor exercise intensity.
heart rate reserve (HRR)—Maximal heart rate minus the resting heart rate.
heart rate variability (HRV)—Differences in the time intervals between consecutive resting heart beats; reflects autonomic
nervous system function.
hemiscan procedure—Used for clients who are too wide for the DXA scan table; client is positioned off center on the DXA
scan table so that one side of the body is completely within the scan field.
hepatitis—Inflammation of the liver characterized by jaundice and gastrointestinal discomfort.
high blood pressure—Hypertension; chronic elevation of blood pressure.
high-density lipoprotein (HDL)—Type of lipoprotein involved in the reverse transport of cholesterol to the liver.
high-intensity interval training (HIT)—Style of cardiometabolic training based on repeated combinations of vigorous-
intensity exertion followed by a rest or recovery period; commonly performed using an aerobic modality; combinations of
exertion and rest can be manipulated so that training focuses on a specific metabolic pathway.
high intensity–low repetitions—Optimal training stimulus for strength development; 85% to 100% 1-RM or 1-RM to 6-
747
RM.
hybrid sphygmomanometer—Device used to measure blood pressure that combines features of electronic and auscultatory
devices.
hydrodensitometry—Method used to estimate body volume by measuring weight loss when the body is fully submerged;
underwater weighing.
hydrostatic weighing (HW)—See hydrodensitometry.
hypercholesterolemia—Excess of total cholesterol, LDL-cholesterol, or both in blood.
hyperlipidemia—Excess lipids in blood.
hypermobility—Excessive range of motion at a joint.
hyperplasia—Increase in number of cells.
hypertension—High blood pressure; chronic elevation of blood pressure.
hyperthyroidism—Overactive thyroid gland that secretes greater than normal amounts of thyroid hormones; also known as
thyrotoxicosis or Graves’ disease.
hypertrophy—Increase in size of cells.
hypoglycemia—Low blood glucose level.
hypokalemia—Inadequate amount of potassium in the blood characterized by an abnormal ECG, weakness, and flaccid
paralysis.
hypomagnesemia—Inadequate amount of magnesium in the blood resulting in nausea, vomiting, muscle weakness, and
tremors.
hypothyroidism—Underactive thyroid gland that secretes lower than normal amounts of thyroid hormones; also known as
myxedema.
hypoxia—Inadequate oxygen at the cellular level.
impedance (Z)—Measure of total amount of opposition to electrical current flowing through the body; function of resistance
and reactance.
improvement stage—Stage of exercise program in which client improves most rapidly; frequency, intensity, duration are
systematically increased; usually lasts 4 to 8 mo.
inclinometer—Gravity-dependent goniometer used to measure the angle between the long axis of the moving segment and
the line of gravity.
initial conditioning stage—Stage of exercise program used as a primer to familiarize client with exercise training, usually
lasting 4 wk.
initial values principle—Training principle; the lower the initial value of a component, the greater the relative gain and the
faster the rate of improvement in that component; the higher the initial value, the slower the improvement rate.
insulin-dependent diabetes mellitus (IDDM)—Type 1 diabetes, caused by lack of insulin production by the pancreas.
interindividual variability principle—Training principle; individual responses to training stimulus are variable and depend on
age, initial fitness level, and health status.
interval training—A repeated series of exercise work bouts interspersed with rest or relief periods.
ischemia—Decreased supply of oxygenated blood to a body part or organ; due to occlusion or restriction of blood flow.
ischemic heart disease—Pathologic condition of the myocardium caused by lack of oxygen to the heart muscle.
isokinetic muscle action—Maximal contraction of a muscle group at a constant velocity throughout entire range of motion.
isometric muscle action—Type of muscle contraction in which there is no visible joint movement; static contraction.
isotonic muscle action—Type of muscle contraction producing visible joint movement; dynamic contraction.
748
joint laxity—Looseness or instability of a joint, increasing risk of musculoskeletal injury.
Karvonen method—Method to prescribe exercise intensity as a percentage of the heart rate reserve added to the resting heart
rate; percent heart rate reserve method.
kettlebell training—Type of resistance training that uses a cast-iron weight (resembling a cannonball with a handle) to
perform ballistic exercises; improves strength, cardiorespiratory fitness, and flexibility.
kilocalorie (kcal)—Amount of heat needed to raise the temperature of 1 kg of water 1 °C; measure of energy need and
expenditure.
lactate threshold—Exercise intensity at which blood lactate production exceeds blood lactate removal; denoted by an increase
of 1 mmol·L−1 between two consecutive stages; an indication of when the primary metabolic pathway switches from
mitochondrial oxidation to glycolysis.
LDL-cholesterol (LDL-C)—Cholesterol transported in the blood by low-density lipoproteins.
limb leads—Three ECG leads (I, II, III) measuring the voltage differential between left and right arms (I) and between the
left leg and right (II) and left (III) arms.
limits of agreement—Statistical method used to assess the extent of agreement between methods; also known as the Bland
and Altman method.
limits of stability—Measure of the maximum excursion of the center of gravity during maintenance of balance over a fixed
supporting base.
linear periodization (LP)—Strength training method that progressively increases training intensity as training volume
decreases between microcycles.
line of best fit—Regression line depicting relationship between reference measure and predictor variables in an equation.
line of gravity—Vertical projection of the center of gravity of the body to the supporting base.
line of identity—Straight line with a slope equal to 1 and an intercept equal to 0; used in a scatter plot to illustrate the
differences in the measured and predicted scores of a cross-validation sample.
lipoprotein—Molecule used to transport and exchange lipids among the liver, intestine, and peripheral tissues.
low back pain—Pain produced by muscular weakness or imbalance resulting from lack of physical activity.
low-density lipoprotein (LDL)—Primary transporter of cholesterol in the blood; product of very low-density lipoprotein
metabolism.
lower body obesity—Type of obesity in which excess body fat is localized in the lower body; gynoid obesity; pear-shaped
body.
low intensity–high repetition—Optimal training stimulus for development of muscular endurance; ≤60% 1-RM or 15-RM
to 20-RM.
lumbar stabilization—Maintaining a static position of the lumbar spine by isometrically cocontracting the abdominal wall
and low back muscles during exercise.
macrocycle—Phase of periodized resistance training program usually lasting 9 to 12 mo; comprised of mesocycles.
maintenance stage—Stage of exercise program designed to maintain level of fitness achieved by end of improvement stage;
should be continued on a regular, long-term basis.
masked hypertension—Condition in which individuals exhibit elevated BP readings outside the physician’s office but have
normal BP values in the office.
masked obesity—Condition in which individuals have a normal body mass index but carry an excessive amount of body fat.
maximal exercise test—Graded exercise test in which exercise intensity increases gradually until the V̇O2 plateaus or fails to
rise with a further increase in workload.
maximum oxygen consumption—Maximum rate of oxygen utilization by muscles during exercise; V̇O2 max.
749
maximum oxygen uptake (V̇O2max)—Maximum rate of oxygen utilization of muscles during aerobic exercise.
maximum voluntary contraction (MVC)—Measure of the maximum force exerted in a single contraction against an
immovable resistance.
McArdle’s syndrome—Inherited metabolic disease characterized by inability to metabolize muscle glycogen, resulting in
excessive amounts of glycogen stored in skeletal muscles.
mesocycle—Phase of a periodized resistance training program usually lasting 3 to 4 mo; comprised of microcycles.
metabolic equivalents (METs)—The ratio of the person’s working (exercising) metabolic rate to the resting metabolic rate.
metabolic syndrome (MetS)—A combination of cardiovascular disease risk factors associated with hypertension,
dyslipidemia, insulin resistance, and abdominal obesity.
MET·min—Index of energy expenditure; product of exercise intensity (METs) and duration (min) of exercise.
microcycle—Phase of a periodized resistance training program usually lasting 1 to 4 wk.
miscuffing—Source of blood pressure measurement error caused by use of a blood pressure cuff that is not appropriately
scaled for the client’s arm circumference.
multicomponent model—Body composition model that takes into account interindividual variations in water, protein, and
mineral content of the fat-free body.
multimodal exercise program—Type of exercise program that uses a variety of exercise modalities.
multiple correlation coefficient (Rmc)—Correlation between reference measure and predictor variables in a prediction
equation.
murmur—Low-pitched fluttering or humming sound.
muscle balance—Ratio of strength between opposing muscle groups, contralateral muscle groups, and upper and lower body
muscle groups.
muscular endurance—Ability of muscle to maintain submaximal force levels for extended periods.
muscular power—Ability to exert force per unit of time; rate of performing work.
muscular strength—Maximal force or tension level produced by a muscle or muscle group.
musculoskeletal fitness—Ability of skeletal and muscular systems to perform work.
myocardial infarction—Heart attack.
myocardial ischemia—Lack of blood flow to the heart muscle.
myocarditis—Inflammation of the heart muscle caused by viral, bacterial, or fungal infection.
myxedema—Disease associated with an underactive thyroid gland that secretes lower than normal amounts of thyroid
hormones; also known as hypothyroidism.
negative energy balance—Excess of energy expenditure in relation to energy intake.net V̇O2—Rate of oxygen consumption
in excess of the resting V̇O2; used to describe the caloric cost of exercise.
neuromotor training—Exercises to improve balance, agility, gait, coordination, and proprioception; especially beneficial as
part of comprehensive exercise programs for older adults.
nonaxial joint—Type of joint allowing only gliding, sliding, or twisting rather than movement about an axis of rotation;
gliding joint.
noncommunicable diseases (NCDs)— Diseases that cannot be transmitted from one person to another; cardiovascular
diseases, diabetes, obesity, chronic respiratory disorders, and most cancers.
non-exercise activity thermogenesis (NEAT)—The portion of physical activity energy expenditure that is not from a defined
exercise (e.g., fidgeting, activities of daily living).
non-insulin-dependent diabetes mellitus (NIDDM)—Type 2 diabetes; caused by decreased insulin receptor sensitivity.
750
normotensive—Referring to normal blood pressure, defined as values less than 120/80 mmHg.
obesity—Excessive amount of body fat relative to body mass; BMI of 30 kg/m2 or more.
obesity paradox—The hypothesis that obesity can be both harmful (i.e., increasing risk of certain disorders) and protective
(i.e., improving survival rates compared with those of nonobese individuals).
objectivity—Intertester reliability; ability of test to yield similar scores for a given individual when the same test is
administered by different technicians.
objectivity coefficient—Correlation between pairs of test scores measured on the same individuals by two different
technicians.
occlusion—Blockage or restriction of blood flow to a body part or organ.
one-repetition maximum (1-RM)—Maximal weight that can be lifted with good form for one complete repetition of a
movement.
oscillometry—Method for measuring blood pressure that uses an automated electronic manometer to measure oscillations in
pressure when the cuff is deflated.
osteoarthritis—Degenerative disease of the joints characterized by excessive amounts of bone and cartilage in the joint.
osteopenia—Low bone mineral mass; precursor to osteoporosis.
osteoporosis—Disorder characterized by low bone mineral and bone density; occurring most frequently in postmenopausal
women and sedentary individuals.
overcuffing—Using a blood pressure cuff with a bladder too large for the arm circumference, leading to an underestimation
of blood pressure.
overload principle—Training principle; physiological systems must be taxed beyond normal to stimulate improvement.
overweight—BMI between 25 and 29.9 kg/m2 in adults; BMI greater than or equal to 95th percentile for age and sex in
children.
pallor—Unnatural paleness or absence of skin color.
palpation—Method used to measure heart rate by feeling the pulse at specific anatomical sites.
palpitations—Racing or pounding of the heart.
passive stretching—Stretching technique that involves a body part being moved by an assistant as the client relaxes the target
muscle group.
pedometer—A device used to count the number of steps taken throughout the day.
pelvic stabilization—Maintenance of a static position of the pelvis during performance of exercises for the low back extensor
muscles.
percent body fat (%BF)—Fat mass expressed relative to body mass; relative body fat.
percent heart rate maximum (%HRmax)—Method used to prescribe exercise intensity as a percentage of the measured or
age-predicted maximum heart rate.
percent heart rate reserve (%HRR) method—Method used to prescribe exercise intensity as a percentage of the heart rate
reserve (HRR = HRmax − HRrest) added to the resting heart rate; Karvonen method.
percent V̇O2 reserve (%V̇O2R)—Method used to prescribe exercise intensity as a percentage of V̇O2 reserve (V̇O2R =
V̇O2max − V̇O2rest) added to the resting V̇O2.
perceptually regulated exercise test (PRET)—Graded exercise test in which the 3 min workloads are incrementally
progressed through intensities the client perceives as being equivalent to RPE values of 9, 11, 13, and 15 on the Borg 6 to 20
scale. Allows extrapolation to RPE values of 19 and 20 and, hence, V̇O2peak estimation via linear regression.
pericarditis—Inflammation of the pericardium caused by trauma, infection, uremia, or heart attack.
751
periodization—Advanced form of training that systematically varies the volume and intensity of the training exercises.
persuasive technology—A computer system, device, or application that is intentionally designed to change a person’s attitude
or behavior.
photoplethysmography (PPG)—A technology found in pulse oximeters and smart watches; uses light to determine heart
rate by detecting changes in blood volume and blood flow in capillary beds.
physical activity level (PAL)—The ratio of total energy expenditure to basal metabolic rate; PAL = TEE / BMR.
physical fitness—Ability to perform occupational, recreational, and daily activities without undue fatigue.
Pilates—An exercise method introduced by Joseph Pilates that blends aspects of gymnastics, yoga, and martial arts to
emphasize precision of body movement.
population-specific equations—Prediction equations intended only for use with individuals from a specific homogeneous
group.
positive energy balance—Excess of energy intake in relation to energy expenditure.
prediabetes—Medical condition identified by fasting blood glucose or glycated hemoglobin levels above normal values yet
below the threshold for diagnosis of diabetes.
PR interval—Part of ECG tracing that indicates delay in the impulse at the atrioventricular node.
progression principle—Training principle; training volume must be progressively increased to impose overload and stimulate
further improvements.
proprioceptive neuromuscular facilitation (PNF)—Mode of stretching that increases range of joint motion through spinal
reflex mechanisms such as reciprocal inhibition.
prosthesis—An artificial replacement of a missing body part, such as an artificial limb or joint.
pulmonary ventilation—Movement of air into and out of the lungs.
pulse pressure—Difference between the systolic and diastolic blood pressures.
P wave—Part of ECG tracing that reflects depolarization of the atria.
pyramiding—Advanced resistance training system in which a relatively light weight is lifted in the first set and progressively
heavier weights are lifted in subsequent sets; light to heavy system.
QRS complex—Part of ECG tracing reflecting ventricular depolarization and contraction.
ramp protocols—Graded exercise tests that are individualized and that provide for continuous frequent increments (every
10-20 sec) in work rate so that V̇O2 increases linearly.
range of motion (ROM)—Degree of movement at a joint; measure of static flexibility.
rating of perceived exertion (RPE)—A scale used to measure a client’s subjective rating of exercise intensity.
reactance (Xc)—Measure of opposition to electrical current flowing through the body due to the capacitance of cell
membranes; a vector of impedance.
reactive balance—Ability to compensate and recover from perturbations while standing or walking.
reciprocal inhibition—Reflex that inhibits the contraction of antagonistic muscles when the prime mover is voluntarily
contracted.
reference method—Gold standard, or criterion method; typically a direct measure of a component used to validate other
tests.
regression line—Line of best fit depicting relationship between reference measure and predictor variables.
relative body fat (%BF)—Fat mass expressed as a percentage of total body mass; percent body fat.
relative strength—Muscular strength expressed relative to the body mass or lean body mass; 1-RM / BM.
relative V̇O2max—Rate of oxygen consumption expressed relative to the body mass (ml·kg−1·min−1) or lean body mass
752
(ml·kgFFM−1·min−1).
reliability—Ability of a test to yield consistent and stable scores across trials and over time.
reliability coefficient—Correlation depicting relationship between trial 1 and trial 2 scores or day 1 and day 2 scores of a test.
repetition maximum (RM)—Measure of intensity for resistance exercise expressed as maximum weight that can be lifted for
a given number of repetitions.
repetitions—Number of times a specific exercise movement is performed in a set.
residual score—Difference between the actual and predicted scores (Y − Y').
residual volume (RV)—Volume of air remaining in the lungs following a maximal expiration.
resistance (R)—Measure of pure opposition to electrical current flowing through the body; a vector of impedance.
resistance index (ht2/R)—Predictor variable in some BIA regression equations that is calculated by dividing standing height
squared by resistance.
respiratory exchange ratio (RER)—Ratio of expired CO2 to inspired O2.
resting energy expenditure (REE)—Energy required to maintain essential physiological processes at rest; resting metabolic
rate.
resting metabolic rate (RMR)—Energy required to maintain essential physiological processes in a relaxed, awake, and
reclined state; resting energy expenditure.
reverse linear periodization (RLP)—Strength training method that progressively decreases training intensity as training
volume increases between microcycles.
reversibility principle—Training principle; physiological gains from training are lost when an individual stops training
(detraining).
rheumatic heart disease—Condition in which the heart valves are damaged by rheumatic fever, contracted from a
streptococcal infection (strep throat).
rheumatoid arthritis—Chronic, destructive disease of the joints characterized by inflammation and thickening of the
synovial membranes and swelling of the joints.
RPE-clamped protocol—Similar to PRET; client adjusts the workload to invoke a specific RPE value response per stage,
with the final stage requiring a workload equivalent to the highest RPE possible on a given scale (e.g., 10 on the 0 to 10 scale
or 20 on the Borg 6 to 20 scale).
sagittal abdominal diameter (SAD)—Measure of the anteroposterior thickness of the abdomen at the umbilical level.
sarcopenia—Age-related loss in muscle mass.
sedentarism—A lifestyle lacking in physical activity and dominated by excessive time spent sitting.
self-determination theory—Theory describing how the presence or absence of specific psychological needs affects behavior.
self-efficacy—Individuals’ perception of their ability to perform a task and their confidence in making a specific behavioral
change.
self-paced protocol—A free-form style of treadmill GXT in which the client adjusts (upward only) speed and incline
periodically, with the stipulation that the client must reach the point of volitional exhaustion within 8 to 12 minutes.
sensitivity—Probability of a test correctly identifying individuals with risk factors for a specific disease.
set—Defines the number of times a specific number of repetitions of a given exercise is repeated; single or multiple sets.
skeletal diameter (D)—Measure of the width of bones.
skinfold (SKF)—Measure of the thickness of two layers of skin and the underlying subcutaneous fat.
social cognitive model—Psychological theory of behavior change; based on concepts of self-efficacy and outcome
expectation.
753
specificity—Measure of a test’s ability to correctly identify individuals with no risk factors for a specific disease.
specificity principle—Training principle; physiological and metabolic responses and adaptations to exercise training are
specific to type of exercise and muscle groups involved.
sphygmomanometer—Device used to measure blood pressure manually, consisting of a blood pressure cuff and a
manometer.
Spinning—Group-led exercise that involves stationary cycling at various cadences and resistances.
split routine—Advanced resistance training system in which different muscle groups are targeted on consecutive days to
avoid overtraining.
sprint interval training (SIT) —Subclass of high-intensity interval training; based on repeated combinations of short (e.g.,
30 sec) sprints and extended (e.g., 4 min) rest or recovery intervals.
stage 1 hypertension—Systolic blood pressure ranging from 130 to 139 mmHg or diastolic blood pressure ranging from 80
to 89 mmHg.
stage 2 hypertension—Systolic blood pressure ≥140 mmHg or diastolic blood pressure ≥90 mmHg.
stages of motivational readiness for change model—Psychological theory of behavior change; ability to make long-term
behavioral change is based on the client’s emotional and intellectual readiness; stages of readiness are precontemplation,
contemplation, preparation, action, and maintenance.
standard error of estimate (SEE)—Measure of error for prediction equation; quantifies the average deviation of individual
data points around the line of best fit.
static balance—Ability to maintain the center of gravity within the supporting base during standing or sitting.
static muscle action—Type of muscle contraction in which there is no visible joint movement; isometric contraction.
static flexibility—Measure of the total range of motion at a joint.
static stretching—Mode of exercise used to increase range of motion by placing the joint at the end of its range of motion
and slowly applying torque to the muscle to stretch it further.
steep ramp cycling protocol (SRP)—Maximal exertion cycling protocol utilizing stage changes every 10 sec; magnitude of
stage increments are determined by the rider’s height.
stress relaxation—Decreased tension within musculotendinous unit when it is held at a fixed length during static stretching.
stretch tolerance—Measure of the amount of resistive force to stretch within target muscles that can be tolerated before
experiencing pain.
stroke—Rupture or blockage of blood flow to the brain caused by an aneurysm, blood clot, or some other particle.
ST segment—Part of ECG tracing reflecting ventricular repolarization; used to detect coronary occlusion and myocardial
infarct.
subcutaneous adipose tissue (SAT)—Fat located beneath the skin and above the muscle.
submaximal exercise test—Graded exercise test in which exercise is terminated at some predetermined submaximal heart rate
or workload; used to estimate V̇O2max.
super circuit resistance training—Type of circuit resistance training that intersperses a short aerobic exercise bout between
each resistance training exercise station.
supersetting—Advanced resistance training system in which exercises for agonist and antagonist muscle groups are done
consecutively without rest.
syncope—Brief lapse in consciousness caused by lack of oxygen to the brain.
systolic blood pressure (SBP)—Highest pressure in the arteries during systole of the heart.
tachycardia—Resting heart rate >100 bpm.
754
talk test—Method to monitor exercise intensity; measure of the client’s ability to converse comfortably while exercising;
based on the relationship between exercise intensity and pulmonary ventilation.
tare weight—Weight of the chair or platform and its supporting equipment used in hydrostatic weighing.
telomeres—Repeated DNA sequences that determine structure and function of chromosomes.
terminal digit bias—Tendency of the technician to round BP values to the nearest 0 or 5 mmHg.
theory of planned behavior—An extension of the theory of reasoned action that takes into consideration the individual’s
perception of behavioral control.
theory of reasoned action—Theory that proposes a way to understand and predict an individual’s behavior; intention is the
most important determinant of behavior.
thoracic gas volume (TGV)—Volume of air in the lungs and thorax.
thrombus—Lump of cellular elements of the blood attached to the inner walls of an artery or vein, sometimes blocking
blood flow through the vessel.
thyrotoxicosis—Overactive thyroid gland that secretes greater than normal amounts of thyroid hormones; also known as
Graves’ disease or hyperthyroidism.
tonic vibration reflex—Reflex that activates muscle spindles and alpha motor neurons of muscles stimulated by vibration
loading.
total cholesterol (TC)—Absolute amount of cholesterol in the blood.
total energy expenditure (TEE)—Sum of energy expenditures for resting metabolic rate, dietary thermogenesis, and physical
activity.
total energy expenditure (TEE) method—Method for determining energy expenditure measured by doubly labeled water or
predicted from equations.
total error (TE)—Average deviation of individual scores of the cross-validation sample from the line of identity.
training volume—Total amount of training as determined by the number of sets and exercises for a muscle group, intensity,
and frequency of training.
transcranial magnetic stimulation (TMS)—Method used to study adaptations in the central nervous system in response to
strength training.
transcriptome signature of resistance exercise—The approximately 660 genes that are affected by resistance training.
transtheoretical model—Model describing the process a client goes through when adopting a change in health behavior.
treading—Type of group-led interval training that involves walking, jogging, and running at various speeds and grades on a
treadmill with relief intervals interspersed.
triaxial joint—Type of joint allowing movement in three planes; ball-and-socket joint.
tri-sets—Advanced resistance training system in which three different exercises for the same muscle group are performed
consecutively with little or no rest between the exercises.
T wave—Part of ECG tracing corresponding to ventricular repolarization.
two-component model—Body composition model that divides the body into fat and fat-free body components.
type A activity—Endurance activity requiring minimal skill or fitness (e.g., walking).
type B activity—Endurance activity requiring minimal skill but average fitness (e.g., jogging).
type C activity—Physical activity requiring both skill and physical fitness (e.g., swimming).
type D activity—Recreational sports that may improve physical fitness (e.g., basketball).
type 1 diabetes—Insulin-dependent diabetes, caused by lack of insulin production by the pancreas.
type 2 diabetes—Non-insulin-dependent diabetes, caused by decreased insulin receptor sensitivity.
755
ultrasound—A noninvasive alternative to SKFs for estimating subcutaneous body fat at specific sites; uses sound frequencies
sent and received by a handheld probe (wand) to determine tissue interfaces and, hence, depth of various tissues at a specific
site (e.g., from basal layer of the skin and start of subcutaneous adipose tissue layer to end of the subcutaneous adipose layer
and start of underlying skeletal muscle); associated computer software displays and calculates the tissue thickness of interest.
undercuffing—Using a blood pressure cuff with a bladder too small for the arm circumference, leading to an overestimation
of blood pressure.
underwater weight (UWW)—Method used to estimate body volume by measuring weight loss when the body is fully
submerged; hydrostatic weighing.
underweight—BMI <18.5 kg/m2.
undulating periodization (UP)—Strength training method that varies training intensity and volume weekly or even daily.
uniaxial joint—Type of joint allowing movement in one plane; hinge or pivot joint.
upper body obesity—Type of obesity in which excess fat is localized to the upper body; android obesity; apple-shaped body.
uremia—Excessive amounts of urea and other nitrogen waste products in the blood associated with kidney failure.
validity—Ability of a test to accurately measure, with minimal error, a specific component.
validity coefficient—Correlation between reference measure and predicted scores.
valvular heart disease—Congenital disorder of a heart valve characterized by obstructed blood flow, valvular degeneration,
valvular stenosis, and regurgitation of blood.
variable-resistance exercise—Type of exercise in which resistance changes during the range of motion due to levers, pulleys,
and cams.
ventilatory threshold—Point at which there is an exponential increase in pulmonary ventilation relative to exercise intensity
and rate of oxygen consumption.
ventricular ectopy—Premature (out of sequence) contraction of the ventricles.
ventricular fibrillation—Cardiac dysrhythmia marked by rapid, uncoordinated, and unsynchronized contractions of the
ventricles, so that no blood is pumped by the heart.
verification bout—Exercise against a constant load approximately 10% higher than the highest workload achieved during a
maximal exertion ramped GXT; used to confirm V̇O2max was attained during a maximal exertion GXT effort that failed to
meet the criterion of a plateau in oxygen consumption.
vertigo—Dizziness or inability to maintain normal balance in a standing or seated position.
very low-density lipoprotein (VLDL)—Lipoprotein made in the liver for transporting triglycerides.
visceral adipose tissue (VAT)—Fat located around the internal organs.
viscoelastic creep—A small increase in joint angle during constant-torque stretching, due to elongation of the muscle-tendon
unit.
viscoelastic properties—Tension within the muscle-tendon unit caused by the elastic and viscous deformation of the unit
when force is applied during stretching.
viscous deformation—Deformation of the muscle-tendon unit that is proportional to the speed at which tension is applied
during stretching.
volume of exercise—Quantity of exercise determined by frequency, intensity, and time of exercise.
V̇O2max—Maximum rate of oxygen utilization of muscles during exercise.
V̇O2peak—Measure of highest rate of oxygen consumption during an exercise test regardless of whether or not a V̇O2
plateau is reached.
V̇O2reserve—The V̇O2max minus the V̇O2rest.
756
waist-to-height ratio (WHTR)—Waist circumference divided by standing height; used as a measure of abdominal obesity.
waist-to-hip ratio (WHR)—Waist circumference divided by hip circumference; used as a measure of upper body or
abdominal obesity.
wearable technology—Data collection or data monitoring devices that may be worn during the day as well as during bouts of
activity (e.g., heart rate monitors, pedometers, accelerometers); these devices do not restrict movement, thereby providing
insights into physiological responses to activity and daily patterns of activity.
white coat effect—Acute elevation of blood pressure when measured in the doctor’s office regardless of usual blood pressure
readings outside of that environment or antihypertensive medication prescription status.
white coat hypertension—Condition in which individuals have normal blood pressure and are not taking any
antihypertensive medications but become hypertensive when blood pressure is measured by a health professional.
whole-body vibration training (WBV)—Training method that uses whole-body mechanical vibration to increase strength,
balance, and bone integrity.
757
References
Aagaard, P., Andersen, J.L., Bennekou, M., Larsson, B., Olesen, J.L., Crameri, R., Magnusson, S.P., and Kjaer, M.
2011. Effects of resistance training on endurance capacity and muscle fiber composition in young top-level cyclists.
Scandinavian Journal of Medicine and Science in Sports 21(6): e298-e307.
Abercromby, A.F.J., Amonette, W.E., Layne, C.S., McFarlin, B.K., Hinman, M.R., and Paloski, W.H. 2007.
Vibration exposure and biodynamic responses during whole-body vibration training. Medicine & Science in Sports &
Exercise 39: 1794-1800.
Abraham, P., Noury-Desvaux, B., Gernigon, M., Mahe, G., Sauvaget, T., Leftheriotis, G., and LeFaucheur, A. 2012.
The inter- and intra-unit variability of a low-cost GPS data logger/receiver to study human outdoor walking in
view of health and clinical studies. PLoS One 7: e31338. doi:10.1371/journal.pone.0031338. Accessed November
10, 2012.
Abraham, W.M. 1977. Factors in delayed muscle soreness. Medicine and Science in Sports 9: 11-20.
Adams, J., Mottola, M., Bagnall, K.M., and McFadden, K.D. 1982. Total body fat content in a group of professional
football players. Canadian Journal of Applied Sport Sciences 7: 36-44.
Ades, P.A., Savage, P.D., Marney, A.M., Harvey, J., and Evans, K.A. 2015. Remission of recently diagnosed type 2
diabetes mellitus with weight loss and exercise. Journal of Cardiopulmonary Rehabilitation Prevention 35: 193-197.
Ahlback, S.O., and Lindahl, O. 1964. Sagittal mobility of the hip-joint. Acta Orthopaedica Scandinavica 34: 310-313.
Ahluwalia, N., Dalmasso, P., Rasmussen, M., Lipsky, L., Currie, C., Haug, E., Kelly, C., Damsgaard, M.T., Due, P.,
Tabak, I., Ercan, O., Maes, L., Aasvee, K., and Cavallo, F. 2015. Trends in overweight prevalence among 11-, 13-
and 15-year-olds in 25 countries in Europe, Canada and USA from 2002 to 2010. European Journal of Public
Health 25(Suppl. 2): 28-32.
Ainsworth, B.E., Haskell, W.L., Whitt, M.C., Irwin, M.L., Swartz, A.M., Strath, S.J., O’Brien, W.L., Bassett, D.R.
Jr., Schmitz, K.H., Emplaincourt, P.O., Jacobs, D.R., and Leon, A.S. 2000. Compendium of physical activities:
An update of activity codes and MET intensities. Medicine & Science in Sports & Exercise 32(Suppl.): S498-S516.
Akuthota, V., Ferreiro, A., Moore, T., and Fredericson, M. 2008. Core stability exercise principles. Current Sports
Medicine Reports 7: 39-44.
Albasini, A., Krause, M., and Rembitzki, I. 2010. Using WBV therapy in physical therapy and sport. London: Churchill
Livingstone.
Alberti, K.G., Eckel, R.H., Grundy, S.M., Zimmet, P.Z., Cleeman, J.I., Donato, K.A., Fruchart, J-C., James, W.P.,
Loria, C.M., and Smith, S.C. Jr. 2009. Harmonizing the metabolic syndrome: A joint interim statement of the
International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood
Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and
International Association for the Study of Obesity. Circulation 120: 1640-1645.
Alcaraz, A.B., Perez-Gomez, J., Chavarrias, M., and Blazevich, A.J. 2011. Similarity in adaptations to high-resistance
circuit vs. traditional strength training in resistance-trained men. Journal of Strength and Conditioning Research 25:
2519-2527.
Allen, L., Williams, J., Townsend, N., Mikkelsen, B., Roberts, N., Foster, C., and Wickramasinghe, K. 2017.
758
Socioeconomic status and non-communicable disease behavioural risk factors in low-income and lower-middle-
income countries: A systematic review. Lancet Global Health 2017 5: e277-e289.
Allison, M.K., Baglole, J.H., Martin, B.J., Macinnis, M.J., Gurd, B.J., and Gibala, M.J. 2017. Brief intense stair
climbing improves cardiorespiratory fitness. Medicine & Science in Sports & Exercise 49: 298-307.
Allison, K.F., Keenan, K.A., Sell, T.C., Abt, J.P., Nagai, T., Deluzio, J., McGrail, M., and Lephart, S.M. 2015.
Musculoskeletal, biomechanical, and physiological gender differences in the U.S. military. U.S. Army Medical
Department Journal April-June: 22-32.
Al Kandari, J.R., Mohammad, S., Al-Hashem, R., Telahoun, G., and Barac-Nieto, M. 2016. Practical use of stairs to
assess fitness, prescribe and perform physical activity training. Health 8: 1402-1410.
Almoallim, H., Alwafi, S., Albazli, K., Alotaibi, M., and Bazuhair, T. 2014. A simple approach of low back pain.
International Journal of Clinical Medicine 5: 1087-1098.
Almuzaini, K.S., and Fleck, S.J. 2008. Modification of the standing long jump test enhances ability to predict
anaerobic performance. Journal of Strength and Conditioning Research 22: 1265-1272.
Alter, M.J. 2004. Science of flexibility, 3rd ed. Champaign, IL: Human Kinetics.
Alves, A.R., Marta, C.C., Neiva, H.P., Izquierdo, M., and Marques, M.C. 2016. Does intrasession concurrent
strength and aerobic training order influence training-induced explosive strength and V̇O2max in prepubescent
children? Journal of Strength and Conditioning Research 30: 3267-3277.
Alway, S.E., Grumbt, W.H., Gonyea, W.J., and Stray-Gundersen, J. 1989. Contrasts in muscle and myofibers of elite
male and female bodybuilders. Journal of Applied Physiology 67: 24-31.
Amaral, T.F., Restivo, M.T., Guerra, R.S., Marques, E., Chousal, M.F., and Mota, J. 2011. Accuracy of a digital
skinfold system for measuring skinfold thickness and estimating body fat. British Journal of Nutrition 105: 478-484.
American Alliance for Health, Physical Education, Recreation and Dance. 1988. The AAHPERD physical best
program. Reston, VA: Author.
American Cancer Society. 2017. ACS guidelines for nutrition and physical activity. www.cancer.org/healthy/eat-
healthy-get-active/acs-guidelines-nutrition-physical-activity-cancer-prevention/guidelines.html. Accessed April
13, 2017.
American College of Sports Medicine. 2004. NCCA accreditation. ACSM’s Certified News 14(3): 1.
American College of Sports Medicine. 2006. ACSM’s guidelines for exercise testing and prescription, 7th ed.
Philadelphia: Lippincott Williams & Wilkins.
American College of Sports Medicine. 2009. Appropriate physical activity intervention strategies for weight loss and
prevention of weight regain for adults. Medicine & Science in Sports & Exercise 41: 459-471.
American Council on Exercise. 1997. Absolute certainty: Do abdominal trainers work any better than the average
crunch? ACE Fitness Matters 3(2): 1-2.
American Diabetes Association. 2017. Statistics about diabetes. http://diabetes.org/diabetes-basics/statistics. Accessed

April 9, 2017.
American Dietetic Association. 2000. Position of the American Dietetic Association, Dietitians of Canada, and the
American College of Sports Medicine: Nutrition and athletic performance. Journal of American Dietetic Association
759
100: 1543-1556.
American Dietetic Association. 2003. Let the evidence speak: Indirect calorimetry and weight management guides.
Chicago: Author.
American Fitness Professionals and Associates. 2004. AFPA news flash: What is the National Board of Fitness
Examiners (NBFE) and how does it work? www.afpafitness.com.
American Heart Association. 2001. International cardiovascular disease statistics. Dallas: Author.
American Heart Association. 2004. Heart disease and stroke statistics—2004 update. Dallas: Author.
American Heart Association. 2012. Heart disease and stroke statistics 2012 update: A report from the American
Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation 125: e2-e220.
American Heart Association. 2017. Heart disease and stroke statistics 2017 update. Circulation 135.
doi:10.1161/CIR.0000000000000485.
American Medical Association. 1988. Guides to the evaluation of permanent impairment, 3rd ed. Chicago, IL: Author.
American Society of Exercise Physiologists. 2018. Standards of practice. https://www.asep.org/organization/practice/.
American Society of Hand Therapists. 1992. Clinical assessment recommendations, 2nd ed. Chicago, IL: Author.
Aminian-Far, A., Hadian, M.R., Olyaei, G., Talebian, S., and Bakhtiary, A.H. 2011. Whole-body vibration and the
prevention and treatment of delayed-onset muscle soreness. Journal of Athletic Training 46: 43-49.
Andersen, J.L., and Aagaard, P. 2000. Myosin heavy chain IIX overshooting in human skeletal muscle. Muscle and
Nerve 23: 1095-1104.
Andersen, J.L., and Aagaard, P. 2010. Effects of strength training on muscle fiber types and size: Consequences for
athletes training for high-intensity sport. Scandinavian Journal of Medicine and Science in Sports 20(Suppl. 2): S32-
S38.
Anderson, G.S. 1992. The 1600 m and multistage 20 m shuttle run as predictive tests of aerobic capacity in children.
Pediatric Exercise Science 4: 312-318.
Anderson, L.J., Erceg, D.N., and Schroeder, E.T. 2012. Utility of multifrequency bioelectrical impedance compared
with dual-energy X-ray absorptiometry for assessment of total and regional body composition varies between men
and women. Nutrition Research 32: 479-485.
Andres, S., Ziegenhagen, R., Trefflich, I., Pevny, S., Schultrich, K., Braun, H., Schänzer, W., Hirsch-Ernst, K.I.,
Schäfer, B., and Lampen, A. 2017. Creatine and creatine forms intended for sports nutrition. Molecular Nutrition
and Food Research 61(6): article 1600772.
Andrews, A.W., Thomas, M.W., and Bohannon, R.W. 1996. Normative values for isometric muscle force
measurements obtained with hand-held dynamometers. Physical Therapy 76: 248-259.
Androutsos, O., Gerasimidis, K., Karanikolou, A., Reilly, J.J., and Edwards, C.A. 2015. Impact of eating and
drinking on body composition measurements by bioelectrical impedance. Journal of Human Nutrition and Dietetics
28: 165-171.
Ansai, J.H., Aurichio, T.R., Goncalves, R., and Rebelatto, J.R. 2016. Effects of two physical exercise protocols on
physical performance related to falls in the oldest old: A randomized controlled trial. Geriatrics and Gerontology
International 16: 492-499.
Antoine-Jonville, S., Sinnapah, S., and Hue, O. 2012. Relationship between body mass index and body composition
in adolescents of Asian Indian origin and their peers. European Journal of Public Health 22: 887-889.
Antonio, J., and Gonyea, W.J. 1993. Skeletal muscle fiber hyperplasia. Medicine & Science in Sports & Exercise 25:
760
1333-1345.
Aragon, A.A., Schoenfeld, B.J., Wildman, R., Kleiner, S., VanDusseldorp, T., Taylor, L., Earnest, C.P., Arciero,
P.J., Wilborn, C., Kalman, D.S., Stout, J.R., Willoughby, D.S., Campbell, B., Arent, S.M., Bannock, L., Smith-
Ryan, A.E., and Antonio, J. 2017. International society of sports nutrition position stand: Diets and body
composition. Journal of the International Society of Sports Nutrition 14: 16.
Ardern, C.I., Katzmarzyk, P.T., and Ross, R. 2003. Discrimination of health risk by combined body mass index and
waist circumference. Obesity Research 11: 135-142.
Arem, H., Moore, S.C., Patel, A., Hartge, P., Berrington de Gonzalez, A., Visvanathan, K., Campbell, P.T.,
Freeman, M., Weiderpass, E., Adami, H.O., Linet, M.S., Lee, I-M., and Matthews, C.E. 2015. Leisure time
physical activity and mortality: A detailed pooled analysis of the dose-response relationship. JAMA Internal
Medicine 175: 959-967.
Arena, S.K., Simon, L., and Peterson, E.L. 2016. Aneroid blood pressure manometer calibration rates in physical
therapy curricula: A descriptive study. Cardiopulmonary Physical Therapy Journal 27: 56-61.
Armstrong, R.B. 1984. Mechanisms of exercise-induced delayed onset muscular soreness: A brief review. Medicine &
Science in Sports & Exercise 16: 529-538.
Artero, E.G., Espada-Fuentes, J.C., Arguelles-Cienfuegos, J., Roman, A., Gomez-Lopez, P.J., and Gutierrez, A.
2012. Effects of whole-body vibration and resistance training on knee extensors muscular performance. European
Journal of Applied Physiology 112: 1371-1378.
Artero, E.G., España-Romero, V., Castro-Piñero, J., Ruiz, J.R., Jiménez-Pavón, D., Aparicio, V., Gatto-Cardia, M.,
Baena, P., Vicente-Rodríguez, G., Castillo, M.J., and Ortega, F.B. 2012. Criterion-related validity of field-based
muscular fitness tests in youth. Journal of Sports Medicine and Physical Fitness 52(3): 263-272.
Asayama, K., Ohkubo, T., Hoshide, S., Kario, K., Ohya, Y., Rakugi, H., and Umemura, S., on behalf of the Japanese
Society of Hypertension Working Group on Mercury Sphygmomanometer and Minamata Convention on
Mercury. 2016. From mercury sphygmomanometer to electric device on blood pressure measurement:
Correspondence of Minamata Convention on Mercury. Hypertension Research 39: 179-182.
Ashford, S., Edmunds, J., and French, D.P. 2010. What is the best way to change self-efficacy to promote lifestyle
and recreational physical activity? A systematic review with meta-analysis. British Journal of Health Psychology 15:
265-280.
Ashwell, M., Gunn, P., and Gibson, S. 2011. Waist-to-height ratio is a better screening tool than waist circumference
and BMI for adult cardiometabolic risk factors: Systematic review and meta-analysis. Obesity Reviews.
doi:10.1111/j.1467-789X.2011.00952.x.
Ashwell, M., and Hsieh, S.D. 2005. Six reasons why the waist-to-height ratio is a rapid and effective global indicator
for health risks of obesity and how its use could simplify the international public health message on obesity.
International Journal of Food Sciences and Nutrition 56: 303-307.
Ashwell, M., Mayhew, L., Richardson, J., and Rickayzen, B. 2014. Waist-to-height ratio is more predictive of years
of life lost than body mass index. PLoS One 9(9): e103483. doi:10.1371/journal.pone.0103483. Accessed August
14, 2017.
Ashwell, M., McCall, S.A., Cole, T.J., and Dixon, A.K. 1985. Fat distribution and its metabolic complications:
Interpretations. In Human body composition and fat distribution, ed. N.G. Norgan, 227-242. Wageningen,
Netherlands: Euronut.
Åstrand, I. 1960. Aerobic capacity in men and women with special reference to age. Acta Physiologica Scandinavica
49(Suppl. 169): S1-S92.
761
Åstrand, P.O. 1956. Human physical fitness with special reference to age and sex. Physiological Reviews 36: 307-335.
Åstrand, P.O. 1965. Work tests with the bicycle ergometer. Varberg, Sweden: AB Cykelfabriken Monark.
Åstrand, P.O., and Rodahl, K. 1977. Textbook of work physiology. New York: McGraw-Hill.
Åstrand, P.O., and Ryhming, I. 1954. A nomogram for calculation of aerobic capacity (physical fitness) from pulse
rate during submaximal work. Journal of Applied Physiology 7: 218-221.
Atterhog, J.H., Jonsson, B., and Samuelsson, R. 1979. Exercise testing: A prospective study of complication rates.
American Heart Journal 98: 572-580.
Aune, K.T., and Powers, J.M. 2017. Injuries in an extreme conditioning program. Sports Health 9: 52-58.
Avila, J.J., Gutierres, J.A., Sheehy, M.E., Lofgren, I.E., and Delmonico, M.J. 2010. Effect of moderate intensity
resistance training during weight loss on body composition and physical performance in overweight older adults.
European Journal of Applied Physiology 109: 517-525.
Axler, C.T., and McGill, S.M. 1997. Low back loads over a variety of abdominal exercises: Searching for the safest
abdominal challenge. Medicine & Science in Sports & Exercise 29: 804-810.
Azevedo, L.F., Perlingeiro, P.S., Brum, P.C., Braga, A.M.W., Negrao, C.E., and de Matos, L.D.N.J. 2011. Exercise
intensity optimization for men with high cardiorespiratory fitness. Journal of Sports Sciences 29: 555-561.
Bacon, A.P., Carter, R.E., Ogle, E.A., and Joyner, M.J. 2013. V̇O2max trainability and high intensity interval training
in humans: A meta-analysis. PLoS One 8(9): e73182. doi:10.1371/journal.pone.00731825.
Baechle, T.R., Earle, R.W., and Wathen, D. 2000. Resistance training. In Essentials of strength training and
conditioning, ed. T.R. Baechle and R.W. Earle. Champaign, IL: Human Kinetics.
Bahk, J., and Khang, Y-H. 2016. Trends in measures of childhood obesity in Korea from 1998 to 2012. Journal of
Epidemiology 26: 199-207.
Bahr, R., Ingnes, I., Vaage, O., Sjersted, O.M., and Newsholme, E.A. 1987. Effect of duration of exercise on excess
post-exercise O2 consumption. Journal of Applied Physiology 62: 485-490.
Bai, Y., Welk, G.J., Nam, Y.H., Lee, J.A., Lee, J-M., Kin, Y., Meier, N.F., and Dixon, P.M. 2016. Comparison of
consumer and research monitors under semistructured settings. Medicine & Science in Sports & Exercise 48(1): 151-
158.
Bailey, B.W., and McInnis, K. 2011. Energy cost of exergaming: A comparison of the energy cost of 6 forms of
exergaming. Archives of Pediatric and Adolescent Medicine 165: 597-602.
Baker, D., Wilson, G., and Carlyon, R. 1994. Periodization: The effect on strength of manipulating volume and
intensity. Journal of Strength and Conditioning Research 8: 235-242.
Balachandran, A., Martins, M.M., De Faveri, F.G., Alan, O., Cetinkaya, F., and Signorile, J.F. 2016. Functional
strength training: Seated machine vs standing cable training to improve physical function in elderly. Experimental
Gerontology 82: 131-138.
Balady, G.J., Arena, R., Sietsema, K., Myers, J., Coke, L., Fletcher, G.F., Forman, D., Franklin, B., Guazzi, M.,
Gulati, M., Keteyian, S.J., Lavie, C.J., Macko, R., Mancini, D., and Milani, R.V. 2010. Clinician’s guide to
cardiopulmonary exercise testing in adults: A scientific statement from the American Heart Association.
Circulation 122: 191-225.
Balke, B. 1963. A simple field test for the assessment of physical fitness. Civil Aeromedical Research Institute Report,
63-18. Oklahoma City: Federal Aviation Agency.
Balke, B., and Ware, R. 1959. An experimental study of physical fitness of Air Force personnel. US Armed Forces
762
Medical Journal 10: 675-688.
Ball, T.E., and Rose, K.S. 1991. A field test for predicting maximum bench press lift of college women. Journal of
Applied Sport Science Research 5: 169-170.
Ballor, D.L., and Keesey, R.E. 1991. A meta-analysis of the factors affecting exercise-induced changes in body mass,
fat mass, and fat-free mass in males and females. International Journal of Obesity 15: 717-726.
Balsamo, S., Tibana, R.A., Nascimento, D., de Farias, G.L., Petruccelli, Z., de Santana, F., Martins, O.V., de
Aguiar, F., Pereira, G.B., de Souza, J.C., and Prestes, J. 2012. Exercise order affects the total training volume and
the ratings of perceived exertion in response to a super-set resistance training session. International Journal of
General Medicine 5: 123-127.
Bandura, A. 1982. Self-efficacy mechanism in human agency. American Psychologist 37: 122-147.
Bankoski, A., Chen, K.Y., Harris, T.B., Berrigan, D., McClain, J.J., Troiano, R.P., Brychta, R.J., Koster, A., and
Caserotti, P. 2011. Sedentary activity associated with metabolic syndrome independent of physical activity.
Diabetes Care 34: 497-503.
Baranauskas, M.N., Johnson, K.E., Juvancic-Heltzel, J.A., Kappler, R.M., Richardson, L., Jamieson, S., and
Otterstetter, R. 2017. Seven-site versus three-site method of body composition using BodyMetrix ultrasound
compared to dual-energy X-ray absorptiometry. Clinical Physiology and Functional Imaging 37: 317-321.
Barbieri, E., Agostini, D., Polidori, E., Potenza, L., Guescini, M., Lucertini, F., Annibalini, G., Stocchi, L., DeSanti,
M., and Stocchi, B. 2015. The pleiotropic effect of physical exercise on mitochondrial dynamics in aging skeletal
muscle. Oxidative Medicine and Cellular Longevity doi:10.1155/2015/917085. Accessed May 6, 2017.
Barbosa, T.M., Marinho, D.A., Reis, V.M., Silva, A.J., and Bragada, J.A. 2009. Physiological assessment of heat-out
aquatic exercises in healthy subjects: A qualitative review. Journal of Sports Science and Medicine 8: 179-189.
Bergamin, M., Zanuso, S., Alvar, B.A., Ermolao, A., and Zaccaria, M. 2012. Is water-based exercise training
sufficient to improve physical fitness in the elderly? European Review of Aging and Physical Activity 9: 129-141.
Barker, A.R., Williams, C.A., Jones, A.M., and Armstrong, N. 2011. Establishing maximal oxygen uptake in young
people during a ramp cycle test to exhaustion. British Journal of Sports Medicine 45: 498-503.
Barnes, J.N. 2015. Exercise, cognitive function, and aging. Advances in Physiology Education 39: 55-62.
Barry, G., van Schaik, P., MacSween, A., Dixon, J., and Martin, D. 2016. Exergaming (XBOX KinectTM) versus
traditional gym-based exercise for postural control, flow and technology acceptance in healthy adults: A
randomised controlled trial. BMC Sports Science, Medicine and Rehabilitation 8: 25. doi:10.1186/s13102-016-0050-
0. Accessed November 15, 2016.
Bartlett, J.D., Close, G.L., Maclaren, D.P.M., Gregson, W., Drust, B., and Morton, J.P. 2011. High-intensity
interval running is perceived to be more enjoyable than moderate-intensity continuous exercise: Implications for
exercise adherence. Journal of Sports Sciences 29: 547-553.
Barquera, S., Pedroza-Tobias, A., Medina, C., Hernandez-Barrera, L., Bibbins-Domingo, K., Lozano, R., and
Moran, A.E. 2015. Global overview of the epidemiology of atherosclerotic cardiovascular disease. Archives of
Medical Research 46: 328-338.
Baumert, P., Lake, M.J., Stewart, C.E., Drust, B., and Erskine, R.M. 2016. Genetic variation and exercise-induced
muscle damage: Implications for athletic performance, injury and ageing. European Journal of Applied Physiology
116: 1595-1625.
Baumgartner, R.N., Heymsfield, S.B., and Roche, A.F. 1995. Human body composition and the epidemiology of
chronic disease. Obesity Research 3: 73-95.
763
Baumgartner, R.N., Heymsfield, S.B., Lichtman, S., Wang, J., and Pierson, R.N. 1991. Body composition in elderly
people: Effect of criterion estimates on predictive equations. American Journal of Clinical Nutrition 53: 1-9.
Baumgartner, T.A. 1978. Modified pull-up test. Research Quarterly 49: 80-84.
Baumgartner, T.A., and Jackson, A.S. 1975. Measurement for evaluation in physical education. Boston: Houghton
Mifflin.
Baumgartner, T.A., East, W.B., Frye, P.A., Hensley, L.D., Knox, D.F., and Norton, C.J. 1984. Equipment
improvements and additional norms for the modified pull-up test. Research Quarterly for Exercise and Sport 55: 64-
68.
Baun, W.B., Baun, M.R., and Raven, P.B. 1981. A nomogram for the estimate of percent body fat from generalized
equations. Research Quarterly for Exercise and Sport 52: 380-384.
Baxter, C., McNaughton, L.R., Sparks, A., Norton, L., and Bentley, D. 2017. Impact of stretching on the
performance and injury risk of long-distance runners. Research in Sports Medicine 25: 78-90.
Bazzocchi, A., Filonzi, G., Ponti, F., Albisinni, U., Guglielmi, F., and Battista, G. 2016. Ultrasound: Which role in
body composition? European Journal of Radiology 85: 1469-1480.
Bazzocchi, A., Ponti, F., Albisinni, U., Battista, G., and Guglielmi, G. 2016. DXA: Technical aspects and
application. European Journal of Radiology 85: 1481-1492.
Beardsley, C., and Contreras, B. 2014. The role of kettlebells in strength and conditioning: A review of the literature.
Strength and Conditioning Journal 36(3): 64-70.
Beaulieu, J.E. 1980. Stretching for all sports. Pasadena, CA: Athletic Press.
Beenakker, E.A.C., van der Hoeven, J.H., Fock, J.M., and Maurits, N.M. 2001. Reference values of maximum
isometric muscle force obtained in 270 children aged 4-16 years by hand-held dynamometry. Neuromuscular
Disorders 11: 441-446.
Behm, D.G., Bambury, A., Farrell, C., and Power, K. 2004. Effect of acute static stretching on force, balance,
reaction time and movement time. Medicine & Science in Sports & Exercise 36: 1397-1402.
Behm, D.G., Blazevich, A.J., Kay, A.D., and McHugh, M. 2016. Acute effects of muscle stretching on physical
performance, range of motion, and injury incidence in healthy active individuals: A systematic review. Applied
Physiology, Nutrition, and Metabolism 41: 1-11.
Behm, D.G., Drinkwater, E.J., Willardson, J.M., and Cowley, P.M. 2010a. The use of instability to train the core
musculature. Applied Physiology, Nutrition and Metabolism 35: 91-108.
Behm, D.G., Drinkwater, E.J., Willardson, J.M., and Cowley, P.M. 2010b. Canadian Society for Exercise Physiology
position stand: The use of instability to train the core in athletic and nonathletic conditioning. Applied Physiology,
Nutrition and Metabolism 35: 109-112.
Behm, D.G., Faigenbaum, A.D., Falk, B., and Klentrou, P. 2008. Canadian Society for Exercise Physiology position
paper: Resistance training in children and adolescents. Applied Physiology, Nutrition, and Metabolism 33: 547-561.
Behm, D.G., Young, J.D., Whitten, J.H.D., Reid, J.C., Quigley, P.J., Low, J., Li, Y., Lima, C.D., Hodgson, D.D.,
Chaouachi, A., Prieske, O., and Granacher, U. 2017. Effectiveness of traditional strength vs. power training on
muscle strength, power and speed with youth: A systematic review and meta-analysis. Frontiers in Physiology 8:
423.
Behnke, A.R. 1961. Quantitative assessment of body build. Journal of Applied Physiology 16: 960-968.
Behnke, A.R., and Wilmore, J.H. 1974. Evaluation and regulation of body build and composition. Englewood Cliffs, NJ:
Prentice Hall.
764
Beime, B., Deutsch, C., Gomez, T., Zwingers, T., Mengden, T., and Bramlage, P. 2016. Validation protocols for
blood pressure-measuring devices: Status quo and development needs. Blood Pressure Monitoring 21: 1-8.
Bell, D.R., Guskiewicz, K.M., Clark, M.A., and Padua, D.A. 2011. Systematic review of the balance error scoring
system. Sports Health 3: 287-295.
Beltz, N.M., Gibson, A.L., Janot, J.M., Kravitz, L., Mermier, C.M., and Dalleck, L.C. 2016. Graded exercise testing
protocols for the determination of V̇O2max: Historical perspectives, progress, and future considerations. Journal of
Sports Medicine 2016: article 3968393. doi:10.1155/2016/3968393. Accessed June 15, 2017.
Beltz, N., Erbes, D., Porcari, J.P., Martinez, R., Doberstein, S., and Foster, C. 2013. Effects of kettlebell training on
aerobic capacity, muscular strength, balance, flexibility, and body composition. Journal of Fitness Research 2: 4-13.
Bemben, D.A., Palmer, I.J., Bemben, M.G., and Knehans, A.W. 2010. Effects of combined whole-body vibration
and resistance training on muscular strength and bone metabolism in postmenopausal women. Bone 47: 650-656.
Bendiksen, M., Ahler, R., Clausen, H., Wedderkopp, N., and Krustrup, P. 2012. The use of Yo-Yo IR1 and
Andersen testing for fitness and maximal heart rate assessments of 6-10 yr old school children. Journal of Strength
and Conditioning Research [Epub ahead of print]. doi:10.1519/JSC.0b013e318270fd0b.
Benatti, F.B., and Ried-Larsen, M. 2015. The effects of breaking up prolonged sitting time: A review of experimental
studies. Medicine & Science in Sports & Exercise 47: 2053-2061.
Benson, A.C., Bruce, L., and Gordon, B.A. 2015. Reliability and validity of a GPS-enabled iPhoneTM “app” to
measure physical activity. Journal of Sports Sciences 22: 1421-1428.
Bentzur, K.M., Kravitz, L., and Lockner, D.W. 2008. Evaluation of the Bod Pod for estimating percent body fat in
collegiate track and field female athletes: A comparison of four methods. Journal of Strength and Conditioning
Research 22: 1985-1991.
Berg, K.O., Wood-Dauphinee, S.L., Williams, J.I., and Maki, B. 1992. Measuring balance in the elderly: Validation
of an instrument. Canadian Journal of Public Health 83(2): S7-S11.
Bergamin, M., Zanuso, S., Alvar, B.A., Ermolao, A., and Zaccaria, M. 2012. Is water-based exercise training
sufficient to improve fitness in the elderly? European Review of Aging and Physical Activity 9: 129-141.
Bergen, G., Stevens, M.R., and Burns, E.R. 2016. Falls and fall injuries among adults aged ≥65 years—United States,
2014. Morbidity and Mortality Weekly Report 65: 993-998.
Bergeron, M.F., Nindl, B.C., Deuster, P.A., Baumgartner, N., Kane, S.F., Kraemer, W.J., Sexauer, L.R., Thompson,
W.R., and O’Connor, F.G. 2011. Consortium for health and military performance and American College of
Sports Medicine consensus paper on extreme conditioning programs in military personnel. Current Sports Medicine
Reports 10(6): 383-389.
Berglund, E., Birath, G., Bjure, J., Grimby, G., Kjellmar, I., Sandvist, L., and Soderholm, B. 1963. Spirometric
studies in normal subjects. I. Forced expirograms in subjects between 7 and 70 years of age. Acta Medica
Scandinavica 173: 185-192.
Bergouignan, A., Legget, K.T., DeJong, N., Kealey, E., Nikolovski, J., Groppel, J.L., Jordan, C., O’Day, R., Hill,
J.O., and Bessesen, D.H. 2016. Effect of frequent interruptions of prolonged sitting on self-perceived levels of
energy, mood, food cravings and cognitive function. International Journal of Behavioral Nutrition and Physical
Activity 13: 113. doi:10.1186/s12966-016-0437-z. Accessed April 6, 2017.
Bergsma-Kadijk, J.A., Baumeister, B., and Deurenberg, P. 1996. Measurement of body fat in young and elderly
women: Comparison between a four-compartment model and widely used reference methods. British Journal of
Nutrition 75: 649-657.
765
Berry, M.J., Cline, C.C., Berry, C.B., and Davis, M. 1992. A comparison between two forms of aerobic dance and
treadmill running. Medicine & Science in Sports & Exercise 24: 946-951.
Best, J.R. 2011. Exergaming immediately enhances children’s executive function. Developmental Psychology 48: 1501-
1510.
Bielinski, R., Schultz, Y., and Jequier, E. 1985. Energy metabolism during the postexercise recovery in man. American
Journal of Clinical Nutrition 42: 69-82.
Biering-Sorensen, F. 1984. Physical measurements as risk indicators for low-back trouble over a one-year period.
Spine 9: 106-119.
Billinger, S.A., Loudon, J.K., and Gajewski, B.J. 2008. Validity of a total body recumbent stepper exercise test to
assess cardiorespiratory fitness. Journal of Strength and Conditioning Research 22: 1556-1562.
Billinger, S.A., van Swearingen, E., McClain, M., Lentz, A.A., and Good, M.B. 2012. Recumbent stepper
submaximal exercise test to predict peak oxygen uptake. Medicine & Science in Sports & Exercise 44: 1539-1544.
Biswas, A., Oh, P.I., Faulkner, G.E., Bajaj, R.R., Silver, M.A., Mitchell, M.S., and Alter, D.A. 2015. Sedentary time
and its association with risk for disease incidence, mortality, and hospitalization in adults: A systematic review and
meta-analysis. Annals of Internal Medicine 162: 123-132.
Bjorntorp, P. 1988. Abdominal obesity and the development of non-insulin diabetes mellitus. Diabetes and Metabolism
Reviews 4: 615-622.
Black, D.M., and Rosen, C.J. 2016. Postmenopausal osteoporosis. New England Journal of Medicine 374: 254-262.
Black, L.F., Offord, K., and Hyatt, R.E. 1974. Variability in the maximum expiratory flow volume curve in
asymptomatic smokers and nonsmokers. American Review of Respiratory Diseases 110: 282-292.
Blair, D., Habricht, J.P., Sims, E.A., Sylwester, D., and Abraham, S. 1984. Evidence of an increased risk for
hypertension with centrally located body fat, and the effect of race and sex on this risk. American Journal of
Epidemiology 119: 526-540.
Blair, S.N. 2009. Physical inactivity: The biggest public health problem of the 21st century. British Journal of Sports
Medicine 43: 1-2.
Bland, J.M., and Altman, D.G. 1986. Statistical methods for assessing agreement between two methods of clinical
measurement. Lancet 12: 307-310.
Bleakley, C., McDonough, S., Gardner, E., Baxter, G.D., Hopkins, J.T., and Davison, G.W. 2012. Cold-water
immersion (cryotherapy) for preventing and treating muscle soreness after exercise. Cochrane Database of Systematic
Reviews [online] 2: CD008262.
Bleakley, C.M., Charles, D., Porter-Armstrong, A., McNeill, M.D.J., McDonnough, S.M., and McCormack, B.
2015. Gaming for health: A systematic review of the physical and cognitive effects of interactive computer games
in older adults. Journal of Applied Gerontology 34: NP166-NP189.
Blum, V., Carriere, E.G.J., Kolsters, W., Mosterd, W.L., Schiereck, P., and Wesseling, K.H. 1997. Aortic and
peripheral blood pressure during isometric and dynamic exercise. International Journal of Sports Medicine 18: 30-34.
Bogaerts, A., Ameye, L., Bijlholt, M., Amuli, K., Heynickx, D., and Devlieger, R. 2017. INTER-ACT: Prevention
of pregnancy complications through an e-health driven interpregnancy lifestyle intervention—study protocol of a
multicentre randomized controlled trial. BMC Pregnancy and Childbirth 17: article 154.
Bogaerts, A.C.G., Delecluse, C., Claessens, A.L., Troosters, T., Boonen, S., Verschueren, S.M.P. 2009. Effects of
whole body vibration training on cardiorespiratory fitness and muscle strength in older individuals (A 1-year
randomized controlled trial). 2009. Age and Ageing 38: 448-454.
766
Bohannon, R.W. 1997. Reference values for extremity muscle strength obtained by hand-held dynamometry from
adults aged 20 to 79 years. Archives of Physical Medicine and Rehabilitation 78: 26-32.
Bohannon, R.W. 2006a. Reference values for the timed up and go test: A descriptive meta-analysis. Journal of
Geriatric Physical Therapy 29(2): 64-68.
Bohannon, R.W. 2006b. Single leg stance times. A descriptive meta-analysis of data from individuals at least 60 years
of age. Topics in Geriatric Rehabilitation 22: 70-77.
Bohannon, R.W., Peolsson, A., Massy-Westropp, N., Desrosiers, J., and Bear-Lehman, J. 2006. Reference values for
adult grip strength measured with a Jamar dynamometer: A descriptive meta-analysis. Physiotherapy 92: 11-15.
Bolam, K.A., Van Uffelen, J.G.Z., and Taaffe, D.R. 2013. The effect of physical exercise on bone density in middle-
aged and older men: A systematic review. Osteoporosis International 24: 2749-2762.
Bompa, T.O., DiPasquale, M.D., and Cornacchia, L.J. 2003. Serious strength training, 2nd ed. Champaign, IL:
Human Kinetics.
Bonge, D., and Donnelly, J.E. 1989. Trials to criteria for hydrostatic weighing at residual volume. Research Quarterly
for Exercise and Sport 60: 176-179.
Bongers, B.C., de Vries, S.I., Helders, P.J.M., and Takken, T. 2013. The Steep Ramp Test in healthy children and
adolescents: Reliability and validity. Medicine & Science in Sports & Exercise 45: 366-371.
Borde, R., Hortobagyi, T., and Granacher, U. 2015. Dose-response relationships of resistance training in healthy old
adults: A systematic review and meta-analysis. Sports Medicine 45: 1693-1720.
Boren, H.G., Kory, R.C., and Syner, J.C. 1966. The Veteran’s Administration-Army cooperative study of pulmonary
function: II. The lung volume and its subdivisions in normal men. American Journal of Medicine 41: 96-114.
Borg, G. 1998. Borg’s perceived exertion and pain scales. Champaign, IL: Human Kinetics.
Bouaziz, W., Lang, P.O., Schmitt, E., Kaltenbach, G., Geny, B., and Vogel, T. 2016. Health benefits of
multicomponent training programmes in seniors: A systematic review. International Journal of Clinical Practice 70:
520-536.
Bouchard, C. 2008. Gene-environment interactions in the etiology of obesity: Defining the fundamentals. Obesity
16(Suppl.): S5-S10.
Bouchard, C., Blair, S.N., and Katzmarzyk, P.T. 2015. Less sitting, more physical activity, or more fitness? Mayo
Clinic Proceedings 90: 1533-1540.
Bouchard, C., Perusse, L., Leblanc, C., Tremblay, A., and Theriault, G. 1988. Inheritance of the amount and
distribution of human body fat. International Journal of Obesity 12: 205-215.
Bouchard, C., Tremblay, A., Despres, J.P., Nadeau, A., Lupien, P.J., Theriault, G., Dussault, J., Moorjani, S.,
Pinault, S., and Fournier, G. 1990. The response of long-term overfeeding in identical twins. New England Journal
of Medicine 322: 1477-1482.
Bracko, M.R. 2004. Can we prevent back injuries? ACSM’s Health & Fitness Journal 8(4): 5-11.
Brahler, C.J., and Blank, S.E. 1995. VersaClimbing elicits higher V̇O2max than does treadmill running or rowing
ergometry. Medicine & Science in Sports & Exercise 27: 249-254.
Braith, R.W., Graves, J.E., Leggett, S.H., and Pollock, M.L. 1993. Effect of training on the relationship between
maximal and submaximal strength. Medicine & Science in Sports & Exercise 25: 132-138.
Branch, J.D. 2003. Effect of creatine supplementation on body composition and performance: A meta-analysis.
International Journal of Sport Nutrition and Exercise Metabolism 13: 198-226.
767
Bray, G.A. 1978. Definitions, measurements and classifications of the syndromes of obesity. International Journal of
Obesity 2: 99-113.
Bray, G.A. 2004. The epidemic of obesity and changes in food intake: The fluoride hypothesis. Physiological Behavior
82: 115-121.
Bray, G.A., Frühbeck, G., Ryan, D.H., and Wilding, J.P.H. 2016. Management of obesity. Lancet 387: 1947-1956.
Bray, G.A., and Gray, D.S. 1988a. Anthropometric measurements in the obese. In Anthropometric standardization
reference manual, ed. T.G. Lohman, A.F. Roche, and R. Martorell, 131-136. Champaign, IL: Human Kinetics.
Bray, G.A., and Gray, D.S. 1988b. Obesity. Part I—Pathogenesis. Western Journal of Medicine 149: 429-441.
Brehm, B.A. 1988. Elevation of metabolic rate following exercise—implications for weight loss. Sports Medicine 6: 72-
78.
British Heart Foundation. 2006. Diet, physical activity, and obesity statistics, 2006 edition. www.bhf.org.
British Heart Foundation. 2015a. Physical activity statistics 2015. www.bhf.org.uk/publications/statistics/physical-

activity-statistics-2015. Accessed March 19, 2017.
British Heart Foundation. 2015b. Cardiovascular disease statistics, 2015. bhf-cvd-satistics-2015-final.pdf. Accessed
April 2, 2017.
Broadbent, S., Rousseau, J.J., Thorp, R.M., Choate, S.L., Jackson, F.S., and Rowlands, D.S. 2010. Vibration therapy
reduces plasma IL6 and muscle soreness after downhill running. British Journal of Sports Medicine 44: 888-894.
Brogan, M., Ledesma, R., Coffino, A., and Chander, P. 2017. Freebie rhabdomyolysis: A public health concern. Spin
class–induced rhabdomyolysis. American Journal of Medicine 130: 484-487.
Bronner, S., Agraharasamakulam, S., and Ojofeitimi, S. 2010. Reliability and validity of electrogoniometry
measurement of lower extremity movement. Journal of Medical Engineering & Technology 34: 232-242.
Bronner, S., Pinsker, R., and Noah, J.A. 2015. Physiological and psychophysiological responses in experienced players
while playing different dance exer-games. Computers in Human Behavior 51: 34-41.
Brooks, G.A., Butte, N.F., Rand, W.M., Flatt, J.P., and Caballero, B. 2004. Chronicle of the Institute of Medicine
physical activity recommendation: How a physical activity recommendation came to be among dietary
recommendations. American Journal of Clinical Nutrition 79(Suppl.): 921S-930S.
Brose, A., Parise, G., and Tarnopolsky, M.A. 2003. Creatine supplementation enhances isometric strength and body
composition improvements following strength exercise training in older adults. Journals of Gerontology, Series A:
Biological Sciences and Medical Sciences 58: 11-19.
Brouha, L. 1943. The step test: A simple method of measuring physical fitness for muscular work in young men.
Research Quarterly 14: 31-36.
Brown, D.A., and Miller, W.C. 1998. Normative data for strength and flexibility of women throughout life. European
Brown, G.A., Cook, C.M., Krueger, R.D., and Heelan, K.A. 2010. Comparison of energy expenditure on a treadmill
vs. an elliptical device at a self-selected exercise intensity. Journal of Strength and Conditioning Research 24: 1643-
1649.
Brozek, J., Grande, F., Anderson, J.T., and Keys, A. 1963. Densiometric analysis of body composition: Revision of
some quantitative assumptions. Annals of the New York Academy of Sciences 110: 113-140.
Bruce, R.A., Kusumi, F., and Hosmer, D. 1973. Maximal oxygen intake and nomographic assessment of functional
aerobic impairment in cardiovascular disease. American Heart Journal 85: 546-562.
768
Brzycki, M. 1993. Strength testing—predicting a one-rep max from reps-to-fatigue. Journal of Physical Education,
Recreation and Dance 64 (1): 88-90.
Brzycki, M. 2000. Assessing strength. Fitness Management 16(7): 34-37.
Buch, A., Kis, O., Carmeli, E., Keinan-Boker, L., Berner, Y., Barer, Y., Shefer, G., Marcus, Y., and Stern, N. 2017.
Circuit resistance training is an effective means to enhance muscle strength in older and middle aged adults: A
systematic review and meta-analysis. Ageing Research Reviews 37: 16-27.
Buckthorpe, M., Morris, J., and Folland, J.P. 2012. Validity of vertical jump measurement devices. Journal of Sports
Sciences 30: 63-69.
Bunt, J.C., Lohman, T.G., and Boileau, R.A. 1989. Impact of total body water fluctuations on estimation of body fat
from body density. Medicine & Science in Sports & Exercise 21: 96-100.
Buresh, R., and Berg, K. 2002. Scaling oxygen uptake to body size and several practical applications. Journal of
Strength and Conditioning Research 16: 461-465.
Burns, R.D., Hannon, J.C., Brusseau, T.A., Eisenman, P.A., Shultz, B.B., Saint-Maurice, P.F., Welk, G.J., and
Mahar, M.T. 2016. Development of an aerobic capacity prediction model from one-mile run/walk performance in
adolescents aged 13-15 years. Journal of Sports Sciences 34: 18-26.
Bushman, B., ed. 2011. Complete guide to fitness & health. Champaign, IL: Human Kinetics.
Bushman, B. 2012. Neuromotor exercise training. ACSM’s Health & Fitness Journal 16(6): 4-7.
Byrne, J.M., Bishop, N.S., Caines, A.M., Crane, K.A., Feaver, A.M., and Pearcey, G.E.P. 2014. Effect of using a
suspension training system on muscle activation during the performance of a front plank. Journal of Strength and
Conditioning Research 28: 3049-3055.
Byrnes, W.C., Clarkson, P.M., and Katch, F.I. 1985. Muscle soreness following resistive exercise with and without
eccentric contraction. Research Quarterly for Exercise and Sport 56: 283-285.
Cable, A., Nieman, D.C., Austin, M., Hogen, E., and Utter, A.C. 2001. Validity of leg-to-leg bioelectrical
impedance measurement in males. Journal of Sports Medicine and Physical Fitness 41: 411-414.
Cadore, E.L., González-Izal, M., Pallarés, J.G., Rodriguez-Falces, J., Häkkinen, K., Kraemer, W.J., Pinto, R.S., and
Izquierdo, M. 2014. Muscle conduction velocity, strength, neural activity, and morphological changes after
eccentric and concentric training. Scandinavian Journal of Medicine and Science in Sports 24(5): e343-e352.
Cadore, E.L., Rodriguez-Manas, L., Sinclair, A., and Izquierdo, M. 2013. Effects of different exercise interventions
on risk of falls, gait ability, and balance in physically frail older adults: A systematic review. Rejuvenation Research
16: 105-114.
Callaway, C.W., Chumlea, W.C., Bouchard, C., Himes, J.H., Lohman, T.G., Martin, A.D., Mitchell, C.D.,
Mueller, W.H., Roche, A.F., and Seefeldt, V.D. 1988. Circumferences. In Anthropometric standardization reference
manual, ed. T.G. Lohman, A.F. Roche, and R. Martorell, 39-54. Champaign, IL: Human Kinetics.
Camhi, S.M., Bray, G.A., Bouchard, C., Greenway, F.L., Johnson, W.D., Newton, R.I., Ravussin, E., Ryan, D.H.,
Smith, S.R., and Katzmarzyk, P.T. 2011. The relationship of waist circumference and BMI to visceral,
subcutaneous, and total body fat: Sex and race differences. Obesity 19: 402-408.
Campbell, N.R.C., Gelfer, M., Stergiou, G.S., Alpert, B.S., Myers, M.G., Rakotz, M.K., Padwal, R., Schutte, A.E.,
O’Brien, E., Lackland, D.T., Niebylski, M.L., Nilsoson, P.M., Redburn, K.A., Zhang, X-H., Prabhakaran, D.,
Ramirez, A.J., Schiffrin, E.L., Touyz, R.M., Wang, J-G., and Weber, M.A. 2016. A call to regulate manufacture
and marketing of blood pressure devices and cuffs: A position statement from the World Hypertension League,
International Society of Hypertension and supporting hypertension organizations. Journal of Clinical Hypertension
769
18: 378-379.
Canadian Society for Exercise Physiology. 2013. Physical activity training for health (CSEP-PATH) resource manual.
Ottawa, ON: Author.
Candow, D.G., Chilibeck, P.D., Abeysekara, S., and Zello, G.A. 2011. Short-term heavy resistance training
eliminates age-related deficits in muscle mass and strength in healthy older males. Journal of Strength and
Cao, C., Liu, Y., Zhu, W., and Ma, J. 2016. Effect of active workstation on energy expenditure and job performance:
A systematic review and meta-analysis. Journal of Physical Activity and Health 13: 562-571.
Cardinal, B.J., Park, E.A., Kim, M.S., and Cardinal, M.K. 2015. If exercise is medicine, where is exercise in
medicine? Review of U.S. medical education curricula for physical-activity-related content. Journal of Physical
Activity and Health 12: 1336-1342.
Carey, M.A., Laird, D.E., Murray, K.A., and Stevenson, J.R. 2010. Reliability, validity, and clinical usability of a
digital goniometer. Work 36: 55-66.
Carneiro, N.H., Ribeiro, A.S., Nascimento, M.A., Gobbo, L.A., Schoenfeld, B.J., Achour Júnior, A., Gobbi, S.,
Oliveira, A.R., and Cyrino, E. 2015. Effects of different resistance training frequencies on flexibility in older
women. Clinical Interventions in Aging 10: 531-538.
Carns, M.L., Schade, M.L., Liba, M.R., Hellebrandt, F.A., and Harris, C.W. 1960. Segmental volume reduction by
localized and generalized exercise. Human Biology 32: 370-376.
Carpenter, D.M., and Nelson, B.W. 1999. Low back strengthening for the prevention and treatment of low back
pain. Medicine & Science in Sports & Exercise 31: 18-24.
Carrick-Ranson, G., Hastings, J.L., Bhella, P.S., Shibata, S., Fujimoto, N., Palmer, D., Boyd, K., and Levine, B.D.
2012. The effect of age-related differences in body size and composition on cardiovascular determinants of V̇O2
max. Journal of Gerontology. doi:10.1093/gerona/gls220. Accessed December 18, 2012.
Carroll, T.J., Barton, J., Hsu, M., and Lee, M. 2009. The effect of strength training on the force of twitches evoked
by corticospinal stimulation in humans. Acta Physiologica 197: 161-173.
Carter, B.D., Abnet, C.C., Feskanich, D., Freedman, N.D., Hartge, P., Lewis, C.E., Ockene, J.K., Prentice, R.L.,
Speizer, F.E. Thun, M.J., and Jacobs, E.J. 2015. Smoking and mortality: Beyond established causes. New England
Journal of Medicine 372: 631-640.
Carter, N.D., Kannus, P., and Khan, K.M. 2001. Exercise in the prevention of falls in older people. A systematic
literature review examining the rationale and the evidence. Sports Medicine 31: 427-438.
Carter, S., Hartman, Y., Holder, S., Thijssen, D.H., and Hopkins, N.D. 2017. Sedentary behavior and cardiovascular
disease risk: Mediating mechanisms. Exercise and Sports Sciences Reviews 45: 80-86.
Casanova, C., Ceili, B.R., Barria, P., Casas, A., Cote, C., de Torres, J.P., Jardim, J., Lopez, M.V., Marin, J.M.,
Montes de Oca, M., Pinto-Plata, V., and Aguirre-Jaime, A. 2011. The 6 min walk distance in healthy subjects:
Reference standards from seven countries. European Respiratory Journal 37: 150-156.
Casartelli, N., Muller, R., and Maffiuletti, N.A. 2010. Validity and reliability of the Myotest accelerometric system
for the assessment of vertical jump height. Journal of Strength and Conditioning Research 24: 3186-3193.
Casiglia, E., Tikhonoff, V., Albertini, F., and Palatini, P. 2016. Poor reliability of wrist blood pressure self-
measurement at home: A population-based study. Hypertension. doi:10.1161/HYPERTENSION
AHA.116.07961. Accessed May 2, 2017.
Cataldo, D., and Heyward, V. 2000. Pinch an inch: A comparison of several high-quality and plastic skinfold calipers.
770
ACSM’s Health & Fitness Journal 4(3): 12-16.
Catenacci, V.A., Grunwald, G.K., Ingebrigtsen, J.P., Jakicic, J.M., McDermott, M.D., Phelan, S., Wing, R.R., Hill,
J.O., and Wyatt, H.R. 2011. Physical activity patterns using accelerometry in the National Weight Control
Registry. Obesity 19(6): 1163-1170.
Catley, M.J., and Tomkinson, G.R. 2013. Normative health-related fitness values for children: Analysis of 85347 test
results on 9-17-year-old Australians since 1985. British Journal of Sports Medicine 47: 98-108.
Caton, J.R., Mole, P.A., Adams, W.C., and Heustis, D.S. 1988. Body composition analysis by bioelectrical
impedance: Effect of skin temperature. Medicine & Science in Sports & Exercise 20: 489-491.
Cavallo, D.N., Tate, D.F., Ries, A.V., Brown, J.D., DeVellis, R.F., and Ammerman, A.S. 2012. A social media-
based physical activity intervention: A randomized controlled trial. American Journal of Preventive Medicine 43:
527-532.
Cayir, Y., Menekse, S., and Akturk, Z. 2015. The effect of pedometer use on physical activity and body weight in
obese women. European Journal of Sport Science 15: 351-356.
Centers for Disease Control and Prevention. 2013. National Health and Nutrition Examination Survey (NHANES):
Body composition procedures manual.
www.cdc.gov/nchs/data/nhanes/nhanes_13_14/2013_Body_Composition_DXA.pdf. Accessed August 6, 2017.
Centers for Disease Control and Prevention. 2014. National diabetes statistics report, 2014.
www.cdc.gov/diabetes/pdfs/data/2014-report-estimates-of-diabetes-and-its-burden-in-the-united-states.pdf.
Accessed April 9, 2017.
Centers for Disease Control and Prevention. 2015a. Heart disease facts. www.cdc.gov/heartdisease/facts.htm.
Centers for Disease Control and Prevention. 2015b. Health, United States, 2015.
www.cdc.gov/nchs/hus/contents2015.htm#057. Accessed August 9, 2016.
Centers for Disease Control and Prevention. 2016. High Blood Pressure Facts.
www.cdc.gov/bloodpressure/facts.htm. Accessed April 2, 2017.
Chalmers, G. 2004. Re-examination of the possible role of Golgi tendon organ and muscle spindle reflexes in
proprioceptive neuromuscular facilitation muscle stretching. Sports Biomechanics 3: 159-183.
Chamberlin, B., and Gallagher, R. 2008. Exergames: Using video games to promote physical activity. Paper presented
at Children, Youth and Families at Risk (CYFAR) Conference, San Antonio, TX.
Chandler, J.M., Duncan, P.W., and Studenski, S.A. 1990. Balance performance on the postural stress test:
Comparison of young adults, healthy elderly, and fallers. Physical Therapy 70: 410-415.
Chapman, E.A., deVries, H.A., and Swezey, R. 1972. Joint stiffness: Effects of exercise on young and old men.
Journal of Gerontology 27: 218-221.
Charlton, P.C., Mentiplay, B.F., Pua, Y-H., Clark, R.A. 2015. Reliability and concurrent validity of a smartphone,
bubble inclinometer and motion analysis system for measurement of hip joint range of motion. Journal of Science
and Medicine in Sport 18: 262-267.
Charro, M.A., Aoki, M.S., Coutts, A.J., Araujo, R.C., and Bacurau, R.F. 2010. Hormonal, metabolic and perceptual
responses to different resistance training systems. Journal of Sports Medicine and Physical Fitness 50: 229-234.
Chen, C-H., Chen, T.C., Jan, M-H., and Lin, J-J. 2015. Acute effects of static active or dynamic active stretching on
eccentric-exercise-induced hamstring muscle damage. International Journal of Sports Physiology and Performance 10:
346-352.
771
Chen, C., Nosaka, K., Chen, H., Lin, M., Tseng, K., and Chen, T.C. 2011. Effects of flexibility training on eccentric
exercise-induced muscle damage. Medicine & Science in Sports & Exercise 43: 491-500.
Chen, G., Doumatey, A.P., Zhou, J., Lei, L., Bentley, A.B. Tekola-Ayele, F., Adebamowo, S.N., Baker, J.L.,
Fasanmade, O., Okafor, G., Eghan, B. Jr., Agyenum-Boateng, K., Amoult, A., Adebamowo, C., Acheampong, J.,
Johnson, T., Oli, J., Shriner, D., Adeyemo, A.A., and Rotimi, C.N. 2017. Genome-wide analysis identifies an
African-specific variant in SEMA4D associated with body mass index. Obesity 25: 794-800.
Chen, Y-L., Chiou, W-K, Tzeng, Y-T., Lu, C-Y., and Chen, S-C. 2017. A rating of perceived exertion scale using
facial expressions for conveying exercise intensity for children and young adults. Journal of Science and Medicine in
Sport 20: 66-69.
Cherkas, L.F., Hunkin, J.L., Kato, B.S., Richards, J.B., Gardner, J.P., Surdulescu, G.L., Kimura, M., Lu, X.,
Spector, T.D., and Aviv, A. 2008. The association between physical activity in leisure time and leukocyte telomere
length. Archives of Internal Medicine 168(2): 154-158.
Cheung, A.M., and Giangregorio, L. 2012. Mechanical stimuli and bone health: What is the evidence? Current
Opinions in Rheumatology 24: 561-566.
Cheung, A.S., de Rooy, C., Hoermann, R., Gianatti, E.J., Hamilton, E.J., Roff, G., Zajac, J.D., and Grossmann, M.
2016. Correlation of visceral adipose tissue measured by Lunar Prodigy dual X-ray absorptiometry with MRI and
CT in older men. International Journal of Obesity 40(8): 1325-1328.
Chidnok, W., DiMenna, F.J., Bailey, S.J., Burnley, M., Wilderson, D.P., and Vanhatalo, A. 2013. V̇O2max is not
altered by self-pacing during incremental exercise. European Journal of Applied Physiology 113: 529-539.
Chillon, P., Castro-Pinero, J., Ruiz, J.R., Soto, V.M., Carbonell-Baeza, A., Dafos, J., Vincente-Rodriguez, G.,
Castillo, M.J., and Ortega, F.B. 2010. Hip flexibility is the main determinant of the back-saver sit-and-reach test
in adolescents. Journal of Sport Sciences 28: 641-648.
Cho, G-H., Rodriguez, D.A., and Evenson, K.R. 2011. Identifying walking trips using GPS data. Medicine & Science
in Sports & Exercise 43: 365-372.
Cho, K., Tian, M., Lan, Y., Zhao, X., and Yan, L.L. 2013. Validation of the Omron HEM-7201 upper arm blood
pressure monitor, for self-measurement in a high-altitude environment, according to the European Society of
Hypertension International Protocol revision 2010. Journal of Human Hypertension 27: 487-491.
Chodzko-Zajko, W.J., Proctor, D.N., Fiatarone, S., Maria, A., Minson, C.T., Nigg, C.R., Claudio, R., Salem, G.J.,
and Skinner, J.S. 2009. Exercise and physical activity for older adults. ACSM position stand. Medicine & Science in
Sports & Exercise 41: 1510-1530.
Christie, A., and Kamen, G. 2014. Cortical inhibition is reduced following short-term training in young and older
adults. Age 36(2): 749-758.
Churchward-Venne, T.A., Murphy, C.H., Longland, T.M., and Phillips, S.M. 2013. Role of protein and amino
acids in promoting lean mass accretion with resistance exercise and attenuating lean mass loss during energy deficit
in humans. Amino Acids 45: 231-240.
Cipriani, D., Abel, B., and Pirrwitz, D. 2003. A comparison of two stretching protocols on hip range of motion:
Implications for total daily stretch duration. Journal of Strength and Conditioning Research 17: 274-278.
Clark, B.C., and Manini, T.M. 2008. Sarcopenia ≠ dynapenia. Journal of Gerontology 63A: 829-834.
Clark, R.A., Bryant, A.L., Pua, Y., McCrory, P., Bennell, K., and Hunt, M. 2010. Validity and reliability of the
Nintendo Wii balance board for assessment of standing balance. Gait & Posture 31: 307-310.
Clark, S., Iltis, P.W., Anthony, C.J., and Toews, A. 2005. Comparison of older adult performance during the
772
functional-reach and limits-of-stability tests. Journal of Aging and Physical Activity 13: 266-275.
Clark, S., Rose, D.J., and Fujimoto, K. 1997. Generalizability of the limits of stability test in the evaluation of
dynamic balance among older adults. Archives of Physical Medicine and Rehabilitation 78: 1078-1084.
Clarkson, P.M., Byrnes, W.C., McCormick, K.M., Turcotte, L.P., and White, J.S. 1986. Muscle soreness and serum
creatine kinase activity following isometric, eccentric and concentric exercise. International Journal of Sports
Medicine 7: 152-155.
Clarys, J.P., Martin, A.D., Drinkwater, D.T., and Marfell-Jones, M.J. 1987. The skinfold: Myth and reality. Journal
of Sports Sciences 5: 3-33.
Cleary, M.A., Hetzler, R.K., Wages, J.J., Lentz, M.A., Stickley, C.D., and Kimura, I.F. 2011. Comparisons of age-
predicted maximum heart rate equations in college-aged subjects. Journal of Strength and Conditioning Research 25:
2591-2597.
Clemons, J.M., Duncan, C.A., Blanchard, O.E., Gatch, W.H., Hollander, D.B., and Doucer, J.L. 2004.
Relationships between the flexed-arm hang and select measures of muscular fitness. Journal of Strength and
Clinical Exercise Physiology Association. 2013. State Updates. https://www.acsm-cepa.org/i4a/pages/index.cfm?

pageid=3339. Accessed May 5, 2017.
Cloutier, L., Daskalopoulou, S.S., Padwal, R.S., Lamarre-Cliché, M., Bolli, P., McLean, D., Milot, A., Tobe, S.W.,
Tremblay, G., McKay, D.W., Townsend, R., Campbell, N., and Gelfer, M. 2015. A new algorithm for the
diagnosis of hypertension in Canada. Canadian Journal Cardiology 31: 620-630.
Cobb, N.K., and Graham, A.L. 2012. Health behavior interventions in the age of Facebook. American Journal of
Preventive Medicine 43: 571-572.
Cochrane, D. 2013. The sports performance application of vibration exercise for warm-up, flexibility and sprint speed.
European Journal of Sport Science 13: 256-271.
Cohen, A. 2004. It’s getting personal. Athletic Business July: 52-54, 56, 58, 60.
Cohen, A., Baker, J., and Ardern, C.I. 2016. Association between body mass index, physical activity, and health-
related quality of life in Canadian adults. Journal of Aging and Physical Activity. 24: 32-38.
Colberg, S.R., Rubin, R.R., Sigal, R.J., Chasa-Taber, L., Fernall, B., Albright, A.L., Regensteiner, J.G., Braun, B.,
and Blissmer, B.J. 2010. Exercise and type 2 diabetes. Diabetes Care. 33: 2692-2696.
Cole, T.J., Bellizzi, M.C., Flegal, K.M., and Dietz, W.H. 2000. Establishing a standard definition for child
overweight and obesity worldwide: International survey. British Medical Journal 320: 1240-1245.
Collins, M., Millard-Stafford, M., Sparling, P., Snow, T., Rosskopf, L., Webb, S., and Omer, J. 1999. Evaluation of
the Bod Pod for assessing body fat in collegiate football players. Medicine & Science in Sports & Exercise 31: 1350-
1356.
Collora, C. 2017. Exercise physiologist: Career overview. www.exercise-science-guide.com/careers/exercise-

physiologist. Accessed May 5, 2017.
Comstock, B.A., Solomon-Hill, G., Flanagan, S.D., Earp, J.E., Luk, H.Y., Dobbins, K.A., Dunn-Lewis, C., Fragala,
M.S., Ho, J.Y., Hatfield, D.L., Vingren, J.L., Denegar, C.R., Volek, J.S., Kupchak, B.R., Maresh, C.M., and
Kraemer, W.J. 2011. Validity of the Myotest in measuring force and power production in the squat and bench
press. Journal of Strength and Conditioning Research 25: 2293-2297.
Conley, D., Cureton, K., Dengel, D., and Weyand, P. 1991. Validation of the 12-min swim as a field test of peak
aerobic power in young men. Medicine & Science in Sports & Exercise 23: 766-773.
773
Conley, D., Cureton, K., Hinson, B., Higbie, E., and Weyand, P. 1992. Validation of the 12-minute swim as a field
test of peak aerobic power in young women. Research Quarterly for Exercise and Sport 63: 153-161.
Conlon, J.A., Newton, R.U., Tufano, J.J., Banyard, H.G., Hopper, A.J., Ridge, A.J., and Haff, G.G. 2016.
Periodization strategies in older adults: Impact on physical function and health. Medicine & Science in Sports &
Exercise 48: 2426-2436.
Conroy, R.M., Pyörälä, K., Fitzgerald, A.P., Sans, S., Menotti, A., DeBacker, G., DeBacquer, D., Ducimetière, P.,
Jousilahti, P., Keil, U., Njølstad, I., Oganov, R.G., Thomsen, T., Turnstall-Pedoe, H., Tverdal, A., Wedel, H.,
Whincup, P., Wilhelmsen, L., and Graham, I.M., on behalf of the SCORE project group. 2003. Estimation of
ten-year risk of fatal cardiovascular disease in Europe: The SCORE project. European Heart Journal 24: 987-1003.
Coombes, J.S., Law, J., Lancashire, B., and Fassett, R.G. 2015. “Exercise is medicine”: Curbing the burden of chronic
disease and physical inactivity. Asia-Pacific Journal of Public Health 27: NP600-NP605.
Cooper Institute for Aerobics Research. 1992. The Prudential FitnessGram test administration manual. Dallas: Author.
Cooper Institute for Aerobics Research. 1994. FitnessGram user’s manual. Dallas: Author.
Cooper Institute for Aerobics Research. 2005. The fitness specialist certification manual. Dallas: Author.
Cooper, K.H. 1968. A means of assessing maximal oxygen intake. Journal of the American Medical Association 203:
201-204.
Cooper, K.H. 1977. The aerobics way. New York: Evans.
Cooper, R., Naclerio, F., Allgrove, J., and Jimenez, A. 2012. Creatine supplementation with specific view to
exercise/sports performance: An update. Journal of the International Society of Sports Nutrition 9: article 33.
Coquart, J., Tabben, M., Farooq, A., Tourney, C., and Eston, R. 2016. Submaximal, perceptually regulated exercise
testing predicts maximal oxygen uptake: A meta-analysis study. Sports Medicine 46: 885-897.
Corbin, C.B., Dowell, L.J., Lindsey, R., and Tolson, H. 1978. Concepts in physical education. Dubuque, IA: Brown.
Costa, P.B., Graves, B.S., Whitehurst, M., and Jacobs, P.L. 2009. The acute effects of different durations of static
stretching on dynamic balance performance. Journal of Strength and Conditioning Research 23: 141-147.
Costill, D.L., Coyle, E.F., Fink, W.F., Lesmes, G.R., and Witzmann, F.A. 1979. Adaptations in skeletal muscle
following strength training. Journal of Applied Physiology 46: 96-99.
Costill, D.L., and Fox, E.L. 1969. Energetics of marathon running. Medicine and Science in Sports 1: 81-86.
Costill, D.L., Thomason, H., and Roberts, E. 1973. Fractional utilization of the aerobic capacity during distance
running. Medicine and Science in Sports 5: 248-252.
Cote, D.K., and Adams, W.C. 1993. Effect of bone density on body composition estimates in young adult black and
white women. Medicine & Science in Sports & Exercise 25: 290-296.
Cotten, D.J. 1971. A modified step test for group cardiovascular testing. Research Quarterly 42: 91-95.
Cotten, D.J. 1972. A comparison of selected trunk flexibility tests. American Corrective Therapy Journal 26: 24.
Coughlan, G.F., Fullam, K., Delahunt, E., Gissane, C., and Caulfield, B.M. 2012. A comparison between
performance on selected directions of the star excursion balance test and the Y balance test. Journal of Athletic
Training 47: 366-371.
Cowell, J.F., Cronin, J., and Brughelli, M. 2012. Eccentric muscle actions and how the strength and conditioning
specialist might use them for a variety of purposes. Strength and Conditioning Journal 34: 33-48.
Coyle, E.F. 1995. Fat metabolism during exercise. Sports Science Exchange 8(6).
Coyle, E.F., Feiring, D.C., Rotkis, T.C., Cote, R.W. III, Roby, F.B., Lee, W., and Wilmore, J.H. 1981. Specificity
774
of power improvements through slow and fast isokinetic training. Journal of Applied Physiology 51: 1437-1442.
Crandall, C.J., Hovey, K.M., Cauley, J.A., Andrews, C.A., Curtis, J.R., Wactawski-Wende, J., Wright, N.C., Li, W.,
and LeBoff, M.S. 2015. Wrist fracture and risk of subsequent fracture: Findings from the Women’s Health
Initiative Study. Journal of Bone and Mineral Research 11: 2086-2095.
Crandall, K.J., Zagdsuren, B., Schafer, M.A., and Lyons, T.S. 2016. Static and active workstations for improving
workplace physical activity and sitting time. International Journal of Human Movement and Sports Sciences 4: 20-25.
Crewther, B.T., Kilduff, L.P., Cunningham, D.J., Cook, C., Owen, N., and Yang, G.Z. 2011. Validating two
systems for estimating force and power. International Journal of Sports Medicine 32: 254-258.
Cribb, P.J., Williams, A.D., and Hayes, A. 2007. A creatine-carbohydrate supplement enhances responses to
resistance training. Medicine & Science in Sports & Exercise 39: 1960-1968.
Cribb, P.J., Williams, A.D., Hayes, A., and Carey, M.F. 2006. The effect of whey isolate on strength, body
composition, and plasma glutamine. International Journal of Sports Nutrition and Exercise Metabolism 16: 494-509.
Cribb, P.J., Williams, A.D., Stathis, C.G., Carey, M.F., and Hayes, A. 2007. Effect of whey isolate, creatine, and
resistance training on muscle hypertrophy. Medicine & Science in Sports & Exercise 39: 298-307.
Critoph, C.H., Patel, V., Mist, B., Thomas, M.D., and Elliott, P.M. 2013. Non-invasive assessment of cardiac
output at rest and during exercise by finger plethysmography. Clinical Physiology and Functional Imaging 33: 338-
343.
Crommett, A., Kravitz, L., Wongsathikun, J., and Kemerly, T. 1999. Comparison of metabolic and subjective
response of three modalities in college-age subjects. Medicine & Science in Sports & Exercise 31(Suppl.): S158
[abstract].
Crook, T.A., Armbya, N., Cleves, M.A., Badger, T.M., and Andres, A. 2012. Air displacement plethysmography,
dual-energy X-ray absorptiometry, and total body water to evaluate body composition in preschool-age children.
Journal of the Academy of Nutrition and Dietetics 112: 1993-1998.
Cug, M. 2017. Stance foot alignment and hand positioning alter star excursion balance test scores in those with
chronic ankle instability: What are we really assessing? Physiotherapy Theory and Practice 33: 316-322.
Cullinen, K., and Caldwell, M. 1998. Weight training increases fat-free mass and strength in untrained young
women. Journal of the American Dietetic Association 98(4): 414-418.
Curb, J.D., Ceria-Ulep, C.D., Rodriquez, B.L., Grove, J., Guralnik, J., Willcox, B.J., Donlon, T.A., Masaki, K.H.,
and Chen, R. 2006. Performance-based measures of physical function for high-function populations. Journal of the
American Geriatrics Society 54: 737-742.
Cureton, K.J., Collins, M.A., Hill, D.W., and McElhannon, F.M. Jr. 1988. Muscle hypertrophy in men and women.
Medicine & Science in Sports & Exercise 20: 338-344.
Cureton, K.J., Sloniger, M., O’Bannon, J., Black, D., and McCormack, W. 1995. A generalized equation for
prediction of V̇O2peak from 1-mile run/walk performance. Medicine & Science in Sports & Exercise 27: 445-451.
Cureton, K.J., Sparling, P.B., Evans, B.W., Johnson, S.M., Kong, U.D., and Purvis, J.W. 1978. Effect of
experimental alterations in excess weight on aerobic capacity and distance running performance. Medicine and
Science in Sports 10: 194-199.
Cureton, T.K., and Sterling, L.F. 1964. Interpretation of the cardiovascular component resulting from the factor
analysis of 104 test variables measured in 100 normal young men. Journal of Sports Medicine and Physical Fitness 4:
1-24.
Cuthbertson, D.J., Steele, T., Wilding, J.P., Halford, J.C., Harrold, J.A., Hamer, M., and Karpe, F. 2017. What have
775
human experimental overfeeding studies taught us about adipose tissue expansion and susceptibility to obesity and
metabolic complications? International Journal of Obesity 41: 853-865.
Cyrino, E.S., Okano, A.H., Glaner, M.F., Ramanzini, M., Gobbo, A., Makoski, A., Bruna, N., Cordeiro de Melo, J.,
and Tassi, G.N. 2003. Impact of the use of different skinfold calipers for the analysis of the body composition.
Revista Brasileira de Medicina do Esporte 9: 150-153.
dabl® Educational Trust. 2017. Classification of sphygmomanometers.

www.dableducational.org/sphygmomanometers.html. Accessed April 29, 2017.
da Silva, D.F., Bianchini, J.A.A., Lopera, C.A., Capelato, D.A., Hintze, L.J., Narido, C.C.S., Ferraro, Z.M., and
Junior, N.N. 2015. Impact of readiness to change behavior on the effects of a multidisciplinary intervention in
obese Brazilian children and adolescents. Appetite 87: 229-235.
Dalleck, L.C., Kravitz, L., and Robergs, R.A. 2006. Development of a submaximal test to predict elliptical cross-
trainer V̇O2max. Journal of Strength and Conditioning Research 20: 278-283.
Dalleck, L.C., Roos, K.A., Byrd, B.R., and Weatherwax, R.M. 2015. Zumba Gold®: Are the physiological responses
sufficient to improve fitness in middle-age to older adults? Journal of Sports Science and Medicine 14: 689-690.
Daly, R.M. 2017. Exercise and nutritional approaches to prevent frail bones, falls and fractures: An update. Climacteric
20: 119-124.
Danaei, G., Finucane, M.M., Lu, Y., Singh, G.M., Cowan, M.J., Paciorek, C.J., Lin, J.K, Farzadfar, F., Khang, Y-
H., Stevens, G.A., Rao, M., Ali, M.K., Riley, L.M., Robinson, C.A., and Ezzati, M. 2011. National, regional,
and global trends in fasting plasma glucose and diabetes prevalence since 1980: Systematic analysis of health
examination surveys and epidemiological studies with 370 country-years and 2.7 million participants. Lancet 378:
31-40.
Davin, J., and Callaghan, M. 2016. BET2: Core stability versus conventional exercise for treating non-specific low
back pain. Emergency Medicine Journal 33: 162-163.
Davis, D.S., Quinn, R.O., Whiteman, C.T., Williams, J.D., and Young, C.R. 2008. Concurrent validity of four
clinical tests to measure hamstring flexibility. Journal of Strength and Conditioning Research 22: 583-588.
Davis, J.A., Dorado, S., Keays, K.A., Reigel, R.A., Valencia, K.S., and Pham, P.H. 2007. Reliability and validity of
the lung volume measurement made by the Bod Pod body composition system. Clinical Physiology and Functional
Imaging 27: 42-46.
Dawes, J. 2017. Complete guide to TRX suspension training. Champaign, IL: Human Kinetics.
Day, J.R., Rossiter, H.B., Coats, E.M., Skasick, A., and Whipp, B.J. 2003. The maximally attainable V̇O2 during
exercise in humans: The peak vs. maximum issue. Journal of Applied Physiology 95: 1901-1907.
de Bruin, E.D., Swanenburg, J., Betschon, E., and Murer, K. 2009. A randomized controlled trial investigating motor
skill training as a function of attentional focus in old age. BMC Geriatrics 9: 15-24.
Deci, E.L., and Ryan, R.M. 2000. The “what” and “why” of goal pursuits: Human needs and the self-determination
of behavior. Psychological Inquiry 11(4): 227-268.
deJong, A. 2010. Active video gaming: An opportunity to increase energy expenditure throughout aging. ACSM’s
Health & Fitness Journal 14: 44-46.
del Consuelo Velazquez-Alva, M., Irogyen-Camacho, M.E., Huerta-Huerta, R., and Delgadillo-Velazquez, J. 2014.
A comparison of dual energy X-ray absorptiometry and two bioelectrical impedance analyzers to measure body fat
percentage and fat-free mass index in a group of Mexican young women. Nutrición Hospitalaria 29: 1038-1046.
Delecluse, C., Roelants, M., and Verschueren, S. 2003. Strength increase after whole-body vibration compared with
776
resistance training. Medicine & Science in Sports & Exercise 35: 1033-1041.
Demerath, E.W., Guo, S.S., Chumlea, W.C., Towne, B., Roche, A.F., and Siervogel, R.M. 2002. Comparison of
percent body fat estimates using air displacement plethysmography and hydrodensitometry in adults and children.
International Journal of Obesity and Related Metabolic Disorders 26: 389-397.
de Melo dos Santos, R., Costa e Costa, F., Saraiva, T.S., Maniglia de Resende, M., Carvalho, N.C.S., Beda, A., and
Callegari, B. 2015. Short-term adaptations in sedentary individuals during indoor cycling classes. Archives of Sports
Medicine 32: 374-381.
Demont, R.G., Lephart, S.M., Giraldo, J.L., Giannantonio, F.P., Yuktanandana, P., and Fu, F.H. 1999. Comparison
of two abdominal training devices with an abdominal crunch using strength and EMG measurements. Journal of
Sports Medicine and Physical Fitness 39: 253-258.
Dempster, P., and Aitkens, S. 1995. A new air displacement method for the determination of human body
composition. Medicine & Science in Sports & Exercise 27: 1692-1697.
Demura, S., Yamaji, S., Goshi, F., Kobayashi, H., Sato, S., and Nagasawa, Y. 2002. The validity and reliability of
relative body fat estimates and the construction of new prediction equations for young Japanese adult males.
Journal of Sports Sciences 20: 153-164.
Deschenes, M.R., and Kraemer, W.J. 2002. Performance and physiologic adaptations to resistance training. American
Journal of Physical Medicine and Rehabilitation 8(Suppl.): S3-S16.
Desgorces, F.D., Berthelot, G., Dietrich, G., and Testa, M.S.A. 2010. Local muscular endurance and prediction of 1
repetition maximum for bench in 4 athletic populations. Journal of Strength and Conditioning Research 24: 394-400.
Despres, J.P., and Lamarche, B. 1994. Low-intensity endurance training, plasma lipoproteins, and the risk of coronary
heart disease. Journal of Internal Medicine 236: 7-22.
Despres, J.P., Bouchard, C., Tremblay, A., Savard, R., and Marcotte, M. 1985. Effects of aerobic training on fat
distribution in male subjects. Medicine & Science in Sports & Exercise 17: 113-118.
Deurenberg, P. 2001. Universal cut-off BMI points for obesity are not appropriate. British Journal of Nutrition 85:
135-136.
Deurenberg, P., and Deurenberg-Yap, M. 2001. Differences in body-composition assumptions across ethnic groups:
Practical consequences. Current Opinion in Clinical Nutrition and Metabolic Care 4: 377-383.
Deurenberg, P., and Deurenberg-Yap, M. 2002. Validation of skinfold thickness and hand-held impedance
measurements for estimation of body fat percentage among Singaporean Chinese, Malay and Indian subjects. Asia
Pacific Journal of Clinical Nutrition 11: 1-7.
Deurenberg, P., van der Kooy, K., Evers, P., and Hulshof, T. 1990. Assessment of body composition by bioelectrical
impedance in a population aged >60 y. American Journal of Clinical Nutrition 51: 3-6.
Deurenberg, P., van der Kooy, K., and Leenan, R. 1989. Differences in body impedance when measured with
different instruments. European Journal of Clinical Nutrition 43: 885-886.
Deurenberg, P., Weststrate, J.A., Paymans, I., and van der Kooy, K. 1988. Factors affecting bioelectrical impedance
measurements in humans. European Journal of Clinical Nutrition 42: 1017-1022.
Deurenberg, P., Yap, M., and van Staveren, W.A. 1998. Body mass index and percent body fat: A meta analysis
among different ethnic groups. International Journal of Obesity 22: 1164-1171.
Deurenberg-Yap, M., Schmidt, G., van Staveren, W.A., Hautvast, J.G.A.J., and Deurenberg, P. 2001. Body fat
measurement among Singaporean Chinese, Malays and Indians: A comparative study using a four-compartment
model and different two-compartment models. British Journal of Nutrition 85: 491-498.
777
deVries, H.A. 1961. Prevention of muscular distress after exercise. Research Quarterly 32: 177-185.
deVries, H.A. 1962. Evaluation of static stretching procedures for improvement of flexibility. Research Quarterly 33:
222-229.
deVries, H.A., and Klafs, C.E. 1965. Prediction of maximal oxygen intake from submaximal tests. Journal of Sports
Medicine and Physical Fitness 5: 207-214.
de Vries, R.A.J., Truong, K.P., Swint, S., Drossaert, C.H.C., and Evers, V. 2016. Crowd-designed motivation:
Motivational messages for exercise adherence based on behavior change theory. Persuasive Technology.
doi:10.1007/978-3-319-31510-2_4. Accessed May 3, 2017.
deWeijer, V.C., Gorniak, G.C., and Shamus, E. 2003. The effect of static stretch and warm-up exercise on hamstring
length over the course of 24 hours. Journal of Orthopaedic and Sports Physical Therapy 33: 727-733.
Dewit, O., Fuller, N.J., Fewtrell, M.S., Elia, M., and Wells, J.C.K. 2000. Whole body air displacement
plethysmography compared with hydrodensitometry for body composition analysis. Archives of Disease in Childhood
82: 159-164.
Dickin, D.C. 2010. Obtaining reliable performance measures on the sensory organization test: Altered testing
sequence in young adults. Clinical Journal of Sport Medicine 20: 278-285.
Dickin, D.C., and Clark, S. 2007. Generalizability of the sensory organization test in college-aged males: Obtaining a
reliable performance measure. Clinical Journal of Sport Medicine 17: 109-115.
Dickinson, R.V. 1968. The specificity of flexibility. Research Quarterly 39: 792-793.
Disch, J., Frankiewicz, R., and Jackson, A. 1975. Construct validation of distance run tests. Research Quarterly 46:
169-176.
Dishman, R.K. 1994. Prescribing exercise intensity for healthy adults using perceived exertion. Medicine & Science in
Dishman, R.K., Jackson, A.S., and Bray, M.S. 2014. Self-regulation of exercise behavior in the TIGER Study. Annals
of Behavioral Medicine 48: 80-91.
Dishman, R.K., Sallis, J.F., and Orenstein, D.R. 1985. The determinants of physical activity and exercise. Public
Health Reports 100: 158-171.
Dolezal, B.A., and Potteiger, J.A. 1998. Concurrent resistance and endurance training influence basal metabolic rate
in nondieting individuals. Journal of Applied Physiology 85: 695-700.
Domene, P.A., Moir, J.J., Pummell, E., and Easton, C. 2016. Salsa dance and Zumba fitness: Acute responses during
community-based classes. Journal of Sport and Health Science 5: 190-196.
Donahue, B., Turner, D., and Worrell, T. 1994. The use of functional reach as a measurement of balance in boys and
girls without disabilities ages 5 to 15 years. Pediatric Physical Therapy 6: 189-193.
Donahue, C.P., Lin, D.H., Kirschenbaum, D.S., and Keesey, R.E. 1984. Metabolic consequence of dieting and
exercise in the treatment of obesity. Journal of Counseling and Clinical Psychology 52: 827-836.
Donath, L., Rossler, R., and Faude, O. 2016. Effects of virtual reality training (exergaming) compared to alternative
exercise training and passive control on standing balance and functional mobility in healthy community-dwelling
seniors: A meta-analytical review. Sports Medicine 46: 1293-1309.
Donath, L., Roth, R., Hürlimann, C., Zahner, L., and Faude, O. 2016. Pilates vs. balance training in healthy
community-dwelling seniors: A 3-arm, randomized controlled trial. International Journal of Sports Medicine 37:
202-210.
778
Donnelly, J.R., Brown, T.E., Israel, R.G., Smith-Sintek, S., O’Brien, K.F., and Caslavka, B. 1988. Hydrostatic
weighing without head submersion: Description of a method. Medicine & Science in Sports & Exercise 20: 66-69.
Dourado, V.Z., and McBurnie, M.A. 2012. Allometric scaling of 6 min walking distance by body mass as a
standardized measure of exercise capacity. European Journal of Applied Physiology 112: 2503-2510.
Downs, D.S. 2006. Understanding exercise intention in an ethnically diverse sample of postpartum women. Journal of
Sport and Exercise Psychology 28: 159-180.
Drenowatz, C., Hand, G.A., Sagner, M., Shook, R.P., Burgess, S., and Blair, S.N. 2015. The prospective association
between different types of exercise and body composition. Medicine & Science in Sports & Exercise 47: 2535-2541.
Drenowatz, C., Hill, J.O., Peters, J.C., Soriano-Maldonado, A., and Blair, S.N. 2017. The association of change in
physical activity and body weight in the regulation of total energy expenditure. European Journal of Clinical
Drystad, S.M., Edvardsen, E., Hansen, B.H., and Anderssen, S.A. 2017. Waist circumference thresholds and
cardiorespiratory fitness. Journal of Sport and Health Science [Epub ahead of print]. doi:10.1016/j.jshs.2017.03.011.
Accessed August 5, 2017.
Dubin, D. 2000. Rapid interpretation of EKGs: An interactive course, 6th ed. Tampa: Cover.
Dubow, J., and Fink, M.E. 2011. Impact of hypertension on stroke. Current Atherosclerosis Reports 13: 298-305.
Ducimetier, P., Richard, J., and Cambien, F. 1989. The pattern of subcutaneous fat distribution in middle-aged men
and the risk of coronary heart disease: The Paris prospective study. International Journal of Obesity 10: 229-240.
Duncan, P.W., Studenski, S., Chandler, J., and Prescott, B. 1992. Functional reach: Predictive validity in a sample of
elderly male veterans. Journal of Gerontology 47(3): M93-M98.
Duncan, P.W., Weiner, D.K., Chandler, J., and Studenski, S. 1990. Functional reach: A new clinical measure of
balance. Journal of Gerontology 45: M192-M197.
Eather, N., Morgan, P.J., and Lubans, D.R. 2016. Improving health-related fitness in adolescents: The CrossFit
Teens™ randomized controlled trial. Journal of Sports Sciences 34: 209-223.
Ebbeling, C., Ward, A., Puleo, E., Widrick, J., and Rippe, J. 1991. Development of a single-stage submaximal
treadmill walking test. Medicine & Science in Sports & Exercise 23: 966-973.
Eckert, S., and Horstkotte, D. 2002. Comparison of Portapres non-invasive blood pressure measurement in the finger
with intra-aortic pressure measurement during incremental bicycle exercise. Blood Pressure Monitoring 7: 179-183.
Edgerton, V.R. 1970. Morphology and histochemistry of the soleus muscle from normal and exercised rats. American
Journal of Anatomy 127: 81-88.
Edgerton, V.R. 1973. Exercise and the growth and development of muscle tissue. In Physical activity, human growth
and development, ed. G.L. Rarick, 1-31. New York: Academic Press.
Edinborough, L., Fisher, J.P., and Steele, J. 2016. A comparison of the effect of kettlebell swings and isolated lumbar
extension training on acute torque production of the lumbar extensors. Journal of Strength and Conditioning
Research 30: 1189-1195.
Edvardsen, E., Hem, E., and Anderssen, S.A. 2014. End criteria for reaching maximal oxygen uptake must be strict
and adjusted to sex and age: A cross-sectional study. PLOS One 9: 1 e85276. doi:10.1371/journal.pone.0085276.
Accessed June 15, 2017.
Edwards, D.A., Hammond, W.H., Healy, M.J., Tanner, J.M., and Whitehouse, R.H. 1955. Design and accuracy of
calipers for measuring subcutaneous tissue thickness. British Journal of Nutrition 9: 133-143.
779
Edwards, H.L., Simpson, J.A.R., and Buchholz, A.C. 2011. Air displacement plethysmography for fat-mass
measurement in healthy young women. Canadian Journal for Dietetic Practice and Research 72: 85-87.
Edwards, M.K., Addoh, O., and Loprinzi, P.D. 2016. Predictive validity of the ACC/AHA pooled cohort equations
in predicting residual-specific mortality in a national prospective cohort study of adults in the United States.
Postgraduate Medicine 128: 865-868.
Egaña, M., and Donne, B. 2004. Physiological changes following a 12 week gym based stair-climbing, elliptical
trainer and treadmill running program in females. Journal of Sports Medicine and Physical Fitness 44: 141-146.
Egli, T., Bland, H.W., Melton, B.F., and Czech, D.R. 2011. Influence of age, sex, and race on college students’
exercise motivation of physical activity. Journal of American College Health 59: 399-406.
Ehrampoush, E., Arasteh, P., Homayounfar, R., Cheraghpour, M., Alipour, M., Naghizadeh, M.M.,
Hadibarhaghtalab, M., Daboodi, S.H., Askari, A., and Razaz, J.M. 2016. New anthropometric indices or old
ones: Which is the better predictor of body fat? Diabetes & Metabolic Syndrome: Clinical Research Reviews [Epub
ahead of print]. doi:10.1016/j.dsx.2016.08.027. Accessed August 14, 2017.
Ehrler, F., Weber, C., and Lovis, C. 2016. Influence of pedometer position on pedometer accuracy at various walking
speeds: A comparative study. Journal of Medical Internet Research 18: e268. doi:10.2196/jmir.5916. Accessed May
10, 2017.
Eickhoff-Shemek, J., and Herbert, D.L. 2007. Is licensure in your future? Issues to consider—part 1. ACSM’s Health
& Fitness Journal 11(5): 35-37.
Eickhoff-Shemek, J., and Herbert, D.L. 2008a. Is licensure in your future? Issues to consider—part 2. ACSM’s Health
& Fitness Journal 12 (1): 36-38.
Eickhoff-Shemek, J., and Herbert, D.L. 2008b. Is licensure in your future? Issues to consider—part 3. ACSM’s Health
& Fitness Journal 12 (3): 36-38.
Eijsvogels, T.M.H., and Thompson, P.D. 2015. Exercise is Medicine: At any dose? Journal of the American Medical
Association 314: 1915-1916.
Ekelund, U., Steene-Johannessen, J., Brown, W.J., Fagerland, M.W., Owen, N., Powell, K.E., Bauman, A., and Lee,
I-M. 2016. Does physical activity attenuate, or even eliminate, the detrimental association of sitting time with
mortality? A harmonized meta-analysis of data from more than 1 million men and women. Lancet 388: 1302-
1310.
El-Amrawy, F., Pharm, B., and Nounou, M.I. 2015. Are currently available wearable devices for activity tracking and
heart rate monitoring accurate, precise, and medically beneficial? Health Informatics Research 21: 315-320.
Ellis, K.J., Bell, S.J., Chertow, G.M., Chumlea, W.C., Knox, T.A., Kotler, D.P., Lukaski, H.C., and Schoeller, D.A.
1999. Bioelectrical impedance methods in clinical research: A follow-up to the NIH technology assessment
conference. Nutrition 15: 874-880.
Elsen, R., Siu, M.L., Pineda, O., and Solomons, N.W. 1987. Sources of variability in bioelectrical impedance
determinations in adults. In In vivo body composition studies, ed. K.J. Ellis, S. Yasamura, and W.D. Morgan, 184-
188. London: Institute of Physical Sciences in Medicine.
Emery, C.A. 2003. Is there a clinical standing balance measurement appropriate for use in sports medicine? A review
of the literature. Journal of Science and Medicine in Sport 6: 492-504.
Emery, C.A., Cassidy, J.D., Klassen, T.P., Rosychuk, R.J., and Rowe, B.H. 2005. Development of a clinical static
and dynamic standing balance measurement tool appropriate for use in adolescents. Physical Therapy 85(6): 502-
514.
780
Emmanuel, J. 2013. Guidance on maintaining and calibrating non-mercury clinical thermometers and
sphygmomanometers. UNDP GEF Global Healthcare Waste Project. https://noharm.org/sites/default/files/lib
/downloads/mercury/Guidance_Hg_2013.pdf. Accessed April 29, 2017.
Englund, D.A., Sharp, R.L., Selsby, J.T., Ganesan, S.S., and Franke, W.D. 2017. Resistance training performed at
distinct angular velocities elicits velocity-specific alterations in muscle strength and mobility status in older adults.
Experimental Gerontology 91: 51-56.
Enwemeka, C.S. 1986. Radiographic verification of knee goniometry. Scandinavian Journal of Rehabilitation Medicine
18: 47-49.
Epstein, L.H., Beecher, M.D., Graf, J.L., and Roemmich, J.L. 2007. Choice of interactive dance and bicycle games in
overweight and non-overweight youth. Annals of Behavioral Medicine 33: 124-131.
Esco, M.R., Olson, M.S., Williford, H.N., Lizana, S.N., and Russell, A.R. 2011. The accuracy of hand-to-hand
bioelectrical impedance analysis in predicting body composition in college-age female athletes. Journal of Strength
and Conditioning Research 25: 1040-1045.
Esco, M.R., Snarr, R.L., Leatherwood, M.D., Chamberlain, N.A., Redding, M.L., Flatt, A.A., Moon, J.R., and
Williford, H.N. 2015. Comparison of total and segmental body composition using DXA and multifrequency
bioimpedance in collegiate female athletes. Journal of Strength and Conditioning Research 29: 918-925.
Esmarck, B., Andersen, J.L., Olsen, S., Richter, E.A., Mizuno, M., and Kjaer, M. 2001. Timing of postexercise
protein intake is important for muscle hypertrophy with resistance training in elderly humans. Journal of Physiology
535: 301-311.
Eston, R., Evans, H., Faulkner, J., Lambrick, D., Al-Rahamneh, H., and Parfitt, G. 2012. A perceptually regulated,
graded exercise test predicts peak oxygen update during treadmill exercise in active and sedentary participants.
Evans, E.M., Rowe, D.A., Misic, M.M., Prior, B.M., and Arngrimsson, S.A. 2005. Skinfold prediction equation for
athletes developed using a four-component model. Medicine & Science in Sports & Exercise 37: 2006-2011.
Evans, H., Parfitt, G., and Eston, R. 2014. Use of a perceptually-regulated test to measure maximal oxygen uptake is
valid and feels better. European Journal of Sport Science. 14: 452-458.
Evans, H.J.L., Ferrar, K.E., Smith, A.E., Parfitt, F., and Eston, R.G. 2015. A systematic review of methods to
predict maximal oxygen uptake from submaximal, open circuit spirometry in healthy adults. Journal of Science and
Medicine in Sport 18: 183-188.
Evans, W., and Rosenberg, I. 1992. Biomarkers. New York: Simon & Schuster.
Fahey, T.D., Rolph, R., Moungmee, P., Nagel, J., and Mortara, S. 1976. Serum testosterone, body composition, and
strength of young adults. Medicine and Science in Sports 8: 31-34.
Falatic, J.A., Plato, P.A., Holder, C., Fiinch, D., Han, K., and Cisar, C.J. 2015. Effects of kettlebell training on
aerobic capacity. Journal of Strength and Conditioning Research 29: 1943-1947.
Faigenbaum, A.D., Kraemer, W.J., Blimkie, C.J.R., Jeffreys, I., Micheli, L.J., Nitka, M., and Rowland, T.W. 2009.
Youth resistance training: Updated position statement paper from the National Strength and Conditioning
Association. Journal of Strength & Conditioning Research 23: S60-S79.
Faigenbaum, A.D., Milliken, L.A., and Westcott, W.L. 2003. Maximal strength testing in healthy children. Journal of
Faigenbaum, A.D., and Myer, G.D. 2011. Exercise deficit disorder: Play now or pay later. Current Sports Medicine
Reports 11: 196-200.
781
Faigenbaum, A.D., Westcott, W.L., Loud, R.L., and Long, C. 1999. The effects of different resistance training
protocols on muscular strength and endurance development in children. Pediatrics 104(1): e5.
Fairbarn, M.S., Blackie, S.P., McElvaney, N.G., Wiggs, B.R., Pare, P.D., and Purdy, R.L. 1994. Prediction of heart
rate and oxygen uptake during incremental and maximal exercise in healthy adults. Chest 105: 1365-1369.
Farrar, R.E., Mayhew, J.L., and Koch, A.J. 2010. Oxygen cost of kettlebell swings. Journal of Strength and
Farthing, J.P., and Chilibeck, P.D. 2003. The effects of eccentric and concentric training at different velocities on
muscle hypertrophy. European Journal of Applied Physiology 89: 578-586.
Faulkner, S.H., Pugh, J.K., Hood, T.M., Menon, K., King, J.A., Nimmo, M.A. 2015. Group studio cycling: An
effective intervention to improve cardiometabolic health in overweight physically inactive individuals. Journal of
Fitness Research 4: 16-25.
Feigenbaum, M.S., and Pollock, M.L. 1999. Prescription of resistance training for health and disease. Medicine &
Feland, J.B., and Marin, H.N. 2004. Effect of submaximal contraction intensity in contract-relax proprioceptive
neuromuscular facilitation stretching. British Journal of Sports Medicine 38: e18.
Femina, H.A., Beevi, M.E., Miranda, J., Pedersen, C.F., and Wagner, S. 2016. An evaluation of commercial
pedometers for monitoring slow walking speed populations. Telemedicine and e-Health 22: 441-449.
Fenstermaker, K., Plowman, S., and Looney, M. 1992. Validation of the Rockport walking test in females 65 years
and older. Research Quarterly for Exercise and Sport 63: 322-327.
Ferber, R., Osternig, L., and Gravelle, D. 2002. Effect of PNF stretch techniques on knee flexor muscle EMG
activity in older adults. Journal of Electromyography and Kinesiology 12: 391-397.
Ferguson, T., Rowlands, A.V., Olds, T., and Maher, C. 2015. The validity of consumer-level activity monitors in
healthy adults worn in free-living conditions: A cross-sectional study. International Journal of Behavioral Nutrition
and Physical Activity 12: 42. doi:10.1186/s12966-015-0201-9.
Ferland, M., Despres, J.P., Tremblay, A., Pinault, S., Nadeau, A., Moorjani, S., Lupien, P.J., Theriault, G., and
Bouchard, C. 1989. Assessment of adipose distribution by computed axial tomography in obese women:
Association with body density and anthropometric measurements. British Journal of Nutrition 61: 139-148.
Ferrar, K., Evans, H., Smith, A., Parfitt, G., and Eston, R. 2014. A systematic review and meta-analysis of
submaximal exercise-based equations to predict maximal oxygen uptake in young people. Pediatric Exercise Science
26: 342-357.
Ferreira, H.R., Gill, P., Filho, J.F., and Fernandes, L.C. 2015. Effects of 12-weeks of supplementation with β-
hydroxy-β-methylbutyrate-ca (HMB-Ca) on athletic performance. Journal of Exercise Physiology Online 18(2): 85-
94.
Ferreira, H.R., Rodacki, A.L.F., Gill, P., Tanhoffer, R., Filho, J.F., and Fernandes, L.C. 2013. The effects of
supplementation of β-hydroxy-β-melthylbutyrate on inflammatory markers in high performance athletes. Journal
of Exercise Physiology Online 16(1): 53-63.
Fess, E.E. 1992. Grip Strength. In Clinical assessment recommendations, American Society of Hand Therapists, 41-45,
Chicago, IL: American Society of Hand Therapists.
Fields, D.A., and Allison, D.B. 2012. Air-displacement plethysmography pediatric option in 2-6 year olds using the
four-compartment model as a criterion method. Obesity 20: 1732-1737.
Fields, D.A., and Goran, M.I. 2000. Body composition techniques and the four-compartment model in children.
782
Fields, D.A., Goran, M.I., and McCrory, M.A. 2002. Body-composition assessment via air-displacement
plethysmography in adults and children: A review. American Journal of Clinical Nutrition 75: 453-467.
Fields, D.A., Hunter, G.R., and Goran, M.I. 2000. Validation of the Bod Pod with hydrostatic weighing: Influence
of body clothing. International Journal of Obesity 24: 200-205.
Fields, D.A., Wilson, G.D., Gladden, L.B., Hunter, G.R., Pascoe, D.D., and Goran, M.I. 2001. Comparison of the
Bod Pod with the four-compartment model in adult females. Medicine & Science in Sports & Exercise 33: 1605-
1610.
Fisher, G., Brown, A.W., Brown, M.M.B., Alcorn, A., Noles, C., Winwood, L., Resuehr, H., George, B., Jeansonne,
M.M., and Allison, D.B. 2015. High intensity interval- vs. moderate intensity-training for improving
cardiometabolic health in overweight or obese males: A randomized controlled trial. PLoS One 10: e0138853.
doi:10.1371/journal.pone.0138853. Accessed July 27, 2017.
Fitzmaurice, C., and the Global Burden of Disease Cancer Collaboration. 2017. Global, regional, and national cancer
incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 32 cancer
groups, 1990 to 2015: A systematic analysis for the Global Burden of Disease study. JAMA Oncology 3: 524-548.
Fleck, S.J. 1999. Periodized strength training: A critical review. Journal of Strength and Conditioning Research 13(1):
82-89.
Fleck, S.J., and Falkel, J.E. 1986. Value of resistance training for the reduction of sports injuries. Sports Medicine 3:
61-68.
Fleck, S.J., and Kraemer, W.J. 2014. Designing resistance training programs, 4th ed. Champaign, IL: Human Kinetics.
Flegal, K.M., Carroll, M.D., Kit, B.K., and Ogden, C.L. 2012. Prevalence of obesity and trends in the distribution of
body mass index among US adults, 1999-2010. Journal of the American Medical Association 307: 491-497.
Flegal, K.M., Kruszon-Moran, D., Carroll, M.D., Fryar, C.D., and Ogden, C.L. 2016. Prevalence of obesity and
trends in the distribution of body mass index among US adults, 2005 to 2014. Journal of the American Medical
Association 315: 2284-2291.
Flegal, K.M., Shepherd, J.A., Looker, A.C., Graubard, B.I., Borrud, L.G., Ogden, C.L., Harris, T.B., Everhart, J.E.,
and Schenker, N. 2009. Comparisons of percentage body fat, body mass index, waist circumference, and waist-
stature ratio in adults. American Journal of Clinical Nutrition 89: 500-508.
Fletcher, G.F., Ades, P.A., Kligfield, P., Arena, R., Balacy, G.J., Bittner, V.A., Coke, L.A., Fleg, J.L., Forman, D.E.,
Gerber, T.C., Gulati, M., Madan, K., Rhodes, J., Thompson, P.D., Williams, M.A., on behalf of the American
Heart Association Exercise, Cardiac Rehabilitation, and Prevention Committee of the Council on Clinical
Cardiology, Council on Nutrition, Physical Activity and Metabolism, Council on Cardiovascular and Stroke
Nursing, and Council on Epidemiology and Prevention. 2013. Exercise standards for testing and training: A
scientific statement from the American Heart Association. Circulation 128: 873-934.
Fogelholm, G.M., Sievanan, H.T., Kukkonen-Harjula, K., Oja, P., and Vuori, I. 1993. Effects of a meal and its
electrolytes on bioelectrical impedance. In Human body composition: In vivo methods, models and assessment, ed. K.J.
Ellis and J.D. Eastman, 331-332. New York: Plenum Press.
Fogg, B.J. 2003. Persuasive technology: Using computers to change what we think and do. New York: Morgan Kaufmann.
Fogg, B.J., and Eckles, D., eds. 2007. Mobile persuasion: 20 perspectives on the future of behavior change. Palo Alto, CA:
Stanford University.
Fohlin, L. 1977. Body composition, cardiovascular and renal function in adolescent patients with anorexia nervosa.
783
Acta Paediatrica Scandinavica 268(Suppl.): S7-S20.
Forbes, G.B. 1976. Adult decline in the lean body mass. Human Biology 48: 151-173.
Forbes, S.C., Little, J.P., and Candow, D.G. 2012. Exercise and nutritional interventions for improving aging muscle
health. Endocrine 42: 29-38.
Ford, G.S., Mazzone, M.A., and Taylor, K. 2005. The effect of 4 different durations of static hamstring stretching on
passive knee-extension range of motion. Journal of Sport Rehabilitation 14: 95-107.
Fornetti, W.C., Pivarnik, J.M., Foley, J.M., and Fiechtner, J.J. 1999. Reliability and validity of body composition
measures in female athletes. Journal of Applied Physiology 87: 1114-1122.
Fort, A., Romero, D., Bagur, C., and Guerra, M. 2012. Effects of whole-body vibration training on explosive
strength and postural control in young female athletes. Journal of Strength and Conditioning Research 26: 926-936.
Foster, C., Jackson, A.S., Pollock, M.L., Taylor, M.M., Hare, J., Sennett, S.M., Rod, J.L., Sarwar, M., and Schmidt,
D.H. 1984. Generalized equations for predicting functional capacity from treadmill performance. American Heart
Journal 107: 1229-1234.
Foster, C., Pollock, M.L., Rod, J.L., Dymond, D.S., Wible, G., and Schmidt, D.H. 1983. Evaluation of functional
capacity during exercise radionuclide angiography. Cardiology 70: 85-93.
Forouzanfar, M., Dajani, H.R., Groza, V.Z., Bolic, M., Rajan, S., and Batkin, I. 2015. Oscillometric blood pressure
estimation: Past, present, and future. IEEE Reviews in Biomedical Engineering 8: 44-63.
Fox, E.L. 1973. A simple, accurate technique for predicting maximal aerobic power. Journal of Applied Physiology 35:
914-916.
Franchignoni, F., Tesio, L., Martino, M.T., and Ricupero, C. 1998. Reliability of four simple, quantitative tests of
balance and mobility in healthy elderly females. Aging 10(1): 26-31.
Francis, P.R., Kolkhorst, F.W., Pennuci, M., Pozos, R.S., and Buono, M.J. 2001. An electromyographic approach to
the evaluation of abdominal exercises. ACSM’s Health & Fitness Journal 5(4): 8-14.
Franklin, S.S., Thijs, L., Asayama, K., Li, Y., Hansen, T.W., Boggia, J., Jacobs, L., Zhang, Z., Kikuya, M.,
Björklund-Bodegård, K., Ohkubo, T., Yang, W-Y., Jeppesen, J., Dolan, E., Kuznetsova, T., Stolarz-Skrzpek, K.,
Tikhonoff, V., Malyutina, S., Casiglia, E., Nikitin, Y., Lind, L., Sandoya, E., Kawecka-Jaszcz, K., Filipovsky, J.,
Imai, Y., Wang, J-G., O-Brien, E., and Staessen, J.A., on behalf of the IDACCO Investigators. 2016. The
cardiovascular risk of white-coat hypertension. Journal of the American College of Cardiology 68: 2033-2043.
Frederick, A., and Frederick, C. 2017. Stretch to win, 2nd ed. Champaign, IL: Human Kinetics.
Freedman, D.S., Blanck, J.M., Dietz, W.H., DasMahapatra, P., Srinivasan, S.R., and Berenson, G.S. 2012. Is the
body adiposity index (hip circumference/height1.5) more related to skinfold thicknesses and risk factor levels than is
BMI? The Bogalusa Heart Study. British Journal of Nutrition. doi:10.1017/S0007114512000979.
Freedman, D.S., and Ford, E.S. 2015. Are the recent secular increases in the waist circumferences independent of
changes in BMI? American Journal of Clinical Nutrition 101: 425-431.
Freitas, S.R., Vilarinho, D., Vaz, J.R., Bruno, P.M., Costa, P.B., and Mil-ho-mens, P. 2015. Responses to static
stretching are dependent on stretch intensity and duration. Clinical Physiology and Functional Imaging 35: 478-484.
Friden, J. 2002. Delayed onset muscle soreness. Scandinavian Journal of Medicine and Science in Sports 12: 327-328.
Friden, J., Sjostrom, M., and Ekblom, B. 1983. Myofibrillar damage following intense eccentric exercise in man.
International Journal of Sports Medicine 4: 170-176.
Friedl, K.E., DeLuca, J.P., Marchitelli, L.J., and Vogel, J.A. 1992. Reliability of body-fat estimations from a four-
compartment model by using density, body water, and bone mineral measurements. American Journal of Clinical
784
Frisancho, A.R. 1984. New standard of weight and body composition by frame size and height for assessment of
nutritional status of adults and the elderly. American Journal of Clinical Nutrition 40: 808-819.
Frohlich, M., Emrich, E., and Schmidtbleicher, D. 2010. Outcome effects of single-set versus multiple-set training—
An advanced replication study. Research in Sports Medicine 18: 157-175.
Fry, A.C. 2004. The role of resistance exercise intensity on muscle fibre adaptations. Sports Medicine 34: 663-679.
Fullam, K., Caulfield, B., Coughlan, G.F., and Delahunt, E. 2014. Kinematic analysis of selected reach directions of
the star excursion balance test compared with the Y-balance test. Journal of Sport Rehabilitation 23: 27-35.
Gába, A., Kapuš, O., Cuberek, R., and Botek, M. 2015. Comparison of multi- and single-frequency bioelectrical
impedance analysis with dual-energy X-ray absorptiometry for assessment of body composition in post-
menopausal women: Effects of body mass index and accelerometer-determined physical activity. Journal of
Nutrition and Human Dietetics 28: 390-400.
Gajdosik, R.L., Vander Linden, D.W., and Williams, A.K. 1999. Influence of age on length and passive elastic
stiffness characteristics of the calf muscle-tendon unit of women. Physical Therapy 79: 827-838.
Gallagher, D., Visser, M., Sepulveda, D., Pierson, R.N., Harris, T., and Heymsfield, S.B. 1996. How useful is body
mass index for comparison of body fatness across age, sex, and ethnic groups? American Journal of Epidemiology 143:
228-239.
Gallagher, M.R., Walker, K.Z., and O’Dea, K. 1998. The influence of a breakfast meal on the assessment of body
composition using bioelectrical impedance. European Journal of Clinical Nutrition 52: 94-97.
Garatachea, N., Pareja-Galeano, H., Sanchis-Gomar, F., Santos-Lozano, A., Fiuza-Luces, C., Morán, M.,
Emanuele, E., Joyner, M.J., and Lucia, A. 2015. Exercise attenuates the major hallmarks of aging. Rejuvenation
Research 18: 57-89.
Garber, C.E., Blissmer, B., Deschenes, M.R., Franklin, B.A., Lamonte, M.J., Lee, I., Nieman, D.C., and Swain,
D.P. 2011. Quantity and quality of exercise for developing and maintaining cardiorespiratory, musculoskeletal, and
neuromotor fitness in apparently healthy adults: Guidance for prescribing exercise. Medicine & Science in Sports &
Exercise 43: 1334-1359.
Garcia, T.B. 2015. 12-lead ECG: The art of interpretation. Burlington, MA: Jones and Bartlett Learning.
Garnacho-Castano, M.V., Lopez-Lastra, S., and Mate-Munoz, J.L. 2015. Reliability and validity assessment of a
linear position transducer. Journal of Sports Science and Medicine 14: 128-136.
GBD 2015 Mortality and Causes of Death Collaborators. 2016. Global, regional, and national life expectancy, all-
cause mortality, and cause-specific mortality for 249 causes of death, 1980-2015: A systematic analysis for the
Global Burden of Disease Study 2015. Lancet 388: 1459-1544.
Gellish, R.L., Goslin, B.R., Olson, R.E., McDonald, A., Russi, G.D., and Moudgil, V.K. 2007. Longitudinal
modeling of the relationship between age and maximal heart rate. Medicine & Science in Sports & Exercise 39: 822-
829.
Gennuso, K.P., Gangnon, R.E., Thraen-Borowski, K.M., and Colbert, L.H. 2015. Dose-response relationships
between sedentary behavior and the metabolic syndrome and its components. Diabetologia 58: 485-492.
Gentil, P., de Lira, C.A.B., Filho, S.G.C., La Scala Teixeira, C.V., Steele, J., Fisher, J., Carneiro, J.A., and Campos,
M.H. 2017. High intensity interval training does not impair strength gains in response to resistance training in
premenopausal women. European Journal of Applied Physiology 117: 1257-1265.
Genton, L., Hans, D., Kyle, U.G., and Pichard, C. 2002. Dual-energy X-ray absorptiometry and body composition:
785
Differences between devices and comparison with reference methods. Nutrition 18: 66-70.
George, J.D., Stone, W.J., and Burkett, L.N. 1997. Non-exercise V̇O2max estimation for physically active students.
George, J., Vehrs, P., Allsen, P., Fellingham, G., and Fisher, G. 1993. V̇O2max estimation from a submaximal 1-mile
track jog for fit college-age individuals. Medicine & Science in Sports & Exercise 25: 401-406.
Gesche, H., Grosskaurth, D., Kuchler, G., and Patzak, A. 2012. Continuous blood pressure measurement by using
the pulse transit time: Comparison to a cuff-based method. European Journal of Applied Physiology 112: 309-315.
Gettman, L.R., Ayres, J.J., Pollock, M.L., and Jackson, A. 1978. The effect of circuit weight training on strength,
cardiorespiratory function, and body composition of adult men. Medicine and Science in Sports 10: 171-176.
Gettman, L.R., and Pollock, M.L. 1981. Circuit weight training: A critical review of its physiological benefits. The
Physician and Sportsmedicine 9: 44-60.
Gibbons, R.J., Balady, G.J., Bricker, J.T., Chaitman, B.R., Fletcher, G.F., Froelicher, V.F., Mark, D.B., McCallister,
B.D., Mooss, A.N., O’Reilly, M.G., and Winters, W.L. Jr. 2002. ACC/AHA 2002 guideline update for exercise
testing: A report of the American College of Cardiology/American Heart Association Task Force on Practice
Guidelines (Committee on Exercise Testing). www.acc.org/clinical/guidelines/exercise/dirIndex.htm.
Gibby, J.T., Njeru, D.K., Cvetko, S.T., Heiny, E.L., Creer, A.R., and Gibby, W.A., 2017. Whole-body computed
tomography-based body mass and body fat quantification: A comparison to hydrostatic weighing and air
displacement plethysmography. Journal of Computer Assisted Tomography 41: 302-308.
Gibson, A., Heyward, V., and Mermier, C. 2000. Predictive accuracy of Omron Body Logic Analyzer in estimating
relative body fat of adults. International Journal of Sport Nutrition and Exercise Metabolism 10: 216-227.
Gibson, A.L., Beam, J.R., Alencar, M.K., Zuhl, M.N., and Mermier, C.M. 2015. Time course of supine and
standing shifts in total body, intracellular and extracellular water for a sample of healthy adults. European Journal of
Clinical Nutrition 69: 14-19.
Gibson, A.L., Holmes, J.C., Desautels, R.L., Edmonds, L.B., and Nuudi, L. 2008. Ability of new octapolar
bioimpedance spectroscopy analyzers to predict 4-component-model percentage body fat in Hispanic, black, and
white adults. American Journal of Clinical Nutrition 87: 332-338.
Gibson, A.L., Roper, J.L., and Mermier, C.M. 2016. Intraindividual variability in test-retest air displacement
plethysmography measurements of body density for men and women. International Journal of Sport Nutrition and
Exercise Metabolism 26: 404-412.
Gillespie, B.D., McCormick, J.J., Mermier, C.M., and Gibson, A.L. 2015. Talk test as a practical method to estimate
exercise intensity in highly trained competitive male cyclists. Journal of Strength and Conditioning Research 29: 894-
898.
Gillen, J.G., Martin, B.J. MacInnis, M.J., Skelly, L.E., Tarnopolsky, M.A., and Gibala, M.J. 2016. Twelve weeks of
sprint interval training improves indices of cardiometabolic health similar to traditional endurance training despite
a five-fold lower exercise volume and time commitment. PLoS One 11: e0154075.
doi:10.1371/journal.pone.0154075. Accessed July 30, 2017.
Gillman, M.W. 2008. The first months of life: A critical period for development of obesity. American Journal of
Clinical Nutrition 87: 1587-1589.
Girouard, C.K., and Hurley, B.F. 1995. Does strength training inhibit gains in range of motion from flexibility
training in older adults? Medicine & Science in Sports & Exercise 27: 1444-1449.
Gledhill, N., and Jamnik, R. 1995. Determining power outputs for cycle ergometers with different sized flywheels.
786
Gleichauf, C.N., and Rose, D.A. 1989. The menstrual cycle’s effect on the reliability of bioimpedance measurements
for assessing body composition. American Journal of Clinical Nutrition 50: 903-907.
Glowacki, S.P., Martin, S.E., Maurer, A., Baek, W., Green, J.S., and Crouse, S.F. 2004. Effects of resistance,
endurance, and concurrent exercise on training outcomes in men. Medicine & Science in Sports & Exercise 36: 2119-
2127.
Goble, D.J., Cone, B.L., and Fling, B.W. 2014. Using the Wii Fit as a tool for balance assessment and
neurorehabilitation: The first half decade of “Wii-search.” Journal of NeuroEngineering and Rehabilitation 11: 12.
Gökbayrak, N.S., Paiva, A.L., Blissmer, B.J., and Prochaska, J.O. 2015. Predictors of relapse among smokers:
Transtheoretical effort variables, demographics, and smoking severity. Addictive Behaviors 42: 176-179.
Goldberg, A., Etlinger, J., Goldspink, D., and Jablecki, C. 1975. Mechanism of work-induced hypertrophy of skeletal
muscle. Medicine and Science in Sports 7: 185-198.
Goldenberg, L., and Twist, P. 2016. Strength ball training, 3rd ed. Champaign, IL: Human Kinetics.
Golding, L. 2000. The Y’s way to physical fitness. Champaign, IL: Human Kinetics.
Goldman, H.I., and Becklake, M.R. 1959. Respiratory function tests: Normal values at medium altitudes and the
prediction of normal results. American Review of Tuberculosis and Respiratory Diseases 79: 457-467.
Gonyea, W.J., Ericson, G.C., and Bonde-Petersen, F. 1977. Skeletal muscle fiber splitting induced by weight-lifting
exercise in cats. Acta Physiologica Scandinavica 99: 105-109.
Goode, A.P., Hall, K.S., Batch, B.C., Huffman, K.M., Hastings, S.N., Allen, K.D., Shaw, R.J., Kanach, F.A.,
McDuffie, J.R., Kosinski, A.S., Williams, J.W. Jr., and Gierisch, J.M. 2017. The impact of interventions that
integrate accelerometers on physical activity and weight loss: A systematic review. Annals of Behavioral Medicine
51(1): 79-93.
Goodman, J.M., Thomas, S.G., and Burr, J. 2011. Evidence-based risk assessment and recommendations for exercise
testing and physical activity clearance in apparently healthy individuals. Applied Physiology, Nutrition, and
Metabolism 36: S14-S32.
Goran, M.I., Allison, D.B., and Poehlman, E.T. 1995. Issues relating to normalization of body fat content in men
and women. International Journal of Obesity 19: 638-643.
Goran, M.I., Toth, M.J., and Poehlman, E.T. 1998. Assessment of research-based body composition techniques in
healthy elderly men and women using the 4-component model as a criterion method. International Journal of
Obesity 22: 135-142.
Gordon, D.J., Probstfield, J.L., Garrison, R.J., Neaton, J.D., Castelli, W.P., Knoke, J.D., Jacobs, D.R., Bangdiwala,
S., and Tyroler, H.A. 1989. High-density lipoprotein cholesterol and cardiovascular disease: Four prospective
American studies. Circulation 79: 8-15.
Gordon-Larsen, P., Hou, N., Sidney, S., Sternfeld, B., Lewis, C., Jacobs, D. Jr., and Popkin, B. 2009. Fifteen-year
longitudinal trends in walking patterns and their impact on weight change. American Journal of Clinical Nutrition
89: 19-26.
Gordon, C.M., Zemel, B.S., Wren, T.A.L., Leonard, M.B., Bachrach, L.K., Rauch, F., Gilsanz, V., Rosen, C.J, and
Winer, K.K. 2017. The determinants of peak bone mass. Journal of Pediatrics 180: 261-269.
Gordon, R., and Bloxham, S. 2016. A systematic review of the effects of exercise and physical activity on non-specific
chronic low back pain. Healthcare 4: 22. doi:10.3390/healthcare402002. Accessed April 15, 2017.
Gosselin, L.E., Kozlowski, K.F., de Vinney-Boymel, L., and Hambridge, C. 2012. Metabolic response of different
787
high-intensity aerobic interval exercise protocols. Journal of Strength and Conditioning Research 26: 2866-2871.
Gothe, N.P., and McAuley, E. 2016. Yoga is as good as stretching-strengthening exercises in improving functional
fitness outcomes: Results from a randomized controlled trial. Journals of Gerontology, Series A: Biological Sciences and
Medical Sciences 71: 406-411.
Granacher, U. 2011. Balance and strength performance in children, adolescents, and seniors. Hamburg, Germany: Verlag
Dr. Kovac.
Granacher, U., Gollhofer, A., Hortobagyi, T., Kressig, R.W., and Muehlbauer, T. 2013. The importance of trunk
muscle strength for balance, functional performance, and fall prevention in seniors: A systematic review. Sports
Medicine 43: 627-641.
Granacher, U., Gruber, M., and Gollhofer. 2010. Force production capacity and functional reflex activity in young
and elderly men. Aging Clinical and Experimental Research 22: 374-382.
Granacher, U., Kressig, R.W., Borde, R., Lesinski, M., Bohm, S., Mersmann, F., and Arampatzis, A. 2017. Muscular
strength and balance in old age: Effects and dose-response relationships following resistance and balance training.
Neurologie und Rehabilitation 23: 61-76.
Granacher, U., Muehlbauer, T., Zahner, L., Gollhofer, A., and Kressig, R.W. 2011. Comparison of traditional and
recent approaches in the promotion of balance and strength in older adults. Sport Medicine 41: 377-400.
Granacher, U., Muehlbauer, T., and Gruber, M. 2012. A qualitative review of balance and strength performance in
healthy older adults: Impact for testing and training. Journal of Aging Research 2012: 708905.
doi:10.1155/2012/708905. Accessed November 30, 2012.
Gras, L.Z., Ganley, K.J., Bosch, P.R., Mayer, J.E., and Pohl, P.S. 2017. Convergent validity of the sharpened
Romberg. Physical and Occupational Therapy in Geriatrics [Epub ahead of print]. Epub:
doi:10.1080/02703181.2017.1307897
Graversen, P., Abildstrøm, S.Z., Jespersen, L., Borglykke, A., and Prescott, E. 2016. Cardiovascular risk prediction:
Can Systematic COronary Risk Evaluation (SCORE) be improved by adding simple risk markers? Results from
the Copenhagen City Heart Study. European Journal of Preventive Cardiology 23: 1546-1556.
Graves, J.D., Webb, M., Pollock, M.L., Matkozich, J., Leggett, S.H., Carpenter, D.M., Foster, D.N., and Cirulli, J.
1994. Pelvic stabilization during resistance training: Its effect on the development of lumbar extension strength.
Archives of Physical Medicine and Rehabilitation 75: 211-215.
Graves, J.E., Pollock, M.L., Colvin, A.B., Van Loan, M., and Lohman, T.G. 1989. Comparison of different
bioelectrical impedance analyzers in the prediction of body composition. American Journal of Human Biology 1: 603-
611.
Graves, L., Stratton, G., Ridgers, N.D., and Cable, N.T. 2007. Comparison of energy expenditure in adolescents
when playing new generation and sedentary computer games: Cross-sectional study. British Medical Journal 335:
1282-1284.
Gray, D.S., Bray, G.A., Gemayel, N., and Kaplan, K. 1989. Effect of obesity on bioelectrical impedance. American
Gray, M., and Paulson, S. 2014. Developing a measure of muscular power during a functional task for older adults.
BMC Geriatrics 14: 145.
Green, J.M., Crews, T.R., Pritchett, R.C., Mathfield, C., and Hall, L. 2004. Heart rate and ratings of perceived
exertion during treadmill and elliptical exercise training. Perceptual and Motor Skills 98: 340-348.
Greene, P.F., Durall, C.J., and Kernozek, T.W. 2012. Intersession reliability and concurrent validity of isometric
788
endurance tests for the lateral trunk muscles. Journal of Sport Rehabilitation 21: 161-166.
Greene, W.B., and Heckman, J.D. 1994. The clinical measurement of joint motion. Rosemont, IL: American Academy
of Orthopaedic Surgeons.
Grembowski, D., Patrick, D., Diehr, P., Durham, M., Beresford, S., Kay, E., and Hecht, J. 1993. Self-efficacy and
health behavior among older adults. Journal of Health and Social Behavior 34(6): 89-104.
Grenier, S.G., Russell, C., and McGill, S.M. 2003. Relationships between lumbar flexibility, sit-and-reach test, and a
previous history of low back discomfort in industrial workers. Canadian Journal of Applied Physiology 28: 165-177.
Gribble, P.A., and Hertel, J. 2003. Considerations for normalizing measures of the star excursion balance test.
Measurement in Physical Education and Exercise Science 7: 89-100.
Grier, T., Canham-Chervak, M., McNulty, V., and Jones, B.H. 2013. Extreme conditioning programs and injury risk
in a US Army Brigade Combat Team. U.S. Army Medical Department Journal (1 October): 36-47.
Griffin, S., Robergs, R., and Heyward, V. 1997. Assessment of exercise blood pressure: A review. Medicine & Science
Grossman, J.C., and Deitrick, R.W. 2015. Air displacement plethysmography and resistance exercise. Internet Journal
of Allied Health Sciences and Practice. http://nsuworks.nova.edu/ijahsp/vol13/iss2/4. Accessed August 5, 2017.
Gruber, J.J., Pollock, M.L., Graves, J.E., Colvin, A.B., and Braith, R.W. 1990. Comparison of Harpenden and Lange
calipers in predicting body composition. Research Quarterly for Exercise and Sport 61: 184-190.
Guariglia, D.A., Pereira, L.M., Dias, J.M., Pereira, H.M., Menacho, M.O., Silva, D.A., Ayrino, E.S., and Cardoso,
J.R. 2011. Time-of-day effect on hip flexibility associated with the modified sit-and-reach test in males.
Gudivaka, R., Schoeller, D., and Kushner, R.F. 1996. Effect of skin temperature on multifrequency bioelectrical
impedance analysis. Journal of Applied Physiology 81: 838-845.
Guglani, R., Shenoy, S., and Singh, J. 2014. Effect of progressive pedometer based walking intervention on quality of
life and general well being among patients with type 2 diabetes. Journal of Diabetes & Metabolic Disorders 13: 110-
120.
Guidetti, L., Sgadari, A., Buzzachera, C.F., Broccatelli, M., Utter, A.C., Goss, F.L., and Baldari, C. 2011. Validation
of the OMNI-cycle scale of perceived exertion in the elderly. Journal of Aging and Physical Activity 19: 214-224.
Guimaraes, R.M., and Isaacs, B. 1980. Characteristics of gait in old people who fall. International Rehabilitation
Medicine 2: 177-180.
Guimarães-Ferreira, L., Cholewa, J.M., Naimo, M.A., Zhi, X.I., Magagnin, D., de Sá, R.B., Streck, E.L., Teixeira
Tda, S., and Zanchi, N.E. 2014. Synergistic effects of resistance training and protein intake: Practical aspects.
Nutrition 30(10): 1097-1103.
Guralnik, J.M., Seeman, T.E., Tinetti, M.E., Nevitt, M.C., and Berkman, L.F. 1994. Validation and use of
performance measures of functioning in a non-disabled older population: MacArthur studies of successful aging.
Aging Clinical and Experimental Research 6: 410-419.
Guskiewicz, K.M. 2011. Balance assessment in the management of sport-related concussion. Clinics in Sports Medicine
30: 89-102.
Guskiewicz, K.M., and Perrin, D.H. 1996. Research and clinical applications of assessing balance. Journal of Sport
Rehabilitation 5: 45-63.
Gustavsen, P.H., Hoegholm, A., Bang, L.E., and Kristensen, K.S. 2003. White coat hypertension is a cardiovascular
risk factor. A 10-year follow-up study. Journal of Human Hypertension 17: 811-817.
789
Guy, J.A., and Micheli, L.J. 2001. Strength training for children and adolescents. Journal of the American Academy of
Orthopaedic Surgeons 9: 29-36.
Habash, D. 2002. Tactile and interpersonal techniques for fatfold anthropometry. School of Medicine. Ohio State
University. Unpublished paper.
Habib, Z., and Westcott, S. 1998. Assessment of anthropometric factors on balance tests in children. Pediatric Physical
Therapy 10: 101-109.
Haff, G.G. 2016. Periodization. In Essentials of strength training and conditioning, 4th ed., ed. G.G. Haff and N.T.
Triplett, 583-604. Champaign, IL: Human Kinetics.
Hagerman, F. 1993. Concept II rowing ergometer nomogram for prediction of maximal oxygen consumption [abstract].
Morrisville, VT: Concept II.
Hall, K.D., Sacks, G., Chandramohan, D., Chow, C.C., Wang, C., Gortmaker, S.L., and Swinburn, B.A. 2011.
Quantification of the effect of energy imbalance on bodyweight. Lancet 378(9793): 826-837.
Halvarsson, A., Dohrn, I-M., and Stahle, A. 2015. Taking balance training for older adults one step further: The
rationale for and description of a proven balance training programme. Clinical Rehabilitation 29: 417-425.
Han, L., and Yang, F. 2015. Strength or power, which is more important to prevent slip-related falls? Human
Movement Science 44: 192-200.
Handelsman, Y., Bloomgarden, Z.T., Grungerger, G., Umpierrrez, G., Zimmerman, R.S., Bailey, T.S., Blonde, L.,
Bray, G.A., Cohen, A.J., Dagogo-Jack, S., Davidson, J.A., Einhorn, D., Ganda, O.P., Garber, A.J., Garvey,
W.T., Henry, R.R., Hirsch, I.B., Horton, E.S., Hurley, D.L., Jellinger, P.S., Jovanovič, L., Lebovitz, H.E.,
LeRoith, D., Levy, P., McGill, J.G., Mechanick, J.I., Mestman, J.H., Moghissi, E.S., Orzeck, E.A., Pessah-
Pollack, R., Rosenblit, P.D., Vinik, A.I., Wyne, K., and Zzangeneh, F. 2015. American Association of Clinical
Endocrinologists and American College of Endocrinology: Clinical practice guidelines for developing a diabetes
mellitus comprehensive care plan—2015. Endocrine Practice 21(Suppl. 1): 1-87.
Hansen, D., Jacobs, N., Bex, S., D’Haene, G., Dendale, P. and Claes, N., 2011. Are fixed-rate step tests medically
safe for assessing physical fitness? European Journal of Applied Physiology 111: 2593-2599.
Harmer, P., and Li, F. 2008. Tai chi and falls prevention in older people. Medicine and Sport Science 52: 124-134.
Harridge, S.D. 2007. Plasticity of human skeletal muscle: Gene expression to in vivo function. Experimental Physiology
92: 783-797.
Harries, S.K., Lubans, D.R., and Callister, R. 2015. Systematic review and meta-analysis of linear and undulating
periodized resistance training programs on muscular strength. Journal of Strength and Conditioning Research 29:
1113-1125.
Harris, J.A., and Benedict, F.G. 1919. A biometric study of basal metabolism in man (publication no. 279). Washington,
D.C.: Carnegie Institute.
Harris, M.L. 1969. A factor analytic study of flexibility. Research Quarterly 40: 62-70.
Harrison, G.G., Buskirk, E.R., Carter, L.J.E., Johnston, F.E., Lohman, T.G., Pollock, M.L., Roche, A.F., and
Wilmore, J.H. 1988. Skinfold thicknesses and measurement technique. In Anthropometric standardization reference
manual, ed. T.G. Lohman, A.F. Roche, and R. Martorell, 55-70. Champaign, IL: Human Kinetics.
Harrop, B.J., and Woodruff, S.J. 2015. Effects of acute and 2-hour postphysical activity on the estimation of body fat
made by the Bod Pod. Journal of Strength and Conditioning Research 29: 1527-1533.
Hartley, L.H. 1975. Growth hormone and catecholamine response to exercise in relation to physical training.
Medicine and Science in Sports 7: 34-36.
790
Hartley, L.H., Mason, J.W., Hogan, R.P., Jones, L.G., Kotchen, T.A., Mougey, E.H., Wherry, R., Pennington, L.,
and Ricketts, P. 1972. Multiple hormonal responses to graded exercise in relation to physical conditioning. Journal
of Applied Physiology 33: 602-606.
Hartley-O’Brien, S.J. 1980. Six mobilization exercises for active range of hip flexion. Research Quarterly for Exercise
and Sport 51: 625-635.
Hasanpour-Dehkordi, A., Dehghani, A., and Solati, K. 2017. A comparison of the effects of Pilates and McKenzie
training on pain and general health in men with chronic low back pain: A randomized trial. Indian Journal of
Palliative Care 23: 36-40.
Haskell, W.L., Lee, I.M., Pate, R.R., Powell, K.E., Blair, S.N., Franklin, B.A., Macera, C.A., Heath, G.W.,
Thompson, P.D., and Bauman, A. 2007. Physical activity and public health: Updated recommendation for adults
from the American College of Sports Medicine and the American Heart Association. Medicine & Science in Sports
& Exercise 39(8): 1423-1434.
Hass, C.J., Garzarella, L., De Hoyas, D., and Pollock, M. 2000. Single versus multiple sets in long-term recreational
weightlifters. Medicine & Science in Sports & Exercise 32: 235-242.
Hastuti, J., Kagawa, M., Byrne, N.M., and Hills, A.P. 2016. Proposal of new body composition prediction equations
from bioelectrical impedance for Indonesian men. European Journal of Clinical Nutrition 70: 1271-1277.
Hawk, C., Hyland, J.K., Rupert, R., Colonvega, M., and Hall, S. 2006. Assessment of balance and risk for falls in a
sample of community-dwelling adults aged 65 and older. Chiropractic & Osteology 14: 3-10.
Hawkins, M.N., Raven, P.B., Snell, P.G., Stray-Gundersen, J., and Levine, B.D. 2007. Maximal oxygen uptake as a
parametric measure of cardiorespiratory capacity. Medicine & Science in Sports & Exercise 39: 103-107.
Hayes, A., and Cribb, P.J. 2008. Effect of whey protein isolate on strength, body composition, and muscle
hypertrophy during resistance training. Current Opinion in Clinical Nutrition and Metabolic Care 11: 40-44.
Hayes, P.A., Sowood, P.J., Belyavin, A., Cohen, J.B., and Smith, F.W. 1988. Sub-cutaneous fat thickness measured
by magnetic resonance imaging, ultrasound, and calipers. Medicine & Science in Sports & Exercise 20: 303-309.
Health Canada. 2003. Canada’s physical activity guide to healthy active living. Version 9. www.hc-
sc.ca/english/lifestyles/index.html.
Hebden, L., Balestracci, K., McGeechan, K., Denney-Wilson, E., Harris, M., Bauman, A., and Allman-Farnelli, M.
2013. ‘TXT2BFIT’ a mobile phone-based healthy lifestyle program for preventing unhealthy weight gain in young
adults: Study protocol for a randomized controlled trial. Trials 14: 75. www.trialsjournal.com/content/14/1/7.
Hegedus, E.J., McDonough, S.M., Bleakley, C., Baxter, D., and Cook, C.E. 2015. Clinician-friendly lower extremity
physical performance tests in athletes: A systematic review of measurement properties and correlation with injury.
Part 2—the tests for hip, thigh, foot and ankle including the star excursion balance test. British Journal of Sports
Medicine 49: 649-656.
Heil, D.P. 1997. Body mass scaling of peak oxygen uptake in 20- to 79-year-old adults. Medicine & Science in Sports &
Exercise 29: 1602-1608.
Heinrich, K.M., Patel, P.M., O’Neal, J.L., and Heinrich, B.S. 2014. High-intensity compared to moderate-intensity
training for exercise initiation, enjoyment, adherence, and intentions: An intervention study. BMC Public Health
14: article 789.
Henschke, N., and Lin, C.C. 2011. Stretching before or after exercise does not reduce delayed-onset muscle soreness.
British Journal of Sport Medicine 45: 1249-1250.
Henwood, T.R., and Taaffe, D.R. 2003. Beneficial effects of high-velocity resistance training in older adults. Medicine
791
& Science in Sports & Exercise 35(Suppl.): S292 [abstract].
Herbert, D.L. 1995. First state licenses exercise physiologists. Fitness Management October: 26-27.
Herbert, D.L. 2004. New law to regulate personal trainers proposed in Oregon. The Exercise Standards and Malpractice
Reporter 18(2): 17, 20-24.
Herbert, R.D., de Noronha, M., and Kamper, S.J. 2011. Stretching to prevent or reduce muscle soreness after
exercise. Cochrane Database of Systematic Reviews [online] 7: CD004577.
Herda, T.J., Costa, P.B., Walter, A.A., Ryan, E.D., Hoge, K.M., Kerksick, C.M., Stout, J.R., and Cramer, J.T.
2011. Effects of two modes of static stretching on muscle strength and stiffness. Medicine & Science in Sports &
Exercise 43: 1777-1784.
Herda, T.J., Herda, N.D., Costa, P.B., Walter-Herda, A.A., Valdez, A.M., and Cramer, J.T. 2013. The effects of
dynamic stretching on the passive properties of the muscle-tendon unit. Journal of Sports Sciences 31: 479-487.
Herman, T., Giladi, N., and Hausdorff, J.M. 2011. Properties of the ‘timed up and go’ test: More than meets the eye.
Hermansen, L., and Saltin, B. 1969. Oxygen uptake during maximal treadmill and bicycle exercise. Journal of Applied
Physiology 26: 31-37.
Hertel, J., Braham, R.A., Hale, S.A., and Olmsted-Kramer, L.C. 2006. Simplifying the star excursion balance test:
Analyses of subjects with and without chronic ankle instability. Journal of Orthopaedic & Sports Physical Therapy 36:
131-137.
Hertel, J., Miller, S.J., and Denegar, C.R. 2000. Intratester and intertester reliability during the star excursion balance
tests. Journal of Sport Rehabilitation 9: 104-116.
Hess, J.A., and Woollacott, M. 2005. Effect of high-intensity strength-training on functional measures of balance
ability in balance-impaired older adults. Journal of Manipulative and Physiological Therapeutics 28: 582-590.
Hettinger, T., and Muller, E.A. 1953. Muskelleistung und muskeltraining. European Journal of Applied Physiology 15:
111-126.
Heymsfield, S.B., Peterson, C.M., Thomas, D.M., Heo, M., and Schuna, J.M. Jr. 2016. Why are there race/ethnic
differences in adult body mass index-adiposity relationships? A quantitative critical review. Obesity Reviews 17:
262-275.
Heymsfield, S.B., Wang, J., Lichtman, S., Kamen, Y., Kehayias, J., and Pierson, R.N. 1989. Body composition in
elderly subjects: A critical appraisal of clinical methodology. American Journal of Clinical Nutrition 50: 1167-1175.
Heyward, V.H., and Wagner, D.R. 2004. Applied body composition assessment, 2nd ed. Champaign, IL: Human
Kinetics.
Hickson, R.C., and Rosenkoetter, M.A. 1981. Reduced training frequencies and maintenance of increased aerobic
power. Medicine & Science in Sports & Exercise 13: 13-16.
Higgins, P.B., Fields, D.A., Hunter, G.R., and Gower, B.A. 2001. Effect of scalp and facial hair on air displacement
plethysmography estimates of percentage of body fat. Obesity Research 9: 326-330.
Hill, J.O., and Melanson, E.L. 1999. Overview of the determinants of overweight and obesity: Current evidence and
research issues. Medicine & Science in Sports & Exercise 31(Suppl.): S515-S521.
Hillsdon, M., Coombes, E., Griew, P., and Jones, A. 2015. An assessment of the relevance of the home
neighbourhood for understanding environmental influences on physical activity: How far from home do people
roam? International Journal of Behavioral Nutrition 12: 100. doi:10.1186/s12966-015-0260-y. Accessed May 25,
2017.
792
Himes, J.H., and Frisancho, R.A. 1988. Estimating frame size. In Anthropometric standardization reference manual, ed.
T.G. Lohman, A.F. Roche, and R. Martorell, 121-124. Champaign, IL: Human Kinetics.
Hindle, K.B., Whitcomb, T.J., Briggs, W.O., and Hong, J. 2012. Proprioceptive neuromuscular facilitation (PNF):
Its mechanisms and effects on range of motion and muscular function. Journal of Human Kinetics 31: 105-113.
Hirsh, J. 1971. Adipose cellularity in relation to human obesity. Advances in Internal Medicine 17: 289-300.
Ho, M., Garnett, S.P., Baur, L.A., Burrows, T., Stewart, L., Neve, M., and Collins, C. 2013. Impact of dietary and
exercise interventions on weight change and metabolic outcomes in obese children and adolescents: A systematic
review and meta-analysis of randomized trials. JAMA Pediatrics 167: 759-768.
Ho, N-T-V.S., Olds, T., Schranz, N., and Maher, C. 2017. Secular trends in the prevalence of childhood overweight
and obesity across Australian states: A meta-analysis. Journal of Science and Medicine in Sport 20: 480-488.
Hodgkins, J., and Skubic, V. 1963. Cardiovascular efficiency test scores for college women in the United States.
Hoeger, W.W.K. 1989. Lifetime physical fitness and wellness. Englewood Cliffs, NJ: Morton.
Hoeger, W.W.K., and Hopkins, D.R. 1992. A comparison of the sit-and-reach and the modified sit-and-reach in the
measurement of flexibility in women. Research Quarterly for Exercise and Sport 63: 191-195.
Hoeger, W.W.K., Hopkins, D.R., Button, S., and Palmer, T.A. 1990. Comparing the sit and reach with the
modified sit and reach in measuring flexibility in adolescents. Pediatric Exercise Science 2: 156-162.
Hofsteenge, G.H., Chinapaw, M.J.M., Delemarre-van de Waal, H.A., and Weijs, P.J.M. 2010. Validation of
predictive equations for resting energy expenditure in obese adolescents. American Journal of Clinical Nutrition 91:
1244-1254.
Hogrel, J-Y. 2015. Grip strength measured by high precision dynamometry in healthy subjects from 5 to 80 years.
BMC Musculoskeletal Disorders. 16: 139.
Hoppeler, H. 2016. Moderate load eccentric exercise: A distinct novel training modality. Frontiers in Physiology 7:
article 483.
Hoshang Bakhtiary, A., Aminian-Far, A., and Hedayati, R. 2013. Acute effects of static stretch on the static and
dynamic balance indices in the young healthy non-athletic females. Koomesh 14: 431-438.
Houtkooper, L.B., Going, S.G., Lohman, T.G., Roche, A.F., and VanLoan, M. 1992. Bioelectrical impedance
estimation of fat-free body mass in children and youth: A cross-validation study. Journal of Applied Physiology 72:
366-373.
Houtkooper, L.B., Going, S.B., Westfall, C.H., Lohman, T.G. 1989. Prediction of fat-free body corrected for bone
mass from impedance and anthropometry in adult females. Medicine & Science in Sports & Exercise 21: 539
[abstract].
Howatson, G., and van Someren, K.A. 2008. The prevention and treatment of exercise-induced muscle damage.
Sports Medicine 38: 483-503.
Howe, T.E., Rochester, L., Jackson, A., and Blair, V.A. 2007. Exercise for improving balance in older people
(review). Cochrane Database of Systematic Reviews 4: CD004963.
Howe, T.E., Rochester, L., Neil, F., Skelton, D.A., and Ballinger, C. 2011. Exercise for improving balance in older
people (review). Cochrane Database of Systematic Reviews 11: CD004963. doi:10.1002/14651858.CD004963.
Howley, E.T. 2007. V̇O2max and the plateau—needed or not? Medicine & Science in Sports & Exercise 39: 101-102.
Howley, E. 2008. Physical activity guidelines for Americans. President’s Council on Physical Fitness and Sports Research
793
Digest Series 9(4): December.
Howley, E.T., Colacino, D.L., and Swensen, T.C. 1992. Factors affecting the oxygen cost of stepping on an
electronic stepping ergometer. Medicine & Science in Sports & Exercise 24: 1055-1058.
Hoxie, R.E. Rubenstein, L.Z., Hoenig, H., and Gallagher, B.R. 1994. The older pedestrian. Journal of the American
Geriatrics Society 42: 444-450.
Hsieh, S.D., Yoshinaga, H., and Muto, T. 2003. Waist-to-height ratio, a simple and practical index for assessing
central fat distribution and metabolic risk in Japanese men and women. International Journal of Obesity 27: 610-
616.
Huang, Y., Cai, X., Liu, C., Zhu, D., Hua, J., Hu, Y., Peng, J., and Xu, D. 2015. Prehypertension and the risk of
coronary heart disease in Asian and Western populations: A meta-analysis. Journal of the American Heart
Association. doi:10.1161/JAHA.114.001519. Accessed April 23, 2017.
Huang, Y., and Liu, X. 2015. Improvement of balance control ability and flexibility in the elderly tai chi chuan (TCC)
practitioners: A systematic review and meta-analysis. Archives of Gerontology and Geriatrics 60: 233-238.
Hubley-Kozey, C.L. 1991. Testing flexibility. In Physiological testing of the high-performance athlete, ed. J.D.
MacDougall, H.A. Wenger, and H.J. Green, 309-359. Champaign, IL: Human Kinetics.
Hübscher, M., Zech, A., Pfeifer, K., Hänsel, F., Vogt, L., and Banzer, W. 2010. Neuromuscular training for sports
injury prevention: A systematic review. Medicine & Science in Sports & Exercise 42: 413-421.
Hudson, J., Hiripi, E., Pope, H., and Kessler, R. 2007. The prevalence and correlates of eating disorders in the
National Comorbidity Survey Replication. Biological Psychiatry 61(3): 348-358.
Hui, S.C., and Yuen, P.Y. 2000. Validity of the modified back-saver sit-and-reach test: A comparison with other
protocols. Medicine & Science in Sports & Exercise 32: 1655-1659.
Hui, S.C., Yuen, P.Y., Morrow, J.R., and Jackson, A.W. 1999. Comparison of the criterion-related validity of sit-
and-reach tests with and without limb length adjustment in Asian adults. Research Quarterly for Exercise and Sport
70: 401-406.
Hui, S.S-C., Xie, Y.J., Woo, J., and Kwok, T.C-Y. 2015. Effects of tai chi and walking exercises on weight loss,
metabolic syndrome parameters, and bone mineral density: A cluster randomized controlled trial. Evidence-Based
Complementary and Alternative Medicine. doi:10.1155/2015/976123. Accessed April 16, 2017.
Hulsey, C.R., Soto, D.T., Koch, A.J., and Mayhew, J.L. 2012. Comparison of kettlebell swings and treadmill running
at equivalent rating of perceived values. Journal of Strength and Conditioning Research 26: 1203-1207.
Human Kinetics. 1995. Practical body composition kit. Champaign, IL: Author.
Hunt, T.N., Ferrara, M.S., Bornstein, R.A., and Baumgartner, T.A. 2009. The reliability of the modified balance
error scoring system. Clinical Journal of Sports Medicine 19: 471-475.
Hunter, G.R., Brock, D.W., Byrne, N.M., Chandler-Laney, P.C., Del Corral, P., and Gower, B.A. 2010. Exercise
training prevents regain of visceral fat for 1 year following weight loss. Obesity 18: 690-695.
Hunter, G.R., Wetzstein, C.J., McLafferty, C.L., Zuckerman, P.A., Landers, K.A., and Bamman, M.M. 2001.
High-resistance versus variable-resistance training in older adults. Medicine & Science in Sports & Exercise 33: 1759-
1764.
Hurkmans, H.L., Ribbers, G.M., Streur-Kranenburg, M.F., Stam, H.J., and van den Berg-Emons, R. 2011. Energy
expenditure in chronic stroke patients playing Wii Sports: A pilot study. Journal of NeuroEngineering and
Rehabilitation 8: 38-44.
Hurst, P.R., Walsh, D.C.I., Conlon, C.A., Ingram, M., Kruger, R., and Stonehouse, W. 2016. Validity and reliability
794
of bioelectrical impedance analysis to estimate body fat percentage against air displacement plethysmography and
dual-energy X-ray absorptiometry. Nutrition and Dietetics 73: 197-204.
Husu, P., and Suni, J. 2012. Predictive validity of health-related fitness tests on back pain and related disability: A 6-
year follow-up study among high-functioning older adults. Journal of Physical Activity and Health 9: 249-258.
Hyldahl, R.D., and Hubal, M.J. 2014. Lengthening our perspective: Morphological, cellular, and molecular responses
to eccentric exercise. Muscle and Nerve 49: 155-170.
Idema, R.N., van den Meiracker, A.H., and Imholz, B.P.M. 1989. Comparison of Finapres non-invasive beat-to-beat
finger blood pressure with intrabrachial artery pressure during and after bicycle ergometry. Journal of Hypertension
7(Suppl. 6): S58-S59.
Ikai, M., and Fukunaga, T. 1968. Calculation of muscle strength per unit cross-sectional area of human muscle by
means of ultrasonic measurement. European Journal of Applied Physiology 26: 26-32.
Imboden, M.T., Nelson, M.B., Kaminsky, L.A., and Montoye, A.H.K. 2017. Comparison of four Fitbit and Jawbone
activity monitors with a research-grade ActiGraph accelerometer for estimating physical activity and energy
expenditure. British Journal of Sports Medicine 0:1. doi:10.1136/bjsports-2016-096990. Accessed May 18, 2017.
Imtiyaz, S., Veqar, Z., and Shareef, M.Y. 2014. To compare the effect of vibration therapy and massage in prevention
of delayed onset muscle soreness (DOMS). Journal of Clinical and Diagnostic Research 8: 133-136.
Instebo, A., Helgheim, V., and Greve, G. 2012. Repeatability of blood pressure measurements during treadmill
exercise. Blood Pressure Monitoring 17: 69-72.
Institute of Medicine. 2002/2005. Dietary reference intakes for energy, carbohydrates, fiber, fat, fatty acids, cholesterol,
protein, and amino acids. Washington, D.C.: National Academies Press.International Association for the Study of
Obesity. 2012. Estimates of relative risk of disease per unit of BMI above 22 kg/m2.
www.iaso.org/policy/healthimpactobesity/estimatesrelativerisk. Accessed October 5, 2012.
International Atomic Energy Association. 2010. Dual energy X-ray absorptiometry for bone mineral density and body
composition assessment. IAEA Human Health Series number 15, Vienna.
International Diabetes Foundation. 2006. IDF consensus worldwide definition of the metabolic syndrome.
www.idf.org/e-library/consensus-statements.html. Accessed August 14, 2017.
International Osteoporosis Foundation. 2015. Epidemiology. www.iofbonehealth.org/health-professionals/about-

osteoporosis/epidemiology.
Invergo, J.J., Ball, T.E., and Looney, M. 1991. Relationship of pushups and absolute muscular endurance to bench
press strength. Journal of Applied Sport Science Research 5: 121-125.
Irving, B.A., Davis, C.K., Brock, D.W., Weltman, J.Y., Swift, D., Barrett, E.J., Gaesser, G.A., and Weltman, A.
2008. Effect of exercise training intensity on abdominal visceral fat and body composition. Medicine & Science in
Isacowitz, R. 2014. Pilates, 2nd ed. Champaign, IL: Human Kinetics.
Ishikawa, J., Ishikawa, Y., Edmondson, D., Pickering, T.G., and Schwartz, J.E. 2011. Age and the difference
between awake ambulatory blood pressure and office blood pressure: A meta-analysis. Blood Pressure Monitoring 16:
159-167.
Ishikawa, S., Kim, Y., Kang, M., and Morgan, D.W. 2013. Effects of weight-bearing exercise on bone health in girls:
A meta-analysis. Sports Medicine 43: 875-892.
Ismail, I., Keating, S.E., Baker, M.K., and Johnson, N.A. 2012. A systematic review and meta-analysis of the effect of
aerobic vs. resistance exercise training on visceral fat. Obesity Reviews 13: 68-91.
795
Ito, T., Shirado, O., Suzuki, H., Takahaski, M., Kaneda, K., and Strax, T.E. 1996. Lumbar trunk muscle endurance
testing: An expensive alternative to a machine for evaluation. Archives of Physical Medicine and Rehabilitation 77:
75-79.
Jackson, A. 1984. Research design and analysis of data procedures for predicting body density. Medicine & Science in
Jackson, A.S., Ellis, K.J., McFarlin, B.K., Sailors, M.H., and Bray, M.S. 2009. Cross-validation of generalized body
composition equations with diverse young men and women: The Training Intervention and Genetics of Exercise
Response (TIGER) Study. British Journal of Nutrition 101: 871-878.
Jackson, A.S., and Pollock, M.L. 1976. Factor analysis and multivariate scaling of anthropometric variables for the
assessment of body composition. Medicine & Science in Sports & Exercise 8: 196-203.
Jackson, A.S., and Pollock, M.L. 1978. Generalized equations for predicting body density of men. British Journal of
Jackson, A.S., and Pollock, M.L. 1985. Practical assessment of body composition. The Physician and Sportsmedicine 13:
76-90.
Jackson, A.S., Pollock, M.L., Graves, J.E., and Mahar, M.T. 1988. Reliability and validity of bioelectrical impedance
in determining body composition. Journal of Applied Physiology 64: 529-534.
Jackson, A.S., Pollock, M.L., and Ward, A. 1980. Generalized equations for predicting body density of women.
Jackson, A.W., and Langford, N.J. 1989. The criterion-related validity of the sit-and-reach test: Replication and
extension of previous findings. Research Quarterly for Exercise and Sport 60: 384-387.
Jackson, A.W., Morrow, J.R., Brill, P.A., Kohl, H.W., Gordon, N.F., and Blair, S.N. 1998. Relations of sit-up and
sit-and-reach tests to low back pain in adults. Journal of Orthopaedic and Sports Physical Therapy 27: 22-26.
Jäger, R., Kerksick, C.M., Campbell, B.I., Cribb, P.J., Wells, S.D., Skwiat, T.M., Purpura, M., Ziegenfuss, T.N.,
Ferrando, A.A., Arent, S.M., Smith-Ryan, A.E., Stout, J.R., Arciero, P.J., Ormsbee, M.J., Taylor, L.W.,
Wilborn, C.D., Kalman, D.S., Kreider, R.B., Willoughby, D.S., Hoffman, J.R., Krzykowski, J.L., and Antonio, J.
2017. International society of sports nutrition position stand: Protein and exercise. Journal of the International
Society of Sports Nutrition 14: 20.
Jahnke, R., Larkey, L., Rogers, C., Etnier, J., and Lin, F. 2010. A comprehensive review of health benefits of qigong
and tai chi. American Journal of Health Promotion 24: e1-e25.
Jakicic, J.M., Davis, K.K., Rogers, R.J., King, W.C., Marcus, M.D., Helsel, D., Rickman, A.D., Wahed, A.S., and
Belle, S.H. 2016. Effect of wearable technology combined with a lifestyle intervention on long-term weight loss:
The IDEA randomized clinical trial. Journal of the American Medical Association 316(11): 1161-1171.
James, P.A., Oparil, S., Carter, B.L., Cushman, W.C., Dennison-Himmelfarb, C., Handler, J., Lackland, D.T.,
LeFevre, M.L., MacKenzie, T.D., Ogedegbe, O., Smith, S.C. Jr., Svetkey, L.P., Taler, S.J., Townsend, R.R.,
Wright, J.T. Jr., Narva, A.S., and Ortiz, E. 2014. 2014 Evidence-based guideline for the management of high
blood pressure in adults: Report from the panel members appointed to the Eighth Joint National Committee
(JNC8). Journal of the American Medical Association 311: 507-520.
Jankowska, M.M., Schipperijn, J., and Kerr, J. 2015. A framework for using GPS in physical activity and sedentary
behavior studies. Exercise and Sport Sciences Reviews 43: 48-56.
Jankowski, M., Niedzielska, A., Brzezinski, M., and Drabik, J. 2015. Cardiorespiratory fitness in children: A simple
screening test for population studies. Pediatric Cardiology 36(1): 27-32.
796
Janssen, P. 2001. Lactate Threshold Training. Champaign, IL: Human Kinetics.
Jay, K., Frisch, D., Hansen, K., Zebis, M.K., Andersen, C.H., Mortensen, O.S., and Andersen, L.L. 2011. Kettlebell
training for musculoskeletal and cardiovascular health: A randomized controlled trial. Scandinavian Journal of
Work, Environment and Health 37: 196-203.
Jdanov, D.A., Deev, A.D., Jasilionis, D., Shalnova, S.A., Shkolnikova, M.A., and Shkolnikov, V.M. 2014.
Recalibration of the SCORE risk chart for the Russian population. European Journal of Epidemiology 29: 621-628.
Jeans, E.A., Foster, C., Porcari, J.P., Gibson, M., and Doberstein, S. 2011. Translation of exercise testing to exercise
prescription using the Talk Test. Journal of Strength and Conditioning Research 25: 590-596.
Jenkins, W.L., Thackaberry, M., and Killian, C. 1984. Speed-specific isokinetic training. Journal of Orthopaedic and
Sports Physical Therapy 6: 181-183.
Jeter, P.E., Nkodo, A-F., Moonaz, S.H., and Dagnelie, G. 2014. A systematic review of yoga for balance in a healthy
population. Journal of Alternative and Complementary Medicine 20: 221-232.
Johansson, J., Nordström, A., and Nordström, P. 2015. Objectively measured physical activity is associated with
parameters of bone in 70-year-old men and women. Bone 81: 72-79.
Johns, R.J., and Wright, V. 1962. Relative importance of various tissues in joint stiffness. Journal of Applied Physiology
17: 824-828.
Johnson, A.W., Mitchell, U.H., Meek, K., and Feland, J.B. 2014. Hamstring flexibility increases the same with 3 or 9
repetitions of stretching held for a total time of 90s. Physical Therapy in Sport 15: 101-105.
Johnson, B.L., and Nelson, J.K., eds. 1986. Practical measurements for evaluation in physical education. Minneapolis:
Burgess.
Jones, B.H., and Knapik, J.J. 1999. Physical training and exercise-related injuries. Sports Medicine 27: 111-125.
Jones, C.J., Rikli, R.E., Max, J., and Noffal, G. 1998. The reliability and validity of a chair sit-and-reach test as a
measure of hamstring flexibility in older adults. Research Quarterly for Exercise and Sport 69: 338-343.
Jones, D.W., Frohlich, E.D., Grim, C.M., Grim, C.E., and Taubert, K.A. 2001. Mercury sphygmomanometers
should not be abandoned: An advisory statement from the Council for High Blood Pressure Research, American
Heart Association. Hypertension 37: 185-186.
Jones, H.A., Putt, G.E., Rabinovitch, A.E., Hubbard, R., and Snipes, D. 2017. Parenting stress, readiness to change,
and child externalizing behaviors in families of clinically referred children. Journal of Child and Family Studies 26:
225-233.
Jones, M.T., and Lorenzo, D.C. 2013. Assessment of power, speed, and agility in athletic, preadolescent youth.
Journal of Sports Medicine and Physical Fitness 53: 693-700.
Jørstad, J.T., Boekholdt, S.M., Wareham, N.J., Khaw, K.T., and Peters, R.J.G. 2017. The Dutch SCORE-based risk
charts seriously underestimate the risk of cardiovascular disease. Netherlands Heart Journal 25: 173-180.
Joshua, A.M., D’Souza, V., Unnikrishnan, B., Mithra, P., Kamath, A., Acharya, V., and Venugopal, A. 2014.
Effectiveness of progressive resistance training versus traditional balance exercise in improving balance among the
elderly—a randomized controlled trial. Journal of Clinical and Diagnostic Research 8: 98-102.
Jowko, E., Ostaszewski, P., and Jank, M. 2001. Creatine and β-hydroxy-β-methylbutyrate (HMB) additively increase
lean body mass and muscle strength during weight-training program. Nutrition 17: 558-566.
Judex, S., and Rubin, C.T. 2010. Is bone formation induced by high-frequency mechanical signals modulated by
muscle activity? 2010. Journal of Musculoskeletal and Neuronal Interactions 10: 3-11.
797
Juker, D., McGill, S., Kropf, P., and Steffen, T. 1998. Quantitative intramuscular myoelectric activity of lumbar
portions of psoas and the abdominal wall during a wide variety of tasks. Medicine & Science in Sports & Exercise 30:
301-310.
Kahn, H.S., Gu, Q., Bullard, K.M., Freedman, D.S., Ahluwalia, N., and Ogden, C.L. 2014. Population distribution
of the sagittal abdominal diameter (SAD) from a representative sample of US adults: Comparison of SAD, waist
circumference and body mass index for identifying dysglycemia. PLoS One 9(10): e108707.
doi:10.1371/journal.pone.0108707. Accessed August 14, 2017.
Kahraman, B.O., Sengul, Y.S., Kahraman, T., and Kalemci, O. 2016. Developing a reliable core stability assessment
battery for patients with nonspecific low back pain. Spine 41: E844-E850.
Kallioinen, N., Hill, A., Horswill, M.S., Ward, H.E., and Watson, M.O., 2017. Sources of inaccuracy in the
measurement of adult patients’ resting blood pressure in clinical settings: A systematic review. Journal of
Hypertension 35: 421-441.
Kalisch, T., Kattenstroth, J.C., Noth, S., Tegenthoff, M., and Dinse, H.R. 2011. Rapid assessment of age-related
differences in standing balance. Journal of Aging Research 2011: 160490. doi:10.4061/2011/160490. Accessed
November 2012.
Kametas, N.A., McAuliffe, F., Krampl, E., Nicolaides, K.H., and Shennan, A.H. 2006. Can aneroid
sphygmomanometers be used at altitude? Journal of Human Hypertension 20: 517-522.
Kaminsky, L.A., and Whaley, M.H. 1998. Evaluation of a new standardized ramp protocol: The BSU/Bruce ramp
protocol. Journal of Cardiopulmonary Rehabilitation 18: 438-444.
Kamioka, H., Tsutani, K., Katsumata, Y., Yoshizaki, T., Okuizumi, H., Okada, S., Park, S.J., Kitayuguchi, J., Abe,
T., and Mutoh, Y. 2016. Effectiveness of Pilates exercise: A quality evaluation and summary of systematic reviews
based on randomized controlled trials. Complementary Therapies in Medicine 25: 1-19.
Kanis, J.A., Borgstrom, F., De Laet, C., Johansson, H., Johnell, O., Jonsson, B., Oden, A., Zethraeus, N., Pfleger, B.,
and Khaltaev, N. 2005. Assessment of fracture risk. Osteoporosis International 16: 581-589.
Kanis, J.A., Oden, A., McCloskey, E.V., Johansson, H., Wahl, D.A., and Cooper, C. 2012. A systematic review of
hip fracture incidence and probability of fracture.
Katanista, A., Król-Zielińska, M., Borowiec, J., Glapa, A., Lisowski, P., and Bronikowski, M. 2015. Physical activity
of female children and adolescents based on step counts: Meeting the recommendation and relation to VMI.
Biomedical Human Kinetics 7: 66-72.
Katch, F.I., Clarkson, P.M., Kroll, W., McBride, T., and Wilcox, A. 1984. Effects of sit-up exercise training on
adipose cell size and adiposity. Research Quarterly for Exercise and Sport 55: 242-247.
Katch, F.I., McArdle, W.D., Czula, R., and Pechar, G.S. 1973. Maximal oxygen intake, endurance running
performance, and body composition in college women. Research Quarterly 44: 301-312.
Kattus, A.A., Hanafee, W.N., Longmire, W.P., MacAlpin, R.N., and Rivin, A.U. 1968. Diagnosis, medical and
surgical management of coronary insufficiency. Annals of Internal Medicine 69: 115-136.
Kaur, J. 2014. A comprehensive review on metabolic syndrome. Cardiology Research and Practice.
doi:10.1155/2014/943162. Accessed April 13, 2017.
Kay, A.D., Dods, S., and Blazevich, A.J. 2016. Acute effects of contract-relax (CR) stretch versus a modified CR
technique. European Journal of Applied Physiology 116: 611-621.
Keim, N.L., Blanton, C.A., and Kretsch, M.J. 2004. America’s obesity epidemic: Measuring physical activity to
promote an active lifestyle. Journal of the American Dietetic Association 104: 1398-1409.
798
Kell, A.B. 2011. The influence of periodized resistance training on strength changes in men and women. Journal of
Kelley, G.A., and Kelley, K.S. 2006. Aerobic exercise and lipids and lipoproteins in men: A meta-analysis of
randomized controlled trials. Journal of Men’s Health & Gender 3(1): 61-70.
Kendall, K.L., Fukuda, D.H., Hyde, P.N., Smith-Ryan, A.E., Moon, J.R., and Stout, J.R. 2017. Estimating fat-free
mass in elite-level male rowers: A four-compartment model validation of laboratory and field methods. Journal of
Sports Sciences 35(7): 624-633.
Kendrick, D., Kumar, A., Carpenter, H., Zijlstra, G.A., Skelton, D.A., Cook, J.R., Stevens, Z., Belcher, C.M.,
Haworth, D., Gawler, S.J., Gage, H., Masud, T., Bowling, A., Pearl, M., Morris, R.W., Iliffe, S., and Delbaere,
K. 2014. Exercise for reducing fear of falling in older people living in the community. Cochrane Database of
Systematic Reviews 11: CD009848.
Kerr, A., Slater, G.J., Byrne, N., and Nana, A. 2016. Reliability of 2 different positioning protocols for dual-energy
X-ray absorptiometry measurement of body composition in healthy adults. Journal of Clinical Densitometry:
Assessment & Management of Musculoskeletal Health 19: 282-289.
Kesäniemi, A., Riddoch, C.J., Reeder, B., Blair, S.N., and Sorensen, T.I.A. 2010. Advancing the future of physical
activity guidelines in Canada: An independent expert panel interpretation of the evidence. International Journal of
Behavioral Nutrition and Physical Activity 7: 41. www.ijbnpa.org/content/7/1/41. Accessed August 25, 2012.
Kessler, H.S., Sisson, S.B., and Short, K.R. 2012. The potential for high-intensity interval training to reduce
cardiometabolic disease risk. Sports Medicine 42: 489-509.
Keys, A., and Brozek, J. 1953. Body fat in adult man. Physiological Reviews 33: 245-325.
Khaled, M.A., McCutcheon, M.J., Reddy, S., Pearman, P.L., Hunter, G.R., and Weinsier, R.L. 1988. Electrical
impedance in assessing human body composition: The BIA method. American Journal of Clinical Nutrition 47: 789-
792.
Kibar, S., Yardimci, F.O., Evcik, D., Ay, S., Alhan, A., Manco, M., and Ergin, E.S. 2016. Can a Pilates exercise
program be effective on balance, flexibility and muscle endurance? A randomized controlled trial. Journal of Sports
Medicine and Physical Fitness 56: 1139-1146.
Kibler, W.B., Press, J., and Sciascia, A. 2006. The role of core stability in athletic function. Sports Medicine 36: 189-
198.
Kidgell, D.J., and Pearce, A.J. 2011. What has transcranial magnetic stimulation taught us about neural adaptations to
strength training? A brief review. Journal of Strength and Conditioning Research 25: 3208-3217.
Kidgell, D.J., Stokes, M.A., Castricum, T.J., and Pearce, A.J. 2010. Neurophysiological responses after short-term
strength training of the biceps brachii muscle. Journal of Strength and Conditioning Research 24: 3123-3132.
Kim, H.I., Kim, J.T., Yu, S.H., Kwak, S.H., Jang, H.C., Park, K.S., Kim, S.Y., Lee, H.K., and Cho, Y.M. 2011.
Gender differences in diagnostic values of visceral fat and waist circumference for predicting metabolic syndrome
in Koreans. Journal of Korean Medicine and Science 26: 906-913.
Kim, J.C., Chon, J., Kim, H.S., Lee, J.H., Yoo, S.D., Kim, D.H., Lee, S.A., Han, Y.J., Lee, H.S., Lee, B.Y., Soh,
Y.S., and Won, C.W. 2017. The association between fall history and physical performance tests in the
community-dwelling elderly: A cross-sectional analysis. Annals of Rehabilitation Medicine 41(2): 239-247.
Kim, J.H., Ko, J.H., Lee, D., Lim, I., and Bang, H. 2012. Habitual physical exercise has beneficial effects on telomere
length in postmenopausal women. Menopause 19. doi:10.1097/gme.0b013e3182503e97.
Kim, P.S., Mayhew, J.L., and Peterson, D.F. 2002. A modified bench press test as a predictor of 1 repetition
799
maximum bench press strength. Journal of Strength and Conditioning Research 16: 440-445.
Kim, Y., and Welk, G.J. 2015. Criterion validity of competing accelerometry-based activity monitoring devices.
Kirby, R.L., Simms, F.C., Symington, V.J., and Garner, J.B. 1981. Flexibility and musculoskeletal symptomatology in
female gymnasts and age-matched controls. American Journal of Sports Medicine 9: 160-164.
Klein, I.E., White, J.B., and Rana, S.R. 2016. Comparison of physiological variables between the elliptical bicycle and
run training in experienced runners. Journal of Strength and Conditioning Research 30: 2998-3006.
Klein, P.J., Fiedler, R.C., and Rose, D.J. 2011. Rasch analysis of the Fullerton advanced balance (FAB) scale.
Physiotherapy Canada 63: 115-125.
Klein, S., Allison, D.B., Heymsfield, S.B., Kelley, D.E., Leibel, R.L., Nonas, C., and Kahn, R. 2007. Waist
circumference and cardiometabolic risk: A consensus statement from Shaping America’s Health: Association for
Weight Management and Obesity Prevention; NAASO, the Obesity Society; the American Society for Nutrition;
and the American Diabetes Association. American Journal of Clinical Nutrition 85: 1197-1202.
Kline, G.M., Porcari, J.P., Hintermeister, R., Freedson, P.S., Ward, A., McCarron, R.F., Ross, J., and Rippe, J.M.
1987. Estimation of V̇O2max from a one-mile track walk, gender, age, and body weight. Medicine & Science in
Kloubec, J. 2011. Pilates: How does it work and who needs it? Muscles, Ligaments and Tendons Journal 1: 61-66.
Knapik, J.J. 2015. Extreme conditioning programs: Potential benefits and potential risks. Journal of Special Operations
Medicine 15(3): 108-113.
Knight, A.C., Holmes, M.E., Chander, H., Kimble, A., and Stewart, J.T. 2016. Assessment of balance among
adolescent track and field athletes. Sports Biomechanics 15: 169-179.
Knight, E., Stuckey, M.I., and Petrella, R.J. 2014. Validation of the step test and exercise prescription tool for adults.
Canadian Journal of Diabetes 38: 164-171.
Knight, H., Stetson, B., Krishnasamy, S., and Mokshagundam, S.P. 2015. Diet self-management and readiness to
change in underserved adults with type 2 diabetes. Primary Care Diabetes 9: 219-215.
Knudson, D. 2001. The validity of recent curl-up tests in young adults. Journal of Strength and Conditioning Research
15: 81-85.
Knudson, D., and Johnston, D. 1995. Validity and reliability of a bench trunk-curl test of abdominal endurance.
Journal of Strength and Conditioning Research 9: 165-169.
Knudson, D.V. 1999. Issues in abdominal fitness: Testing and technique. Journal of Physical Education, Recreation &
Dance 70(3): 49-55.
Knudson, D.V., Magnusson, P., and McHugh, M. 2000. Current issues in flexibility fitness. President’s Council on
Physical Fitness and Sports Research Digest 3(10): 1-8.
Knuttgen, H.G., and Kraemer, W.J. 1987. Terminology and measurement in exercise performance. Journal of Applied
Sport Science Research 1: 1-10.
Knutzen, K.M., Brilla, L.R., and Caine, D. 1999. Validity of 1RM prediction equations for older adults. Journal of
Komi, P.V., Viitasalo, J.T., Rauramaa, R., and Vihko, V. 1978. Effect of isometric strength training on mechanical,
electrical, and metabolic aspects of muscle function. European Journal of Applied Physiology 40: 45-55.
Konrad, A., Stafilidis, S., and Tilp, M. 2017. Effects of acute static, ballistic, and PNF stretching exercise on the
muscle and tendon tissue properties. Scandinavian Journal of Medicine & Science in Sports 27(10): 1070-1080.
800
Kosek, D.J., Kim, J.S., Petrella, J.K., Cross, J.M., and Bamman, M.M. 2006. Efficacy of 3 days/wk resistance training
on myofiber hypertrophy and myogenic mechanisms in young vs. older adults. Journal of Applied Physiology 101:
531-544.
Kostek, M.A., Pescatello, L.S., Seip, R.L., Angelopoulos, T.J., Clarkson, P.M., Gordon, P.M., Moyna, N.M.,
Visich, P.S., Zoeller, R.F., Thompson, P.D., Hoffman, R.P., and Price, T.B. 2007. Subcutaneous fat alterations
resulting from an upper-body resistance training program. Medicine & Science in Sports & Exercise 39: 1177-1185.
Kotanidou, E.P., Grammatikopoulou, M.G., Spiliotis, B.E., Kanaka-Gantenbein, C., Tsigga, M., and Galli-
Tsinopoulou, A. 2013. Ten-year obesity and overweight prevalence in Greek children: A systematic review and
meta-analysis of 2001-2010. Hormones 12: 537-549.
Koulmann, N., Jimenez, C., Regal, D., Bolliet, P., Launay, J., Savourey, G., and Melin, B. 2000. Use of bioelectrical
impedance analysis to estimate body fluid compartments after acute variations of the body hydration level. Medicine
& Science in Sports & Exercise 32: 857-864.
Kraemer, W.J. 2003. Strength training basics. The Physician and Sportsmedicine 31(8): 39-45.
Kraemer, W.J., and Fleck, S.J. 2007. Optimizing strength training. Champaign, IL: Human Kinetics.
Kraemer, W.J., Fleck, S.J., and Evans, W.J. 1996. Strength and power training: Physiological mechanisms of
adaptation. In Exercise and Sport Sciences Reviews, ed. J.O. Holloszy, 363-397. Baltimore: Williams & Wilkins.
Kraemer, W.J., Gordon, S.J., Fleck, S.J., Marchitelli, L.J., Mello, R., Dziados, J.E., Friedl, K., Harman, E., Maresh,
C., and Fry, A.C. 1991. Endogenous anabolic hormonal and growth factor responses to heavy resistance exercise
in males and females. International Journal of Sports Medicine 12: 228-235.
Kraemer, W.J., Häkkinen, K., Newton, R.U., Nindl, B.C., Volek, J.S., McCormick, M., Gotshalk, L.A., Gordon,
S.E., Fleck, S.J., Campbell, W.W., Putukian, M., and Evans, W.J. 1999. Effects of heavy-resistance training on
hormonal response patterns in younger vs. older men. Journal of Applied Physiology 87: 982-992.
Kraemer, W.J., Hooper, D.R., Szivak, T.K., Kupchak, B.R., Dunn-Lewis, C., Comstock, B.A., Flanagan, S.D.,
Looney, D.P., Sterczala, A.J., DuPont, W.H., Pryor, J.L., Luk, H.Y., Maladoungdock, J., McDermott, D., Volek,
J.S., and Maresh, C.M. 2015. The addition of beta-hydroxy-beta-methylbutyrate and isomaltulose to whey protein
improves recovery from highly demanding resistance exercise. Journal of the American College of Nutrition 34(2): 91-
99.
Kraemer, W.J., Noble, B.J., Clark, M.J., and Culver, B.W. 1987. Physiologic responses to heavy-resistance exercise
with very short rest periods. International Journal of Sports Medicine 8: 247-252.
Kraemer, W.J., and Ratamess, N.A. 2004. Fundamentals of resistance training: Progression and exercise prescription.
Kraemer, W.J., Volek, J.S., Clark, K.L., Gordon, S.E., Puhl, S.M., Koziris, L.P., McBride, J.M., Triplett-McBride,
N.T., Putukian, M., Newton, R.U., Häkkinen, K., Bush, J.A., and Sabastianelli, W.J. 1999. Influence of exercise
training on physiological and performance changes with weight loss in men. Medicine & Science in Sports & Exercise
31: 1320-1329.
Kravitz, L., Heyward, V., Stolarczyk, L., and Wilmerding, V. 1997a. Effects of step training with and without
handweights on physiological profiles of women. Journal of Strength and Conditioning Research 11: 194-199.
Kravitz, L., and Heyward, V.H. 1995. Flexibility training. Fitness Management 11(2): 32-38.
Kravitz, L., Robergs, R., and Heyward, V. 1996. Are all aerobic exercise modes equal? Idea Today 14: 51-58.
Kravitz, L., Robergs, R.A., Heyward, V.H., Wagner, D.R., and Powers, K. 1997b. Exercise mode and gender
comparisons of energy expenditure at self-selected intensities. Medicine & Science in Sports & Exercise 29: 1028-
801
1035.
Kravitz, L., Wax, B., Mayo, J.J., Daniels, R., and Charette, K. 1998. Metabolic response of elliptical exercise training.
Medicine & Science in Sports & Exercise 30(Suppl.): S169 [abstract].
Kraus, H. 1970. Clinical treatment of back and neck pain. New York: McGraw-Hill.
Krause, M.P., Goss, F.L., Robertson, R.J., Kim, K., Elsangedy, H.M., Keinski, K., and da Silva, S.G. 2012.
Concurrent validity of an OMNI rating of perceived exertion scale for bench stepping exercise. Journal of Strength
and Conditioning Research 26: 506-512.
Kreider, R.B., Wilborn, C.D., Taylor, L., Campbell, B., Almada, A.L., Collins, R., Cooke, M., Earnest, C.P.,
Greenwood, M., Kalman, D.S., Kersick, C.M., Kleiner, S.M., Leutholtz, B., Lopez, H., Lowery, L.M., Mendel,
R., Smith, A., Spano, M., Wildman, R., Willoughby, D.S., Ziegenfuss, T.N., and Antonio, J. 2010. ISSN exercise
& sport nutrition review: Research & recommendations. Journal of the International Society of Sports Nutrition 7: 6-
43.
Krieger, J.W. 2010. Single vs. multiple sets of resistance exercise for muscle hypertrophy: A meta-analysis. Journal of
Krishnan, S., Tokar, T.N., Boylan, M.M., Griffin, K., McMurry, L., Esperat, C., and Cooper, J.A. 2015. Zumba®
dance improves health in overweight/obese or type 2 diabetic women. American Journal of Health Behavior 39: 109-
120.
Kruger, J., Yore, M.M., and Kohl, H.W. 2007. Leisure-time physical activity patterns by weight control status: 1999-
2002 NHANES. Medicine & Science in Sports & Exercise 39: 788-795.
Kubo, K., Kaneshisa, H., Takeshita, D., Kawakami, Y., Fukashiro, S., and Fukunaga, T. 2000. In vivo dynamics of
human medial gastrocnemius muscle-tendon complex curing stretch-shortening cycle exercise. Acta Physiologica
Scandinavica 170: 127-135.
Kubo, K., Kawakami, Y., and Fukunaga, T. 1999. Influence of elastic properties of tendon structures on jump
performance in humans. Journal of Applied Physiology 87: 2090-2096.
Kumar, N., Khunger, M., Gupta, A., and Garg, N. 2015. A content analysis of smartphone-based applications for
hypertension management. Journal of the American Society of Hypertension 9: 130-136.
Kuramoto, A.K., and Payne, V.G. 1995. Predicting muscular strength in women: A preliminary study. Research
Quarterly for Exercise and Sport 66: 168-172.
Kurucz, R., Fox, E.L., and Mathews, D.K. 1969. Construction of a submaximal cardiovascular step test. Research
Quarterly 40: 115-122.
Kushner, R.F. 1992. Bioelectrical impedance analysis: A review of principles and applications. Journal of the American
College of Nutrition 11: 199-209.
Kushner, R.F., Gudivaka, R., and Schoeller, D.A. 1996. Clinical characteristics influencing bioelectrical impedance
analysis measurements. American Journal of Clinical Nutrition 64: 423S-427S.
Kushner, R.F., and Schoeller, D.A. 1986. Estimation of total body water in bioelectrical impedance analysis. American
Kuukkanen, T., and Malkia, E. 2000. Effects of a three-month therapeutic exercise programme on flexibility in
subjects with low back pain. Physiotherapy Research International 5: 46-61.
Kwak, D.H., and Ryu, Y.U. 2015. Applying proprioceptive neuromuscular facilitation stretching: Optimal
contraction intensity to attain the maximum increase in range of motion in young males. Journal of Physical Therapy
Science 27: 2129-2032.
802
Kwon, S., Janz, K.F., Letuchy, E.M., Burns, T.L., and Levy, S.M. 2015. Active lifestyle in childhood and adolescence
prevents obesity development in young adulthood: Iowa Bone Development Study. Obesity (Silver Springs) 23:
2462-2469.
Kyle, U.G., Genton, L., Karsegard, L., Slosman, D.O., and Pichard, C. 2001. Single prediction equation for
bioelectrical impedance analysis in adults aged 20-94 years. Nutrition 17: 248-253.
Lacour, J-R., and Bourdin, M. 2015. Factors affecting the energy cost of level running at submaximal speed. European
Journal of Applied Physiology 115(4): 651-673.
Lake, J.P., and Lauder, M.A. 2012. Kettlebell swing training improves maximal and explosive strength. Journal of
Lakhal, K., Ehrmann, S., Martin, M., Faiz, S., Réminiac, F., Cinotti, R., Capdevila, X., Asehnoune, K., Blanloeil, Y.,
Rozec, B., and Boulain, T. 2015. Blood pressure monitoring during arrhythmia: Agreement between automatic
brachial cuff and intra-arterial measurements. British Journal of Anaesthesia 115: 540-549.
Lambrick, D., Jakeman, J., Grigg, R., Kaufmann, S., and Faulkner, J. 2017. The efficacy of a discontinuous graded
exercise test in measuring peak oxygen update in children aged 8 to 10 years. Biology of Sport 34: 57-61.
Landram, M.J., Utter, A.C., Baldari, C., Guidetti, L., McAnulty, S.R., and Collier, S.R. 2016. Differential effects of
continuous versus discontinuous aerobic training on blood pressure and hemodynamics.
doi:10.1519/JSC.0000000000001661. Accessed July 20, 2017.
Larsen, G.E., George, J.D., Alexander, J.L., Fellingham, G.W., Aldana, S.G., and Parcell, A.C. 2002. Prediction of
maximum oxygen consumption from walking, jogging, or running. Research Quarterly for Exercise and Sport 73: 66-
72.
LaStayo, P., Marcus, R., Dibble, L., Frajacomo, F., and Lindstedt, S. 2014. Eccentric exercise in rehabilitation:
Safety, feasibility, and application. Journal of Applied Physiology 116: 1426-1434.
Lau, R.W.K., Liao, L-R., Yu, F., Teo, T., Chung, R.C.K., and Pang, M.Y.C. 2011. The effects of whole body
vibration therapy on bone mineral density and leg muscle strength in older adults: A systematic review and meta-
analysis. Clinical Rehabilitation 25: 975-988.
Lauby-Secretan, B., Scoccianti, C., Loomis, D., Grosse, Y., Bianchini, F., and Straif, K. 2016. Body fatness and
cancer—Viewpoint of the IARC Working Group. New England Journal of Medicine 375(8): 794-798.
Lavie, C.J., McAuley, P.A., Church, T.S., Milani, R.V., and Blair, S.N. 2014. Obesity and cardiovascular diseases:
Implications regarding fitness, fatness, and severity in the Obesity Paradox. Journal of the American College of
Cardiology 63: 1345-1354.
Law, R.Y.W., and Herbert, R.D. 2007. Warm-up reduces delayed-onset muscle soreness but cool-down does not: A
randomized controlled trial. Australian Journal of Physiotherapy 53: 91-95.
Layne, J.E., and Nelson, M.E. 1999. The effects of progressive resistance training on bone density: A review.
Leahy, S., O’Neill, C., Sohun, R., and Jakeman, P. 2012. A comparison of dual energy X-ray absorptiometry and
bioelectrical impedance analysis to measure total and segmental body composition in healthy young adults.
Leal, V.O., Moraes, C., Stockler-Pinto, M.B., Lobo, J.C., Farage, N.E., Velarde, L.G., Fouque, D., and Mafra, D.
2012. Is a body mass index of 23 kg/m2 a reliable marker of protein-energy wasting in hemodialysis patients?
Leard, J.S., Cirillo, M.A., Katsnelson, E., Kimiatek, D.A., Miller, T.W., Trebincevic, K., and Garbalosa, J.C. 2007.
803
Validity of two alternative systems for measuring vertical jump height. Journal of Strength and Conditioning Research
21: 1296-1299.
LeBoeuf, S.F., Aumer, M.E., Kraus, W.E., Johnson, J.L., and Duscha, B. 2014. Earbud-based sensor for the
assessment of energy expenditure, HR, and V̇O2max. Medicine & Science in Sports & Exercise 46: 1046-1052.
Lee, I-M., Shiroma, E.J., Lobelo, F., Puska, P., Blair, S.N., and Katzmarzyk, P.T. 2012. Impact of physical inactivity
on the world’s major non-communicable diseases. Lancet 380: 219-229. doi:10.1016/S0140-6736(12)61031-9.
Lee, J.A., Williams, S.M., Brown, D.D., and Laurson, K.R. 2015. Concurrent validation of the Actigraph gt3x+,
Polar Active accelerometer, Omron HJ-720 and Yamax Digiwalker SW-701 pedometer step counts in lab-based
and free-living settings. Journal of Sports Sciences. 33: 991-1000.
Lee, M.S., and Ernst, E. 2012. Systematic reviews of t’ai chi: An overview. British Journal of Sports Medicine 46: 713-
718.
Leger, L.A., Mercier, D., Gadoury, C., and Lambert, J. 1988. The multistage 20-metre shuttle run test for aerobic
fitness. Journal of Sports Sciences 6: 93-101.
Leighton, J.R. 1955. An instrument and technique for measurement of range of joint motion. Archives of Physical
Medicine and Rehabilitation 36: 571-578.
Leitzmann, M., Powers, H., Anderson, A.S., Scoccianti, C., Berrino, F., Boutron-Ruault, M-C., Cecchini, M.,
Espina, C., Key, T.I., Norat, T., Wiseman, M., and Romier, I., 2015. European code against cancer 4th edition:
Physical activity and cancer. Cancer Epidemiology 39S: S46-S55.
Leonska-Duniec, A., Jastrzebski, Z., Zarebska, A., Maciejewska, A., Ficek, K., and Cieszczyk, P. 2017. Assessing
effect of interaction between the FTO A/T polymorphism (rs9939609) and physical activity on obesity-related
traits. Journal of Sport and Health Science. Advance online publication. doi:10.1016/j.jshs.2016.08.013.
Lesinski, M., Hortobagyi, T., Muehlbauer, T., Gollhofer, A., and Granacher, U. 2015. Dose-response relationships
of balance training in healthy young adults: A systematic review and meta-analysis. Sports Medicine 45: 557-576.
Lesmes, G.R., Costill, D.L., Coyle, E.F., and Fink, W.J. 1978. Muscle strength and power changes during maximal
isokinetic training. Medicine and Science in Sports 10: 266-269.
Levine, B., Zuckerman, J., and Cole, C. 1998. Medical complications of exercise. In ACSM’s resource manual for
guidelines for exercise testing and prescription, ed. J.L. Roitman, 488-498. Philadelphia: Lippincott Williams &
Wilkins.
Levine, J.A. 2015. Sick of sitting. Diabetologia 58: 1751-1758.
Lewis, P.B., Ruby, D., and Bush-Joseph, C.A. 2012. Muscle soreness and delayed-onset muscle soreness. Clinics in
Li, S., Zhao, X., Ba, S., He, G., Lam, C.T., Ke, L., Li, N., Yan, L.L., Li, X., and Wu, Y. 2012. Can electronic
sphygmomanometers be used for measurement of blood pressure at high altitudes? Blood Pressure Monitoring 17:
62-68.
Liang, M.T.C., Su, H., and Lee, N. 2000. Skin temperature and skin blood flow affect bioelectrical impedance study
of female fat-free mass. Medicine & Science in Sports & Exercise 32: 221-227.
Liang, M.Y., and Norris, S. 1993. Effects of skin blood flow and temperature on bioelectrical impedance after
exercise. Medicine & Science in Sports & Exercise 25: 1231-1239.
Liebenson, C. 2011. Functional training with the kettlebell. Journal of Bodywork and Movement Therapies 15: 542-544.
Lim, S., Kim, J.H., Yoon, J.W., Kang, S.M., Choi, S.H., Park, Y.J., Kim, K.W., Cho, N.H., Shin, H., Park, K.S.,
and Jang, H.C. 2012. Optimal cut points of waist circumference (WC) and visceral fat area (VFA) predicting for
804
metabolic syndrome (MetS) in elderly population in the Korean Longitudinal Study on Health and Aging
(KLoSHA). Archives of Gerontology and Geriatrics 54: E29 -E34.
Lin, H-T., Hung, W-C., Hung, J-L., Wu, P.S., Liaw, L-J., and Chang, J-H. 2016. Effects of Pilates on patients
with chronic non-specific low back pain: A systematic review. Journal of Physical Therapy Science 28: 2961-2969.
Lin, X., Zhang, X., Guo, J., Roberts, C.K., McKenzie, S., Wu, W-C., Liu, S., and Song, Y. 2015. Effects of exercise
training on cardiorespiratory fitness and biomarkers of cardiometabolic health: A systematic review and meta-
analysis of randomized controlled trials. Journal of the American Heart Association 4: e002014.
doi:10.1161/JAHA.115.002014.
Litchell, H., and Boberg, J. 1978. The lipoprotein lipase activity of adipose tissue from different sites in obese women
and relationship to cell size. International Journal of Obesity 2: 47-52.
Liu, S., Brooks, D., Thomas, S., Eysenbach, G., and Nolan, R.P. 2015. Lifesource XL-18 pedometer for measuring
steps under controlled and free-living conditions. Journal of Sports Sciences 33: 1001-1006.
Liu, H., and Frank, A. 2010. Tai chi as a balance improvement exercise for older adults: A systematic review. Journal
of Geriatric Physical Therapy 33: 103-109.
Lixandrão, M.E., Damas, F., Chacon-Mikahil, M.P., Cavaglieri, C.R., Ugrinowitsch, C., Bottaro, M., Vechin, F.C.,
Conceição, M.S., Berton, R., and Libardi, C.A. 2016. Time course of resistance training-induced muscle
hypertrophy in the elderly. Journal of Strength and Conditioning Research 30(1): 159-163.
Lockner, D., Heyward, V., Baumgartner, R., and Jenkins, K. 2000. Comparison of air-displacement
plethysmography, hydrodensitometry, and dual X-ray absorptiometry for assessing body composition of children
10 to 18 years of age. Annals of the New York Academy of Sciences 904: 72-78.
Loenneke, J.P., Barnes, J.T., Wagganer, J.D., Wilson, J.M., Lowery, R.P., Green, C.E., and Pujol, T.J. 2014.
Validity and reliability of an ultrasound system for estimating adipose tissue. Clinical Physiology and Functional
Imaging 34: 159-162.
Löffler-Wirth, H., Willscher, E., Ahnert, P., Wirkner, K., Engel, C., Loeffler, M., and Binder, H. 2016. Novel
anthropometry based on 3D-bodyscans applied to a large population based cohort. PLoS One 11(7): e0159887.
Logghe, I.H.J., Verhagen, A.P., Rademaker, A.C.H.J., Bierma-Zeinstra, S.M.A., van Rossum, E., Faber, M.J., and
Koes, B.W. 2010. The effects of tai chi on fall prevention, fear of falling and balance in older people: A meta-
analysis. Preventive Medicine 51: 222-227.
Lohman, T.G. 1981. Skinfolds and body density and their relation to body fatness: A review. Human Biology 53: 181-
115.
Lohman, T.G. 1987. Measuring body fat using skinfolds [videotape]. Champaign, IL: Human Kinetics.
Lohman, T.G. 1989. Bioelectrical impedance. In Applying new technology to nutrition: Report of the ninth roundtable on
medical issues, 22-25. Columbus, OH: Ross Laboratories.
Lohman, T.G. 1992. Advances in body composition assessment. Current issues in exercise science series. Monograph no. 3.
Champaign, IL: Human Kinetics.
Lohman, T.G. 1996. Dual energy X-ray absorptiometry. In Human body composition, ed. A.F. Roche, S.B.
Heymsfield, and T.G. Lohman, 63-78. Champaign, IL: Human Kinetics.
Lohman, T.G., Boileau, R.A., and Slaughter, M.H. 1984. Body composition in children and youth. In Advances in
pediatric sport sciences, ed. R.A. Boileau, 29-57. Champaign, IL: Human Kinetics.
Lohman, T.G., Harris, M., Teixeira, P.J., and Weiss, L. 2000. Assessing body composition and changes in body
805
composition: Another look at dual-energy X-ray absorptiometry. Annals of the New York Academy of Sciences 904:
45-54.
Lohman, T.G., Pollock, M.L., Slaughter, M.H., Brandon, L.J., and Boileau, R.A. 1984. Methodological factors and
the prediction of body fat in female athletes. Medicine & Science in Sports & Exercise 16: 92-96.
Lohman, T.G., Roche, A.F., and Martorell, R., eds. 1988. Anthropometric standardization reference manual.
Londeree, B., and Moeschberger, M. 1984. Influence of age and other factors on maximal heart rate. Journal of
Cardiac Rehabilitation 4: 44-49.
Looker, A.C., Borrud, L.F., Dawson-Hughes, B., and Shepherd, J.A. 2012. Osteoporosis or low bone mass at the
femur neck or lumbar spine in older adults: United States, 2005-2008. NCHS Data Brief. No. 93. Hyattsville, MD:
National Center for Health Statistics. http://inflpro.com/nchs/data/databriefs/db93.pdf. Accessed September 30,
2012.
Loose, B.D., Christiansen, A.M., Smolczyk, J.E., Roberts, K.L., Budziszewska, A., Hollatz, C.G., and Norman, J.F.
2012. Consistency of the counting talk test for exercise prescription. Journal of Strength and Conditioning Research
26: 1701-1707.
Loprinzi, P.D. 2015. Dose-response association of moderate-to-vigorous physical activity with cardiovascular
biomarkers and all-cause mortality: Considerations by individual sports, exercise and recreational physical
activities. Preventive Medicine 81: 73-77.
Loprinzi, P.D., Loenneke, J.P., and Blackburn, E.H. 2015. Movement-based behaviors and leukocyte telomere length
among U.S. adults. Medicine & Science in Sports & Exercise 47: 2347-2352.
Lorant, V., Soto, V.E., Alves, J., Federico, B., Kinnunen, J., Kuipers, M., Moor, I., Perelman, J., Richter, M.,
Rimpelä, A., Robert, P-O., Roscillo, F., and Kunst, A. 2015. BMC Research Notes 8:91. doi:10.1186/s13104-015-
1041-z. Accessed April 8, 2016.
Loudon, J.K., Cagle, P.E., Figoni, S.F., Nau, K.L., and Klein, R.M. 1998. A submaximal all-extremity exercise test to
predict maximal oxygen consumption. Medicine & Science in Sports & Exercise 30: 1299-1303.
Lounana, J., Campion, F., Noakes, T.D., and Medelli, J. 2007. Relationship between %HRmax, %HR reserve,
%V̇O2max, and %V̇O2 reserve in elite cyclists. Medicine & Science in Sports & Exercise 39: 350-357.
Low, D.C., Walsh, G.S., and Arkesteijn, M. 2017. Effectiveness of exercise interventions to improve postural control
in older adults: A systematic review and meta-analysis of centre of pressure measurements. Sports Medicine 47: 101-
112.
Lowery, R.P., Joy, J.M., Rathmacher, J.A., Baier, S.M., Fuller, J.C. Jr., Shelley, M.C. Jr., Jäger, R., Purpura, M.,
Wilson, S.M., and Wilson, J.M. 2016. Interaction of beta-hydroxy-beta-methylbutyrate free acid and adenosine
triphosphate on muscle mass, strength, and power in resistance trained individuals. Journal of Strength and
Lowry, D.W., and Tomiyama, A.J. 2015. Air displacement plethysmography versus dual-energy X-ray absorptiometry
in underweight, normal-weight, and overweight/obese individuals. PLoS One 10(1): e0115086.
Loy, S., Likes, E., Andrews, P., Vincent, W., Holland, G.J., Kawai, H., Cen, S., Swenberger, J., VanLoan, M.,
Tanaka, K., Heyward, V., Stolarczyk, L., Lohman, T.G., and Going, S.B. 1998. Easy grip on body composition
measurements. ACSM’s Health & Fitness Journal 2(5): 16-19.
Lozano, A., Rosell, J., and Pallas-Areny, R. 1995. Errors in prolonged electrical impedance measurements due to
806
electrode repositioning and postural changes. Physiological Measurement 16: 121-130.
Lu, T.W., Chien, H.L., and Chen, H.L. 2007. Joint loading in the lower extremities during elliptical exercise.
Lu, Y.M., Lin, J.H., Hsiao, S.F., Liu, M.F., Chen, S.M., and Lue, Y.J. 2011. The relative and absolute reliability of
leg muscle strength testing by a handheld dynamometer. Journal of Strength and Conditioning Research 25: 1065-
1071.
Luettengen, M., Foster, C., Doberstein, S., Mikat, R., and Porcari, J. 2012. Zumba®: Is the “fitness-party” a good
workout?” Journal of Sports Science and Medicine 11: 357-358.
Lukaski, H.C. 1986. Use of the tetrapolar bioelectrical impedance method to assess human body composition. In
Human body composition and fat patterning, ed. N.G. Norgan, 143-158. Wageningen, Netherlands: Euronut.
Lukaski, H.C., and Bolonchuk, W.W. 1988. Estimation of body fluid volumes using tetrapolar impedance
measurements. Aviation, Space, and Environmental Medicine 59: 1163-l169.
Lukaski, H.C., Johnson, P.E., Bolonchuk, W.W., and Lykken, G.I. 1985. Assessment of fat-free mass using
bioelectric impedance measurements of the human body. American Journal of Clinical Nutrition 41: 810-817.
Lundin-Olsson, L., Nyberg, L., and Gustafson, Y. 1997. “Stops walking when talking” as a predictor of falls in elderly
people. Lancet 349: 617.
Lundqvist, S., Börjesson, M., Larsson, M.E.H., Hagberg, L., and Cider, Å. 2017. Physical activity on prescription
(PAP), in patients with metabolic risk factors. A 6-month follow-up study in primary health care. PLoS One 12:
e0175190. doi:10.1371/journal.pone.0175190.
Lyden, K., Keadle, S.K., Staudenmayer, J., and Freedson, P.S. 2017. The activPAL accurately classifies activity
intensity categories in healthy adults. Medicine & Science in Sports & Exercise 49: 1022-1028.
Lynch, E., and Barry, S. 2012. The effectiveness of ice water immersion in the treatment of delayed onset muscle
soreness in the lower leg. Physiotherapy Practice and Research 33: 9-15.
Ma, W-Y., Liu, P-H., Yang, C-Y., Hua, C-H., Shih, S-R., Hsein, Y-C., Hsieh, H-J., Chuang, L-M., Hung, C.S.,
Lin, J-W., Chiu, F-C., Wei, J-N., Lin, M-S., and Li, H-Y. 2012. Diabetes Care. doi:10.2337/dc12-1452.
MacDonald, E.Z., Vehrs, P.R., Fellingham, G.W., Eggett, D., George, J.D., and Hager, R. 2017. Validity and
reliability of assessing body composition using a mobile application. Medicine & Science in Sports & Exercise [Epub
ahead of print]. doi:10.1249/MSS.0000000000001378. Accessed August 13, 2017.
MacDougall, J.D., Sale, D.G., Moroz, J.R., Elder, G.C., Sutton, J.R., and Howalk, H. 1979. Mitochondrial volume
density in human skeletal muscle following heavy resistance training. Medicine and Science in Sports 11: 164-166.
Macedonio, M.A., and Dunford, M. 2009. The athlete’s guide to making weight. Champaign, IL: Human Kinetics.
Machado, A., Garcia-Lopez, D., Gonzalez-Gallego, J., and Garatachea, N. 2010. Whole-body vibration training
increases muscle strength and mass in older women: A randomized-controlled trial. Scandinavian Journal of
Medicine & Science in Sports 20: 200-207.
Mackey, A.L., Bojsen-Moller, J., Qvortrup, K., Langberg, H., Suetta, C., Kalliokoski, K.K., Kjaer, M., and
Magnusson, S.P. 2008. Evidence of skeletal muscle damage following electrically stimulated isometric muscle
contractions in humans. Journal of Applied Physiology 105: 1620-1627.
MacRae, I.F., and Wright, V. 1969. Measurement of back movement. Annals of Rheumatic Diseases 28: 584-589.
Maddigan, M.E., Peach, A.A., and Behm, D.G. 2012. A comparison of assisted and unassisted proprioceptive
neuromuscular facilitation techniques and static stretching. Journal of Strength and Conditioning Research 26: 1238-
1244.
807
Maddison, R., Foley, L., Mhurchu, C.N., Jiang, Y., Jull, A., Prapavessis, H., Hohepa, M., and Rodgers, A. 2011.
Effects of active video games on body composition: A randomized controlled trial. American Journal of Clinical
Magnan, R.E., Kwan, B.M., Ciccolo, J.T., Gurney, B., Mermier, C.M., and Bryan, A.D. 2013. Aerobic capacity
testing with inactive individuals: The role of subjective experience. Journal of Physical Activity and Health 10: 271-
279.
Magnusdottir, A., Porgilsson, B., and Karlsson, B. 2014. Comparing three devices for jump height measurement in a
heterogeneous group of subjects. Journal of Strength and Conditioning Research 28: 2837-2844.
Magnusson, S.P. 1998. Passive properties of human skeletal muscle during stretch maneuvers. A review. Scandinavian
Journal of Medicine and Science in Sports 8(2): 65-77.
Magnusson, S.P., Aagaard, P., Larsson, B., and Kjaer, M. 2000. Passive energy absorption by human muscle-tendon
unit is unaffected by increase in intramuscular temperature. Journal of Applied Physiology 88: 1215-1220.
Magnusson, S.P., Simonsen, E.B., Aagaard, P., Bueson, J., Johannson, F., and Kjaer, M. 1997. Determinants of
musculoskeletal flexibility: Viscoelastic properties, cross-sectional area, EMG and stretch tolerance. Scandinavian
Journal of Medicine and Science in Sports 7: 195-202.
Magnusson, S.P., Simonsen, E.B., Aagaard, P., Dyhre-Poulsen, P., McHugh, M.P., and Kjaer, M. 1996. Mechanical
and physiological responses to stretching with and without preisometric contraction in human skeletal muscle.
Mahar, M.T., Guerieri, A.M., Hanna, M.S., and Kemble, D. 2011. Estimation of aerobic fitness from 20-M
multistage shuttle run test performance. American Journal of Preventive Medicine 41: S117-S123.
Mahieu, N.N., McNair, P., DeMuynck, M., Stevens, V., Blanckaert, I., Smits, N., and Witvrouw, E. 2007. Effect of
static and ballistic stretching on the muscle-tendon tissue properties. Medicine & Science in Sports & Exercise 39:
494-501.
Maksud, M.G., and Coutts, K.D. 1971. Comparison of a continuous and discontinuous graded treadmill test for
maximal oxygen uptake. Medicine and Science in Sports 3: 63-65.
Malek, M.H., Nalbone, D.P., Berger, D.E., and Coburn, J.W. 2002. Importance of health science education for
personal fitness trainers. Journal of Strength and Conditioning Research 16: 19-24.
Manini, T.M., and Clark, B.C. 2012. Dynapenia and aging: An update. Journals of Gerontology, Series A: Biological
Sciences and Medical Sciences 67 A: 28-40.
Mansoubi, M., Pearson, N., Clemes, S.A., Biddle, S.J.H., Bodicoat, D.H., Tolfrey, K., Edwardson, C., and Yates, T.
2015. Energy expenditure during common sitting and standing tasks: Examining the 1.5 MET definition of
sedentary behavior. BMC Public Health 15: 516. doi:10.1186/s12889-015-1851-x. Accessed July 3, 2017.
Marcus, B.H., Rakowski, W., and Rossi, R.S. 1992. Assessing motivational readiness and decision-making for
exercise. Health Psychology 11: 257-261.
Markland, D., and Ingledew, L. 1997. The measurement of exercise motives: Factorial validity and invariance across
gender of a revised exercise motivation inventory. British Journal of Health Psychology 2: 361-376.
Markland, D., and Tobin, V.J. 2004. A modification of the Behavioral Regulation in Exercise Questionnaire to
include an assessment of amotivation. Journal of Sport and Exercise Psychology 26: 191-196.
Marley, W., and Linnerud, A. 1976. A three-year study of the Åstrand-Ryhming step test. Research Quarterly 47:
211-217.
Marocolo, M., Marocolo, I.C., Cunha, F.S.B., Da Mota, G.R., and Maior, A.S. 2016. Influence of percentage of
808
1RM strength test repetition performance during resistance exercise of upper and lower limbs. Archivos de Medicina
del Deporte 33: 387-392.
Marsh, C.E. 2012. Evaluation of the American College of Sports Medicine submaximal treadmill running equation
for predicting V̇O2max. Journal of Strength and Conditioning Research 26: 548-554.
Martin, A.D., Drinkwater, D.T., and Clarys, J.P. 1992. Effects of skin thickness and skinfold compressibility on
skinfold thickness measurements. American Journal of Human Biology 4: 453-460.
Martin, A.D., Ross, W.D., Drinkwater, D.T., and Clarys, J.P. 1985. Prediction of body fat by skinfold caliper:
Assumptions and cadaver evidence. International Journal of Obesity 9(Suppl. 1): S31-S39.
Martin, C.A., and McGrath, B.P. 2014. Ambulatory and home blood pressure measurement in the management of
hypertension: White-coat hypertension. Clinical and Experimental Pharmacology and Physiology 41: 22-29.
Martin, S.B., Jackson, A.W., Morrow, J.R., and Liemohn, W. 1998. The rationale for the sit and reach test revisited.
Martindale, J.L, and Brown, D.F.M. 2017. A visual guide to ECG interpretation. 2nd ed. Philadelphia: Wolters
Kluwer.
Martuscello, J.M., Nuzzo, J.L., Ashley, C.D., Campbell, B.I., Orriola, J.J., and Mayer, J.M. 2013. Systematic review
of core muscle activity during physical fitness exercises. Journal of Strength and Conditioning Research 27: 1684-
1698.
Marx, J.O., Ratamess, N.A., Nindl, B.C., Gotshalk, L.A., Volek, J.S., Dohi, K., Bush, J.A., Gomez, A.L., Mazzetti,
S.A., Fleck, S.J., Hakkinen, K., Newton, R.U., and Kraemer, W.J. 2001. Low-volume circuit versus high-volume
periodized resistance training in women. Medicine & Science in Sports & Exercise 33: 635-643.
Mat, S., Tan, M.P., Kamaruzzaman, S.B., and Ng, C.T. 2015. Physical therapies for improving balance and reducing
falls risk in osteoarthritis of the knee: A systematic review. Age and Ageing 44: 16-24.
Mauger, A.R., and Sculthorpe, N. 2012. A new V̇O2max protocol allowing self-pacing in maximal incremental
exercise. British Journal of Sports Medicine 46: 59-63.
Mayer, J. 1968. Overweight: Causes, costs and control. Englewood Cliffs, NJ: Prentice Hall.
Mayer, T.G., Tencer, A.F., and Kristoferson, S. 1984. Use of noninvasive technique for quantification of spinal
range-of-motion in normal subjects and chronic low back dysfunction patients. Spine 9: 588-595.
Mayhew, J.L., Brechue, W.F., Smith, A.E., Kemmler, W., Lauber, D., and Koch, A.J. 2011. Impact of testing
strategy on expression of upper-body work capacity and one-repetition maximum prediction after resistance
training in college-aged men and women. Journal of Strength and Conditioning Research 25: 2796-2807.
Mayhew, J.L., Ball, T.E., Arnold, M.D., and Bowen, J.C. 1992. Relative muscular endurance performance as a
predictor of bench press strength in college men and women. Journal of Applied Sport Science Research 6: 200-206.
Mayorga-Vega, D., Aguilar-Soto, P., and Viciana, J. 2015. Criterion-related validity of the 20-m shuttle run test for
estimating cardiorespiratory fitness: A meta-analysis. Journal of Sports Science and Medicine 14: 536-547.
Mayorga-Vega, D., Bocanegra-Parrilla, R., Ornelas, M., and Viciana, J. 2016. Criterion-related validity of the
distance and time-based walk/run field tests for estimating cardiorespiratory fitness: A systematic review and meta-
analysis. PLOS One. doi:10.1371/journal.pone.015167. Accessed June 22, 2016.
Mays, R.J., Boér, N.F., Mealey, L.M., Kim, K.H., and Goss, F.L. 2016. A comparison of practical assessment
methods to determine treadmill, cycle and elliptical ergometer V̇O2peak. Journal of Strength and Conditioning
Research 24: 1325-1331.
Mays, R.J., Goss, F.L., Schafer, M.A., Kim, K.H., Nagle-Stilley, E.F., Robertson, R.J. 2010. Validation of adult
809
OMNI perceived exertion scales for elliptical ergometry. Perceptual and Motor Skills 111: 848-862.
Mayson, D.J., Kiely, D.K., LaRose, S.I., and Bean, J.F. 2008. Leg strength or velocity of movement. Which is more
influential on the balance of mobility limited elders? American Journal of Physical Medicine and Rehabilitation 87:
969-976.
Mazess, R.B., Barden, H.S., and Ohlrich, E.S. 1990. Skeletal and body-composition effects of anorexia nervosa.
American Journal of Clinical Nutrition 52: 438-441.
McArdle, W.D., Katch, F.I., and Katch, V.L. 1996. Exercise physiology: Energy, nutrition and human performance, 4th
ed. Baltimore: Williams & Wilkins.
McArdle, W.D., Katch, F.I., and Pechar, G.S. 1973. Comparison of continuous and discontinuous treadmill and
bicycle tests for V̇O2max. Medicine and Science in Sports 5: 156-160.
McArdle, W.D., Katch, F.I., Pechar, G.S., Jacobson, L., and Ruck, S. 1972. Reliability and interrelationships
between maximal oxygen intake, physical working capacity and step-test scores in college women. Medicine and
Science in Sports 4: 182-186.
McAtee, R.E., and Charland, J. 2014. Facilitated stretching, 4th ed. Champaign, IL: Human Kinetics.
McBride, J.M., Nuzzo, J.L., Dayne, A.M., Israetel, M.A., Nieman, D.C., and Triplett, N.T. 2010. Effect of an acute
bout of whole body vibration exercise on muscle force output and motor neuron excitability. Journal of Strength and
McCarthy, J.P., Agre, J.C., Graf, B.K., Pozniak, M.A., and Vailas, A.C. 1995. Compatibility of adaptive responses
with combining strength and endurance training. Medicine & Science in Sports & Exercise 27: 429-436.
McConnell, T., and Clark, B. 1987. Prediction of maximal oxygen consumption during handrail-supported treadmill
exercise. Journal of Cardiopulmonary Rehabilitation 7: 324-331.
McCrory, M.A., Gomez, T.D., Bernauer, E.M., and Mole, P.A. 1995. Evaluation of a new displacement
plethysmograph for measuring human body composition. Medicine & Science in Sports & Exercise 27: 1686-1691.
McCrory, M.A., Mole, P.A., Gomez, T.D., Dewey, K.G., and Bernauer, E.M. 1998. Body composition by air
displacement plethysmography using predicted and measured thoracic gas volumes. Journal of Applied Physiology 84:
1475-1479.
McCue, B.F. 1953. Flexibility of college women. Research Quarterly 24: 316-324.
McGill, S. 2016. Low back disorders: Evidence-based prevention and rehabilitation, 3rd ed. Champaign, IL: Human
Kinetics.
McGill, S.M. 2001. Low back stability: From formal description to issues for performance and rehabilitation. Exercise
and Sport Sciences Reviews 29(1): 26-31.
McGill, S.M., Childs, A., and Liebenson, D.C. 1999. Endurance times for low back stabilization exercises: Clinical
targets for testing and training from a normal database. Archives of Physical Medicine and Rehabilitation 80: 941-
944.
McGill, S.M., and Marshall, L.W. 2012. Kettlebell swing, snatch, and bottoms-up carry: Back and hip muscle
activation, motion, and low back loads. Journal of Strength and Conditioning Research 26: 16-27.
McGlory, C., Devries, M.C., and Phillips, S.M. 2017. Skeletal muscle and resistance exercise training: The role of
protein synthesis in recovery and remodeling. Journal of Applied Physiology 122: 541-548.
McGrath, L.J., Hopkins, W.G., and Hinckson, E.A. 2015. Associations of objectively measured built-environment
attributes with youth moderate-to-vigorous physical activity: A systematic review and meta-analysis. Sports
Medicine 45: 841-865.
810
McHugh, M.P., and Cosgrave, C.H. 2010. To stretch or not to stretch: The role of stretching in injury prevention
and performance. Scandinavian Journal of Medicine and Science in Sports 20: 169-181.
McHugh, M.P. Kremenic, I.J., Fox, M.B., and Gleim, G.W. 1998. The role of mechanical and neural restraints to
joint range of motion during passive stretch. Medicine & Science in Sports & Exercise 30: 928-932.
McHugh, M.P., Magnusson, S.P., Gleim, G.W., and Nicholas, J.A. 1992. Viscoelastic stress relaxation in human
skeletal muscle. Medicine & Science in Sports & Exercise 24: 1375-1382.
McInnis, K., and Balady, G. 1994. Comparison of submaximal exercise responses using the Bruce vs modified Bruce
protocols. Medicine & Science in Sports & Exercise 26: 103-107.
McKeon, P.O., and Hertel, J. 2008. Systematic review of postural control and lateral ankle instability. Part II: Is
balance training clinically effective? Journal of Athletic Training 43(3): 305-315.
McMurray, R.G., Butte, N.F., Crouter, S.E., Trost, S.G., Pfeiffer, K.A., Bassett, D.R., Puyay, M.R., Berrigan, D.,
Watson, K.B., and Fulton, J.E. 2015. Exploring metrics to express energy expenditure of physical activity in youth.
PLoS One. 10: e0130869. doi:10.1371/journal.pone.0130869. Accessed July 4, 2017.
McRae, G., Payne, A., Zelt, J.G.E., Scribbans, T.D., Jung, M.E., Little, J.P., and Gurd, B.J. 2012. Extremely low
volume, whole-body aerobic-resistance training improves aerobic fitness and muscular endurance in females.
Applied Physiology, Nutrition, and Metabolism 37: 1124-1131.
Mears, J., and Kilpatrick, M. 2008. Motivation for exercise: Applying theory to make a difference in adoption and
adherence. ACSM’s Health & Fitness Journal 12(1): 20-26.
Menant, J.C., Schoene, D., Sarofim, M., and Lord, S.R. 2014. Single and dual task tests of gait speed are equivalent
in the prediction of falls in older people: A systematic review and meta-analysis. Ageing Research Reviews 16: 83-
104.
Mesquita, L.S.A., de Carvalho, F.T., Freire, L.S.A., Neto, O.P., and Zangaro, R.A. 2015. Effects of two exercise
protocols on postural balance of elderly women: A randomized controlled trial. BMC Geriatrics 15: 61.
Messier, S.P., Royer, T.D., Craven, T.E., O’Toole, M.L., Burns, R., and Ettinger W.H. Jr. 2000. Long-term
exercise and its effect on balance in older, osteoarthritic adults: Results from the Fitness, Arthritis, and Seniors
Trial (FAST). Journal of the American Geriatrics Society 48: 131-138.
Metcalfe, L. 2010. The BEST strength training program for osteoporosis prevention. ACSM’s Certified News 20(4): 7-
8, 11.
Micozzi, M.S., Albanes, D., Jones, Y., and Chumlea, W.C. 1986. Correlations of body mass indices with weight,
stature, and body composition in men and women in NHANES I and II. American Journal of Clinical Nutrition 44:
725-731.
Midgley, A.W., Bentley, D.J., Luttikholt, H., McNaughton, L.R., and Millet, G.P. 2008. Challenging a dogma of
exercise physiology. Does an incremental exercise test for valid V̇O2max determination really need to last between
8 and 12 minutes? Sports Medicine 38: 441-447.
Mier, C.M., Alexander, R.P., and Mageean, A.L. 2012. Achievement of V̇O2max criteria during a continuous graded
exercise test and a verification stage performed by college-aged athletes. Journal of Strength and Conditioning
Research 26: 2648-2654.
Mier, C.M., and Feito, Y. 2006. Metabolic cost of stride rate, resistance, and combined use of arms and legs on the
elliptical trainer. Research Quarterly for Exercise and Sport 77: 507-513.
Mifflin, M.D., St. Jeor, S.T., Hill, L.A., Scott, B.J., Daugherty, S.A., and Koh, Y.O. 1990. A new predictive
equation for resting energy expenditure in healthy individuals. American Journal of Clinical Nutrition 51: 241-247.
811
Milani, P., Coccetta, C.A., Rabini, A., Sciarra, T., Massazza, G., and Ferriero, G. 2014. Mobile smartphone
applications for body position measurement in rehabilitation: A review of goniometric tools. PM&R 6: 1038-1043.
Milanović, Z., Sporiš, G., and Weston, M. 2015. Effectiveness of high-intensity interval training (HIT) and
continuous endurance training for V̇O2max improvements: A systematic review and meta-analysis of controlled
trials. Sports Medicine 45: 1469-1481.
Millard-Stafford, M.L., Collins, M.A., Evans, E.M., Snow, T.K., Cureton, K.J., and Rosskopf, L.B. 2001. Use of air
displacement plethysmography for estimating body fat in a four-component model. Medicine & Science in Sports &
Exercise 33: 1311-1317.
Mills, K.T., Bundy, J.D., Kelly, T.N., Reed, J.E., Kearney, P.M., Reynolds, K., Chen, J., and He, J. 2016. Global
disparities on hypertension prevalence and control. Circulation 134: 441-450.
Mingji, C., Onakpoya, I.J., Heneghan, C.J., and Ward, A.M. 2016. Assessing agreement of blood pressure-
measuring devices in Tibetan areas of China: A systematic review. Heart Asia 8: 46-51.
Minkler, S., and Patterson, P. 1994. The validity of the modified sit-and-reach test in college-age students. Research
Miranda, A.B., Simao, F., Rhea, M., Bunker, D., Prestes, J., Leite, R.D., Miranda, H., de Salles, B.F., and Novaes, J.
2011. Effects of linear vs. daily undulating periodized resistance training on maximal and submaximal strength
gains. Journal of Strength and Conditioning Research 25: 1824-1830.
Mitchell, J.A., Cousminer, D.L., Zemel, B.S., Grant, S.F.A., and Chesi, A. 2016. Genetics of pediatric bone
strength. BoneKEy Reports 5: article 823. doi:10.1038/bonekey.2016.50. Accessed April 15, 2017.
Mitros, M., Gabriel, K.P., Ainsworth, B., Lee, C.M., Herrmann, S., Campbell, K., and Swan, P. 2011.
Comprehensive evaluation of a single-stage submaximal treadmill walking protocol in healthy, middle-aged
women. European Journal of Applied Physiology 111: 47-56.
Miyachi, M., Yamamoto, K., Ohkawara, K., and Tanaka, S. 2010. METs in adults while playing active video games:
A metabolic chamber study. Medicine & Science in Sports & Exercise 42: 1149-1153.
Miyazaki, R., Kotani, K., Tszaki, K., Sakane, N., Yonei, Y., and Ishii, K. 2015. Effects of a year-long pedometer-
based walking program on cardiovascular disease risk factors in active older people. Asia-Pacific Journal of Public
Health 27: 155-163.
Mizumura, K., and Taguchi, T. 2016. Delayed onset muscle soreness: Involvement of neurotrophic factors. Journal of
Physiological Sciences 66: 43-52.
Mizuno, T., and Umemura, Y. 2016. Dynamic stretching does not change the stiffness of the muscle-tendon unit.
Moffatt, R.J., Stamford, B.A., and Neill, R.D. 1977. Placement of tri-weekly training sessions: Importance regarding
enhancement of aerobic capacity. Research Quarterly 48: 583-591.
Moffroid, M.T., and Whipple, R.H. 1970. Specificity of speed of exercise. Physical Therapy 50: 1699-1704.
Moholdt, T., Wisløff, U., Lydersen, S., and Nauman, J. 2014. Current physical activity guidelines for health are
insufficient to mitigate long-term weight gain: More data in the fitness versus fatness debate (the HUNT study,
Norway). British Journal of Sports Medicine 48: 1489-1496.
Mole, P.A., Oscai, L.B., and Holloszy, J.O. 1971. Adaptation of muscle to exercise: Increase in levels of palmityl
CoA synthetase, carnitine palmityl-transferase, and palmityl CoA dehydrogenase and the capacity to oxidize fatty
acids. Journal of Clinical Investigation 50: 2323-2329.
Molnar, D., Jeges, S., Erhardt, E., and Schutz, Y. 1995. Measured and predicted resting metabolic rate in obese and
812
nonobese adolescents. Journal of Pediatrics 127: 571-577.
Montalvo, A.M., Shaefer, H., Rodriguez, B., Li, T., Epnere, K., and Myer, G.D. 2017. Retrospective injury
epidemiology and risk factors for injury in CrossFit. Journal of Sports Science and Medicine 16: 53-59.
Montoye, H.J., and Faulkner, J.A. 1964. Determination of the optimum setting of an adjustable grip dynamometer.
Moon, J.R., Stout, J.R., Walter, A.A., Smith, A.E., Stock, M.S., Herda, T.J., Sherk, V.D., Young, K.C., Lockwood,
C.M., Kendall, K.L., Fukuda, D.H., Graff, J.L., Cramer, J.T., Beck, T.W., and Esposito, E.N. 2011. Mechanical
scale and load cell underwater weighing: A comparison of simultaneous measurements and the reliability of
methods. Journal of Strength and Conditioning Research 25: 652-661.
Moon, J.R., Tobkin, S.E., Costa, P.B., Smalls, M., Mieding, W.K., O’Kroy, J.A., Zoeller, R.F., and Stout, J.R. 2008.
Validity of the Bod Pod for assessing body composition in athletic high school boys. Journal of Strength and
Mooney, V., Kron, M., Rummerfield, P., and Holmes, B. 1995. The effect of workplace based strengthening on low
back injury rates: A case study in the strip mining industry. Journal of Occupational Rehabilitation 5: 157-167.
Moore, D.R., Young, M., and Phillips, S.M. 2012. Similar increases in muscle size and strength in young men after
training with maximal shortening or lengthening contractions when matched for total work. European Journal of
Applied Physiology 112: 1587-1592.
Moore, M.A., and Hutton, R.S. 1980. Electromyographic investigation of muscle stretching techniques. Medicine &
Moore, S.C. 2009. Waist versus weight—which matters more for mortality? American Journal of Clinical Nutrition 89:
1003-1004.
Moore, S.C., Lee, I-M., Weiderpass, E., Campbell, P.T., Sampson, J.N., Kitahara, C.M., Keadle, S.K., Arem, J.,
Berrington de Bonzalez, A., Hartge, P., Adami, H-O, Blair, C.K., Borch, K.B., Boyd, E., Check, D.P., Fournier,
A., Freedman, N.D., Gunter, M., Johannson, M., Khaw, K-T., Linet, M.S., Orsini, N., Park, Y., Riboli, E.,
Robien, K., Schairer, C., Sesso, H., Spriggs, M., Van Dusen, R., Wolk, A., Matthews, C.E., and Patel, A.V.
2016. Association of leisure-time physical activity with risk of 26 types of cancer in 1.44 million adults. Journal of
the American Medical Association: Internal Medicine 176: 816-825.
Moran, S., Booker, H., Staines, J., and Williams, S. 2017. Rates and risk factors of injury in CrossFit™: A
prospective cohort study. Journal of Sports Medicine and Physical Fitness 57: 1147-1153.
Morán-Navarro, R., Mora-Rodríguez, R., Rodríguez-Rielves, V., de la Fuente-Pérez, P., and Pallarés, J.G. 2016.
Heart rate reserve at ventilator thresholds, maximal lactate steady state and maximal aerobic power in well-trained
cyclists: Training application. European Journal of Human Movement 36: 150-162.
Morehouse, L.E. 1972. Laboratory manual for physiology of exercise. St. Louis: Mosby.
Moritani, T., and deVries, H.A. 1979. Neural factors versus hypertrophy in the time course of muscle strength gain.
American Journal of Physical Medicine 58: 115-130.
Morrison, S.A., Petri, R.M., Hunter, H.L., Raju, D., and Gower, B. 2016. Comparison of the Lunar Prodigy and
iDXA dual-energy X-ray absorptiometers for assessing total and regional body composition. Journal of Clinical
Densitometry: Assessment & Measurement of Musculoskeletal Health 19: 290-297.
Morrow, J.R., Jackson, A.S., Bradley, P.W., and Hartung, G.H. 1986. Accuracy of measured and predicted residual
lung volume on body density measurement. Medicine & Science in Sport & Exercise 18: 647-652.
Motalebi, S.A., Iranagh, J.A., Abdollahi, A., and Lim, W.K. 2014. Applying of theory of planned behavior to
813
promote physical activity and exercise behavior among older adults. Journal of Physical Education and Sport 14: 562-
568.
Muehlbauer, A.B., Roth, T., Mueller, S., and Granacher, U. 2011. Intra and intersession reliability of balance
measures during one-leg standing in young adults. Journal of Strength and Conditioning Research 25: 2228-2234.
Muir, S.W., Berg, K., Chesworth, B., and Speechley, M. 2008. Use of the Berg Balance Scale for predicting multiple
falls in community-dwelling elderly people: A prospective study. Physical Therapy 88: 449-459.
Muller, M.J., Bosy-Westphal, A., Klaus, S., Kreymann, G., Luhrmann, P.M., Neuhauser-Berthold, M., Noack, R.,
Pirke, K.M., Platte, P., Selberg, O., and Steiniger, J. 2004. World Health Organization equations have
shortcomings for predicting resting energy expenditure in persons from a modern, affluent population: Generation
of a new reference standard from a retrospective analysis of a German database of resting energy expenditure.
Müller, W., Lohman, T.G., Stewart, A.D., Maughan, R.J., Meyer, N.L., Sardinha, L.B., Kirihennedige, N.,
Reguant-Closa, A., Risoul-Salas, V., Sundgot-Borgen, J., Ahammer, H., Anderhuber, F., Fürhapter-Rieger, A.,
Kainz, P., Matrna, W., Pilsl, U., Pirstinger, W., and Ackland, T.R. 2016. Subcutaneous fat patterning in athletes:
Selection of appropriate sites and standardisation of a novel ultrasound measurement technique: Ad hoc working
group on body composition, health and performance, under the auspices of the IOC Medical Commission. British
Journal of Sports Medicine 50(1): 45-54.
Muñoz-Martinez, F.A., Rubio-Arias, J.Á., Ramos-Campo, D.J., and Alcaraz, P.E. 2017. Effectiveness of resistance
circuit-based training for maximum oxygen uptake and upper-body one-repetition maximum improvements: A
systematic review and meta-analysis. Sports Medicine [in press].
Munroe, R.A., and Romance, T.J. 1975. Use of the Leighton flexometer in the development of a short flexibility test
battery. American Corrective Therapy Journal 29: 22.
Murach, K.A., and Bagley, J.R. 2016. Skeletal muscle hypertrophy with concurrent exercise training: Contrary
evidence for an interference effect. Sports Medicine 46: 1029-1039.
Murlasits, Z., Kneffel, Z., and Thalib, L. 2017. The physiological effects of concurrent strength and endurance
training sequence: A systematic review and meta-analysis. Journal of Sports Sciences [in press].
Murphy, E.C.S, Carson, L., Neal, W., Baylis, C., Donley, D., and Yeater, R. 2009. Effects of an exercise intervention
using Dance Dance Revolution on endothelial function and other risk factors in overweight children. International
Journal of Pediatric Obesity 4: 205-214.
Murphy, J.R., Di Santo, M.C., Alkanani, T., and Behm, D.G. 2010. Aerobic activity before and following short-
duration static stretching improves range of motion and performance vs. a traditional warm-up. Applied Physiology,
Nutrition, and Metabolism 35: 679-690.
Muyor, J.M., Vaquero-Cristobal, R., Alacid, F., and Lopez-Minarro, P.A. 2014. Criterion-related validity of sit-and-
reach and toe-touch tests as a measure of hamstring extensibility in athletes. Journal of Strength and Conditioning
Research 28: 546-555.
Myers, J., Forman, D.E., Balady, G.J., Franklin, B.A., Nelson-Worel, J., Martin, B-J. Herbert, W.G., Guazzi, M.,
and Arena, R. 2014. Supervision of exercise testing by nonphysicians: A scientific statement from the American
Heart Association. Circulation 130: 1014-1027.
Myers, T.R., Schneider, M.G., Schmale, M.S., and Hazell, T.J. 2015. Whole-body aerobic resistance training circuit
improves aerobic fitness and muscle strength in sedentary young females. Journal of Strength and Conditioning
Research 29: 1592-1600.
Myers, M.G., Valdivieso, M., and Kiss, A. 2009. Use of automated office blood pressure measurement to reduce the
814
white coat response. Journal of Hypertension 27: 280-286.
Naclerio, A.B., Rodriguez-Romo, G., Barriopedro-Moro, M.I., Jimenez, A., Alvar, B.A., and Triplett, N.T. 2011.
Control of resistance training intensity by the OMNI perceived exertion scale. Journal of Strength and Conditioning
Research 25: 1879-1888.
Nagle, F.S., Balke, B., and Naughton, J.P. 1965. Gradational step tests for assessing work capacity. Journal of Applied
Nakamura, M., Ikezoe, T., Takeno, Y., and Ichihashi, N. 2011. Acute and prolonged effect of static stretching on the
passive stiffness of the human gastrocnemius muscle tendon unit in vivo. Journal of Orthopedic Research 29: 1759-
1763.
Nana, A., Slater, G.J., Hopkins, W.G., and Burke, L.M. 2012. Effects of daily activities on dual-energy X-ray
absorptiometry measurements of body composition in active people. Medicine & Science in Sports & Exercise 44(1):
180-189.
Nana, A., Slater, G.J., Stewart, A.D., and Burke, L.M. 2015. Methodology review: Using dual-energy X-ray
absorptiometry (DXA) for assessment of body composition in athletes and active people. International Journal of
Sport Nutrition and Exercise Metabolism 25(2): 198-215.
Napolitano, M.A., Lewis, B.A., Whitely, J.A., and Marcus, B.H. 2010. Principles of health behavior change. In
ACSM’s resource manual for guidelines for exercise testing and prescription, 710-723. Philadelphia: Wolters
Kluwer/Lippincott Williams & Wilkins.
Nashner, L.M. 1997. In Handbook of balance function testing, ed. G.P. Jacobson, C.W. Newman, and J.M. Kartush,
280-307. San Diego: Singular Publishing Group.
National Cholesterol Education Program. 2001. Executive summary of the third report of the National Cholesterol
Education Program (NCEP) Expert Panel on detection, evaluation, and treatment of high blood cholesterol in
adults (Adult Treatment Panel III). Journal of the American Medical Association 285(19): 2486-2497.
National Institutes of Health. 2012. Mad as a hatter campaign for a mercury-free NIH.
www.nems.nih.gov/Pages/madhatter.aspx. Accessed October 27, 2012.
National Osteoporosis Foundation. 2004. America’s bone health: The state of osteoporosis and low bone mass.
www.nof.org/advocacy/prevalence.
National Osteoporosis Foundation. 2017. Bone health basics: Get the facts. www.nof.org/preventing-
fractures/general-facts. Accessed April 14, 2017.
National Strength and Conditioning Association. 2017. Strength training, 2nd ed. Champaign, IL: Human Kinetics.
National Strength and Conditioning Association. 2016. Essentials of strength training and conditioning, 4th ed.
Naughton, J., Balke, B., and Nagle, F. 1964. Refinement in methods of evaluation and physical conditioning before
and after myocardial infarction. American Journal of Cardiology 14: 837.
NCD Risk Factor Collaboration. 2017. Worldwide trends in blood pressure from 1975 to 2015: A pooled analysis of
1479 population-based measurement studies with 19.1 million participants. Lancet 389: 37-55.
Nelson, A.G., and Kokkonen, J. 2014. Stretching anatomy, 2nd ed. Champaign, IL: Human Kinetics.
Nelson, A.G., Kokkonen, J., Arnall, D.A., and Li, L. 2012. Acute stretching increases postural stability in non-
balance-trained individuals. Journal of Strength and Conditioning Research 26: 3095-3100.
Nelson, M.E., and Folta, S.C. 2009. Further evidence for the benefits of walking. American Journal of Clinical
815
Nelson, M.E., Rejeski, W.J., Blair, S.N., Duncan, P.W., Judge, J.O., King, A.C., Macera, C.A., and Castaneda-
Sceppa, C. 2007. Physical activity and public health in older adults: Recommendations from the American College
of Sports Medicine and the American Heart Association. Medicine & Science in Sports & Exercise 39(8): 1435-
1445.
Neuhauser, H.K., Ellert, U., Thamm, M., and Adler, C. 2015. Calibration of blood pressure data after replacement of
the standard mercury sphygmomanometer by an oscillometric device and concurrent change of cuffs. Blood Pressure
Monitoring 20: 39-42.
Ng, B.K., Hinton, B.J., Fan, B., Kanaya, A.M., and Shepherd, J.A. 2016. Clinical anthropometrics and body
composition from 3D whole-body surface scans. European Journal of Clinical Nutrition 70: 1265-1270.
Ng, J.K., Kippers, V., Richardson, C.A., and Parnianpour, M. 2001. Range of motion and lordosis of the lumbar
spine: Reliability of measurement and normative values. Spine 26: 53-60.
Ng, M., Freeman, M.K., Fleming, T.D., Robinson, M., Dwyer-Lindgren, L., Thomson, B., Wollum, A., Sanman,
E., Wulf, S., Lopez, A.D., Murray, C.J.L., and Gakidou, E. 2014. Smoking prevalence and cigarette consumption
in 187 countries, 1980-2012. Journal of the American Medical Association 311: 183-192.
Ng, N. 1995. Metcalc. Champaign, IL: Human Kinetics.
NHS Digital. Health survey for England, 2014: Trend tables. http://content.digital.nhs.uk/catalogue/PUB19297.
Ni, M., Mooney, K., Richards, L., Balachandran, A., Sun, M., Harriell, K., Potiaumpai, M., and Signorile, J.F. 2014.
Comparative impacts of tai chi, balance training, and a specially-designed yoga program on balance in older fallers.
Archives of Physical Medicine and Rehabilitation 95(9): 1620-1628.
Nichols, D.L., Sanborn, C.F., and Love, A.M. 2001. Resistance training and bone mineral density in adolescent
females. Journal of Pediatrics 139: 494-499.
Nichols, J.F., Sherman, C.L., and Abbott, E. 2000. Treading is new and hot: 30 minutes meets the ACSM
recommendations for cardiorespiratory fitness and caloric expenditure. ACSM’s Health & Fitness Journal 4(2): 12-
17.
Nick, N., Petramfar, P., Ghodsbin, F., Keshavarzi, S., and Jahanbin, I. 2016. The effect of yoga on balance and fear
of falling in older adults. PM&R 8: 145-151.
Nicklas, B.J., Wang, X., You, T., Lyles, M.F., Demons, J., Easter, L., Berry, M.J., Lenchik, L., and Carr, J.J. 2009.
Effect of exercise intensity on abdominal fat loss during calorie restriction in overweight and obese postmenopausal
women: A randomized, controlled trial. American Journal of Clinical Nutrition 89: 1043-1052.
Nicklas, J.M., Huskey, K.W., Davis, R.B., and Wee, C.C. 2012. Successful weight loss among obese U.S. adults.
American Journal of Preventive Medicine 42: 481-485.
Nieman, D.C. 2003. Exercise testing and prescription: A health related approach. New York: McGraw-Hill.
Nissen, S.L., and Sharp, R.L. 2003. Effect of dietary supplements on lean mass and gains with resistance training: A
meta-analysis. Journal of Applied Physiology 94: 651-659.
Noakes, T.D. 2008. How did A V Hill understand the V̇O2max and the “plateau phenomenon”? Still no clarity?
British Journal of Sports Medicine 42: 574-580.
Noland, M., and Kearney, J.T. 1978. Anthropometric and densitometric responses of women to specific and general
exercise. Research Quarterly 49: 322-328.
Noreen, E.E., and Lemon, P.W.R. 2006. Reliability of air displacement plethysmography in a large, heterogeneous
sample. Medicine & Science in Sports & Exercise 38: 1505-1509.
816
Norkin, C.C., and White, D.J. 1995. Measurement of joint motion: A guide to goniometry. Philadelphia: Davis.
Norris, C. 2000. Back stability. Champaign, IL: Human Kinetics.
Norris, R.A., Wilder, E., and Norton, J. 2008. The functional reach test in 3- to 5-year-old children without
disabilities. Pediatric Physical Therapy 20: 47-52.
North American Spine Society. 2009. Exercise for a healthy back.

www.spine.org/Pages/ConsumerHealth/SpineHealthAndWellness/PreventBackPain.
Northey, J.M., Cherbuin, N., Pumpa, K.L., Smee, D.J., and Rattray, B. 2017. Exercise interventions for cognitive
function in adults older than 50: A systematic review with meta-analysis. British Journal of Sports Medicine.
doi:10.1136/bjsports-2016-09658. Accessed April 27, 2017.
Norton, K., Marfell-Jones, M., Whittingham, N., Kerr, D., Carter, L., Saddington, K., and Gore, C. 2000.
Anthropometric assessment protocols. In Physiological tests for elite athletes, ed. C. Gore, 66-85. Champaign, IL:
Human Kinetics.
Nunez, C., Kovera, A., Pietrobelli, A., Heshka, S., Horlick, M., Kehayias, J., Wang, Z., and Heymsfield, S. 1999.
Body composition in children and adults by air displacement plethysmography. European Journal of Clinical
Nuri, L., Ghotbi, N., and Faghihzadeh, S. 2013. Acute effects of static stretching, active warm up, or passive warm up
on flexibility of the plantar flexors of Iranian professional female taekwondo athletes. Journal of Musculoskeletal Pain
21: 263-268.
Nuzzo, J.L., Anning, J.H., and Scharfenberg, J.M. 2011. The reliability of three devices used for measuring vertical
jump height. Journal of Strength and Conditioning Research 25: 2580-2590.
Nye, N.S., Carnahan, D.H., Jackson, J.D., Covey, C.J., Zarazabal, L.A., Chao, S.Y., Bockhorst, A.D., and Crawford,
P.F. 2014. Abdominal circumference is superior to body mass index in estimating musculoskeletal injury risk.
O’Brien, E., Atkins, N., Stergiou, G., Karpettas, N., Parati, G., Asmar, R., Imai, Y., Want, J., Mengden, T., and
Sheenan, A., on behalf of the Working Group on Blood Pressure Monitoring of the European Society of
Hypertension. 2010. European Society of Hypertension International Protocol revision 2010 for the validation of
blood pressure measuring devices in adults. Blood Pressure Monitoring 15: 23-38.
O’Brien, R.J., and Drizd, T.A. 1983. Roentgenographic determination of total lung capacity: Normal values from a
national population survey. American Review of Respiratory Diseases 128: 949-952.
O’Connor, D.M., and Crowe, M.J. 2007. Effects of six weeks of β-hydroxy-β-methylbutyrate (HMB) and
HMB/creatine supplementation on strength, power, and anthropometry of highly trained athletes. Journal of
Ogawa, E.F., Leveille, S.G., Write, J.A., Shi, L., Cambi, S.M., and You, T. 2017. Physical activity
domains/recommendations and leukocyte telomere length in U.S. adults. Medicine & Science in Sports & Exercise.
doi:10.1249/MSS.0000000000001253. Accessed March 25, 2017.
Ogden, C.L., Carroll, M.D., Fryar, C.D., and Flegal, K.M. 2015. Prevalence of obesity among adults and youth:
United States, 2011-2014. NCHS Data Brief. No. 219. Hyattsville, MD: National Center for Health Statistics.
Ogden, C.L., Carroll, M.D., Kit, B.K., and Flegal, K.M. 2014. Prevalence of childhood and adult obesity in the
United States, 2011-2012. Journal of the American Medical Association 311: 806-814.
Ogedegbe, G., and Pickering, T. 2010. Principles and techniques of blood pressure measurement. Cardiology Clinics
28: 571-586.
817
Ogedegbe, G., Agyemang, C., and Ravenell, J.E. 2010. Masked hypertension: Evidence of the need to treat. Current
Hypertension Reports 12: 349-355.
Oh, K.Y., Kim, S.A., Lee, S.Y., and Lee, S.L. 2011. Comparison of manual balance and balance board tests in
healthy adults. Annals of Rehabilitation Medicine 35: 873-879.
Ohkubo, T., Kikuya, M., Metoki, H., Asayama, K., Obara, T., Hashimoto, J., Totsune, K., Hoshi, H., Satoh, H.,
and Imai, Y. 2005. Prognosis of “masked” hypertension and “white-coat” hypertension detected by 24-h
ambulatory blood pressure monitoring: A 10-year follow-up from the Ohasama study. Journal of American College of
Cardiology 46: 508-515.
Ohrvall, M., Berglund, L., and Vessby, B. 2000. Sagittal abdominal diameter compared with other anthropometric
measurements in relation to cardiovascular risk. International Journal of Obesity 24: 497-501.
Oldroyd, B., Treadgold, L., and Hind, K. 2017. Cross calibration of the GE Prodigy and iDXA for the measurement
of total and regional body composition in adults. Journal of Clinical Densitometry: Assessment & Measurement of
Musculoskeletal Health [Epub ahead of print]. doi:10.1016/j.jocd.2017.05.009. Accessed August 6, 2017.
Oliveira, G.B.F., Avezum, A., and Roever, L. 2015. Cardiovascular disease burden: Evolving knowledge of risk
factors in myocardial infarction and stroke through population-based research and perspectives in global
prevention. Frontiers in Cardiovascular Medicine 2: article 32. doi:10.3389/fcvm.2015.00032. Accessed August 14,
2017.
Olmsted, L.C., Carcia, C.R., Hertel, J., and Schultz, S.J. 2002. Efficacy of the star excursion balance tests in detecting
reach deficits in subjects with chronic ankle instability. Journal of Athletic Training 37: 501-506.
O’Neill, D.C., Cronin, O., O’Neill, S.B., Woods, T., Keohane, D.M., Molloy, M.G., and Falvey, E.C. 2016.
Application of a sub-set of skinfold sites for ultrasound measurement of subcutaneous adiposity and percentage
body fat estimation in athletes. International Journal of Sports Medicine 37: 359-363.
O’Neill, S., and O’Driscoll, L. 2015. Metabolic syndrome: A closer look at the growing epidemic and its associated
pathologies. Obesity Reviews 16: 1-12.
Opplert, J., Gentry, J.B., and Babault, N. 2016. Do stretch durations affect muscle mechanical and neurophysiological
properties? International Journal of Sports Medicine 37: 673-679.
Oppliger, R.A., Nielsen, D.H., and Vance, C.G. 1991. Wrestlers’ minimal weight: Anthropometry, bioimpedance,
and hydrostatic weighing compared. Medicine & Science in Sports & Exercise 23: 247-253.
O’Riordan, C.F., Metcalf, B.S., Perkins, J.M., and Wilkin, T.J. 2010. Reliability of energy expenditure prediction
equations in the weight management clinic. Journal of Human Nutrition and Dietetics 23: 169-175.
Orr, R. 2010. Contribution of muscle weakness to postural instability in the elderly: A systematic review. European
Journal of Physical and Rehabilitation Medicine 46: 183-220.
Orr, R., de Vos, N.J., Singh, N.A., Ross, D.A., Stavrinos, T.M., and Fiatarone-Singh, M.A. 2006. Power training
improves balance in healthy older adults. Journals of Gerontology, Series A: Biological Sciences and Medical Sciences 61:
78-85.
Orr, R., Raymond, J., and Singh, M.F. 2008. Efficacy of progressive resistance training on balance performance in
older adults. A systematic review of randomized controlled trials. Sports Medicine 38: 317-343.
Ortega, F.B., Cadenas-Sánchez, C., Sánchez-Delgado, G., Mora-González, J., Martinez-Téllez, B., Artero, E.G.,
Castro-Piñero, J., Labayen, I., Chillón, P., Löf, M., and Ruiz, J.R. 2015. Systematic review and proposal of a
field-based physical fitness-test battery in preschool children: The PREFIT battery. Sports Medicine 45: 533-555.
Ortiz, O., Russell, M., Daley, T.L., Baumgartner, R.N., Waki, M., Lichtman, S., Wang, S., Pierson, R.N., and
818
Heymsfield, S.B. 1992. Differences in skeletal muscle and bone mineral mass between black and white females and
their relevance to estimates of body composition. American Journal of Clinical Nutrition 55: 8-13.
Osawa, Y., and Oguma, Y. 2013. Effects of vibration on flexibility: A meta-analysis. Journal of Musculoskeletal
Neuronal Interactions 13: 442-453.
Ostchega, Y., Hughes, J.P., Prineas, R.J., Zhang, G., Nwankwo, T., and Chiappa, M.M. 2014. Mid-arm
circumference and recommended blood pressure cuffs for children and adolescents aged between 3 and 19 years:
Data from the National Health and Nutrition Examination Survey, 1999-2010. Blood Pressure Monitoring 19: 26-
31.
Ostchega, Y., Prineas, R.J., Dillon, C., McDowell, M., and Carroll, M. 2004. Estimating equations and tables for
adult mid-arm circumference based on measured height and weight: Data from the third National Health and
Nutrition Examination Survey (NHANES III) and NHANES 1999-2000. Blood Pressure Monitoring 9: 123-131.
Osternig, L.R., Robertson, R.N., Troxel, R.K., and Hansen, P. 1990. Differential responses to proprioceptive
neuromuscular facilitation (PNF) stretch techniques. Medicine & Science in Sports & Exercise 22: 106-111.
Otto, W.H. III, Coburn, J.W., Brown, L.E., Spiering, B.A. 2012. Effects of weightlifting vs. kettlebell training on
vertical jump, strength, and body composition. Journal of Strength and Conditioning Research 26: 1199-1202.
Page, P., and Ellenbecker, T. 2011. Strength band training, 2nd ed. Champaign, IL: Human Kinetics.
Pajala, S., Era, P., Koskenvuo, M., Kaprio, J., Tormakangas, T., and Rantanen, T. 2008. Force platform balance
measures as predictors of indoor and outdoor falls in community-dwelling women 63-76 years. Journal of
Pajunen, P., Heliovaara, M., Rissanen, H., Reunanen, A., Laaksonen, M.A., and Knekt, P. 2013. Sagittal abdominal
diameter as a new predictor for incident diabetes. Diabetes Care 36(2): 283-288. doi:10.2337/dc11-2451.
Palatini, P., Benetti, E., Fania, C., Malipiero, G., and Saladini, F. 2012. Rectangular cuffs may overestimate blood
pressure in individuals with large conical arms. Journal of Hypertension 30: 530-536.
Panagiotakos, D.B., Georgousopoulou, E.N., Fitzgerald, A.P., Pitsavos, C., and Stefanadis, C. 2015. Validation of
the HellenicSCORE (a calibration of the ESC SCORE Project) regarding 10-year risk of fatal cardiovascular
disease in Greece. Hellenic Journal of Cardiology 56: 302-308.
Parati, G., and Ochoa, J.E. 2012. Automated-auscultatory (Hybrid) sphygmomanometers for clinic blood pressure
measurement: A suitable substitute to mercury sphygmomanometer as reference standard? Journal of Human
Hypertension 26: 211-213.
Parfitt, G., Evans, H., and Eston, R. 2012. Perceptually regulated training at RPE13 is pleasant and improves
physical health. Medicine & Science in Sports & Exercise 44: 1613-1618.
Parker, S.B., Hurley, B.F., Hanlon, D.P., and Vaccaro, P. 1989. Failure of target heart rate to accurately monitor
intensity during aerobic dance. Medicine & Science in Sports & Exercise 21: 230-234.
Parry, I., Carbullido, C., Kawada, J., Baglesy, A., Sen, S., Greenhalgh, D., and Palmieri, T. 2014. Keeping up with
video game technology: Objective analysis of Xbox KinextTM and PlayStation 3 MoveTM for use in burn
rehabilitation. Burns 40: 852-859.
Partridge, S.R., McGeechan, K., Hebden, L., Balestracci, K., Wong, A.T.Y., Denney-Wilson, E., Harris, M.F.,
Phongsavan, P., Bauman, A., and Allman-Farinelli, M. 2015. Effectiveness of a mHealth lifestyle program with
telephone support (TXT2BFIT) to prevent unhealthy weight gain in young adults: Randomized clinical trial.
Journal of Medical Internet Research 3: e66. doi:10.2196/mhealth.4530. Accessed June 10, 2017.
PAR-Q+ Collaboration. 2017. The new PARQ-X+ and ePARmed-X+: Official website. http://eparmedx.com.
819
Pata, R.W., Lord, K., and Lamb, J. 2014. The effect of Pilates based exercise on mobility, postural stability, and
balance in order to decrease fall risk in older adults. Journal of Bodywork & Movement Therapies 18: 361-367.
Pate, R.R., Pratt, M., Blair, S.N., Haskell, W.L., Macera, C.A., Bouchard, C., Buchner, D., Ettinger, W., Heath,
G.W., and King, A.C. 1995. Physical activity and public health: A recommendation from the Centers for Disease
Control and Prevention and the American College of Sports Medicine. Journal of the American Medical Association
273: 402-407.
Patel, R., Sulzberger, L., Li, G., Mair, J., Morley, H., Shing, M.N-W., O’Leary, C., Prakash, A., Robilliard, N.,
Rutherford, M., Sharpe, C., Shie, C., Sritharan, L., Turnbull, J., Whyte, I. Yu, H., Cleghorn, C., Leung, W., and
Wilson, N. 2015. Smartphone apps for weight loss and smoking cessation: Quality ranking of 120 apps [Letter].
2015. New Zealand Medical Journal 128(1421): 73-76.
Patrick, N., Emanski, E., and Knaub, M.A. 2014. Acute and chronic low back pain. Medical Clinics in North America
98: 777-789.
Patterson, P., Wiksten, D.L., Ray, L., Flanders, C., and Sanphy, D. 1996. The validity and reliability of the backsaver
sit-and-reach test in middle school girls and boys. Research Quarterly for Exercise and Sport 67: 448-451.
Pavlou, K.N., Steffee, W.P., Lerman, R.H., and Burrows, B.A. 1985. Effects of dieting and exercise on lean body
mass, oxygen uptake, and strength. Medicine & Science in Sports & Exercise 17: 466-471.
Payne, N., Gledhill, N., Kazmarzyk, P.T., Jamnik, V., and Keir, P.J. 2000. Canadian musculoskeletal fitness norms.
Canadian Journal of Applied Physiology 25: 430-442.
Peeters, M.W. 2012. Subject positioning in the BodPod only marginally affects measurement of body volume and
estimation of body fat in young adult men. PLoS One 7: E32722. doi:10.1371/journal.pone.0032722.
Peeters, M.W., and Claessens, A.L. 2011. Effect of different swim caps on the assessment of body volume and
percentage body fat by air displacement plethysmography. Journal of Sports Sciences 29: 191-196.
Pekmezi, D., Barbera, B., and Marcus, B.H. 2010. Using the transtheoretical model to promote physical activity.
ACSM’s Health & Fitness Journal 14: 8-13.
Perk, J., DeBacker, G., Gohlke, H., Graham, I., Reiner, Z., Verschuren, W.M.M., Albus, C., Benlian, P., Boysen,
G., Cifkova, R., Deaton, C., Ebrahim, S., Fisher, M., Germano, G., Hobbs, R., Hoes, A., Karadeniz, S., Messani,
A., Prescott, E., Ryden, L., Scherer, M., Syvänne, M., Scholte, W.J.M., Reimer, O., Vrints, C., Wood, D.,
Zamorano, J.L., and Zannad, F. 2012. European Guidelines on cardiovascular disease prevention in clinical
practice (version 2012). European Heart Journal 33: 1635-1701.
Perrier, E.T., Pavol, M.J., and Hoffman, M.A. 2011. The acute effects of a warm-up including static or dynamic
stretching on countermovement jump height, reaction time, and flexibility. Journal of Strength and Conditioning
Research 25: 1925-1931.
Persinger, R., Foster, C., Gibson, M., Fater, D.C.W., and Porcari, J.P. 2004. Consistency of the talk test for exercise
prescription. Medicine & Science in Sports & Exercise 36: 1632-1636.
Pescatello, L.S., Franklin, B.A., Fagard, R., Farquhar, W.B., Kelley, G.A, and Ray, C.A. 2004. American College of
Sports Medicine position stand. Exercise and hypertension. Medicine & Science in Sports & Exercise 36: 533-553.
Peters, D., Fox, K., Armstrong, N., Sharpe, P., and Bell, M. 1992. Assessment of children’s abdominal fat
distribution by magnetic resonance imaging and anthropometry. International Journal of Obesity 16(Suppl. 2): S35
[abstract].
Peters, M.J.H., van Nes, S.I., Vanhoutte, E.K., Bakkers, M., van Doorn, P.A., Merkies, I.S.J., and Faber, C.G. 2011.
820
Revised normative values for grip strength with the Jamar dynamometer. Journal of the Peripheral Nervous System
16: 47-50.
Peters, S.A.E., Huxley, R.R., and Woodward, M. 2014. Diabetes as risk factor for incident coronary heart disease in
women compared with men: A systematic review and meta-analysis of 64 cohorts including 858,507 individuals
and 28203 coronary events. Diabetologia 57: 1542-1551.
Peterson, M.D. 2010. Resistance exercise for sarcopenic outcomes and muscular fitness in aging adults. Strength and
Conditioning Journal 32(3): 52-61.
Peterson, M., Chandlee, M., and Abraham, A. 2008. Cost effectiveness analysis of a statewide media campaign to
promote adolescent physical activity. Health Promotion Practice 9: 126-133.
Peterson, M.D., and Gordon, P.M. 2011. Resistance exercise for the aging adult: Clinical implications and
prescription guidelines. American Journal of Medicine 124: 194-198.
Peterson, M.D., Rhea, M.R., and Alvar, B.A. 2004. Maximizing strength development in athletes: A meta-analysis to
determine the dose-response relationship. Journal of Strength and Conditioning Research 18: 377-382.
Peterson, M.D., Rhea, M.R., Sen, A., and Gordon, P.M. 2010. Resistance exercise for muscular strength in older
adults: A meta-analysis. Ageing Research Reviews 9: 226-237.
Peterson, M.D., Sen, A., and Gordon, P.M. 2011. Influence of resistance exercise on lean body mass in aging adults:
A meta-analysis. Medicine & Science in Sports & Exercise 43: 249-258.
Petrella, J.K., Kim, J.S., Mayhew, D.L., Cross, J.M., and Bamman, M.M. 2008. Potent myofiber hypertrophy during
resistance training in humans is associated with satellite cell-mediated myonuclear addition: A cluster analysis.
Petrella, R., Koval, J., Cunningham, D., and Paterson, D. 2001. A self-paced step test to predict aerobic fitness in
older adults in the primary care clinic. Journal of the American Geriatrics Society 49: 632-638.
Pickering, T.G., Hall, J.E., Appel, L.J., Falkner, B.E., Graves, J., Hill, M.N., Jones, D.W., Kurtz, T., Sheldon, G.,
and Rocella, E.J. 2005. Recommendations for blood pressure measurement in humans and experimental animals:
Part 1: Blood pressure measurement in humans: A statement for professionals from the subcommittee of
Professional and Public Education of the American Heart Council on High Blood Pressure Research. Hypertension
45(1): 142-161.
Pierce, P., and Herman, S. 2004. Obtaining, maintaining, and advancing your fitness certification. Journal of Physical
Education, Recreation and Dance 75(7): 50-53.
Pietrobelli, A., Formica, C., Wang, Z., and Heymsfield, S.B. 1996. Dual-energy X-ray absorptiometry body
composition model: Review of physical concepts. American Journal of Physiology 271: E941-E951.
Pimentel, G.D., Moreto, F., Takahashi, M.M., Portero-Mclellan, K.D., and Burini, R.C. 2011. Sagittal abdominal
diameter, but not waist circumference is strongly associated with glycemia, triacylglycerols and HDL-c levels in
overweight adults. Nutricion Hospitalaria 25: 1125-1129.
Pisani, P., Renna, M.D., Conversano, F., Casciaro, E., Di Paola, M., Quarta, E., Muratore, M., and Casiaro, S.
2016. Major osteoporotic fragility fractures: Risk factor updates and societal impact. World Journal of Orthopedics 7:
171-181.
Pi-Sunyer, F.X. 1999. Comorbidities of overweight and obesity: Current evidence and research issues. Medicine &
Science in Sports & Exercise 31: S602-S608.
Piucco, T., Diefenthaeler, F., Soares, R., Murias, J.M., and Millet, G.Y. 2017. Validation of a maximal incremental
skating test performed on a slide board: Comparison with treadmill skating. International Journal of Sports
821
Physiology and Performance [Epub ahead of print]. doi:10.1123/ijspp.2016-0613. Accessed July 4, 2017.
Plisky, P.J., Gorman, P.P., Butler, R.J., Kiesel, K.B., Underwood, F.B., and Elkins, B. 2009. The reliability of an
instrumented device for measuring components of the star excursion balance test. North American Journal of Sports
Physical Therapy 4: 92-99.
Plisky, P.J., Rauh, M.J., Kaminski, T.W., and Underwood, F.B. 2006. Star excursion balance test as a predictor of
lower extremity injury in high school basketball players. Journal of Orthopaedic and Sports Physical Therapy 36: 911-
919.
Podsiadlo, D., and Richardson, S. 1991. The timed “up & go”: A test of basic functional mobility of frail elderly
persons. Journal of the American Geriatrics Society 39: 142-148.
Pollock, M.L. 1973. The quantification of endurance training programs. In Exercise and Sport Sciences Reviews, ed.
J.H. Wilmore, 155-188. New York: Academic Press.
Pollock, M.L., Bohannon, R.L., Cooper, K.H., Ayres, J.J., Ward, A., White, S.R., and Linnerud, A.C. 1976. A
comparative analysis of four protocols for maximal treadmill stress testing. American Heart Journal 92: 39-46.
Pollock, M.L., Broida, J., and Kendrick, Z. 1972. Validity of the palpation technique of heart rate determination and
its estimation of training heart rate. Research Quarterly 43: 77-81.
Pollock, M.L., Cureton, T.K., and Greninger, L. 1969. Effects of frequency of training on working capacity,
cardiovascular function, and body composition of adult men. Medicine and Science in Sports 1: 70-74.
Pollock, M.L., Dimmick, J., Miller, H.S., Kendrick, Z., and Linnerud, A.C. 1975. Effects of mode of training on
cardiovascular function and body composition of middle-aged men. Medicine and Science in Sports 7: 139-145.
Pollock, M.L., Foster, C., Schmidt, D., Hellman, C., Linnerud, A.C., and Ward, A. 1982. Comparative analysis of
physiologic responses to three different maximal graded exercise test protocols in healthy women. American Heart
Journal 103: 363-373.
Pollock, M.L., Gaesser, G.A., Butcher, J.D., Despres, J.P., Dishman, R.K., Franklin, B.A., and Garber, C.E. 1998.
The recommended quantity and quality of exercise for developing and maintaining cardiorespiratory and muscular
fitness, and flexibility in healthy adults. Medicine & Science in Sports & Exercise 30: 975-991.
Pollock, M.L., Garzarella, L., and Graves, J. 1992. Effects of isolated lumbar extension resistance training on BMD
of the elderly. Medicine & Science in Sports & Exercise 24: S66 [abstract].
Pollock, M.L., Gettman, L., Milesis, C., Bah, M., Durstine, L., and Johnson, R. 1977. Effects of frequency and
duration of training on attrition and incidence of injury. Medicine and Science in Sports 9: 31-36.
Pollock, M.L., and Jackson, A.S. 1984. Research progress in validation of clinical methods of assessing body
Pollock, M.L., Miller, H.S., Janeway, R., Linnerud, A.C., Robertson, B., and Valentino, R. 1971. Effects of walking
on body composition and cardiovascular function of middle-aged men. Journal of Applied Physiology 30: 126-130.
Pollock, M.L., Miller, H.S., Linnerud, A.C., and Cooper, K.H. 1975. Frequency of training as a determinant for
improvement in cardiovascular function and body composition of middle-aged men. Archives of Physical Medicine
and Rehabilitation 56: 141-145.
Pollock, M.L., Wilmore, J.H., and Fox, S.M. III. 1978. Health and fitness through physical activity. New York: Wiley.
Pondal, M., and del Ser, T. 2008. Normative data and determinants for the timed “up and go” test in a population-
based sample of elderly individuals without gait disturbances. Journal of Geriatric Physical Therapy 31(2): 57-63.
Poole, D.C., and Jones, A.M. 2017. Measurement of the maximum oxygen uptake V̇O2max: V̇O2 peak is no longer
acceptable. Journal of Applied Physiology 122: 997-1002.
822
Pope, R.P., Herbert, R.D., Kirwan, J.D., and Graham, B.J. 2000. A randomized trial of preexercise stretching for
prevention of lower-limb injury. Medicine and Science in Sports and Exercise 32: 271-277.
Porcari, J., Foster, C., and Schneider, P. 2000. Exercise response to elliptical trainers. Fitness Management 16(9): 50-
53.
Porszasz, J., Casaburi, R., Somfay, A., Woodhouse, L.J., and Whipp, B.J. 2003. A treadmill ramp protocol using
simultaneous changes in speed and grade. Medicine & Science in Sports & Exercise 35: 1596-1603.
Porter, G.H. 1988. Case study evaluation for exercise prescription. In Resource manual for guidelines for exercise testing
and prescription, ed. S.N. Blair, P. Painter, R.R. Pate, L.K. Smith, and C.B. Taylor, 248-255. Philadelphia: Lea &
Febiger.
Porter, M.M. 2006. Power training for older adults. Applied Physiology, Nutrition and Metabolism 31: 87-94.
President’s Council on Physical Fitness and Sports. 1997. The presidential physical fitness award program. Washington,
D.C.: Author.
Prevalence of leisure-time physical activity among overweight adults—United States, 1998. 2000. Morbidity and
Mortality Weekly Report 49(15), April 21.
Price, K., Bird, S.R., Lythgo, N., Raj, I.S., Wong, J.Y.L., and Lynch, C. 2017. Validation of the Fitbit One, Garmin
Vivofit and Jawbone UP activity tracker in estimation of energy expenditure during treadmill walking and running.
Journal of Medical Engineering & Technology 41: 208-215.
Prior, B.M., Cureton, K.J., Modlesky, C.M., Evans, E.M., Sloniger, M.A., Saunders, M., and Lewis, R.D. 1997. In
vivo validation of whole body composition estimates from dual-energy X-ray absorptiometry. Journal of Applied
Prochaska, J.O., and DiClemente, C.C. 1982. Trans-theoretical therapy: Toward a more integrative model of change.
Psychotherapy: Theory, Research, and Practice 19: 276-288.
Proske, U., and Morgan, D.L. 2001. Muscle damage from eccentric exercise: Mechanism, mechanical signs,
adaptation, and clinical applications. Journal of Physiology 537: 333-345.
Province, M.A., Hadley, E.C., Hornbrook, M.C., Lipsitz, L.A., Miller, J.P., Mulrow, C.P., Ory, M.G., Sattin,
R.W., Tinetti, M.E., and Wolf, S.L. 1995. The effects of exercise on falls in elderly patients. A preplanned meta-
analysis of the FICSIT trials. Frailty and injuries: Cooperative studies of intervention techniques. Journal of the
American Medical Association 273: 1341-1347.
Psilander, N., Frank, P., Flockhart, M., and Sahlin, K. 2015. Adding strength to endurance training does not enhance
aerobic capacity in cyclists. Scandinavian Journal of Medicine and Science in Sports 25: e353-e359.
Quatrochi, J.A., Hicks, V.L., Heyward, V.H., Colville, B.C., Cook, K.L., Jenkins, K.A., and Wilson, W. 1992.
Relationship of optical density and skinfold measurements: Effects of age and level of body fatness. Research
Quinn, T.J., and Coons, B.A. 2011. Talk test and its relationship with the ventilatory and lactate thresholds. Journal
Radaelli, R., Fleck, S.J., Leite, T., Leite, R.D., Pinto, R.S., Fernandes, L., and Simao, R. 2015. Dose-response of 1,
3, and 5 sets of resistance exercise on strength, local muscular endurance, and hypertrophy. Journal of Strength and
Raffaelli, C., Galvani, C., Lanza, M., and Zamparo, P. 2012. Different methods for monitoring intensity during
water-based aerobic exercise. European Journal of Applied Physiology 112: 125-134.
Ralston, G.W., Kilgore, L., Wyatt, F.B., and Baker, J.S. 2017. The effect of weekly set volume on strength gain: A
823
meta-analysis. Sports Medicine [in press].
Rankinen, T., Rice, T., Teran-Garcia, M., Rao, D.C., and Bouchard, C. 2010. FTO genotype is associated with
exercise training-induced changes in body composition. Obesity 18: 322-326.
Rapsomaniki, E., Timmis, A., George, J., Pujades-Rodriguez, M., Shah, A.D., Denaxas, S., White, I.R., Caulfield,
M.J. Deanfield, J.E., Smeeth, L., Williams, B., Hingorani, A., and Hemingway, H. 2014. Blood pressure and
incidence of twelve cardiovascular diseases: Lifetime risks, healthy life-years lost, and age-specific associations in
1.25 million people. Lancet 383: 1899-1911.
Ratamess, N.A., Alvar, B.A., Evetoch, T.K., Housh, T.J., Kibler, W.B., Kraemer, W.J., and Triplett, N.T. 2009.
ACSM position stand: Progression models in resistance training for healthy adults. Medicine & Science in Sports &
Exercise 41: 687-708.
Rauch, F., Sievanen, H., Boonen, S., Cardinale, M., Dengens, H., Felsenberg, D., Roth, J., Schoenau, E.,
Verschueren, S., and Rittweger, J. 2010. Reporting whole-body vibration intervention studies: Recommendations
of the International Society of Musculoskeletal and Neuronal Interactions. Journal of Musculoskeletal and Neuronal
Interactions 10: 193-198.
Raue, U., Trappe, T.A., Estrem, S.T., Qian, H.R., Helvering, L.M., Smith, R.C., and Trappe, S. 2012.
Transcriptome signature of resistance training adaptations: Mixed muscle and fiber type specific profiles in young
and old adults. Journal of Applied Physiology 112: 1625-1636.
Rawson, E.S., and Clarkson, P.M. 2003. Scientifically debatable: Is creatine worth its weight? Gatorade Sport Science
Exchange 91 16(4): 1-13.
Rebuffe-Scrive, M. 1985. Adipose tissue metabolism and fat distribution. In Human body composition and fat
distribution, ed. N.G. Norgan, 212-217. Wageningen, Netherlands: Euronut.
Recalde, P.T., Foster, C., Skemp-Arlt, K.M., Fater, D.C.W., Neese, C.A., Dodge, C., and Porcari, J.P. 2002. The
talk test as a simple marker of ventilatory threshold. South African Journal of Sports Medicine 8: 5-8.
Reed, J.L., and Pipe, A.L. 2014. The talk test: A useful tool for prescribing and monitoring exercise intensity. Current
Opinion in Cardiology 29: 475-480.
Reed, J.L., and Pipe, A.L. 2016. Practical approaches to prescribing physical activity and monitoring exercise
intensity. Canadian Journal of Cardiology. 32: 514-522.
Reed-Jones, R.J., Dorgo, S., Hitchings, M.K., and Bader, J.O. 2012. Wii Fit plus balance test scores for the
assessment of balance and mobility in older adults. Gait & Posture 36: 430-433.
Reese, N.B., and Bandy, W.D. 2003. Use of an inclinometer to measure flexibility of the iliotibial band using the
Ober test and the modified Ober test: Differences in magnitude and reliability of measurements. Journal of
Orthopaedic and Sports Physical Therapy 33: 326-330.
Regnier, S.M., and Sargis, R.M. 2014. Adipocytes under assault: Environmental disruption of adipose physiology.
Biochemica et Biophysica Acta 1842: 520-533.
Reid, K.F., and Fielding, R.A. 2012. Skeletal muscle power: A critical determinant of physical functioning in older
adults. Exercise and Sport Sciences Reviews 40: 4-12.
Reiman, M.P., and Manske, R.C. 2009. Functional testing in human performance. Champaign, IL: Human Kinetics.
Reiman, M.P., Krier, A.D., Nelson, J.A., Rogers, M.A., Stuke, Z.O., and Smith, B.S. 2010. Reliability of alternative
trunk endurance testing procedures using clinician stabilization vs. traditional methods. Journal of Strength and
Reinhardt, M., Piaggi, P., DeMers, B., Trinidad, C., and Krakoff, J. 2017. Cross calibration of two dual-energy X-ray
824
densitometers and comparison of visceral adipose tissue measurements by iDXA and MRI. Obesity 25: 332-337.
Rhea, M.R., Alvar, B.A., Burkett, L.N., and Ball, S.D. 2003a. A meta-analysis to determine the dose response for
strength development. Medicine & Science in Sports & Exercise 35: 456-464.
Rhea, M.R., Ball, S.D., Phillips, W.T., and Burkett, L.N. 2002. A comparison of linear and daily undulating
periodized programs with equated volume and intensity for strength. Journal of Strength and Conditioning Research
16: 250-255.
Rhea, M.R., Phillips, W.T., Burkett, L.N., Stone, W.J., Ball, S.D., Alvar, B.A., and Thomas, A.B. 2003b. A
comparison of linear and daily undulating periodized programs with equated volume and intensity for local
muscular endurance. Journal of Strength and Conditioning Research 17: 82-87.
Ribeiro, A.S., Campos-Filho, M.G.A., Avelar, A., dos Santos, L., Achour Júnior, A., Aguiar, A.F., Fleck, S.J.,
Serassuelo Júnior, H., and Cyrino, E.S. 2017. Effect of resistance training on flexibility in young adult men and
women. Isokinetics and Exercise Science 25: 149-155.
Ribeiro, A.S., Schoenfeld, B.J., Fleck, S.J., Pina, F.L.C., Nascimento, M.A., and Cyrino, E.S. 2017. Effects of
traditional and pyramidal resistance training systems on muscular strength, muscle mass, and hormonal responses
in older women: A randomized crossover trial. Journal of Strength and Conditioning Research 31: 1888-1896.
Ribeiro, A.S., Schoenfeld, B.J., Pina, F.L.C., Souza, M.F., Nascimento, M.A., dos Santos, L., Antunes, M., and
Cyrino, E.S. 2015. Resistance training in older women: Comparison of single vs. multiple sets on muscle strength
and body composition. Isokinetics and Exercise Science 23: 53-60.
Ribeiro, A.S., Schoenfeld, B.J., Silva, D.R., Pina, F.L., Guariglia, D.A., Porto, M., Maestá, N., Burini, R.C., and
Cyrino, E.S. 2015. Effect of two- versus three-way split resistance training routines on body composition and
muscular strength in bodybuilders: A pilot study. International Journal of Sport Nutrition and Exercise Metabolism
25(6): 559-565.
Ribeiro, A.S., Schoenfeld, B.J., Souza, M.F., Tomeleri, C.M., Venturini, D., Barbosa, D.S., and Cyrino, E.S. 2016.
Traditional and pyramidal resistance training systems improve muscle quality and metabolic biomarkers in older
women: A randomized crossover study. Experimental Gerontology 79: 8-15.
Richards, J.B., Valdes, A.M., Gardner, J.P., Kato, B.S., Silva, A., Kimura, M., Lu, X., Brown, M.J., Aviv, A., and
Spector, T.D. 2008. Homocysteine levels and leukocyte telomere length. Atherosclerosis 200: 271-277.
Riddle, D.L., and Stratford, P.W. 1999. Interpreting validity indexes for diagnostic tests: An illustration using the
Berg balance test. Physical Therapy 79: 939-948.
Ridley, K., Ainsworth, B.E., and Olds, T.S. 2008. Development of a compendium of energy expenditures for youth.
International Journal of Behavioral Nutrition and Physical Activity 5: 45-52.
Riemann, B.L., Guskiewicz, K.M., and Shields, E.W. 1999. Relationship between clinical and forceplate measures of
postural stability. Journal of Sport Rehabilitation 8: 71-82.
Rikli, R., Petray, C., and Baumgartner, T. 1992. The reliability of distance run tests for children in grades K-4.
Research Quarterly for Exercise and Sport 63: 270-276.
Rikli, R.E., and Jones, C.J. 1999. Development and validation of a functional fitness test for community-residing
older adults. Journal of Aging and Physical Activity 7: 127-159.
Rikli, R.E, and Jones, C.J. 2013. Senior fitness test manual. Champaign, IL: Human Kinetics.
Riley, D.A., and Van Dyke, J.M. 2012. The effects of active and passive stretching on muscle length. Physical
Medicine and Rehabilitation Clinics of North America 23: 51-57.
Ringrose, J., Millay, J., Babwick, S.A., Neil, M., Langkaas, L.A., and Padwal, R. 2015. Effect of overcuffing on the
825
accuracy of oscillometric blood pressure measurements. Journal of the American Society of Hypertension 9: 563-568.
Ripka, W.L., Ulbricht, L., Menghin, L., and Gewehr, P.M. 2016. Portable A-mode ultrasound for body composition
assessment in adolescents. Journal of Ultrasound in Medicine 35: 755-760.
Risérus, U., de Faire, U., Berglund, L., and Hellénius, M-L. 2010. Sagittal abdominal diameter as a screening tool in
clinical research: Cutoffs for cardiometabolic risk. Journal of Obesity 2010: article 757939.
doi:10.1155/2010/757939. Accessed August 14, 2017.
Riva, D., Bianchi, R., Rocca, F., and Mamo, C. 2016. Proprioceptive training and injury prevention in a professional
men’s basketball team: A six-year prospective study. Journal of Strength and Conditioning Research 30: 461-475.
Rixon, K.P., Rehor, P.R., and Bemben, M.G. 2006. Analysis of the assessment of caloric expenditure in four modes
of aerobic dance. Journal of Strength and Conditioning Research 20: 593-596.
Rizzo, A., Lange, B., Suma, E.A., and Bolas, M. 2011. Virtual reality and interactive digital game technology: New
tools to address obesity and diabetes. Journal of Diabetes Science and Technology 5: 256-264.
Roberts, H.C., Denison, J.J., Martin, J.J., Patel, H.P., Syddall, H., Cooper, C., and Sayer, A.A. 2011. A review of the
measurement of grip strength in clinical and epidemiological studies: Towards a standardized approach. Age and
Ageing 40: 423-429.
Roberts, J.M., and Wilson, K. 1999. Effect of stretching duration on active and passive range of motion in the lower
extremity. British Journal of Sports Medicine 33: 259-263.
Robertson, R.J. 2004. Perceived exertion for practitioners: Rating effort with the OMNI picture system. Champaign, IL:
Human Kinetics.
Robertson, R.J., Goss, F.L., Andreacci, J.L., Dube, J.J., Rutkowski, J.J., Frazee, K.M., Aaron, D.J., Metz, K.F.,
Kowallis, R.A., and Snee, B.M. 2005. Validation of the children’s OMNI-resistance exercise scale of perceived
exertion. Medicine & Science in Sports & Exercise 37: 819-826.
Robinson, R.H., and Gribble, P.A. 2008. Support for a reduction in the number of trials needed for the star excursion
balance test. Archives of Physical Medicine and Rehabilitation 89: 364-370.
Roby, R.B. 1962. Effect of exercise on regional subcutaneous fat accumulations. Research Quarterly 33: 273-278.
Rochmis, P., and Blackburn, H. 1971. Exercise tests. A survey of procedures, safety and litigation experience in
approximately 170,000 tests. Journal of the American Medical Association 217: 1061-1066.
Rockport Walking Institute. 1986. Rockport fitness walking test. Marlboro, MA: Author.
Rodd, D., Ho, L., and Enzler, D. 1999. Validity of Tanita TBF-515 bioelectrical impedance scale for estimating body
fat in young adults. Medicine & Science in Sports & Exercise 31(Suppl.): S201 [abstract].
Rodgers, W.M., and Loitz, C.C. 2009. The role of motivation in behavior change: How do we encourage our clients
to be active? ACSM’s Health & Fitness Journal 13(1): 7-12.
Rodriguez, D.A., Brown, A.L., and Troped, P.J. 2005. Portable global positioning units to complement
accelerometry-based physical activity monitors. Medicine & Science in Sports & Exercise 37(Suppl.): S572-S581.
Rodriguez-Sanchez, N., and Galloway, D.R. 2015. Errors in dual energy X-ray absorptiometry estimation of body
composition induced by hypohydration. International Journal of sport Nutrition and Exercise Metabolism 25: 60-68.
Roelants, M., Delecluse, C., Goris, M., and Verschueren, S. 2004. Effects of 24 weeks of whole body vibration
training on body composition and muscle strength in untrained females. International Journal of Sports Medicine 25:
1-5.
Rogan, S., de Bruin, E.D., Radlinger, L., Joehr, C., Wyss, C., Stuck, N.J., Bruelhart, Y., de Bie, R.A., and Hilfiker,
R. 2015. Effects of whole-body vibration on proxies of muscle strength in old adults: A systematic review and
826
meta-analysis on the role of physical capacity level. European Review of Aging and Physical Activity 12: 12.
Roger, V.L., Go, A.S., Lloyd-Jones, D.M., Benjamin, E.J., Berry, J.D., Borden, W.B., Bravata, D.M., Dai, S., Ford,
E.S., Fox, C.S., Fullerton, H.J., Gillespie, C., Jailpern, S.M., Hert, J.A., Howard, V.J., Kissela, B.M., Kittner,
S.J., Lackland, D.T., Lichtman, J.H., Lisabeth, L.D., Makue, D.M., Marcus, G.M., Marielli, A., Matchar, D.B.,
Moy, C.S., Mozaffarian, D., Mussolino, M.E., Nichol, G., Paynter, N.P., Soliman, E.Z., Sorlie, P.D.,
Sotoodehnia, N.O., Turan, T.N., Virani, S.S., Wong, N.D., Woo, D., and Turner, M.B., on behalf of the
American Heart Association Statistics Committee and Stroke Statistics Subcommittee. 2012. Heart disease and
stroke statistics—2012 update: A report from the American Heart Association. Circulation.
doi:10.1161/CIR.0b013e31823ac046.
Rojas, R., Aguilar-Salinas, C.A., Jimenez-Corona, A., Shamah-Levy, T., Rauda, J., Avila-Burgos, L., Villalpando, S.,
and Ponce, E.L. 2010. Metabolic syndrome in Mexican adults: Results from the National Health and Nutrition
Survey 2006. Salud Publica de Mexico 52(Suppl. 1): S11-S18.
Rokholm, B., Baker, J.L., and Sorensen, T.I. 2010. The leveling off of the obesity epidemic since the year 1999: A
review of evidence and perspectives. Obesity Reviews 11: 835-846.
Romo-Perez, V., Schwingel, A., and Chodzko-Zajko, W. 2011. International resistance training recommendations
for older adults: Implications for the promotion of healthy aging in Spain. Journal of Human Sport & Exercise 6:
639-648.
Ronnestad, B.R., Holden, G., Samnoy, L.E., and Paulsen, G. 2012. Acute effect of whole-body vibration on power,
one-repetition maximum, and muscle activation in power lifters. Journal of Strength and Conditioning Research 26:
531-539.
Rose, D.J. 2010. Fall proof: A comprehensive balance and mobility training program, 2nd ed. Champaign, IL: Human
Kinetics.
Rosendale, R.P., and Bartok, C.J. 2012. Air displacement plethysmography for the measurement of body composition
in children aged 6-48 months. Pediatric Research 71: 299-304.
Ross, J., and Pate, R. 1987. The national children and youth fitness study II: A summary of findings. Journal of
Physical Education, Recreation and Dance 58: 51-56.
Ross, R., Blair, S.N., Arena, R., Church, T.S., Després, J-P., Franklin, B.A., Haskell, W.L., Kaminsky, L.A., Levine,
B.D., Lavie, C.J., Myers, J., Niebauer, J., Sallis, R., Sawada, S.S., Sui, X., and Wisloff, U. 2016. Importance of
assessing cardiorespiratory fitness in clinical practice: A case for fitness as a clinical vital sign. Circulation 134:
e653-e399. doi:10.1161/CIR.0000000000000461. Accessed April 25, 2017.
Ross, R., and Janssen, I. 2001. Physical activity, total and regional obesity: Dose-response considerations. Medicine &
Science in Sports & Exercise 33(Suppl.): S521-S527.
Ross, W.D., and Marfell-Jones, M.J. 1991. Kinanthropometry. In Physiological testing of the high-performance athlete,
ed. J.D. MacDougall, H.A. Wenger, and H.J. Green, 75-115, Champaign, IL: Human Kinetics.
Rossi, F.E., Fortaleza, A.C.S., Neves, L.M., Buonani, C., Picolo, M.R., Diniz, T.A. Kalva-Filha, C.A., Papoti, M.,
Lira, F.S., and Freitas, I.F. Jr. 2016. Combined training (aerobic plus strength) potentiates a reduction in body fat
but demonstrates no difference on the lipid profile in postmenopausal women when compared with aerobic
training with a similar training load. Journal of Strength and Conditioning Research 30: 226-234.
Row, B.S., and Cavanagh, P.R. 2007. Reaching upward is more challenging to dynamic balance than reaching
forward. Clinical Biomechanics 22: 155-164.
Rowland, M.L. 1990. Self-reported weight and height. American Journal of Clinical Nutrition 52: 1125-1133.
827
Rowlands, A.V., Marginson, V.F., and Lee, J. 2003. Chronic flexibility gains: Effect of isometric contraction duration
during proprioceptive neuromuscular facilitation stretching techniques. Research Quarterly for Exercise and Sport 74:
47-51.
Roy, J.L.P., Smith, J.F., Bishop, P.A., Hallinan, C., Wang, M., and Hunter, G.R. 2004. Prediction of maximal V̇O2
from a submaximal StairMaster test in young women. Journal of Strength and Conditioning Research 18: 92-96.
Roza, A.M., and Shizgal, H.M. 1984. The Harris Benedict equation reevaluated: Resting energy requirements and
the body cell mass. American Journal of Clinical Nutrition 40: 168-182.
Rubenstein, L.Z., and Josephson, K.R. 2002. The epidemiology of falls and syncope. Clinics in Geriatric Medicine 18:
141-158.
Rubini, E.C., Costa, A.L.L., and Gomes, P.S.C. 2007. The effects of stretching on strength performance. Sports
Medicine 37: 213-224.
Rücker, V., Keil, U., Fitzgerald, A.P., Malzahn, U., Prugger, C., Ertl, G., Heuschmann, P.U., and Neuhauser, H.
2016. Predicting 10-year risk of fatal cardiovascular disease in Germany: An update based on the SCORE-
Deutshland risk charts. PLoS One 11: e0162188. doi:10.1371/journal.pone.0162188. Accessed April 20, 2017.
Rush, E.C., Plank, L.D., Laulu, M.S., and Robinson, S.M. 1997. Prediction of percentage body fat from
anthropometric measurements: Comparison of New Zealand European and Polynesian young women. American
Ryan, D., and Heaner, M. 2014. Preface to the full report. Obesity 22(Suppl. 2): S1-S3.
Ryan, E.E., Rossi, M.D., and Lopez, R. 2010. The effects of the contract-relax-antagonist-contract form of
proprioceptive neuromuscular facilitation stretching on postural stability. Journal of Strength and Conditioning
Research 24: 1888-1894.
Sahrmann, S. 2002. Diagnosis and treatment of movement impairment syndromes. St. Louis: C.V. Mosby.
Saint-Maurice, P.F., Laurson, K.R., Kaj, M., and Csanyi, T. 2015. Establishing normative reference values for
standing broad jump among Hungarian youth. Research Quarterly for Exercise and Sport 86: S37-S44.
Sale, D. 1988. Neural adaptation to resistance training. Medicine & Science in Sports & Exercise 20: S135-S145.
Salem, J.G., Wang, M.Y., and Sigward, S. 2002. Measuring lower extremity strength in older adults: The stability of
isokinetic versus 1RM measures. Journal of Aging and Physical Activity 10: 489-503.
Sallis, J.F., and Owen, N. 1999. Physical activity and behavioral medicine. Thousand Oaks, CA: Sage.
Sallis, J.F., Bull, F., Guthold, R., Heath, G.W., Inoue, S., Kelly, P., Oyeyemi, A.L., Perez, L.G., Richards, J., and
Hallal, P.C. 2016. Progress in physical activity over the Olympic quadrennium. Lancet. doi:10.1016/S0140-
6736(16)30581-5. Accessed March 4, 2017.
Same, R.V., Feldman, D.I., Shah, N., Martin, S.S., Al Rafai, M., Blaha, M.J., Graham, G., and Ahmed, H.M. 2016.
Relationship between sedentary behavior and cardiovascular risk. Current Cardiology Reports 18.
doi:10.1007/s11886-015-0678-5. Accessed March 25, 2017.
Samukawa, M., Hattori, M., Sugama, N., and Takeda, N. 2011. The effects of dynamic stretching on plantar flexor
muscle-tendon tissue properties. Manual Therapy 16: 618-622.
Sanal, E., Ardic, F., and Kirac, S. 2013. Effects of aerobic or combined aerobic resistance exercise on body
composition in overweight and obese adults: Gender differences. A randomized intervention study. European
Journal of Physical and Rehabilitation Medicine 49: 1-11.
Santos, T.M., Gomes, P.S., Oliveira, B.R.R., Ribeiro, L.G., and Thompson, W.R. 2012. A new strategy for the
implementation of an aerobic training session. Journal of Strength and Conditioning Research 28: 87-93.
828
Sanz, C., Gautier, J.F., and Hanaire, H. 2010. Physical exercise for the prevention and treatment of type 2 diabetes.
Diabetes & Metabolism 36: 346-351.
Saris, W.H.M., Blair, S.N., van Baak, M.A., Eaton, S.B., Davies, P.S.W., Di Pietro, L., Fogelholm, M., Rissanen,
A., Schoeller, D., Swinburn, B., Tremblay, A., Westerterp, K.R., and Wyatt, H. 2003. How much physical
activity is enough to prevent unhealthy weight gain? Outcome of the IASO 1st Stock Conference and consensus
statement. Obesity Reviews 4: 101-114.
Sarki, A.M., Nduka, C.U., Stranges, S., Kandala, N-B., and Uthman, O.A. 2015. Prevalence of hypertension in low-
and middle-income countries: A meta-analysis. Medicine 95: e1959. doi:10.1097/MD.0000000000001959.
Sarzynski, M.A., Schuna, J.M. Jr., Carnethon, M.R., Jacobs, D.R. Jr., Lewis, C.E., Quesenberry, C.P. Jr., Sidney, S.,
Schreiner, P.J., and Sternfeld, B. 2015. Association of fitness with incident dyslipidemias over 15 years in the
Coronary Artery Risk Development in Young Adults study. American Journal of Preventatitive Medicine 49: 745-
752.
Sasaki, J.E., Hickey, A., Mavilla, M., Tedesco, J., John, D., Keadle, S.K., and Freedson, P.S. 2015. Validation of the
Fitbit wireless activity tracker for prediction of energy expenditure. Journal of Physical Activity and Health 12: 149-
154.
Sattelmair, J., Pertman, J., Ding, E.L., Kohl, H.W. III, Haskell, W., and Lee, I-M. 2011. Dose response between
physical activity and risk of coronary heart disease: A meta-analysis. Circulation 124: 789-793.
Sawano, M., Kohsaka, S., Okamura, T., Inohara, T., Sugiyama, D., Watanabe, M., Nakamura, Y., Higashiyama, A.,
Kadota, A., Okud, M., Murakami, Y., Ohkubo, T., Fuhiyoshi, A., Miura, K., Okayama, A., and Ueshima, H., for
the National Integrated Project for Prospective Observation of Non-Communicable Disease and its Trends in the
Aged (NIPPON DATA 80) research group. 2016. Atherosclerosis 252: 116-121.
Saydah, S., Bullard, K.M., Cheng, Y., Ali, M.K., Gregg, E.W., Geiss, L., and Imperatore, G. 2014. Trends in
cardiovascular disease risk factors by obesity level in adults in the United States, NHANES 1999-2010. Obesity 22:
1888-1895.
Sayers, S.P., Harackiewicz, D.V., Harman, E.A., Frykman, P.N., and Rosenstein, M.T. 1999. Cross-validation of
three jump power equations. Medicine & Science in Sports & Exercise 31: 572-577.
Schade, M., Hellebrandt, F.A., Waterland, J.C., and Carns, M.L. 1962. Spot reducing in overweight college women:
Its influence on fat distribution as determined by photography. Research Quarterly 33: 461-471.
Schenk, A.K., Witbrodt, B.C., Hoarty, C.A., Carlson, R.H. Jr., Goulding, E.H., Potter, J.F., and Bonasera, S.J.
2011. Cellular telephones measure activity and lifespace in community-dwelling adults: Proof of principle. Journal
of American Geriatric Society 59: 345-352.
Scherr, J., Wolfarth, B., Christle, J.W., Pressler, A., Wagenpfeil, S., and Halle, M. 2013. Associations between Borg’s
rating of perceived exertion and physiological measures of exercise intensity. European Journal of Applied Physiology
113: 147-155.
Schleicher, M.M., Wedam, L., and Wu, G. 2012. Review of tai chi as an effective exercise on falls prevention in
elderly. Research in Sports Medicine 20: 37-58.
Schmidt, C.P., Zwingenberger, S., Walther, A., Reuter, U., Kasten, P., Seifert, J., Gunther, K-P., and Stiehler, M.
2014. Prevalence of low back pain in adolescent athletes: An epidemiological investigation. International Journal of
Sports Medicine 35(8): 684-689.
Schmidt, P.K., and Carter, J.E.L. 1990. Static and dynamic differences among five types of skinfold calipers. Human
Biology 62: 369-388.
829
Schneider, P.J., Jacobs., D.R. Jr., Wong, N.D., and Kiefe, C.I. 2016. Twenty-five year secular trends in lipids and
modifiable risk factors in a population-based biracial cohort: The Coronary Artery Risk Development in Young
Adults (CARDIA) study, 1985-2011. Journal of the American Heart Association. doi:10.1161/JAHA.116.00338.
Accessed July 16, 2016.
Schnohr, P., O’Keefe, J.H., Marott, J.L., Lange, P., and Jensen, G.B. 2015. Dose of jogging and long-term mortality:
The Copenhagen City Heart Study. Journal of the American College of Cardiology 65: 411-419.
Schoenfeld, B.J. 2013. Postexercise hypertrophic adaptations: A reexamination of the hormone hypothesis and its
applicability to resistance training program design. Journal of Strength and Conditioning Research 27: 1720-1730.
Schoenfeld, B.J., Ogborn, D.I., Vigotsky, A.D., Franchi, M.V., and Krieger, J.W. 2017. Hypertrophic effects of
concentric vs. eccentric muscle actions: A systematic review and meta-analysis. Journal of Strength and Conditioning
Research 31: 2599-2608.
Schot, P.K., Knutzen, K.M., Poole, S.M., and Mrotek, L.A. 2003. Sit-to-stand performance of older adults following
strength training. Research Quarterly for Exercise and Sport 74: 1-8.
Schrieks, I.C., Barnes, M.J., and Hodges, L.D. 2011. Comparison study of treadmill versus arm ergometry. Clinical
Physiology and Functional Imaging. 31: 326-331.
Schroeder, M.M., Foster, C., Porcari, J.P., and Mikat, R.P. 2017. Effects of speech passage length on accuracy of
predicting metabolic thresholds using the talk test. Kinesiology 49: 9-14.
Schutz, Y., and Herren, R. 2000. Assessment of speed of human locomotion using a differential satellite global
positioning system. Medicine & Science in Sports & Exercise 32: 612-616.
Schwane, J.A., Johnson, S.R., Vandenakker, C.B., and Armstrong, R.B. 1983. Delayed-onset muscular soreness and
plasma CPK and LDH activities after downhill running. Medicine & Science in Sports & Exercise 15: 51-56.
Scott, S. 2008. ABLE bodies balance training. Champaign, IL: Human Kinetics.
Sedentary Behaviour Research Network. 2012. Standardized use of the terms “sedentary” and “sedentary behaviours.”
Segal, K.R., Van Loan, M., Fitzgerald, P.I., Hodgdon, J.A., and Van Itallie, T.B. 1988. Lean body mass estimation
by bioelectrical impedance analysis: A four-site cross-validation study. American Journal of Clinical Nutrition 47: 7-
14.
Seidell, J.C., and Halberstadt, J. 2015. The global burden of obesity and the challenges of prevention. Annals of
Nutrition & Metabolism 66(Suppl. 2): 7-12.
Seip, R., and Weltman, A. 1991. Validity of skinfold and girth based regression equations for the prediction of body
composition in obese adults. American Journal of Human Biology 3: 91-95.
Sekendiz, B., Altun, O., Korkusuz, F., and Akin, S. 2007. Effects of Pilates exercise on trunk strength, endurance and
flexibility in sedentary adult females. Journal of Bodywork and Movement Therapies 11: 318-326.
Selassie, M., and Sinha, A.C. 2011. The epidemiology and aetiology of obesity: A global challenge. Best Practice &
Research Clinical Anaesthesiology 25: 1-9.
Sell, K.E., Verity, T.M., Worrell, T.W., Pease, B.J., and Wigglesworth, J. 1994. Two measurement techniques for
assessing subtalar joint position: A reliability study. Journal of Orthopaedic and Sports Physical Therapy 19: 162-167.
Sell, K., Lillie, T., and Taylor, J. 2008. Energy expenditure during physically interactive video game playing in male
college students with different playing experience. Journal of American College Health 56: 505-511.
Sendra-Lillo, J., Sabater-Hernandez, D., Sendra-Ortola, A., and Martinez-Martinez, F. 2011. Comparison of the
white-coat effect in community pharmacy versus the physician’s office: The Palmera study. Blood Pressure
830
Seneli, R.M., Ebersole, K.T., O’Connor, K.M., and Snyder, A.C. 2013. Estimated V̇O2max from the Rockport Walk
Test on a nonmotorized curved treadmill. Journal of Strength and Conditioning Research 27: 3495-3505.
Seynnes, O.R., de Boer, M., and Narici, M.V. 2007. Early skeletal muscle hypertrophy and architectural changes in
response to high-intensity resistance training. Journal of Applied Physiology 102: 368-373.
Shahbabu, B., Dasgupta, A., Sarkar, K., and Sahoo, S.K. 2016. Which is more accurate in measuring the blood
pressure? A digital or an aneroid sphygmomanometer. Journal of Clinical and Diagnostic Research 10: LC11-LC14.
Sharkey, B.J., and Gaskill, S.E. 2007. Fitness and health, 6th ed. Champaign, IL: Human Kinetics.
Sharman, M.J., Cresswell, A.G., and Riek, S. 2006. Proprioceptive neuromuscular facilitation stretching: Mechanisms
and clinical applications. Sports Medicine 36: 929-939.
Sharman, J.E., and LaGerche, A. 2015. Exercise blood pressure: Clinical relevance and correct measurement. Journal
of Human Hypertension 29: 351-358.
Sharman, J.E., La Gerche, A., and Coombes, J.S. 2015. Exercise and cardiovascular risk in patients with
hypertension. American Journal of Hypertension 28: 147-158.
Shaw, B. 2016. Beth Shaw’s YogaFit, 3rd ed. Champaign, IL: Human Kinetics.
Shaw, C.E., McCully, K.K., and Posner, J.D. 1995. Injuries during the one repetition maximum assessment in the
elderly. Journal of Cardiopulmonary Rehabilitation 15: 283-287.
Shaw, K., Gennat, H., O’Rourke, P., and Del Mar, C. 2006. Exercise for overweight or obesity. Cochrane Database of
Systematic Reviews 4: CD003817. doi:10.1002/14651858.CD003817.pub3.
Shaw, M.P., Robinson, J., and Peart, D.J. 2017. Comparison of a mobile application to estimate percentage body fat
to other non-laboratory based measurements. Biomedical Human Kinetics 9: 94-98.
Sheard, P.W., and Paine, T.J. 2010. Optimal contraction intensity during proprioceptive neuromuscular facilitation
for maximal increase of range of motion. Journal of Strength and Conditioning Research 24: 416-421.
Shephard, R.J. 1972. Alive man: The physiology of physical activity. Springfield, IL: Charles C Thomas.
Shirato, M., Tsuchiya, Y., Sato, T., Hamano, S., Gushiken, T., Kimura, N., and Ochi, E. 2016. Effects of combined
β-hydroxy-β-methylbutyrate (HMB) and whey protein ingestion on symptoms of eccentric exercise-induced
muscle damage. Journal of the International Society of Sports Nutrition 13: article 7.
Shitara, H., Yamamoto, A., Shimoyama, D., Ichinose, T., Sasaki, T., Hamano, N., Ueno, A., Endo, F., Oshima, A.,
Sakane, H., Tachibana, M., Tomomatsu, Y., Tajika, T., Kobayashi, T., Osawa, T., Iizuka, H., and Takagishi, K.
2017. Shoulder stretching intervention reduces the incidence of shoulder and elbow injuries in high school baseball
players: A time-to-event analysis. Scientific Reports 7: 45304.
Shoenhair, C.L., and Wells, C.L. 1995. Women, physical activity, and coronary heart disease: A review. Medicine,
Exercise, Nutrition and Health 4: 200-206.
Shrier, I., and Gossal, K. 2000. Myths and truths of stretching: Individualized recommendations for healthy muscles.
The Physician and Sportsmedicine 28: 57-63.
Shubert, T.E. 2011. Evidence-based exercise prescription for balance and falls prevention: A current review of the
literature. Journal of Geriatric Physical Therapy 34: 100-108.
Shubert, T.E., Schrodt, L.A., Mercer, V.S., Busby-Whitehead, J., and Giuliani, C.A. 2006. Are scores on balance
screening tests associated with mobility in older adults? Journal of Geriatric Physical Therapy 29(1): 33-39.
Shuger, S.L., Barry, V.W., Sui, X., McClain, A., Hand, G.A., Wilcox, S., Meriwether, R.A., Hardin, J.W., and
831
Blair, S.N. 2011. Electronic feedback in a diet- and physical activity-based lifestyle intervention for weight loss: A
randomized controlled trial. International Journal of Behavioral Nutrition and Physical Activity 8: 41.
Shumway-Cook, A., Baldwin, M., Polissar, N.L., and Gruber, W. 1997. Predicting the probability for falls in
community-dwelling older adults. Physical Therapy 77: 812-819.
Shumway-Cook, A., Brauer, S., and Wollacott, M.H. 2000. Predicting the probability of falls in community-dwelling
older adults using the timed up and go test. Physical Therapy 80: 896-904.
Shumway-Cook, A., and Woollacott, M.H. 1995. Motor control: Theory and practical applications. Baltimore: Williams
& Wilkins.
Siegel, R.L., Miller, K.D., and Jemal, A. 2016. Cancer statistics, 2016. CA: Cancer Journal for Clinicians 66: 7-30.
Simao, R., Spineti, J., Fretas de Salles, B., Matta, T., Ferandes, L., Fleck, S.J., Rhea, M.R., and Strom-Olsen, H.E.
2012. Comparison between nonlinear and linear periodized resistance training: Hypertrophic and strength effects.
Journal of Strength and Conditioning Research 26: 1389-1395.
Simpson, W.F. 2015. Progress for ACSM Certifications. ACSM’s Health & Fitness Journal 19(2): 30-31.
Siri, W.E. 1961. Body composition from fluid space and density. In Techniques for measuring body composition, ed. J.
Brozek and A. Henschel, 223-224. Washington, D.C.: National Academy of Sciences.
Sivén, S.S.E., Niiranen, T.J., Kantola, I.M., and Jula, A.M. 2016. White-coat and masked hypertension as risk factors
for progression to sustained hypertension: The Finn-Home study. Journal of Hypertension 34: 54-60.
Sjodin, A.M., Forslund, A.H., Westerterp, K.R., Andersson, A.B., Forslund, J.M., and Hambraeus, L.M. 1996. The
influence of physical activity on BMR. Medicine & Science in Sports & Exercise 28: 85-91.
Sjostrom, M., Lexell, J., Eriksson, A., and Taylor, C.C. 1992. Evidence of fiber hyperplasia in human skeletal muscles
from healthy young men? European Journal of Applied Physiology 62: 301-304.
Skalski, J., Allison, T.G., and Miller, T.D. 2012. The safety of cardiopulmonary exercise testing in a population with
high-risk cardiovascular diseases. Circulation 126: 2465-2472.
Skatrud-Mickelson, M., Benson, J., Hannon, J.C., and Askew, W.E. 2011. A comparison of subjective and objective
physical exertion. Journal of Sports Sciences 29: 1635-1644.
Skinner, J. 1993. Exercise testing and exercise prescription for special cases. Philadelphia: Lea & Febiger.
Slaughter, M.H., Lohman, T.G., Boileau, R.A., Horswill, C.A., Stillman, R.J., Van Loan, M.D., and Bemben, D.A.
1988. Skinfold equations for estimation of body fatness in children and youth. Human Biology 60: 709-723.
Smith, A.E., Evans, H., Parfitt, G., Eston, R., and Ferrar, K. 2016. Submaximal exercise-based equations to predict
maximal oxygen uptake in older adults: A systematic review. Archives of Physical Medicine and Rehabilitation 97:
1003-1012.
Smith, L.L. 1991. Acute inflammation: The underlying mechanism in delayed onset muscle soreness? Medicine &
Smith, U., Hammerstein, J., Bjorntorp, P., and Kral, J.G. 1979. Regional differences and effect of weight reduction
on human fat cell metabolism. European Journal of Clinical Investigation 9: 327-332.
Smith, K.B., and Smith, M.S. 2016. Obesity statistics. Primary Care: Clinics in Office Practice 43: 121-135.
Smith-Ryan, A.E., Blue, M.N.M., Trexler, E.T., and Hirsch, K.R. 2016. Utility of ultrasound for body fat
assessment: Validity and reliability compared to a multicompartment criterion. Clinical Physiology and Functional
Imaging [Epub ahead of print]. doi:10.1111/cpf.12402. Accessed August 5, 2017.
Smith-Ryan, A.E., Fultz, S.N., Melvin, M.N., Wingfield, H.L., and Woessner, M.N. 2014. Reproducibility and
832
validity of A-Mode ultrasound for body composition measurement and classification in overweight and obese men
and women. PLoS One 9(3): e91750. doi:10.1371/journal.pone.0091750. Accessed August 5, 2017.
Smith-Ryan, A.E., Mock, M.G., Ryan, E.D., Gerstner, G.R., Trexler, E.R., and Hirsch, K.R. 2017. Validity and
reliability of a 4-compartment body composition model using dual energy X-ray absorptiometry-derived body
volume. Clinical Nutrition 36: 825-830.
Smutok, M.A., Skrinar, G.S., and Pandolf, K.B. 1980. Exercise intensity: Subjective regulation by perceived exertion.
Smye, S.W., Sutcliffe, J., and Pitt, E. 1993. A comparison of four commercial systems used to measure whole-body
electrical impedance. Physiological Measurement 14: 473-478.
Snarr, R.L., Hallmark, A.V., Nickerson, B.S., and Esco, M.R. 2016. Electromyographical comparison of pike
variations performed with and without instability devices. Journal of Strength and Conditioning Research 30: 3436-
3442.
Snijder, M.B., Kuyf, B.E., and Deurenberg, P. 1999. Effect of body build on the validity of predicted body fat from
body mass index and bioelectrical impedance. Annals of Nutrition and Metabolism 43: 277-285.
Soileau, L., Bautista, D., Johnson, C., Gao, C., Zhang, K., Li, X., Heymsfield, S.B., Thomas, D., and Zheng, J.
2016. Automated anthropometric phenotyping with novel Kinect-based three-dimensional imaging method:
Comparison with a reference laser imaging system. European Journal of Clinical Nutrition 70: 475-481.
Spalding, K.L., Arner, E., Westermark, P.O., Bernard, S., Buchholz, B.A., Bergmann, O., Blomqvist, L., Hoffstedt,
J., Näslund, E., Britton, T., Concha, H., Hassan, M., Rydén, M., Frisén, J., and Arner, P. 2008. Dynamics of fat
cell turnover in humans. Nature 453(7196): 783-787.
Spennewyn, K.C. 2008. Strength outcomes in fixed versus free-form resistance equipment. Journal of Strength and
Conditioning Research 22(1): 75-81.
Sperandei, S., Vieira, M.C., and Reis, A. 2016. Adherence to physical activity in an unsupervised setting: Explanatory
variables for high attrition rates among fitness center members. Journal of Science and Medicine in Sport 19: 916-
920.
Sperlich, P.F., Holmberg, H-C., Reed, J.L., Zinner, C., Mester, J., and Sperlich, B. 2015. Individual versus
standardized running protocols in the determination of V̇O2max. Journal of Sports Science and Medicine 14: 386-
393.
Spierer, D.K., Rosen, Z., Litman, L.L., and Fujii, K. 2015. Validation of photoplethysmography as a method to
detect heart rate during rest and exercise. Journal of Medical Engineering & Technology 39: 264-271.
Sprey, J.W.C., Ferreira, T., de Lima, M.V., Duarte, A., Jorge, P.B., and Santili, C. 2016. An epidemiological profile
of CrossFit Athletes in Brazil. Orthopaedic Journal of Sports Medicine 4: 29 August.
Springer, B.A., Marin, R., Cyhan, T., Roberts, H., and Gill, N.W. 2007. Normative values for the unipedal stance
test with eyes open and closed. Journal of Geriatric Physical Therapy 30: 8-15.
Staiano, A.E., and Flynn, R. 2014. Therapeutic uses of active videogames: A systematic review. Games for Health
Journal 6: 351-365.
Stark, M., Lukaszuk, J., Prawitz, A., and Salacinski, A. 2012. Protein timing and its effects on muscular hypertrophy
and strength in individuals engaged in weight-training. Journal of the International Society of Sports Nutrition 9:
article 54.
Stark, T., Walker, B., Phillips, J.K., Fejer, R., and Beck, R. 2011. Hand-held dynamometry correlation with the gold
standard isokinetic dynamometry: A systematic review. PM&R: The Journal of Injury, Function, and Rehabilitation
833
3: 472-479.
Stathokostas, L., Little, R.M.D., Vandervoort, A.A., and Paterson, D.H. 2012. Flexibility training and functional
ability in older adults: A systematic review. Journal of Aging Research 2012: article 306818.
Statistics Canada. 2017. Canadian Health Measures Survey: Activity monitor data. http://www.statcan.gc.ca/daily-
quotidien/170419/dq170419e-eng.htm. Accessed March 31, 2018.
Steele, J., Fisher, J., Skivington, M., Dunn, C., Arnold, J., Tew, G., Batterham, A.M., Nunan, D., O’Driscoll, J.M.,
Mann, S., Beedie, C., Jobson, S., Smith, D., Vigotsky, A., Phillips, S., Estabrooks, P., and Winett, R. 2017. A
higher effort-based paradigm in physical activity and exercise for public health: Making a case for a greater
emphasis on resistance training. BMC Public Health 17(1): 300.
Steffens, D., Maher, C.G., Pereira, L.S., Stevens, M.L., Oliveira, V.C., Chapple, M., Teixeira-Salmela, L.F., and
Hancock, M.J. 2016. Prevention of low back pain: A systematic review and meta-analysis. JAMA Internal Medicine
176(2): 199-208.
Steinberg, S.I., Sammel, M.D., Harrel, B.T., Schembri, A., Policastro, C., Bogner, H.R., Negash, S., and Arnold,
S.E. 2015. Exercise, sedentary pastimes, and cognitive performance in healthy older adults. American Journal of
Alzheimer’s Disease & Other Dementias 30: 290-298.
Steinberger, J., Daniels, S.R., Eckel, R.H., Hayman, L., Lustag, R.H., McCrindle, B., and Mietus-Snyder, M. 2009.
Progress and challenges in metabolic syndrome in children and adolescents: A scientific statement from the
American Heart Association Atherosclerosis, Hypertension and Obesity in the Young Committee of the Council
on Cardiovascular Disease in the Young; Council on Cardiovascular Nursing; and Council on Nutrition, Physical
Activity, and Metabolism. Circulation 119: 628-647.
Stergiou, G.S., Karpettas, N., Atkins, N., and O’Brien, E. 2011. Impact of applying the more stringent validation
criteria of the revised European Society of Hypertension International Protocol 2010 on earlier validation studies.
Blood Pressure Monitoring 16: 67-73.
Stergiou, G.S., Karpettas, N., Kollias, A., Destounis, A., and Tzamouranis, D. 2012a. A perfect replacement for the
mercury sphygmomanometer: The case of the hybrid blood pressure monitor. Journal of Human Hypertension 26:
220-227.
Stergiou, G.S., Parati, G., Asmar, R., and O’Brien, E. 2012b. Requirements for professional office blood pressure
monitors. Journal of Hypertension 30: 537-542.
Stiffler, M.R., Bell, D.R., Sanfilippo, J.L., Hetzel, S.J., Pickett, K.A., and Heiderscheit, B.C. 2017. Star excursion
balance test anterior asymmetry is associated with injury status in division I collegiate athletes. Journal of
Orthopaedic and Sports Physical Therapy 47: 339-346.
Stojanovic, M.D., and Ostojic, S.M. 2011. Stretching and injury prevention in football: Current perspectives. Research
in Sports Medicine 19: 73-91.
Stolarczyk, L.M., Heyward, V.H., Hicks, V.L., and Baumgartner, R.N. 1994. Predictive accuracy of bioelectrical
impedance in estimating body composition of Native American women. American Journal of Clinical Nutrition 59:
964-970.
Störchle, P., Müller, W., Sengeis, M., Ahammer, H., Fürhapter-Rieger, A., Bachl, N., Lackner, S., Mörkl, S., and
Holasek, S. 2017. Standardized ultrasound measurement of subcutaneous fat patterning: High reliability and
accuracy in groups ranging from lean to obese. Ultrasound in Medicine and Biology 43: 427-438.
Stracciolini, A., Myer, G.D., and Faigenbaum, A.D. 2013. Exercise-deficit disorder in children: Are we ready to
make this diagnosis? The Physician and Sports Medicine 41. doi:10.3810/psm.2013.02.2003.
834
Studenski, S., Perera, S., Hile, E., Keller, V., Spadola-Bogard, J., and Garcia, J. 2010. Interactive video dance games
for healthy older adults. Journal of Nutrition, Health, and Aging 14: 850-852.
Straight, C.R., Lindheimer, J.B., Brady, A.O., Dishman, R.K., and Evans, E.M. 2016. Effects of resistance training
on lower-extremity muscle power in middle-aged and older adults: A systematic review and meta-analysis of
randomized controlled trials. Sports Medicine 46: 353-364.
Strand, S.L., Hjelm, J., Shoepe, T.C., and Fajardo, M.A. 2014. Norms for an isometric muscle endurance test.
Journal of Human Kinetics 40: 93-1026.
Stuber, K.J., Bruno, P., Sajko, S., and Hayden, J.A. 2014. Core stability exercises for low back pain in athletes: A
systematic review of the literature. Clinical Journal of Sports Medicine 24: 448-456.
Sturm, R., and Hattori, A. 2012. Morbid obesity rates continue to rise rapidly in the United States. International
Journal of Obesity. doi:10.1038/ijo.2012.159.
Stutchfield, B.M., and Coleman, S. 2006. The relationships between hamstring flexibility, lumbar flexion, and low
back pain in rowers. European Journal of Sport Science 6: 255-260.
Sung, R.Y.T., Lau, P., Yu, C.W., Lam, P.K.W., and Nelson, E.A.S. 2001. Measurement of body fat using leg to leg
bioimpedance. Archives of Disease in Childhood 85: 263-267.
Svendsen, O.L., Hassager, C., Bergmann, I., and Christiansen, C. 1992. Measurement of abdominal and intra-
abdominal fat in postmenopausal women by dual energy X-ray absorptiometry and anthropometry: Comparison
with computerized tomography. International Journal of Obesity 17: 45- 51.
Swain, D.P. 1999. V̇O2 reserve: A new method for exercise prescription. ACSM’s Health & Fitness Journal 3(5): 10-14.
Swain, D.P., and Leutholtz, B.C. 1997. Heart rate reserve is equivalent to % V̇O2reserve, not to V̇O2max. Medicine &
Swain, D.P., Leutholtz, B.C., King, M.E., Haas, L.A., and Branch, J.D. 1998. Relationship between % heart rate
reserve and % V̇O2reserve in treadmill exercise. Medicine & Science in Sports & Exercise 30: 318-321.
Swain, D.P., Parrott, J.A., Bennett, A.R., Branch, J.D., and Dowling, E.A. 2004. Validation of a new method for
estimating V̇O2max based on V̇O2 reserve. Medicine & Science in Sports & Exercise 36: 1421-1426.
Swift, D.L., Johannsen, N.M., Lavie, C.J., Earnest, C.P., and Church, T.S. 2014. The role of exercise and physical
activity in weight loss and maintenance. Progress in Cardiovascular Diseases 56: 441-447.
Swinburn, B.A., Sacks, G., Hall, K.D., McPherson, L., Finegood, D.T., Moodie, M.L., and Gortmaker, S.L. 2011.
The global obesity pandemic: Shaped by global drivers and local environments. Lancet 378: 804-814.
Taaffe, D.R., Duret, C., Wheeler, S., and Marcus, R. 1999. Once-weekly resistance exercise improves muscle
strength and neuromuscular performance in older adults. Journal of the American Geriatrics Society 47: 1208-1214.
Takaishi, T., Ishihara, K., Shima, N., and Hayashi, T. 2014. Health promotion with stair exercise. Journal of Physical
Fitness and Sports Medicine 3: 173-179.
Takeshima, N., Rogers, M.E., Watanabe, E., Brechue, W.F., Okada, A., Yamada, T., Islam, M.M., and Hayano, J.
2002. Water-based exercise improves health-related aspects of fitness in older women. Medicine & Science in Sports
& Exercise 34: 544-551.
Talag, T.S. 1973. Residual muscular soreness as influenced by concentric, eccentric, and static contractions. Research
Quarterly 44: 458-469.
Tanaka, H., Monahan, K.D., and Seals, D.R. 2001. Age-predicted maximal heart rate revisited. Journal of the
American College of Cardiology 37: 153-156.
835
Tang, L.H., Zwisler, A-D., Taylor, T.S., Doherty, P., Zangger, G., Berg, S.K., and Langberg, H. 2016. Self-rating
level of perceived exertion for guiding exercise intensity during a 12-week cardiac rehabilitation programme and
the influence of heart rate reducing medication. Journal of Science and Medicine in Sport. 19: 611-615.
Tarleton, H.P., Smith, L.V., Zhang, Z-F., and Kuo, T. 2015. Utility of anthropometric measures in a multiethnic
population: Their association with prevalent diabetes, hypertension and other chronic disease comorbidities.
Journal of Community Health 39: 471-479.
Taylor, D.C., Dalton, J.D., Seaber, A.V., and Garrett, W.E. 1990. Viscoelastic properties of muscle-tendon units.
The biomechanical effects of stretching. American Journal of Sports Medicine 18: 300-309.
Taylor, N.A.S., and Wilkinson, J.G. 1986. Exercise-induced skeletal muscle growth: Hypertrophy or hyperplasia?
Taylor, W.D., George, J.D., Allsen, P.E., Vehrs, P.R., Hager, R.L., and Roberts, M.P. 2002. Estimation of V̇O2max
from a 1.5-mile endurance test. Medicine & Science in Sports & Exercise 35(Suppl.): S257 [abstract].
Tchoukalova, Y.D., Votruba, S.B., Tchkonia, T., Giorgadze, N., Kirkland, J.L., and Jensen, M.D. 2010. Regional
differences in cellular mechanisms of adipose tissue gain with overfeeding. Proceedings of the National Academy of
Sciences 107: 18226-18231.
Tegenkamp, M.H., Clark, R.R., Schoeller, D.A., and Landry, G.L. 2011. Effects of covert subject actions on percent
body fat by air-displacement plethysmography. Journal of Strength and Conditioning Research 25: 2010-2017.
Teichtahl, A.J., Urquhart, D.M., Wang, Y., Wluka, A.E., O’Sullivan, R., Jones, G., and Cicuttini, F.M. 2015.
Physical inactivity is associated with narrower lumbar intervertebral discs, high fat content of paraspinal muscles
and low back pain and disability. Arthritis Research & Therapy 17: 114-120.
Tesch, P.A. 1988. Skeletal muscle adaptations consequent to long-term heavy resistance exercise. Medicine & Science
in Sports & Exercise 20: S132-S134.
Tesch, P.A. 1992. Short- and long-term histochemical and biochemical adaptations in muscle. In Strength and power
in sports: The encyclopaedia of sports medicine, ed. P. Komi, 239-248. Oxford: Blackwell.
Teixeira, P.J., Carraça, E.V., Markland, D., Silva, M.N., and Ryan, R.M. 2012. Exercise, physical activity, and self-
determination theory: A systematic review. Journal of Behavioral Nutrition and Physical Activity 9: 78.
www.ijbnpa.org/content/9/1/78. Accessed June 10, 2017.
Teixeira, P.J., Carraça, E.V., Marques, M.M., Rutter, H., Oppert, J-M., de Bourdeaudhuij, I., Lakerveld, J., and
Brug, J. 2015. Successful behavior change in obesity interventions in adults: A systematic review of self-regulation
mediators. BMC Medicine 13: 84. doi:10.1186/s12916-015-0323-6. Accessed May 3, 2017.
Thacker, S.B., Gilchrist, J., Stroup, D.F., and Kimsey, C.D. 2004. The impact of stretching on sports injury risk: A
systematic review of the literature. Medicine & Science in Sports & Exercise 36: 371-378.
Thaler, M.S. 2015. The only EKG book you’ll ever need, 8th ed. Philadelphia: Wolters Kluers.
Tholl, U., Lüders, S., Bramlage, P., Dechend, R., Eckert, S., Mengden, T., Nürnberger, J., Sanner, B., and Anlauf,
M. 2016. The German Hypertension League (Deutsche Hochdruckliga) Quality Seal Protocol for blood pressure-
measuring devices: 15-year experience and results from 105 devices for home blood pressure control. Blood Pressure
Thomas, J.F., Larson, K.L., Hollander, D.B., and Kraemer, R.R. 2014. Comparison of two-hand kettlebell exercise
and graded treadmill walking: Effectiveness as a stimulus for cardiorespiratory fitness. Journal of Strength and
Thomas, T.R., and Etheridge, G.L. 1980. Hydrostatic weighing at residual volume and functional residual capacity.
836
Thomas, T.R., Ziogas, G., Smith, T., Zhang, Q., and Londeree, B.R. 1995. Physiological and perceived exertion
responses to six modes of submaximal exercise. Research Quarterly for Exercise and Sport 66: 239-246.
Thompson, C.J., and Bemben, M.G. 1999. Reliability and comparability of the accelerometer as a measure of
muscular power. Medicine and Science in Sports & Exercise 31: 897-902.
Thompson, C.J., Cobb, K.M., and Blackwell, J. 2007. Functional training improves club head speed and functional
fitness of older golfers. Journal of Strength and Conditioning Research 21(1): 131-137.
Thompson, J., Manore, M., and Thomas, J. 1996. Effects of diet and diet-plus-exercise programs on resting
metabolic rate: A meta-analysis. International Journal of Sport Nutrition 6: 41-61.
Thompson, M., and Medley, A. 2007. Forward and lateral sitting functional reach in younger, middle-aged, and older
adults. Journal of Geriatric Physical Therapy 30(2): 43-51.
Thompson, P.D. 1993. The safety of exercise testing and participation. In ACSM’s resource manual for guidelines for
exercise testing and prescription, ed. S.N. Blair, P. Painter, R. Pate, L.K. Smith, and C.B. Taylor, 361-370.
Philadelphia: Lea & Febiger.
Thompson, W.R. 2017. Worldwide survey of fitness trends for 2018. ACSM’s Health & Fitness Journal 21(6): 10-19.
Thorstensson, A., Hulten, B., vonDobeln, W., and Karlsson, J. 1976. Effect of strength training on enzyme activities
and fibre characteristics in human skeletal muscle. Acta Physiologica Scandinavica 96: 392-398.
Thurlow, S., Taylor-Covill, G., Sahota, P., Oldroyd, B., and Hind, K. 2017. Effects of procedure, upright
equilibrium time, sex, and BMI on the precision of body fluid measurements using bioelectrical impedance
analysis. European Journal of Clinical Nutrition [Epub ahead of print]. doi:10.1038/ejcn.2017.110. Accessed August
4, 2017.
Tidstrand, J., and Horneij, E. 2009. Inter-rater reliability of three standardized functional tests in patients with low
back pain. BMC Musculoskeletal Disorders 10: 58.
Tiedemann, A., Sherrington, C., Close, J.C.T., and Lord, S.R. 2011. Exercise and sports science Australia position
statement on exercise and falls prevention in older people. Journal of Science and Medicine in Sport 14: 489-495.
Tientcheu, D., Ayers, C., Das, S.R., McGuire, K.K., de Lemos, J.A., Khera, A., Kaplan, N., Victor, R., and
Vongpatanasin, W. 2015. Target organ complications and cardiovascular events associated with masked
hypertension and white-coat hypertension. Journal of the American College of Cardiology 66: 2159-2169.
Timson, B.F., and Coffman, J.L. 1984. Body composition by hydrostatic weighing at total lung capacity and residual
volume. Medicine & Science in Sports & Exercise 16: 411-414.
Tinetti, M.E. 1986. Performance-oriented assessment of mobility problems in elderly patients. Journal of the American
Geriatric Society 34: 119-126.
Tinetti, M.E., Speechley, M., and Ginter, S.F. 1988. Risk factors for falls among elderly persons living in the
community. New England Journal of Medicine 319(26): 1701-1707.
Tinwala, F., Cronin, J., Haemmerle, E., and Ross, A. 2017. Eccentric strength training: A review of the available
technology. Strength and Conditioning Journal 39: 32-47.
Tipton, C.M., Matthes, R.D., Maynard, J.A., and Carey, R.A. 1975. The influence of physical activity on ligaments
and tendons. Medicine and Science in Sports 7: 165-175.
Tjønna, A.E., Leinan, I.M., Bartnes, A.T., Jenssen, B., Gibala, M.J., Winett, R.A., and Wisløff. 2013. Low- and
high-volume of intensive endurance training significantly improves maximal oxygen update after 10-weeks of
training in healthy men. PLoS One 8: e65382. doi:10.1371/journal.pone.0065382.g001. Accessed August 2, 2017.
837
Tognetti, A., Lorussi, F., Carbonaro, N., and de Rossi, D. 2015. Wearable goniometer and accelerometer sensory
fusion for knee joint angle measurement in daily life. Sensors 15: 28435-28455.
Tognetti, A., Lorussi, F., Dalle Mura, G., Carbonaro, N., Pacelli, M., Paradiso, R., and de Rossi, D. 2014. New
generation of wearable goniometers for motion capture systems. Journal of NeuroEngineering and Rehabilitation 11:
56.
Tolonen, H., Koponen, P., Naska, A., Männistö, S., Broda, G., Palossari, T., Kuulasmaa, K., and the EHES Pilot
Project. 2015. Challenges in standardization of blood pressure measurement at the population level. BMC Medical
Research Methodology 15: 33. doi:10.1186/s12874-015-0020-3. Accessed April 29, 2017.
Tomiyama, A.J., Hunger, J.M., Nguyen-Cuu, J., and Wells, C. 2016. Misclassification of cardiometabolic health
when using body mass index categories in NHANES 2005-2012. International Journal of Obesity 40: 883-886.
Toombs, R.J., Ducher, G., Shepherd, J.A., and de Souza, M.J. 2012. The impact of recent technological
advancements on the trueness and precision of DXA to assess body composition. Obesity 20: 30-39.
Toomey, C.M., McCormack, W.G., and Jakeman, P. 2017. The effect of hydration status on the measurement of
lean tissue mass by dual-energy X-ray absorptiometry. European Journal of Applied Physiology 117: 567-574.
Toomey, C., McCreesh, K., Leahy, S., and Jakeman, P. 2011. Technical considerations for accurate measurement of
subcutaneous adipose tissue thickness using B-mode ultrasound. Ultrasound 19: 91-96.
Torbeyns, T., Bailey, S., Bos, I., and Meeusen, R. 2014. Active workstations to fight sedentary behavior. Sports
Medicine 44: 1261-1273.
Torgan, C.E., and Cousineau, T.M. 2012. Leveraging social media technologies to help clients achieve behavior
change goals. ACSM’s Health & Fitness Journal 16: 18-24.
Torres, R., Ribeiro, F., Duarte, J.A., and Cabri, J.M.H. 2012. Evidence of the physiotherapeutic interventions used
currently after exercise-induced muscle damage: Systematic review and meta-analysis. Physical Therapy in Sport 13:
101-114.
Torvinen, S., Kannus, P., Sievanen, H., Jarvinen, T.A.H., Pasanen, M., Kontulainen, S., Jarvinen, T.L.N., Jarvinen,
M., Oja, P., and Vuori, I. 2002. Effect of four-month vertical whole body vibration on performance and balance.
Town, G.P., Sol, N., and Sinning, W. 1980. The effect of rope skipping rate on energy expenditure of males and
females. Medicine & Science in Sports & Exercise 12: 295-298.
Townsend, N., Rutter, H., and Foster, C. 2012. Evaluating the evidence that the prevalence of childhood obesity is
plateauing. Pediatric Obesity 7: 343-346.
Townsend, N., Wildon, L., Bhatnagar, P., Wickramasinghe, K., Rayner, M., and Nichols, M. 2016. Cardiovascular
disease in Europe: Epidemiological update 2016. European Heart Journal 37: 3232-3245.
Tran, Z.V., and Weltman, A. 1988. Predicting body composition of men from girth measurements. Human Biology
60: 167-175.
Tran, Z.V., and Weltman, A. 1989. Generalized equation for predicting body density of women from girth
measurements. Medicine & Science in Sports & Exercise 21: 101-104.
Trapp, E.G., Chisholm, D.J., Freund, J., and Boutcher, S.H. 2008. The effects of high-intensity intermittent exercise
training on fat loss and fasting insulin levels of young women. International Journal of Obesity 32: 684-691.
Tremblay, M.S., Warburton, D.E.R., Janssen, I., Paterson, D.H., Latimer, A.E., Rhodes, R.E., Kho, M.E., Hicks,
A., LeBlanc, A.G., Zehr, L., Murumets, K., and Duggan, M. 2011. New Canadian physical activity guidelines.
838
Troped, P.J., Oliveira, M.S., Matthews, C.E., Cromley, E.K., Melly, S.J., and Craig, B.A. 2008. Prediction of activity
mode with global positioning system and accelerometer data. Medicine & Science in Sports & Exercise 10: 972-978.
Trost, S.G., Owen, N., Bauman, A.E., Sallis, J.F., and Brown, W. 2002. Correlates of adults’ participation in physical
activity: Review and update. Medicine & Science in Sports & Exercise 34: 1996-2001.
Tseng, K., Tseng, W., Lin, M., Chen, H., Nosaka, K., and Chen, T.C. 2016. Protective effect by maximal isometric
contractions against maximal eccentric exercise-induced muscle damage of the knee extensors. Research in Sports
Medicine 24: 243-256.
Tsukamoto, H., Takenaka, S., Suga, T., Tanaka, D., Takeuchi, T., Hamaoka, T., Isaka, T., and Hashimoto, T. 2017.
Effect of exercise intensity and duration on postexercise executive function. Medicine & Science in Sports & Exercise
49: 774-784.
Tucker, L.A., Lechiminant, J.D., and Bailey, B.W. 2014. Test re-test reliability of the BodPod: The effect of multiple
assessments. Perceptual and Motor Skills: Physical Development and Movement 118: 563-570.
Tudor-Locke, C., Bassett, D.R., Shipe, M.F., and McClain, J.J. 2011. Pedometry methods for assessing free-living
adults. Journal of Physical Activity and Health 8: 445-453.
Tudor-Locke, C., Pangrazi, R.P., Corbin, C.B., Rutherford, W.J., Vincent, S.D., Raustorp, A., Tomson, L.M., and
Cuddihy, T.F. 2004. BMI-referenced standards for recommended pedometer-determined steps/day in children.
Preventive Medicine 38: 857-864.
Turcato, E., Bosello, O., Francesco, V.D., Harris, T.B., Zoico, E., Bissoli, L., Fracassi, E., and Zamboni, M. 2000.
Waist circumference and abdominal sagittal diameter as surrogates of body fat distribution in the elderly: Their
relation with cardiovascular risk factors. International Journal of Obesity 24: 1005-1010.
Tyrrell, V.J., Richards, G., Hofman, P., Gillies, G.F., Robinson, E., and Cutfield, W.S. 2001. Foot-to-foot
bioelectrical impedance analysis: A valuable tool for the measurement of body composition in children.
International Journal of Obesity 25: 273-278.
Urwin, S.G., Kader, D.F., Caplan, N., St. Clair Gibson, A., and Stewart, S. 2013. Validation of an electrogoniometry
system as a measure of knee kinematics during activities of daily living. Journal of Musculoskeletal Research 16: article
1350005.
U.S. Department of Health and Human Services. 1996. Physical activity and health: A report of the Surgeon General—At
a glance. Atlanta: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention,
National Center for Chronic Disease Prevention and Health Promotion.
U.S. Department of Health and Human Services. 2007. The Surgeon General’s call to action to prevent overweight and
obesity in children and adolescents. Washington, DC: Author.
www.surgeongeneral.gov/topics/obesity/calltoaction/fact_adolescents.html.
U.S. Department of Health and Human Services. 2008. Physical activity guidelines for Americans. At-a-glance: A
fact sheet for professionals. www.health.gov/paguidelines/factsheetprof.aspx.
U.S. Department of Health and Human Services, Centers for Disease Control and Prevention. 2015. How much
physical activity do adults need? www.cdc.gov/physicalactivity/basics/adults/index.htm. Accessed June 27, 2017.
U.S. Department of Health and Human Services. 2010. Dietary guidelines for Americans 2010. Washington, D.C.:
Author.
U.S. Department of Health and Human Services. 2012. Healthy People 2020. www.healthypeople.gov/2020. Accessed
June 15, 2012.
U.S. Environmental Protection Agency. 2017. Minata Convention on Mercury. www.epa.gov/international-
839
cooperation/minamata-convention-mercury. Accessed April 30, 2017.
Utter, A.C., Nieman, D.C., Ward, A.N., and Butterworth, D.E. 1999. Use of the leg-to-leg bioelectrical impedance
method in assessing body-composition change in obese women. American Journal of Clinical Nutrition 69: 603-607.
Vaisman, N., Corey, M., Rossi, M.F., Goldberg, E., and Pencharz, P. 1988. Changes in body composition during
refeeding of patients with anorexia nervosa. Journal of Pediatrics 113: 925-929.
Vaisman, N., Rossi, M.F., Goldberg, E., Dibden, L.J., Wykes, L.J., and Pencharz, P.B. 1988. Energy expenditures
and body composition in patients with anorexia nervosa. Journal of Pediatrics 113: 919-924.
Van Adrichem, J.A.M., and van der Korst, J.K. 1973. Assessment of flexibility of the lumbar spine: A pilot study in
children and adolescents. Scandinavian Journal of Rheumatology 2: 87-91.
van den Beld, W.A., van der Sanden, G.A.C., Sengers, R.C.A., Verbeek, A.L.M., and Gabreels, F.J.M. 2006.
Validity and reproducibility of hand-held dynamometry in children aged 4-11 years. Journal of Rehabilitation
Medicine 38: 57-64.
van der Kooy, K., Leenen, R., Seidell, J.C., Deurenberg, P., Droop, A., and Bakker, C.J.G. 1993. Waist-hip ratio is a
poor predictor of changes in visceral fat. American Journal of Clinical Nutrition 57: 327-333.
van Genugten, L., Dusseldorp, E., Webb, T.L., and van Empelan, P. 2016. Which combinations of techniques and
modes of delivery in Internet-based interventions effectively change health behavior? A meta-analysis. Journal of
Medical Internet Research. 18: e155. doi:10.2196/jmir.4218. Accessed June 11, 2017.
Vanhelder, W.P., Radomski, M.W., and Goode, R.C. 1984. Growth hormone responses during intermittent weight
lifting exercise in men. European Journal of Applied Physiology 53: 31-34.
Van Loan, M.D., and Mayclin, P.L. 1987. Bioelectrical impedance analysis: Is it a reliable estimator of lean body
mass and total body water? Human Biology 59: 299-309.
Van Mechelen, W., Holbil, H., and Kemper, H.C. 1986. Validation of two running tests as estimates of maximal
aerobic power in children. European Journal of Applied Physiology and Occupational Physiology 55: 503-506.
Van Remoortel, H., Giavedoni, S., Raste, Y., Burtin, C., Louvaris, Z., Gimeno-Santos, E., Langer, D., Glendenning,
A., Hopkinson, N.S., Vogiatzis, I., Peterson, B.T., Wilson, F., Mann, B., Rabinovich, R., Puhan, M.A., and
Troosters, T. 2012. Validity of activity monitors in health and chronic disease: A systematic review. International
Journal of Behavioral Nutrition and Physical Activity 9: 84.
VanWormer, J.J., Martinez, A.M., Martinson, B.C., Crain, A.L., Benson, G.A., Cosentino, D.L., and Pronk, N.P.
2009. Self-weighing promotes weight loss for obese adults. American Journal of Preventive Medicine 36: 70-73.
Vehrs, P.R., Drummond, M., Fellingham, D.K., and Brigham, G.W. 2002. Accuracy of five heart rate monitors
during exercise. Medicine & Science in Sports & Exercise 34(Suppl.): S272 [abstract].
Velthuis, M.J., Schuit, A.J., Peeters, P.H.M., and Monninkhof, E.M. 2009. Exercise program affects body
composition but not weight in postmenopausal women. Menopause: The Journal of the North American Menopause
Society 16: 777-784.
Vera-Garcia, F.J., Grenier, S.G., and McGill, S.M. 2000. Abdominal muscle responses during curl-ups on both
stable and labile surfaces. Physical Therapy 80: 564-569.
Vescovi, J.D., Zimmerman, S.L., Miller, W.C., Hildebrandt, L., Hammer, R.L., and Fernhall, B. 2001. Evaluation
of the Bod Pod for estimating percentage body fat in a heterogeneous group of adult humans. European Journal of
Applied Physiology 85: 326-332.
Vĕtrovska, R., Vilikus, Z., Klaschka, J., Stránská, Z., Svačina, Š., Svobodova, Š., and Matoulek, M. 2014. Does
impedance measure a functional state of body fat? Physiological Research 63(Suppl. 2): S309-S320.
840
Vikmoen, O., Ronnestad, B.R., Ellefsen, S., and Raastad, T. 2017. Heavy strength training improves running and
cycling performance following prolonged submaximal work in well-trained female athletes. Physiological Reports 5:
article e13149.
Vincent, K.R., Braith, R.W., Feldman, R.A., Magyari, P.M., Cutler, R.B., Persin, S.A., Lennon, S.L., Gabr, A.H.,
and Lowenthal, D.T. 2002. Resistance exercise and physical performance in adults aged 60 to 83. Journal of the
American Geriatrics Society 50: 1100-1107.
Vohralik, S.L., Bowen, A.R., Burns, J., Hiller, C.E., and Nightingale, E.J. 2015. Reliability and validity of a
smartphone app to measure joint range. American Journal of Physical Medicine and Rehabilitation 94: 325-330.
von Stengel, S., Kemmler, W., Bebenek, M., Engelke, K., and Kalender, W.A. 2011. Effects of whole-body vibration
training on different devices on bone mineral density. Medicine & Science in Sports & Exercise 43: 1071-1079.
von Stengel, S., Kemmler, W., Engelke, K., and Kalender, W.A. 2011. Effects of whole body vibration on bone
mineral density and falls: Results of the randomized controlled ELVIS study with postmenopausal women.
Osteoporosis International 22: 317-325.
von Stengel, S., Kemmler, W., Engelke, K., and Kalender, W.A. 2012. Effect of whole-body vibration on
neuromuscular performance and body composition for females 65 years and older: A randomized-controlled trial.
Scandinavian Journal of Medicine & Science in Sports 22: 119-127.
Wagner, D.R., 2013. Ultrasound as a tool to assess body fat. Journal of Obesity 2013: article 280713.
doi:10.1155/2013/280713. Accessed August 1, 2014.
Wagner, D.R. 2014. Exercise physiologists in the United States: A 2012 national survey. Journal of Exercise Physiology
[online]. www.asep.org/asep/asep/JEPonlineOCTOBER2014_Wagner.pdf.
Wagner, D.R. 2015. Predicted versus measured thoracic gas volumes of collegiate athletes made by Bod Pod air
displacement plethysmography system. Applied Physiology, Nutrition, and Metabolism 10: 1075-1077.
Wagner, D.R., Cain, D.L., and Clark, N.W. 2016. Validity and reliability of A-mode ultrasound for body
composition assessment of NCAA Division I athletes. PLoS One 11(4): e0153146.
doi:10.1371/journal.pone.0153146.
Wagner, D.R., and Heyward, V.H. 2001. Validity of two-component models of estimating body fat of Black men.
Wagner, D., Heyward, V., and Gibson, A. 2000. Validation of air displacement plethysmography for assessing body
Wagner, K.H., and Brath, H. 2012. A global view on the development on non communicable diseases. Preventive
Medicine 54: s38-s41.
Wallick, M.E., Porcari, J.P., Wallick, S.B., Berg, K.M., Brice, G.A., and Arimond, G.R. 1995. Physiological
responses to in-line skating compared to treadmill running. Medicine & Science in Sports & Exercise 27: 242-248.
Wallman, H.W. 2001. Comparison of elderly nonfallers and fallers on performance measures of functional reach,
sensory organization, and limits of stability. Journal of Gerontology 56: M589-M583.
Wallman, K., Plant, L.A., Rakimov, B., and Maiorana, A.J. 2009. The effects of two modes of exercise on aerobic
fitness and fat mass in an overweight population. Research in Sports Medicine 17: 156-170.
Walts, C.T., Hanson, E.D., Delmonico, M.J., Yao, L., Wang, M.W., and Hurley, B.F. 2008. Do sex or race
differences influence strength training effects on muscle or fat? Medicine & Science in Sports & Exercise 40: 669-
676.
Wan, Y., Henegghan, C., Stevens, R., McManus, R.J., Ward, A., Perera, R., Thompson, M., Tarassenko, L., and
841
Mant, D. 2010. Determining which automatic digital blood pressure device performs adequately: A systematic
review. Journal of Human Hypertension 24: 431-438.
Wang, J., Thornton, J.C., Russell, M., Burastero, S., Heymsfield, S., and Pierson, R.N. 1994. Asians have lower body
mass index (BMI) but higher percent body fat than do whites: Comparison of anthropometric measurements.
Wang, J-G., Zhang, Y., Chen, H-E., Li, Y., Cheng, X-G., Xu, L., Guo, Z., Zhao, X-S., Sato, T., Cao, Q-Y., Chen,
K-M., and Li, B. 2013. Comparison of two bioelectrical impedance analysis devices with dual energy X-ray
absorptiometry and magnetic resonance imaging in the estimation of body composition. Journal of Strength and
Wang, R., Wu, M.J., Ma, X.Q., Zhao, Y.F., Yan, X.Y., Gao, Q.B., and He, J. 2012. Body mass index and health-
related quality of like in adults: A population based study in five cities of China. European Journal of Public Health
22(4): 497-502.
Wang, X.Q., Zheng, J.J., Yu, Z.W., Bi, X., Lou, S.J., Liu, J., Cai, B., Hua, Y.H., Wu, M., Wei, M.L, Shen, H.M.,
Chen, Y., Pan, Y.J., Xu, G.H., and Chen, P.J. 2012. A meta-analysis of core stability exercise versus general
exercise for chronic low back pain. PLoS One 7(1): e52082.
Warburton, D.E.R., and Breden, S.S.D. 2016. Reflections on physical activity and health: What should we
recommend? Canadian Journal of Cardiology 32: 495-504.
Warburton, D.E.R., Sarkany, D., Johnson, M., Rhodes, R.E., Whitford, W., Esch, B.T.A., Scott, J.M., Wong, S.C.,
and Bredin, S.S.D. 2009. Metabolic requirements of interactive video game cycling. Medicine & Science in Sports &
Exercise 41: 920-926.
Ward, R., and Anderson, G.S. 1998. Resilience of anthropometric data assembly strategies to imposed error. Journal
Ward, R., Rempel, R., and Anderson, G.S. 1999. Modeling dynamic skinfold compression. American Journal of
Human Biology 11: 521-537.
Wathen, D. 1994. Load assignment. In Essentials of strength testing, ed. T.R. Baechle, 435-446. Champaign, IL:
Human Kinetics.
Watson, L.P.E., Venables, M.C., and Murgatroyd, P.R. 2017. An investigation into the differences in bone density
and body composition measurements between 2 GE Lunar densitometers and their comparison to a 4-component
model. Journal of Clinical Densitometry: Assessment & Management of Musculoskeletal Health [Epub ahead of print].
doi:10.1016/j.jocd.2017.06.029. Accessed August 6, 2017.
Watson, S.L., Weeks, B.K., Weis, L.J., Horan, S.A., and Beck, B.R. 2015. Heavy resistance training is safe and
improves bone, function, and stature in postmenopausal women with low to very low bone mass: Novel early
findings from the LIFTMOR trial. Osteoporosis International 26: 2889-2894.
Weakley, J.J.S., Till, K., Read, D.B., Roe, G.A.B., Darrall-Jones, J., Phibbs, P.J., and Jones, B. 2017. The effects of
traditional, superset, and tri-set resistance training structures on perceived intensity and physiological responses.
Weaver, T.B., Ma, C., and Laing, A.C. 2017. Use of the Nintendo Wii balance board for studying standing static
balance control: Technical considerations, force-plate congruency, and the effect of battery life. Journal of Applied
Biomechanics 33: 48-55.
Webb, C., Vehrs, P.R., George, J.D., and Hager, R. 2014. Estimating V̇O2max using a personalized step test.
842
Webb, T.L., Joseph, J., Yardley, L., and Michie, S. 2010. Using the Internet to promote health behavior change: A
systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and
mode of delivery on efficacy. Journal of Medicine and Internet Research 12:e4. doi:10.2196/jmir.1376. Accessed
November 4, 2012.
Wei, N., Pang, M.Y.C., Ng, S.S.M., and Ng, G.Y.F. 2016. Optimal frequency/time combination of whole-body
vibration training for improving muscle size and strength of people with age-related muscle loss (sarcopenia): A
randomized controlled trial. Geriatrics and Gerontology International [in press].
Weier, A.T., and Kidgell, D.J. 2012. Strength training with superimposed whole body vibration does not
preferentially modulate cortical plasticity. Scientific World Journal 2012: 876328.
Weiglein, L., Herrick, J., Kirk, S., and Kirk, E.P. 2011. The 1-mile walk test is a valid predictor of V̇O2max and is a
reliable alternative fitness test to the 1.5-mile run in U.S. Air Force males. Military Medicine 176: 669-673.
Weijs, P.J.M. 2008. Validity of predictive equations for resting energy expenditure in U.S. and Dutch overweight and
obese class I and II adults aged 18-65 y. American Journal of Clinical Nutrition 88: 959-970.
Weinheimer, E.M., Sands, L.P., and Campbellnure, W.W. 2010. A systematic review of the separate and combined
effects of energy restriction and exercise on fat-free mass in middle-aged and older adults: Implications for
sarcopenic obesity. Nutrition Reviews 68: 375-388.
Weisenthal, B.M., Beck, C.A., Maloney, M.D., DeHaven, K.E., and Giordano, B.D. 2014. Injury rate and patterns
among CrossFit athletes. Orthopaedic Journal of Sports Medicine 2: article 2325967114531177.
Weiss, E.C., Galuska, D.A., Khan, L.K., and Serdula, M.K. 2006. Weight-control practices among U.S. adults,
2001-2002. American Journal of Preventive Medicine 31: 18-24.
Weiss, L.W., Cureton, K.J., and Thompson, F.N. 1983. Comparison of serum testosterone and androstenedione
responses to weight lifting in men and women. European Journal of Applied Physiology 50: 413-419.
Weits, T., Van der Beek, E.J., Wedel, M., and Ter Haar Romeny, B.M. 1988. Computed tomography measurement
of abdominal fat deposition in relation to anthropometry. International Journal of Obesity 12: 217-225.
Wellmon, R.H., Gulick, D.T., Paterson, M.L., and Gulick, C.N. 2016. Validity and reliability of 2 goniometric
mobile apps: Device, application, and examiner factors. Journal of Sport Rehabilitation 25: 371-379.
Weltman, A., Levine, S., Seip, R.L., and Tran, Z.V. 1988. Accurate assessment of body composition in obese
females. American Journal of Clinical Nutrition 48: 1179-1183.
Weltman, A., Seip, R.L., and Tran, Z.V. 1987. Practical assessment of body composition in adult obese males.
Human Biology 59: 523-535.
Wen, C.P., Wai, J.P., Tsai, M.K., Yang, Y.C., Cheng, T.Y., Lee, M.C., Chan, H.T., Tsao, C.K., Tsai, S.P., and
Wu, X. 2011. Minimum amount of physical activity for reduced mortality and extended life expectancy: A
prospective cohort study. Lancet 378: 1244-1253.
Wessel, H.U., Strasburger, J.F., and Mitchell, B.M. 2001. New standards for Bruce treadmill protocol in children and
adolescents. Pediatric Exercise Science 13: 392-401.
Wewege, M., van den Berg, R., Ward, R.E., and Keech, A. 2017. The effects of high-intensity interval training vs.
moderate-intensity continuous training on body composition in overweight and obese adults: A systematic review
and meta-analysis. Obesity Reviews 18: 635-646.
Whaley, M., Kaminsky, L., Dwyer, G., Getchell, L., and Norton, J. 1992. Predictors of over- and underachievement
of age-predicted maximal heart rate. Medicine & Science in Sports & Exercise 24: 1173-1179.
Whelton, P.K., Carey, R.M., Aronow, W.S., Casey, D.E. Jr., Collins, K.J., Himmelfarb, C.D., DePalma, S.M.,
843
Gidding, S., Jamerson, K.A., MacLaughlin, E.J., Muntner, P., Ovbiagele, B., Smith, S.C. Jr., Stafford, R.S.,
Taler, S.J., Thomas, R.J., Williams Sr., K.A., Williamson, J.D., and Wright, J.T. Jr. 2017. 2017
ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA guideline for the prevention,
detection, evaluation, and management of high blood pressure in adults. Journal of the American College of
Cardiology [e-pub ahead of print]. doi:10.1016/j.jacc.2017.11.006. Accessed November 14, 2017.
Whitmer, T.D., Fry, A.C., Forsythe, C.M., Andre, M.J., Lane, M.T., Hudy, A., and Honnold, D.E. 2015. Accuracy
of a vertical jump contact mat for determining jump height and flight time. Journal of Strength and Conditioning
Research 29: 877-881.
Whitney, S.L., Poole, J.L., and Cass, S.P. 1998. A review of balance instruments for older adults. American Journal of
Occupational Therapy 52: 666-671.
Wibner, T., Doering, K., Kropf-Sanshen, C., Rüdger, S., Blanta, I., Stoiber, K.M., Rottbauer, W., and Schumann,
C. 2014. Pulse transit time and blood pressure during cardiopulmonary exercise tests. Physiological Research 63:
287-296.
Wild, D., Nayak, U.S.L., and Isaacs, B. 1981. Prognosis of falls in old people at home. Journal of Epidemiology and
Community Health 35: 200-204.
Wild, S., Hanley, J., Lewis, S., McKnight, J., McCloughan, L., Padfield, P., Paterson, M., Pinnock, H., and
McKinstry, B. 2013. The impact of supported telemetric monitoring in people with type 2 diabetes: Study
protocol for a randomised controlled trial. Trials 14: 198.
Wilkin, L.D., Cheryl, A., and Haddock, B.L. 2012. Energy expenditure comparison between walking and running in
average fitness individuals. Journal of Strength and Conditioning Research 26: 1039-1044.
Willardson, J.M. 2008. A periodized approach for core training. ACSM’s Health & Fitness Journal 12(1): 7-13.
Willey, J.Z., Gardener, H., Caunca, M.R., Moon, Y.P., Dong, C., Cheung, Y.K., Sacco, R.L., Elkind, M.S.V., and
Wright, C.B. 2016. Leisure-time physical activity associates with cognitive decline: The Northern Manhattan
Study. Neurology. 86: 1897-1903.
Williams, D.P., Going, S.B., Massett, M.P., Lohman, T.G., Bare, L.A., and Hewitt, M.J. 1993. Aqueous and
mineral fractions of the fat-free body and their relation to body fat estimates in men and women aged 49-82 years.
In Human body composition: In vivo methods, models and assessment, ed. K.J. Ellis and J.D. Eastman, 109-113. New
York: Plenum Press.
Williams, M.A. 2001. Exercise testing in cardiac rehabilitation: Exercise prescription and beyond. Cardiology Clinics
19: 415-431.
Williams, P.T. 2001. Physical fitness and activity as separate heart disease risk factors: A meta-analysis. Medicine &
Williams, R., Binkley, J., Bloch, R., Goldsmith, C.H., and Minuk, T. 1993. Reliability of the modified-modified
Schober and double inclinometer methods for measuring lumbar flexion and extension. Physical Therapy 73: 26-37.
Williams, T.D., Tolusso, D.V., Fedewa, M.V., and Esco, M.R. 2017. Comparison of periodized and non-periodized
resistance training on maximal strength: A meta-analysis. Sports Medicine 47: 2083-2100.
Willson, J.D., Dougherty, C.P., Ireland, M.L., and Davis, I.M. 2005. Core stability and its relationship to lower
extremity function and injury. Journal of the American Academy of Orthopaedic Surgery 13: 316-325.
Willson, T., Nelson, S.D., Newbold, J., Nelson, R.E., and LaFleur, J. 2015. The clinical epidemiology of male
osteoporosis: A review of the recent literature. Clinical Epidemiology 7: 65-75.
Wilmore, J.H. 1974. Alterations in strength, body composition, and anthropometric measurements consequent to a
844
10-week weight training program. Medicine and Science in Sports 6: 133-138.
Wilmore, J.H., and Behnke, A.R. 1969. An anthropometric estimation of body density and lean body weight in
young men. Journal of Applied Physiology 27: 25-31.
Wilmore, J.H., and Behnke, A.R. 1970. An anthropometric estimation of body density and lean body weight in
young women. American Journal of Clinical Nutrition 23: 267-274.
Wilmore, J.H., Davis, J.A., O’Brien, R.S., Vodak, P.A., Walder, G.R., and Amsterdam, E.A. 1980. Physiological
alterations consequent to 20-week conditioning programs of bicycling, tennis and jogging. Medicine & Science in
Wilmore, J.H., Frisancho, R.A., Gordon, C.C., Himes, J.H., Martin, A.D., Martorell, R., and Seefeldt, R.D. 1988.
Body breadth equipment and measurement techniques. In Anthropometric standardization reference manual, ed.
T.G. Lohman, A.F. Roche, and R. Martorell, 27-38. Champaign, IL: Human Kinetics.
Wilmore, J.H., Parr, R.B., Girandola, R.N., Ward, P., Vodak, P.A., Barstow, T.J., Pipes, T.V., Romero, G.T., and
Leslie, P. 1978. Physiological alterations consequent to circuit weight training. Medicine and Science in Sports 10:
79-84.
Wilmore, J.H., Royce, J., Girandola, R.N., Katch, F.I., and Katch, V.L. 1970. Body composition changes with a 10-
week program of jogging. Medicine and Science in Sports 2: 113-119.
Wilms, B., Schmid, S.M., Ernst, B., Thurnheer, M., Mueller, M.J., and Schultes, B. 2010. Poor prediction of resting
energy expenditure in obese women by established equations. Metabolism Clinical and Experimental 59: 1181-1189.
Wilson, J.M., Lowery, R.P., Joy, J.M., Andersen, J.C., Wilson, S.M., Stout, J.R., Duncan, N., Fuller, J.C., Baier,
S.M., Naimo, M.A., and Rathmacher, J. 2014. The effects of 12 weeks of beta-hydroxy-beta-methylbutyrate free
acid supplementation on muscle mass, strength, and power in resistance-trained individuals: A randomized,
double-blind, placebo-controlled study. European Journal of Applied Physiology 114(6): 1217-1227.
Wilson, J.M., Marin, P.J., Rhea, M.R., Wilson, S.M.C., Loenneke, J.P., and Anderson, J.C. 2012. Concurrent
training: A meta-analysis examining interference of aerobic and resistance exercises. Journal of Strength and
Wilson, P.K., Winga, E.R., Edgett, J.W., and Gushiken, T.J. 1978. Policies and procedures of a cardiac rehabilitation
program—immediate to long term care. Philadelphia: Lea & Febiger.
Withers, R.T., LaForgia, J., Pillans, R.K., Shipp, N.J., Chatterton, B.E., Schultz, C.G., and Leaney, F. 1998.
Comparisons of two-, three-, and four-compartment models of body composition analysis in men and women.
Witten, C. 1973. Construction of a submaximal cardiovascular step test for college females. Research Quarterly 44: 46-
50.
Woei-Ni Hwang, P., and Braun, K.L. 2015. The effectiveness of dance interventions to improve older adults’ health:
A systematic literature review. Alternative Therapies in Health and Medicine 21: 64-70.
Women’s Exercise Research Center. 1998. Based on figures published by Brown, D.A., and Miller, W.C. 1998.
Normative data for strength and flexibility of women throughout life. European Journal of Applied Physiology 78: 77-
82.
Wolpern, A.E., Burgos, D.J., Janot, J.M., and Dalleck, L.C. 2015. Is a threshold-based model a superior method to
the relative percent concept for establishing individual exercise intensity? A randomized controlled trial. BMC
Sports Science, Medicine and Rehabilitation 7: 16. doi:10.1186/s13102-015-0011-z. Accessed July 3, 2017.
World Health Organization. 1998. Obesity: Preventing and managing a global epidemic. Report of a WHO
845
Consultation on Obesity. Geneva: Author.
World Health Organization. 2002a. Reducing risks, promoting healthy life. World Health Report 2002.
www.who.int/whr/2002/chapter4/en/index4.html.
World Health Organization. 2002b. Smoking statistics. www.wpro.who.int/public/press_release/press_view.asp?

id=219.
World Health Organization. 2010. Global recommendations on physical activity for health.
http://whqlibdoc.who.int/publications/2010/9789241599979_eng.pdf. Accessed July 5, 2012.
World Health Organization. 2011. Global atlas on cardiovascular disease prevention and control.
http://whqlibdoc.who.int/publications/2011/9789241564373_eng.pdf. Accessed September 9, 2012.
World Health Organization. 2012a. Childhood obesity.

www.who.int/dietphysicalactivity/childhood/Childhood_obesity_Tool.pdf. Accessed October 5, 2012.
World Health Organization. 2012b. Global database on body mass index. http://apps.who.int/bmi/index.jsp.
Accessed on September 8, 2012.
World Health Organization. 2013. Global action plan for the prevention and control of noncommunicable diseases
2013-2020. http://apps.who.int/iris/bitstream/10665/94384/1/9789241506236_eng.pdf?ua=1. Accessed March
19, 2017.
World Health Organization. 2014. Global status report on NCDs 2014.

http://apps.who.int/iris/bitstream/10665/148114/1/9789241564854_eng.pdf. Accessed March 24, 2017.
World Health Organization. 2016a. Cardiovascular diseases.

www.who.int.libproxy.unm.edu/mediacentre/factsheets/fs317/en. Accessed April 2, 2017.
World Health Organization. 2016b. Global report on diabetes.

http://apps.who.int/iris/bitstream/10665/204871/1/9789241565257_eng.pdf. Accessed April 9, 2017.
World Health Organization. 2016c. Obesity and overweight fact sheet.

www.who.int/mediacentre/factsheets/fs311/en. Accessed June 25, 2017.
World Health Organization. 2016d. World health statistics 2016: Monitoring health for the SDGs. Geneva, Switzerland:
WHO Press.
World Health Organization. 2018a. Cancer Fact Sheet. http://www.who.int/mediacentre/factsheets/fs297/en/.

Accessed March 31, 2018.
World Health Organization. 2018b. Obesity and Overweight Fact Sheet.

http://www.who.int/mediacentre/factsheets/fs311/en/ Accessed March 31, 2018.
World Health Organization. 2018c. Physical Activity Fact Sheet.

http://www.who.int/mediacentre/factsheets/fs385/en/ Accessed March 31, 2018.
World Medical Association. 2017. Non-communicable diseases.

www.wma.net/en/20activities/30publichealth/10noncommunicablediseases. Accessed March 16, 2017.
Wright, N.C., Sang, K.G., Dawson-Hughes, B., Khosla, S., and Siris, E.S. 2017. The impact of the new National
Bone Health Alliance (NBHA) diagnostic criteria on the prevalence of osteoporosis in the USA. Osteoporosis
International 28: 1225-1232.
Wysocki, A., Butler, M., Shamilyan, T., and Kane, R.L. 2011. Whole-body vibration therapy for osteoporosis: State
of the Science. Annals of Internal Medicine 155: 680-686.
Xian, H., Vasilopoulos, T., Liu, W., Hanger, R.L. Jacobson, K.C., Lyons, M.J., Panizzon, M., Reynolds, C.A.,
Vuoksimua, E., Kremen, W.S., and Franz, C.E. 2017. Steeper change in body mass across four decades predicts
846
poorer cardiometabolic outcomes at midlife. Obesity 25: 773-780.
Xu, J., Lombardi, G., Jiao, W., and Banfi, G. 2016. Effects of exercise on bone status in female subjects, from young
girls to postmenopausal women: An overview of systematic reviews and meta-analyses. Sports Medicine 46: 1165-
1182.
Xu, W., Chafi, H., Guo, B., Heymsfield, S.B., Murray, K.B., Zheng, J., and Jie, G. 2016. Quantitative comparison of
2 dual-energy X-ray absorptiometry systems in assessing body composition and bone mineral measurements.
Journal of Clinical Densitometry 19: 298-304.
Yamanoto, K. 2002. Omron Institute of Life Science [personal communication].
Yamato, T.P., Maher, C.G., Saragiotto, B.T., Hancock, M.J., Ostelo, R.W., Cabral, C.M., Costa, L.C., and Costa,
L.O. 2016. Pilates for low back pain: Complete republication of a Cochrane review. Spine 41(12): 1013-1021.
Yang, Q., Cogswell, M.E., Flanders, W.D., Hong, Y., Zhang, Z., Loustalot, F., Gillespie, C., Merritt, R., and Hu,
F.B. 2012. Trends in cardiovascular health metrics and associations with all-cause and CVD mortality among U.S.
adults. Journal of the American Medical Association 307: 1273-1283.
Yee, A.J., Fuerst, T., Salamone, L., Visser, M., Dockrell, M., Van Loan, M., and Kern, M. 2001. Calibration and
validation of an air-displacement plethysmography method for estimating percentage body fat in an elderly
population: A comparison among compartmental models. American Journal of Clinical Nutrition 74: 637-642.
Yee, S.Y., and Gallagher, D. 2008. Assessment methods in human body composition. Current Opinion in Clinical
Nutrition and Metabolic Care 11: 566-572.
Yessis, M. 2003. Using free weights for stability training. Fitness Management 19(11): 26-28.
Yim-Chiplis, P.K., and Talbot, L.A. 2000. Defining and measuring balance in adults. Biological Research for Nursing
1(4): 321-331.
YMCA of the USA. 2000. YMCA fitness testing and assessment manual, 4th ed. Champaign, IL: Human Kinetics.
Yoke, M., and Kennedy, C. 2004. Functional exercise progressions. Monterey, CA: Healthy Learning.
Yoon, B.K., Kravitz, L., and Robergs, R. 2007. V̇O2max, protocol duration, and the V̇O2 plateau. Medicine & Science
Yoon, Y.S., and Oh, S.W. 2014. Optimal waist circumference cutoff values for the diagnosis of abdominal obesity in
Korean adults. Endocrinology and Metabolism 29: 418-426.
Youkhana, S., Dean, C.M., Wolff, M., Sherrington, C., and Tiedemann, A. 2016. Yoga-based exercise improves
balance and mobility in people aged 60 and over: A systematic review and meta-analysis. Age and Ageing 45: 21-29.
Yu, J-H., and Lee, G-C. 2012. Effect of core stability training using Pilates on lower extremity muscle strength and
postural stability in healthy subjects. Isokinetics and Exercise Science 20: 141-146.
Zakas, A., Balaska, P., Grammatikopoulou, M.G., Zakas, N., and Vergou, A. 2005. Acute effects of stretching
duration on the range of motion of elderly women. Journal of Bodywork and Movement Therapies 9: 270-276.
Zamboni, M., Turcato, E., Armellini, F., Kahn, H.S., Zivelonghi, A., Santana, H., Bergamo-Andreis, I.A., and
Bosello, O. 1998. Sagittal abdominal diameter as a practical predictor of visceral fat. International Journal of Obesity
and Related Metabolic Disorders 22: 655-660.
Zampieri, S., Pietrangelo, L., Loefler, S., Fruhmann, H., Vogelauer, M., Burggraf, S., Pond, A., Grim-Stieger, M.,
Cvecka, J., Sedliak, M., Tirpáková, V., Mayr, W., Sarabon, N., Rossini, K., Barberi, L., DeRossi, M., Romanello,
V., Boncompagni, S., Musarò, A., Sandri, M., and Protasi, F. 2015. Lifelong physical exercise delays age-
associated skeletal muscle decline. Journals of Gerontology: Biological Sciences and Medical Sciences 70: 163-173.
Zancanaro, C., Milanese, C., Lovato, C., Sandri, M., and Giachetti, A. 2015. Reliability of three-dimensional
847
photonic scanner anthropometry performed by skilled and naïve operators. International Journal of Ergonomics 5: 1-
11.
Zanchi, N.E., Gerlinger-Romero, F., Guimaraes-Ferreira, L., de Siqueira Filho, M., Felitti, V., Lira, F.S.,
Seelaender, M., and Lancha, A.H. Jr. 2011. HMB supplementation: Clinical and athletic performance-related
effects and mechanisms of action. Amino Acids 40: 1015-1025.
Zanetti, J.R., Gonçalves da Cruz, L., Lourenço, C.L.M., Ribeiro, G.C., Ferreira de Jusus Leite, M.A., Neves, F.F.,
Sivla-Vergara, M.L., and Mendes, E.L. 2016. Nonlinear resistance training enhances the lipid profile and reduces
inflammation marker in people living with HIV: A randomized clinical trial. Journal of Physical Activity and Health
12: 765-770.Zeni, A.I., Hoffman, M.D., and Clifford, P.S. 1996. Energy expenditure with indoor exercise
machines. Journal of the American Medical Association 275: 1424-1427.
Zhu, S., Heshka, S., Wang, Z., Shen, W., Allison, D.B., Ross, R., and Heymsfield, S.B. 2004. Combination of BMI
and waist circumference for identifying cardiovascular risk factors in whites. Obesity Research 12: 633-645.
Zhu, S., Heymsfield, S.B., Toyoshima, H., Wang, Z., Petrobelli, A., and Heshka, S. 2005. Race-ethnicity-specific
waist circumference cutoffs for identifying cardiovascular disease risk factors. American Journal of Clinical Nutrition
81: 409-415.
Zhu, W. 2008. Promoting physical activity using technology. President’s Council on Physical Fitness and Sports Research
Digest 9(3): 1-8.
Zhuang, J., Huang, L., Wu, Y., and Zhang, Y. 2014. The effectiveness of a combined exercise intervention on
physical fitness factors related to falls in community-dwelling older adults. Clinical Interventions in Aging 9: 131-
140.
Zwald, M.L, Akinbami, L.J., Fakhouri, T.H.I., and Fryar, C.D. 2017. Prevalence of low high-density lipoprotein
cholesterol among adults, by physical activity: United States, 2011-2014. NCHS Data Brief. No. 276. www-cdc-
gov.libproxy.unm.edu/nchs/data/databriefs/db276.pdf. Accessed April 3, 2017.
848
Index
Note: Page numbers followed by italic f and t refer to figures and tables, respectively.
A
AACVPR (American Association of Cardiovascular and Pulmonary Rehabilitation) 81
AAMI (Association for the Advancement of Medical Instrumentation) 42-45
abbreviations, list of 461-463
abdominal training 212, 341, 343
ABLE Bodies Balance Training (Scott) 371
Abraham, W.M. 224
absolute strength 173, 180
absolute V̇O2 80
accelerometers 73, 74, 167, 176, 286, 301
accommodating resistance 172
ACS (American Cancer Society) 21-22
ACSM. See American College of Sport Medicine (ACSM)
action stage of motivational readiness 69
active-assisted stretching 332
active stretching 332, 335-336
active tension 329-330 336
active video games (AVGs) 74-75
active workstations 2
activities of daily living (ADLs)
assessment of ability to perform 119, 121
balance and 349, 354, 364, 366
DXA scans and performance of 245
examples of 4
in Exercise and Physical Activity Pyramid 9, 9f
flexibility and 309, 326-328
in functional fitness testing 181-183
functional training and 199
resistance training and 209, 210
activity trackers 286, 301
acute inflammation theory of muscle soreness 225
acute-onset muscle soreness 224
adherence to exercise programs 67-71, 68t
ADLs. See activities of daily living (ADLs)
adolescents. See children and adolescents
ADP (air displacement plethysmography) 233, 237-242, 238f
aerobic activities 139-145
bioelectrical impedance analysis and 267
blood pressure and 12
body composition and 304
for cardiorespiratory endurance 56
continuous training 139-142
discontinuous training 139, 142-145
endurance and 144
fat loss and 304-305
innovative modes 142
lipid profiles and 15-16, 143
recommendations for 4-5t, 4-6, 9f, 10
resistance training and 216-217
resting energy expenditure and 301
type A, B, C, and D 127-128, 132, 139, 142-143, 150
water-based 141-142
for weight loss 300-302
aerobic capacity 38, 56, 145, 222-223
aerobic dance 140
aerobic fitness, fields tests for assessment of 112-115
aerobic interval training (AIT) 143-144
African Americans
body composition assessment of 231, 232t, 248, 249t
cardiovascular disease among 10
diabetes mellitus among 17
hypertension among 12
obesity among 19, 283
osteoporosis risk in 22
aging. See also older adults
body density and 247
energy expenditure and 292
flexibility and 311
849
healthy body weight and 290
muscle strength loss and 209, 214, 223-224
physical activity and 24-25
AHA. See American Heart Association (AHA)
air displacement plethysmography (ADP) 233, 237-242, 238f
AIT (aerobic interval training) 143-144
Alaska Natives. See Native Americans and Alaska Natives
alcohol consumption 37
alleles 306
altitude, blood pressure and 45
American Association of Cardiovascular and Pulmonary Rehabilitation (AACVPR) 81
American Cancer Society (ACS) 21-22
American College of Sport Medicine (ACSM)
on adherence to exercise programs 71
on balance training 349, 364, 370
on bone health exercise 23-24
on discontinuous training 143
on duration of exercise 65-66, 136
on energy expenditure 137
Exercise is Medicine initiative 26
exercise prescription guidelines from 126, 127, 143, 144
on extreme conditioning programs 200-201
on flexibility assessment and training 314, 320, 337, 338
on frequency of exercise 66, 136
GXT guidelines from 79, 81
health screening process 33
on hypertension exercise prescription 12
on intensity of exercise 130, 131, 133, 134
leg ergometry equation 97
medical clearance guidelines 31t
metabolic equations from 87-89, 88t, 96, 99, 102-103, 105
muscular fitness as defined by 159
on muscular fitness assessment 167, 170, 173, 182
on older adult exercise 25
physical activity recommendations 4, 4t, 297, 299t
on pulmonary screening tests 38
on resistance training 191, 191t, 193, 194, 210
running equation 88t, 89
screening algorithm 35
on skinfold method 251-253
stepping equation 99
on volume of exercise 137
walking equation 88-89, 88t
on weight maintenance 297-298
American Dietetic Association 292, 293
American Heart Association (AHA)
on blood pressure measurement 43-44
on exercise tests 38, 82
on global inactivity trends 11
physical activity recommendations from 4, 4t, 143, 297, 299t
American Indians. See Native Americans and Alaska Natives
American Medical Association 318
American Society of Hand Therapists (ASHT) 162-163
amino acid supplements 217
A-mode (amplitude mode) ultrasound 256-260, 257f
Andersen test for children 118
Anderson, L.J. 266
android obesity 284
aneroid manometers 41, 43, 45
angina pectoris 10
ankle sprains, balance training and 364
ankylosis 309
anorexia nervosa 282
anthropometric assessment 268-278. See also body mass index (BMI)
bony breadth in 275-276, 278, 430
circumference in 269, 271t, 272-273, 276-278, 429
defined 268
in disease risk classification 270-275
equipment for 276, 276f
error sources in 276-278
frame size classification 275-276
mobile app for 269
prediction equations for 270, 271t
sagittal abdominal diameter in 269, 275
skeletal diameter in 269, 275-277
standardized procedures for 276, 277
technician skill in 277
waist-to-height ratio in 274-275
waist-to-hip ratio in 269, 273-274, 274f, 274t, 284
weight-to-height indices for 269
Anthropometric Standardization Reference Manual (Lohman et al.) 61, 251, 252, 256, 273-274
anthropometric tape measures 276, 276f
anxiety, in physical fitness testing 57
850
Archimedes’ principle 233
Arena, S.K. 41
Arkesteijn, M. 365
arm curls 170-171t, 182, 182f, 183t, 412
arm ergometry 84, 88t, 96
Armstrong’s model of delayed-onset muscle soreness 225
ASHT (American Society of Hand Therapists) 162-163
Ashwell Body Shape Chart 275, 431
Asian and Pacific Islander populations
body composition assessment of 232t
hypercholesterolemia in 14
waist circumference of 273
Association for the Advancement of Medical Instrumentation (AAMI) 42-45
Åstrand cycle ergometer maximal test 98, 394
Åstrand-Ryhming cycle ergometer submaximal test 104-105, 104f, 394
Åstrand-Ryhming submaximal step test 108, 109t, 394
atherosclerosis 10, 35
attenuation of X-rays 243
augmented unipolar leads 51, 52f
auscultation 36, 39, 40, 49
autogenic inhibition 336
automated blood pressure devices 38-39, 41, 43-46
autophagy 25
auxotonic muscle action 160, 160-161f
AVGs (active video games) 74-75
B
back and leg dynamometers 162-164, 162f
back pain. See low back pain
back-saver sit-and-reach test 323-324, 323f
back scratch test 327-328, 327f, 328t
back strength testing 162-164, 162f, 164t
balance. See also balance assessment; balance training; falls and fall risk
definitions and nature of 56, 350
direct and indirect measures of 59t
dynamic 350-353, 357-364
exercise modes for 65t
factors affecting 350-352
functional 56, 350
gender differences in 350-351
low back pain and 340-341
muscle balance 179-180, 180t, 193
physical activity and 364-365, 366t
reactive 350, 356-357, 367
resistance training and 210, 364, 366-367, 366t
static 350-357
balance assessment 352-364
Balance Error Scoring System for 355-356, 356-357f
Berg Balance Scale for 351, 363
of children and adolescents 354, 358
computerized systems for 352-353, 352f
direct measures of 352-353
Dynamic Gait Index for 364
equipment for 360
functional reach tests for 357-359, 358f
gait velocity test for 363
indirect measures of 353-364
of older adults 354-355, 359-360, 359-360t, 363
Performance-Oriented Mobility Assessment 363
Romberg tests for 353-354
star excursion test for 361-363, 362f, 362t
test batteries for 363
timed up and go tests for 359-360t, 359-361, 361f
unipedal stance test for 354-355, 354t
Y balance test for 362-363
Balance Error Scoring System (BESS) 355-356, 356-357f
balance training 364-373
ankle sprains and 364
equipment for 373
exercise prescription for 364-369, 366t
multimodal 366t, 371, 372
neuromotor 349, 364
for older adults 364-371
perturbation-based 369
proprioceptive 364
recommendations for 4-5t, 4-6, 9f, 10, 365, 373
resources for 371
851
sample programs 370-372
Balke protocol
in maximal tests 91f, 92
metabolic equations for 92t
MET estimations for 91t
modified 116, 117t, 119, 394
nomogram for 93f
in submaximal tests 103
summary of 394
ballistic stretching 311, 332, 332t, 334-335
ball squeeze 412
Barker, A.R. 116
basal metabolic rate (BMR) 284-285
Baumgartner, T.A. 172-173
BBS (Berg Balance Scale) 351, 363
Behavioral Regulation in Exercise Questionnaire 70
behavior change models 67-71
behavior modification model 67-68
Behm, D.G. 332-333, 338-339, 345
Behnke, A.R. 269
bench press
in endurance tests 169, 171t
norms for 168t, 171t, 180, 410
in resistance training 191
starting position for 177
in strength tests 167, 168t, 169, 170t
YMCA test 169, 179
bench stepping
in aerobic training 140
maximal exercise tests 84, 98-100
protocol summary 394-395
submaximal exercise tests 108-110, 109t
bent-knee curl-ups 343, 459
Berg Balance Scale (BBS) 351, 363
BESS (Balance Error Scoring System) 355-356, 356-357f
%BF. See percent body fat
BHS (British Hypertension Society) 42-46, 42t
BIA. See bioelectrical impedance analysis (BIA)
bias, in prediction equations 62
biaxial joints 310, 310t
Billinger, S.A. 100, 111, 395
biochemical effects of resistance training 221-223
Biodex Stability System 353
bioelectrical impedance analysis (BIA) 260-268
assumptions in 260-261
defined 260
environmental factors in 268
equipment for 261-262, 261-262f
exercise and 267
hydration state in 266-267
positioning for 268
prediction equations for 261-262, 263t, 264
pretesting client guidelines 262
upper and lower body analyzers 263-265
vertical analyzers 266
whole-body method 261, 263, 265, 266
bioimpedance spectroscopy (BIS) 265-266
blacks. See African Americans
Bland and Altman method 62, 62f
Blazevich, A.J. 333
blood chemistry profile 36-37, 36-37t
blood lactate levels 83, 94, 134, 135
blood pressure (BP). See also hypertension
altitude and 45
automated devices for 38-39, 41, 43-46
body and arm position effects on 46
in cardiorespiratory exercise 126
classification of 34t, 37
cuff size determination 47-48, 48t
defined 37
diastolic 11, 12, 37, 45-48
elevated 11, 37
during exercise 45-46, 48-49
in graded exercise tests 83
measurement of 38-49
medications and 11, 12, 32, 37, 148
physical activity and 7f, 12, 143
resting (seated position) 37, 39, 40
sources of measurement error 39, 41-48
systolic 11, 12, 37, 45-48
BMC (bone mineral content) 56, 245
BMD. See bone mineral density (BMD)
852
BMI. See body mass index (BMI)
B-mode (brightness modulation) ultrasound 257-260
BMR (basal metabolic rate) 284-285
Bod Pod 237-242, 238f
BodyCombat 142
body composition. See also body composition assessment
balance and 350
defined 56
exercise and 65, 65t, 299, 300
flexibility and 311
genotype and 306-307
physical activity and 7f
programs for improvement of 304-307
body composition assessment 229-278. See also percent body fat (%BF)
air displacement plethysmography in 233, 237-242, 238f
anthropometric methods of 268-278
bioelectrical impedance analysis in 260-268, 261-262f, 263t
of children and adolescents 230t, 231, 232t, 239-241, 264, 273
classification and uses of 230, 230t
direct and indirect measures for 59t
dual-energy X-ray absorptiometry in 231, 239, 243-247, 243f, 259
hydration state and 245, 254, 256, 266-267
hydrostatic weighing in 231, 233-234f, 233-237, 242
models of 230-231, 232t
obesity and 256, 270, 271t, 277-278
of older adults 231, 232t
racial and ethnic considerations in 231, 232t, 248, 249t
residual volume in 233-237, 234f, 422
skinfold method for 247-256
3D body surface scanners for 246
ultrasound for 256-257f, 256-260
body density (Db) 231, 233-237, 240-242, 247-249, 270
body hair, in body composition assessment 238-240
BodyJam 142
calculation of 18, 271
healthy body weight and 289-290
nomograms for 271, 272f
in obesity and overweight classification 18-19, 270-272, 272f, 272t, 282
plateau phenomenon and 80
racial and ethnic considerations in 271-272
relationship with percent body fat 270, 271
BodyPump 142
BodyStep 142
body surface area (BSA) 239, 291-292, 292f
body volume (BV) 233-237, 234f, 240, 241, 245
body weight (BW). See also weight management
cardiorespiratory fitness and 300
defined 56
energy expenditure and 136-137
evaluation of 288-289
healthy body weight 289-290
oxygen uptake relative to 80
physical activity and 8-9
self-reported 288
setting goals for 288-290
Body Weight Planner (NIH) 295, 303
bone mineral content (BMC) 56, 245
bone mineral density (BMD) 22, 23, 56, 208, 214, 221-222
bone strength 5t, 6, 56, 59t, 65, 65t
bony breadth 275-276, 278, 430
Borg scales 83
Bouchard, C. 287
Boyle’s law 238
BP. See blood pressure (BP)
bradycardia 49
breast cancer 3, 3f, 6, 7, 16, 21
breathing
in body composition assessment 235, 240, 242
intensity of exercise and 66, 135-136
in Pilates 346
shortness of breath 148
stretching and 341
British Hypertension Society (BHS) 42-46, 42t
Bruce protocol
in maximal tests 90f, 92-93
metabolic equations for 92t
MET estimations for 91t
modified 91f, 91t, 93, 116, 394
nomogram for 93f
853
in submaximal tests 102
summary of 394
work rates for 94t
Brzycki equation 178, 181
BSA (body surface area) 239, 291-292, 292f
BV. See body volume
BW. See body weight (BW)
C
Cain, D.L. 259-260
calipers 253-254, 253-254f, 255t, 276
calisthenic-type exercises
in abdominal training 212
in muscular fitness assessment 172-174, 174-175t
in resistance training 215-216
caloric intake
energy balance and 290-291
food records of 290-291, 434
in weight management 20, 288, 290-291, 294, 303
caloric thresholds 136
Canadian Physical Activity Guidelines 349
Canadian Society for Exercise Physiology (CSEP) 173, 208, 320, 339
cancer. See also specific types of cancer
physical activity and 3, 3f, 6, 7, 21-22
prevalence of 21
risk factors for 13t, 16, 17, 21
cardiorespiratory endurance
defined 56, 79
evaluation of 112-113
maximum oxygen uptake and 56, 59t, 80
physiological changes from training 137, 138
resistance training for 196
cardiorespiratory exercise programs 125-155
aerobic training methods 139-145
body weight and 300
case studies 145-146, 149, 404-406
continuous training in 139-142
discontinuous training in 139, 142-145
duration of exercise in 136-137
exercise prescription for 125-139
frequency of exercise in 136
intensity of exercise in 130-136, 132f, 133t
modes of exercise in 126-130, 129-130f
multimodal 150-151, 153-154
for older adults 130, 134, 136
personalized 145-155
phases of 126
progression in 137-139
recommendations for 126, 127
sample programs, 147-155
volume of exercise in 137
cardiorespiratory fitness assessment 79-122. See also graded exercise tests (GXTs)
bench stepping tests 84, 98-100, 108-110, 109t, 394-395
of children and adolescents 113, 115-118, 117t, 394-395
classifications in 81, 81t
continuous vs. discontinuous 85-87, 96, 97f
cycle ergometer tests 95-98, 97f, 104-107f, 104-108, 116, 117t, 394
elliptical cross-trainer test 111-112, 395
equipment for 84, 86, 119, 121
field tests 112-117, 394-395
forms and questionnaires for 375-390
maximal exercise tests 84-100
of older adults 114, 118-122, 120-121t, 394-395
prediction equations in 92-93, 92t, 95, 109t, 113, 114
recumbent stepper test 100, 111, 395
Rockport walking test 60, 61, 114, 396-397
rowing ergometer test 111, 112f, 395
stair climbing test 110-111, 395
step tests 114-115, 118, 119, 121-122, 121t, 398-399
submaximal exercise tests 100-112
terminology related to 79-80
treadmill tests 87-95, 101-103, 115-116, 117t, 119, 394
unit conversions in 89
cardiovascular disease (CVD). See also coronary heart disease (CHD)
aerobic capacity and 38
graded exercise tests and 82
metabolic syndrome and 20
physical activity and 3f, 6, 7
prevalence of 10
854
risk factors for 10, 11, 14, 17, 21, 35
SCORE system for 35
white coat hypertension and 47
Casanova, C. 120
case studies 145-146, 404-406
cat-camel exercise 344, 457, 459
catecholamines 222, 303, 306
CE (constant error) 62, 63
center of pressure 352, 361
Centers for Disease Control and Prevention (CDC) 4, 10, 66, 282
chair sit-and-reach test 326-327, 326t, 327f
CHD. See coronary heart disease (CHD)
chest leads 51, 52f
chest push 411
children and adolescents
active video games for 74-75
balance assessment of 354, 358
blood pressure measurement in 48, 48t
body composition assessment of 230t, 231, 232t, 239-241, 264, 273
bone density in 23
cardiorespiratory fitness assessment of 113, 115-118, 117t, 394-395
cardiovascular disease in 10
diabetes mellitus in 17
exercise deficit disorder in 6
flexibility assessment for 319, 324
graded exercise tests for 82, 83, 83t, 117t, 118, 394-395
informed consent for 35
MET values for 132
muscular fitness assessment of 167, 177, 185-186
obesity among 19, 282, 283, 286-287
persuasive technology for 75-76
physical activity recommendations for 5t, 6, 22, 297
resistance training for 23-24, 193, 208-209
resting energy expenditure of 293
step count goals for 72t, 73
tobacco use among 16
total energy expenditure in 285t
cholesterol
classification of 34t
functions of 14
guidelines on 36-37, 36-37t
hypercholesterolemia 13t, 14
chylomicron 14
circuit resistance training 145, 196-197, 197f
circumference (C) 269, 271t, 272-273, 276-278, 429
Clark, N.W. 259-260
client interaction 63-64, 69, 177, 252
clothing, in body composition assessment 238-239, 241
CMJ (countermovement jump) 174-176, 175t
cognitive performance 25-26
colon cancer 3, 3f, 6, 7, 21
compound sets 195
computerized dynamic posturography 352-353, 352f
concentric muscle action 160, 160-161f, 202
connective tissue damage 224
consent. See informed consent
constant-angle static stretching 335
constant error (CE) 62, 63
constant-resistance, in muscle testing 166-171, 168-171t
constant-torque static stretching 335
contact mats 174, 176
contemplation stage of motivational readiness 69
continuous exercise tests 85-87, 96, 97f
continuous training 139-142
contract-relax agonist contract (CRAC) technique 333, 334f, 336
contract-relax (CR) technique 333
contracture 312
cool-downs 84, 126, 141, 338
core stability
defined 198
muscles involved in 199
in muscular fitness assessment 173-174, 175t
core strengthening 199
coronary heart disease (CHD)
case study 145-146, 404-406
diagnostic tools for 82
obesity and 19, 282, 284
physical activity and 3, 3f, 6, 11
prevalence of 10
risk factors for 10, 13t, 16, 17, 33, 34t
855
signs and symptoms of 36
countermovement jump (CMJ) 174-176, 175t
counting talk test (CTT) 135-136
CRAC (contract-relax agonist contract) technique 333, 334f, 336
creatine supplements 217-218
Cresswell, A.G. 336-337
criterion (reference) methods of testing 57, 59-60, 59t
CrossFit programs 199-201
cross-training 128, 150-151
cross-validation 61-63
CR (contract-relax) technique 333
CSEP. See Canadian Society for Exercise Physiology (CSEP)
CTT (counting talk test) 135-136
cuff hypertension 47
cultural differences. See race and ethnicity
curl-ups
arm curls 170-171t, 182, 182f, 183t, 412
leg curls 167, 170-171t, 413
for low back pain 343, 344, 458, 459
trunk curl tests 173
on unstable surfaces 213
CVD. See cardiovascular disease (CVD)
cycle ergometers 95, 95-96f
cycle ergometer tests
for children and adolescents 116, 117t
duration of 85
maximal 95-98, 97f
protocol summary 394
submaximal 104-107f, 104-108
cycling 115, 139-140, 147, 150, 301-303
D
dabl Educational Trust website 43, 45
Dalleck, L.C. 111-112
damping technique 236
dance, aerobic 140
Dance Dance Revolution (DDR) 74-76
Davis, J.A. 242
Db. See body density (Db)
DBP. See diastolic blood pressure (DBP)
death. See mortality risk factors
decision-making theory 69
delayed-onset muscle soreness (DOMS) 198, 224-226, 339
dementia 6, 12, 25
densitometry 231, 233-237
Department of Health and Human Services, U.S. (USDHHS) 4-6, 5t, 126, 299t
Desgorces, F.D. 179
Deurenberg-Yap, M. 264-265
DGI (Dynamic Gait Index) 364
diabetes mellitus
exercise prescription for 17-18
physical activity and 3, 3f, 6, 7, 17-18
prediabetes 17
prevalence of 16-17
risk factors for 13t, 17, 19, 21, 282, 284
type 1 17
type 2 3, 6, 7, 13t, 17-18, 21
diastolic blood pressure (DBP) 11, 12, 37, 45-48
diet and nutrition. See also caloric intake
food records 290-291, 434
macronutrients 285t
supplements in 217
in weight management 288, 303
dietary thermogenesis 285
dietitians 297, 298t
digital calipers 254
digital dynamometers 164, 165f, 408
digital goniometers 313-314, 314f
diminishing returns principle 64, 203
Dimmick, J. 139-140
discontinuous exercise tests 85, 87, 96, 97f
discontinuous training 139, 142-145
disease. See also specific diseases
anthropometric assessment and 270-275
noncommunicable 1, 3, 10, 16, 26
physical activity in prevention of 3-4, 3f, 6-8, 17-18, 21-22
risk classification for 33-35, 34t, 382-383
distance run/walk tests 109t, 112-114, 395
diversity. See race and ethnicity
Dods, S. 333
DOMS (delayed-onset muscle soreness) 198, 224-226, 339
856
dopamine 222
dorsiflexor flexibility exercises 448
dose-response relationship 7-8, 8f, 24
double-inclinometer technique 318, 319f
drop-step jump test 174
dual-energy X-ray absorptiometry (DXA) 231, 239, 243-247, 243f, 259
duration of exercise 65-66, 136-137, 336-338
DXA. See dual-energy X-ray absorptiometry (DXA)
dynamic balance 350-353, 357-364
dynamic flexibility 309, 310, 312
Dynamic Gait Index (DGI) 364
dynamic hollowing 344
dynamic muscle action 160, 160f
dynamic muscle testing 166-172
calisthenic-type exercises in 172-174, 174-175t
constant- and variable-resistance in 166-171, 168-171t
endurance tests 169, 171-172, 171t, 173t
equipment for 162, 162t
isokinetic 172, 172f, 179
strength tests 162t, 167-169, 168-170t, 172, 173t
dynamic resistance training 190-198
circuit training 145, 196-197, 197f
eccentric training 197-198
exercises for 415-420
frequency of 191-192t, 193, 195, 215
intensity of 190, 191, 191-192t, 194t, 196
methods of 194-195
order of exercises in 193, 194t, 195
periodization in 193, 195-196, 205-208, 211
repetitions in 190, 191, 191t
rest intervals in 191-192t, 193, 194, 194t
sets in 190, 191, 191t, 193-195, 211-212
training volume in 190, 193, 196
dynamic stretching 311, 312, 332, 332t, 339
dynamic systems model of balance 350
dynamometers
back and leg 162-164, 162f
digital 164, 165f, 408
handgrip 162-163, 162f
handheld 164, 165f, 408
hydraulic 162
isoinertial 167
isokinetic 172, 172f, 179, 201
spring-loaded 162-164, 162f, 164t
dynapenia 209, 214, 224
dyslipidemia 14, 15, 19, 282
E
EAT (exercise activity thermogenesis) 285
eating disorders 282
Ebbeling, C. 103
ebike training 141
eccentric muscle action 160, 160-161f, 202
eccentric training 197-198
ECG (electrocardiogram) 50-52, 51-52f
EDD (exercise deficit disorder) 6
EIMD (exercise-induced muscle damage) 224-226
EIM (Exercise is Medicine) initiative 26
elastic deformation 310
elbow breadth 276, 430
elderly populations. See older adults
electrocardiogram (ECG) 50-52, 51-52f
electrogoniometers 314, 317, 318f
electromyography (EMG) 212, 213
elevated blood pressure 11, 37. See also hypertension
elliptical cross-trainer submaximal test 111-112, 395
elliptical training 140-141
EMG (electromyography) 212, 213
Emmanuel, J. 41
endurance. See cardiorespiratory endurance; muscular endurance
endurance running 113
energy balance. See also energy expenditure
caloric intake and 290-291
negative 284, 288, 294
positive 284, 286
in weight management 284-286, 290-294
energy expenditure
activity trackers for 286
body weight and 136-137
equation for 136-137
factorial method for 291-294, 292f, 293t
857
intensity of exercise and 301, 302
measurement of 284-286
METs for 286, 294, 437-439
physical activity levels and 293-294, 293t
prediction equations for 285, 285t, 291-293
resting 285
total 87-88, 96, 285, 285t, 291, 294-295
energy intake 290-291
environmental factors
in bioelectrical impedance analysis 268
in muscular fitness assessment 178
in obesity 20, 287-288
in response to exercise 132, 134
ePARmed-X+ 31, 33, 384-385
epinephrine 222, 286, 300
equipment. See also specific equipment
for anthropometric assessment 276, 276f
for balance assessment and training 360, 373
for bioelectrical impedance analysis 261-262, 261-262f
for cardiorespiratory fitness assessment 84, 86, 119, 121
for flexibility assessment 327, 328
for muscular fitness assessment 162, 162t, 177, 182, 183, 185
for skinfold method 253-254, 253-254f, 255t
Erceg, D.N. 266
errors
in anthropometric assessment 276-278
in bioelectrical impedance analysis 263-268
in blood pressure measurement 39, 41-48, 42t
client factors and 177
constant 62, 63
in DXA scans 245-246
environmental factors and 178
in hydrostatic weighing 236
in muscular fitness assessment 177-178
in self-reported body weight 288
in skinfold method 250-256, 255t
standard error of estimate 58-61, 58f, 59t, 114, 237, 241
technician skill and 177-178, 250-251, 268
total error 59t, 61, 61f
in ultrasound method 258-260
Esco, M.R. 265-266
ethnicity. See race and ethnicity
European Society of Hypertension International Protocol (ESH-IP) 42-46
exercise activity thermogenesis (EAT) 285
Exercise and Physical Activity Pyramid 9-10, 9f
Exercise and Sports Science Australia 351
exercise deficit disorder (EDD) 6
exercise dos and don’ts 451-455
exercise-induced hypertrophy 219-220, 222, 306
exercise-induced muscle damage (EIMD) 224-226
Exercise is Medicine (EIM) initiative 26
exercise machines
for children and adolescents 167, 185
constant-resistance 166, 177, 181, 190
fixed-form 212
free-motion 167, 212
free-weight training vs. 366
isokinetic 161
lumbar extension 343
older adults and 181, 210
variable-resistance 160-161, 166, 177, 190
exercise modalities 65, 65t, 126-130, 129-130f
Exercise Motivation Inventory 70
exercise prescription
art and science of 64-65, 131
for balance training 364-369, 366t
basic elements of 65-66
for body composition changes 307
for bone health 23-24
for cardiorespiratory fitness 125-139
for diabetes mellitus 17-18
duration of exercise 65-66, 136-137, 336-338
for flexibility training 337-340, 442-450
frequency of exercise 66, 136, 338
guidelines for 126, 127
for hypertension 12, 14
intensity of exercise 65, 130-136, 132f, 133t, 337
for interval training 131, 139, 143-144, 151, 155
for low back pain 24, 341, 343-344, 456-460
modes of exercise 65, 65t, 126-130, 129-130f
multimodal 150-151, 153-154
for older adults 130
progression in 66-67, 137-139, 200
858
sample programs 146, 147-155
volume of exercise 137
for weight gain 304
for weight loss 288, 294, 295, 297-303, 299t
exercise programs. See also cardiorespiratory exercise programs; exercise prescription; physical activity
adherence to 67-71, 68t
benefits of 26, 299-301
for body composition improvement 304-307
design principles for 64
multimodal 150-151, 153-154
personalized 145-155
progression in 66-67, 137-139, 200
sample programs 146, 147-155
spot-reduction 302-303
exercise specialists 55-56, 67, 70-71
exercise tests. See also cardiorespiratory fitness assessment; graded exercise tests; maximal exercise tests; muscular fitness assessment; submaximal exercise tests
blood pressure measurement during 45-46, 48-49
continuous vs. discontinuous 85-87
contraindications to 32, 33, 38, 50
electrocardiogram during 52
medical clearance for 30-31, 31t, 33, 36
perceptually regulated 101-102, 104, 394
exergaming 74-75, 365-366
extreme conditioning programs 199-201
F
factorial method 291-294, 292f, 293t
falls and fall risk 349, 351-352, 363, 364, 367-369. See also balance
false negatives 58
false positives 58
Fantastic Lifestyle Checklist 35, 388
fasting, for DXA scans 244, 245
fast-velocity resistance training 210
fat-free body (FFB) 231, 245, 260
fat-free mass (FFM)
in anthropometric assessment 270
DXA scans and 259
exercise and 299, 300
in hydrostatic weighing 234f
oxygen uptake relative to 80
resistance training and 300, 302, 305-306
ultrasound method and 259, 260
fat loss 216, 300-302, 304-307
fat mass (FM) 245, 262, 265, 266
females. See gender differences; women
FFB (fat-free body) 231, 245, 260
FFM. See fat-free mass (FFM)
field tests
of aerobic fitness 112-115
for children and adolescents 116-117, 394-395
heart rate monitoring during 115
of physical fitness 57-60, 59t, 62-63
prediction equations for 109t
finger blood pressure devices 44, 45
FITT-VP principle 126, 127
fixed-form exercise machines 212
flexed-arm hang test 173
flexibility. See also flexibility assessment; flexibility training; range of motion (ROM); static flexibility; stretching
aging and 311
definitions and nature of 56, 309-311
dynamic 309, 310, 312
excessive 312
gender differences in 311
in multimodal exercise prescriptions 150-151
flexibility assessment 312-328
back scratch test for 327-328, 327f, 328t
equipment for 327, 328
flexometers in 317, 319f
859
goniometers in 312-318, 313-314f, 315-316t, 318f
guidelines for 313
inclinometers in 317-320, 319f
lumbar stability tests for 325-326
for older adults 326-328
sit-and-reach tests for 319-327
skin distraction test for 324-325, 325f
flexibility training 331-343
defined 331
exercise prescription for 337-340, 442-450
guidelines for 341
for low back pain 24
for older adults 311, 340
principles of 331-332
recommendations for 4, 4-5t, 7, 9f, 10
safety considerations in 332, 332t, 337-339
stretching methods in 332-337, 332t, 334f
flexometers 317, 319f
FM. See fat mass (FM)
food records 290-291, 434
foot size, balance and 350
forearm plank percentile scores 166, 166t
Fox, S.M., III 169
Fox cycle ergometer maximal test 98, 394
Fox single-stage cycle ergometer test 108
fractures 22, 23, 221
frame size classification 275-276
Frank, A. 367, 368
FRAX methodology 22-23
FRC (functional residual capacity) 236, 239, 242
free-motion exercise machines 167, 212
free weights 56, 166-168, 177, 190, 345, 366
frequency of exercise
in balance training 364
in cardiorespiratory programs 136
fat loss and 305
in flexibility training 338
overview 66
in resistance training 191-192t, 193, 195, 215
FTO genotype 306-307
functional balance 56, 350
functional fitness testing 181-185, 182f, 183-184t, 184f
functional reach tests 357-359, 358f
functional residual capacity (FRC) 236, 239, 242
functional training 198-200, 215
G
gait velocity 363
gate control theory 336
gender differences. See also women
in balance 350-351
in cancer prevalence 21
in cardiorespiratory fitness classifications 81t
in flexibility 311
in hormonal responses to resistance training 222
in hypercholesterolemia 14
in hypertension 12
in hypertrophy 220
in maximum oxygen uptake formulas 103
in obesity 19, 283, 284
in osteoporosis 22
in physical activity trends 1
in ratings of perceived exertion 135
in resting energy expenditure 293
in resting heart rate 49
in strength-to-body-mass ratios 170t
generalized prediction equations 60, 92, 92t, 248, 261, 270
genetic factors
body composition and 306-307
in metabolic syndrome 21
in obesity 19, 287, 306
transcriptome signature 220-221
genome-wide association studies (GWASs) 19
German Hypertension League (DHL) 42-45
Gettman, L.R. 145
GH. See growth hormone (GH)
Gibby, J.T. 239
global population
cancer in 21
860
cardiovascular disease in 10
diabetes mellitus in 16-17
metabolic syndrome among 20-21
obesity in 18-19, 282-283
osteoporosis in 22
physical activity trends in 1-2, 11
tobacco use in 16
global positioning system (GPS) 74, 116
gluteal squeeze 414
goniometers 312-318, 313-314f, 315-316t, 318f
graded exercise tests (GXTs)
ACSM guidelines for 79, 81
of cardiorespiratory endurance 56
for children and adolescents 82, 83, 83t, 117t, 118, 394-395
contraindications to 38
fitness classifications in 81, 81t
general guidelines for 81-84, 86
heart rate response to 82, 83, 86, 103, 132-133
for older adults 82, 118-119, 394-395
in preliminary health screening 38
ramp protocols for 94
risks involved in 82
termination of 84, 85
Greene, P.F. 165-166
Grenier, S.G. 213
grip endurance testing 163
grip strength testing 162-163, 162f, 164t
groin region flexibility exercises 445
gross V̇O2 80, 88, 88t, 99
growth hormone (GH) 222, 286, 300, 306
GWASs (genome-wide association studies) 19
GXT. See graded exercise tests (GXTs)
gynoid obesity 284
H
Hagerman protocol 111, 395
hamstring flexibility exercises 443-444
handgrip dynamometers 162-163, 162f
handheld dynamometers 164, 165f, 408
Hansen, D. 108
Harris-Benedict equations 292-293
HbA1c 17, 18
HDL-cholesterol (HDL-C). See high-density lipoprotein (HDL)
health. See also disease; preliminary health screening
exercise prescription for improvement of 125-127
motivation related to 67-71, 68t
overexercising and 8, 215
physical activity and 3-4, 3f, 6-9, 7f
health belief model 68
healthy body weight 289-290
heart attack. See myocardial infarction
heart rate (HR). See also heart rate reserve; maximal heart rate
auscultation method for determination of 36, 39, 40, 49
in cardiorespiratory exercise 126, 128-129, 129-130f
classification of 49
in cycle ergometer tests 104-105, 107
electrocardiogram recordings of 50-52, 51-52f
during field tests 115
in graded exercise tests 82, 83, 86, 103, 132-133
intensity of exercise and 132-134, 132f, 133t
measurement of 49-52, 51-52f
medication effects on 32, 37, 100, 148
in older adults 101
oxygen uptake and 100
palpation technique for determination of 49-50
in stair climbing 110-111
steady-state 100, 128-129, 129-130f
in treadmill tests 89, 94, 102, 103
workload and 100, 107f
heart rate monitors 50, 73-74, 115
heart rate reserve (HRR)
in exercise tests 86, 101, 107
intensity of exercise and 130, 131, 133, 133t, 302
percent heart rate reserve method 133, 133t, 134
hemiscan procedure 244
hierarchical model of balance 350
high blood pressure. See hypertension
high-density lipoprotein (HDL)
861
functions of 14
guidelines on 11, 15, 36, 36t
physical activity and 7-8, 7f, 15, 143
high-intensity interval training (HIT) 131, 139, 143-144, 151, 155, 216, 305
high-intensity–low-repetition resistance 190-191
Hill, J.O. 287
hip abductor flexibility exercises 446
hip adductor flexibility exercises 445
hip extensor flexibility exercises 443-444
hip flexor flexibility exercises 442
hip fractures 22, 23
Hispanics
body composition assessment of 232t, 248, 249t
osteoporosis among 22
HIT. See high-intensity interval training
HMB (β-hydroxy-β-methylbutyrate) 218
Hoppeler, H. 198
hormones 222, 286, 300, 306
Hosmer, D. 92-93
HR. See heart rate (HR)
HRmax. See maximal heart rate
%HRmax (percent heart rate maximum) 133-134, 133t
HRR. See heart rate reserve
%HRR (percent heart rate reserve) method 133, 133t, 134
Hui, S.C. 324
Hurst, P.R. 266
HW. See hydrostatic weighing (HW)
hybrid sphygmomanometers 44
hydration state
in DXA scans 245
heart rate and 73
skinfold method and 254, 256
hydraulic dynamometers 162
hydrodensitometry 231, 269
hydrostatic weighing (HW) 231, 233-234f, 233-237, 242
hypercholesterolemia 13t, 14
hyperlipidemia 14, 15, 284, 320
hypermobility 309, 312
hyperplasia 286, 287
hypertension. See also blood pressure (BP)
cuff 47
exercise prescription for 12, 14
masked 47
physical activity and 6, 7, 12
prevalence of 10, 12
risk factors for 12, 13t, 19, 282, 284
stage 1 and stage 2 11, 12, 34t, 37
white coat 46-47
hypertrophy
exercise-induced 219-220, 222, 306
of fat cells 286, 287
plateau phenomenon and 220
resistance training and 193, 202, 216-220, 222, 306
supplements and 217, 218
I
IASO (International Association for the Study of Obesity) 297, 298, 299t
iDXA scanners 245-247, 259, 266
IGF (insulin-like growth factor) 222, 306
IHD (ischemic heart disease) 10, 19, 38
impedance (Z) 260-261, 263, 267-268
improvement stage of exercise programs 67, 138-139
inactivity. See sedentarism and sedentary behaviors
InBody analyzers 265-266
inclinometers 317-320, 319f
Indians. See Native Americans and Alaska Natives
inflammation
atherosclerosis as 10
markers of 24
muscle soreness and 224-226
obesity and 19
informed consent 35, 83, 389-390
initial conditioning stage of exercise programs 66-67, 137-138
initial values principle 64, 203
862
Insanity program 199
instability resistance training 198
Institute of Medicine (IOM) 8, 291
insulin-like growth factor (IGF) 222, 306
intensity of exercise. See also moderate-intensity physical activity; vigorous-intensity physical activity
body composition changes and 305
in cardiorespiratory programs 130-136, 132f, 133t
energy expenditure and 301, 302
in flexibility training 337
heart rate methods for 132-134, 132f, 133t
monitoring 135-136
overview 65
percent V̇O2max reserve method for 130-131, 133t
ratings of perceived exertion and 133t, 134-135
in resistance training 190, 191, 191-192t, 194t, 196
V̇O2reserve (MET) method for 131-132, 132f
for weight loss 297-298, 299t, 302
interactive video games 74-75
interindividual variability principle 64, 203, 331
International Association for the Study of Obesity (IASO) 297, 298, 299t
International Diabetes Foundation 273
International Society for Clinical Densitometry 246
International Society for the Advancement of Kinanthropometry 256, 257
International Society of Musculoskeletal and Neuronal Interactions 213
International Society of Sports Nutrition 217, 295, 303
interpersonal skills 63-64, 69, 177, 252
intertester reliability 58, 251, 259, 318
interval training 131, 139, 143-144, 151, 155
IOM (Institute of Medicine) 8, 291
ischemic heart disease (IHD) 10, 19, 38
isoinertial dynamometers 167
isokinetic dynamometers 172, 172f, 179, 201
isokinetic muscle action 160, 160-161f, 161, 333, 336
isokinetic muscle testing 172, 172f, 173t
isokinetic training 201-202, 201t
isometric muscle action 160, 160f
isometric muscle testing 162-166
clinical methods of 165-166, 166t
dynamometers for 162-164, 162f, 164t, 165f
of grip strength and endurance 162-163, 162f, 164t
of leg and back strength 162-164, 162f, 164t
isometric side support exercise 343, 344, 459
isometric stabilization exercises 344
isometric training 190, 190t, 411-414
isotonic muscle action 160
J
Jackson, A.S. 61, 251, 260, 269
Jackson’s skinfold equations 248-249, 249t, 250f, 428
Jakicic, J.M. 301
Jamar grip dynamometer 162-163
Jay, K. 215
jogging 103, 113-114, 139-140, 148, 152, 395
joints. See also flexibility
classification of 309-310, 310t
laxity of 312
stability of 221-222
Just Jump contact mat 176
K
Kahraman, B.O. 345
Kaminsky, L.A. 94
Kanis, J.A. 22-23
Karvonen (%HRR) method 133, 133t, 134
Kasch Pulse Recovery (KPR) Test 118
Kay, A.D. 333
Kessler, H.S. 143-144
kettlebell training 129, 135, 142, 215
kids. See children and adolescents
kilocalories 284
knee squeeze or pull 413
knee-to-chest exercise 343, 456
KPR (Kasch Pulse Recovery) Test 118
Kraemer, W.J. 300
Kraus-Weber test 165
Kravitz, L. 111-112
Kusumi, F. 92-93
863
L
lactate threshold 135, 300
LDL-cholesterol (LDL-C). See low-density lipoprotein (LDL)
LeanScreen app 269
leg curls 167, 170-171t, 413
leg ergometry 84, 88t, 96, 97
leg extensions 167, 170-171t, 344, 412, 458
leg press 167, 169-170t, 180, 413
leg strength testing 162, 162f, 163, 164t
Leighton flexometer 317, 319f
leisure-time physical activity (LTPA) 6, 21, 24, 25, 284
lifestyle evaluation 35, 148, 386-388
limb leads 51, 52f
limits of agreement 62, 62f, 63
limits of stability test 353
linear periodization (LP) 196, 205, 211
line of best fit 58, 58f, 60, 61
line of gravity 350
line of identity 61, 61f
lipid profiles 7-8, 15-16, 143. See also cholesterol
lipoproteins. See also high-density lipoprotein
defined 14
guidelines on 36, 36t
low-density 10, 14-15, 34t, 36, 36-37t
physical activity and 15
very low-density 14, 15
Liu, H. 367, 368
Low, D.C. 365
low back pain 341-346
core stability testing and training for 345
exercise prescription for 24, 341, 343-344, 456-460
kettlebell training and 215
in older adults 340-341
pilates for 346
prevalence of 24, 340
risk factors for 13t, 24
low-density lipoprotein (LDL)
atherosclerosis and 10
functions of 14
guidelines on 14-15, 36, 36-37t
lower body analyzers 263-265
lower body obesity 284
lower body power test 185
low-intensity–high-repetition resistance 190-191
LP (linear periodization) 196, 205, 211
LTPA. See leisure-time physical activity (LTPA)
lumbar extension exercises 343, 344
lumbar extension machines 343
lumbar stability tests 325-326
lumbar stabilization 344
Lunar Prodigy scanners 239, 246-247, 265-266
lung cancer 16, 21
M
macrocycle 196
macronutrients, energy yield and caloric equivalents for 285t
maintenance stage of exercise programs 67, 139, 145
maintenance stage of motivational readiness 69
males. See gender differences
manometers 39, 41-45
Marshall, L.W. 215
masked hypertension 47
maximal exercise tests 84-100
bench stepping 84, 98-100
cycle ergometer 95-98, 97f
general protocols for 84-87
graded 81-84
recumbent stepper 100
treadmill 87-95
maximal heart rate (HRmax)
for children and adolescents 118
in exercise tests 86, 100-103
intensity of exercise and 132-134, 132f, 133t
percent heart rate maximum 133-134, 133t
maximum oxygen uptake (V̇O2max)
in bench stepping 100, 108-109
cardiorespiratory endurance and 56, 59t, 80
864
in cardiorespiratory exercise programs 136
by children 116-118
in classification of cardiorespiratory fitness 81, 81t
in cycle ergometer tests 96, 98, 104-108
defined 56, 79
in distance run tests 113, 114
in elliptical cross-trainer test 111-112
estimation methods 57
in graded exercise tests 81-84
in maximal exercise tests 85-87
percent V̇O2max reserve 130-131, 133t
ramp protocols and 85-86
in recumbent stepper test 100
relative 80
in rowing ergometer test 111, 112f
in stair climbing 110-111
in treadmill tests 92, 92t, 94, 101-103
maximum voluntary contraction (MVC) 162-163, 212, 215, 223, 226, 337
Mays, R.J. 112
McGill, S.M. 213, 215
McMaster cycle ergometer protocol 116, 117t, 394
mechanical efficiency 100
medical clearance 30-31, 31t, 33, 36
medical history questionnaires 32, 145, 380-381
medications
blood pressure and 11, 12, 32, 37, 146
exercise tests and 32
heart rate and 32, 37, 100, 146
medicine balls 212
Medley, A. 359
Melanson, E.L. 287
men. See gender differences
menstrual cycle 237, 256, 267-268, 282
mercury column manometers 41-45
mesocycles 196, 206-207
metabolic disorders 3, 3f, 8, 20-21, 20t
metabolic equations
for bench stepping 99
for cycle ergometer tests 96, 98, 105
for treadmill tests 87-89, 88t, 92, 92t, 93, 102
metabolic equivalents (METs)
defined 4
in elliptical cross-trainer test 112
for energy expenditure 286, 294, 437-439
for exergaming 74, 75
intensity of exercise and 131-133, 132f
in progression of exercise 66
in rope-skipping activities 129-130
in stair climbing 110
in treadmill tests 89, 91t
in V̇O2reserve method 131-132, 132f
volume of exercise and 137
metabolic profile of muscles 222
metabolic syndrome 8, 20-21, 20t
MET·min·wk−1 127, 137
METs. See metabolic equivalents (METs)
MHC (myosin heavy chain) proteins 219, 221
microcycles 196, 206-207
Mifflin equations 292-293
Miller, H.S. 305
Minamata Treaty (2013) 43
MIPA. See moderate-intensity physical activity (MIPA)
miscuffing 47
mitochondrial volume density 222
moderate-intensity physical activity (MIPA)
in aerobic conditioning 138
for cardiorespiratory fitness 126, 136
defined 4
duration of 136
examples of 8, 74-75
frequency of 66, 136
health benefits of 6, 7, 9, 23, 25, 189
lipid profiles and 15
recommendations for 2, 4, 6, 8, 22, 66
for weight loss 302
for weight maintenance 297-298
moderate-load eccentric exercise 198
modes of exercise 65, 65t, 126-130, 129-130f
modified Balke protocol 116, 117t, 119, 394
modified bird dog exercise 460
modified Bruce protocol 91f, 91t, 93, 116, 394
865
modified front bridge exercise 460
modified Schober test 324
modified sit-and-reach tests 321-322, 322f, 323t, 324, 324f
morphological effects of resistance training 219-222
mortality risk factors
cancer 21
cardiovascular disease 10, 11
diabetes mellitus 17
falls 349
hypertension 11-12
obesity and overweight 18, 19
physical activity and 3, 6-8
motivation for exercise 67-71, 68t
multicomponent models of body composition 231
multimodal exercise programs 150-151, 153-154
multiple correlation coefficient (Rmc) 60
muscle action 160, 160f
muscle balance 179-180, 180t, 193
muscle fibers 219-220
muscle hypertrophy. See hypertrophy
muscle soreness 198, 201, 215, 224-226, 339
muscular endurance
calisthenic-type tests of 172-174, 174-175t
of children and adolescents 208
defined 56, 159
dynamic tests of 169, 171-172, 171t, 173t
grip endurance testing 163
isometric tests of 163, 165-166
low back pain and 344
resistance training for 190-191, 196, 201, 222-223
selection of test items for 180
static tests of 162, 163
muscular fitness assessment 159-186
calisthenic-type tests 172-174, 174-175t
of children 167, 177, 185-186
dynamic tests 166-172, 168-171t, 172f, 173t
equipment for 162, 162t, 177, 182, 183, 185
estimation of 1-RM in 178-179, 179t
isometric tests 162-166, 162f, 164t, 165f
muscle balance 179-180, 180t
muscular power 174-177, 175t
norms for 164, 164t, 166t, 168-169t, 171t, 174-175t, 180, 409-410
of older adults 181-185, 182f, 183-184t, 184f
prediction equations in 178-179, 181
terminology related to 159-161, 160-161f
muscular power
assessment of 174-177, 175t
balance and 351, 367
defined 56, 159
of older adults 185, 210
resistance training for 191, 201
whole-body vibration and 214
muscular strength
absolute 173, 180
activity recommendations for 4-5t, 6
age-related loss of 209, 214, 223-224
calisthenic-type tests of 172
of children and adolescents 208
defined 56, 159
dynamic tests of 162t, 167-169, 168-170t, 172, 173t
of older adults 182, 183, 209-210
predictions from endurance tests 178-179
relative 167, 168-170t, 173, 180
resistance training for 191, 193, 196, 201
selection of test items for 180
static tests of 162-164, 162t, 164t
variations in 160, 161f
whole-body vibration and 214
musculoskeletal disorders 3f, 22-24, 148. See also specific disorders
musculoskeletal fitness 56, 214
MVC. See maximum voluntary contraction (MVC)
myocardial infarction
as contraindication to exercise tests 50
in graded exercise tests 82
risk factors for 10-12, 14-16, 21
myocardial ischemia 10
866
Myogrip dynamometer 163
myosin heavy chain (MHC) proteins 219, 221
Myotest accelerometer 167, 176
N
Nagle, Balke, and Naughton step test protocol 99-100, 394
National Bone Health Alliance (NBHA) 23
National Cholesterol Education Program (NCEP) 20-21, 36-37, 273
National Health and Nutrition Examination Survey (NHANES) 23, 48, 245, 271
National Institutes of Health (NIH) 294-295, 303
National Strength and Conditioning Association (NSCA) 167, 170, 208
Native Americans and Alaska Natives
body composition assessment of 232t
tobacco use among 16
Naughton protocol 90f, 91-92t, 394
NBHA (National Bone Health Alliance) 23
NCD. See noncommunicable diseases (NCDs)
NCEP (National Cholesterol Education Program) 20-21, 36-37, 273
NEAT (non-exercise activity thermogenesis) 285
neck pain, kettlebell training and 215
negative energy balance 284, 288, 294
net caloric cost 136-137
net V̇O2 80
Neuhauser, H.K. 44
neural adaptation 223
NeuroCom Balance Master 353
neurological effects of resistance training 221, 223-224
neuromotor training 349, 364
neuromuscular disorders 100
NHANES (National Health and Nutrition Examination Survey) 23, 48, 245, 271
NIH (National Institutes of Health) 294-295, 303
9 min run test 113, 395
nomograms
for body mass index 271, 272f
for body surface area 291, 292f
for cycle ergometer protocols 104-105, 104f
for rowing ergometer protocols 111, 112f
for skinfold prediction equations 249, 250f
for treadmill protocols 92, 93f
for waist-to-hip ratio 274, 274f
nonaxial joints 310, 310t
noncommunicable diseases (NCDs) 1, 3, 10, 16, 26. See also specific diseases
non-exercise activity thermogenesis (NEAT) 285
norepinephrine 222, 286, 300
normotensive 12, 37
North American Spine Society 344
Northey, J.M. 25, 26
NSCA (National Strength and Conditioning Association) 167, 170, 208
nudge test 356
nutrition. See diet and nutrition
nutritionists 295, 297, 298t
O
obesity 281-288. See also weight loss programs
blood pressure measurement and 48, 48t
BMI classification of 18-19, 270-272, 272f, 272t
body composition assessment and 256, 270, 271t, 277-278
body fat percentage for 230, 230t
causes of 19-20, 284-288, 306
in children and adolescents 19, 282, 283, 286-287
defined 282
fat cells, number and size in 286-287
gender differences in 19, 283, 284
health risks related to 19, 22, 229, 281-282
prevalence of 18, 282-283
racial and ethnic considerations 19, 283
risk factors for 13t, 19
types of 284
obesity paradox 19
objectivity coefficient 58
objectivity of physical fitness testing 58
O’Driscoll, L. 20
older adults. See also aging; Senior Fitness Test Battery
active video games for 75
aerobic training for 141-142
balance assessment of 354-355, 359-360, 359-360t, 363
balance training for 364-371
867
body composition assessment of 231, 232t
cardiorespiratory exercise programs for 130, 134, 136
cardiorespiratory fitness assessment of 114, 118-122, 120-121t, 394-395
cognitive performance in 25-26
falls and fall risk among 349, 351-352, 363, 364, 367-369
flexibility assessment for 326-328
flexibility training for 311, 340
functional fitness testing of 181-185, 182f, 183-184t, 184f
graded exercise tests for 82, 118-119
heart rate in 101
hypertension in 12, 47
low back pain in 340-341
metabolic syndrome in 21
muscular fitness assessment of 181-185, 182f, 183-184t, 184f
osteoporosis in 22
physical activity recommendations for 4-5t, 4-6, 25
progression of exercise for 66
ratings of perceived exertion for 66, 210
resistance training for 203, 204, 209-211, 214, 220-221
trends in physical activity among 1
OMNI RPE scales 83, 83t, 400-403
Omron analyzers 261-262, 262f, 264-266
1.0 mile jogging test 109t, 113-114, 395
1.0 mile run/walk test 113, 114, 116-117, 395
1.5 mile run/walk test 109t, 113, 395
O’Neill, D.C. 259
O’Neill, S. 20
one-repetition maximum (1-RM)
in endurance tests 169
estimation of 178-179, 179t, 181
in resistance training 190, 191, 191-192t, 195-196
steps for 170
in strength tests 167, 168-170t
test standards 409-410
organizations, professional 391
oscillometry 39, 41, 43-45
osteopenia 22
osteoporosis 7, 13t, 22-23, 214, 221, 282
overcuffing 47
overexercising/overtraining 8, 215
overload principle
in abdominal training 212
defined 64
in flexibility training 331-332
in interval training 143
in resistance training 203
overweight. See also obesity; weight loss programs
BMI classification of 18-19, 270-272, 272f, 272t
causes of 284-288
defined 282
health risks related to 281, 282
prevalence of 18, 282-283
oxygen uptake (V̇O2). See also maximum oxygen uptake
absolute 80
in bench stepping 99, 100
exercise modality comparisons 128-129
gross 80, 88, 88t, 99
heart rate and 100
net 80
peak 80, 82, 86, 101-102, 111, 116-117
plateau phenomenon and 79-80, 82, 85-86
resting 130-131
terminology related to 79-80
in treadmill tests 87-89, 88t, 92t, 93f, 94
verification bout and 80, 82
V̇O2reserve 131-132, 132f
P
Pacific Islanders. See Asian and Pacific Islander populations
pain. See low back pain
PAL (physical activity level) 294, 297
palpation
in blood pressure measurement 37, 40
in heart rate measurement 49-50
in pulse rate measurement 49, 112
PARmed-X+ (Physical Activity Readiness Medical Examination) 31, 33, 384-385
PAR-Q+ (Physical Activity Readiness Questionnaire for Everyone) 31, 83, 376-379
passive stretching 332, 335-336
Pavlou, K.N. 300
868
peak power 176
Pea Pod 240
pedometers 72-73, 72t, 137
pelvic stabilization 343, 344
pelvic tilt 343, 414, 456
percent body fat (%BF)
by air displacement plethysmography 239-242
by anthropometric assessment 270
by bioelectrical impedance analysis 261-267, 263t
classification levels 230, 230t
conversion of body density into 231, 232t, 242, 248-249
by DXA 244, 245
by hydrostatic weighing 234f, 235, 237
relationship with body mass index 270, 271
by skinfold method 247-249, 253-254
percent heart rate maximum (%HRmax) 133-134, 133t
percent heart rate reserve (%HRR) method 133, 133t, 134
percentile rankings 63, 166t, 322-323t
percent V̇O2max reserve (%V̇O2R) 130-131, 133t
perceptually regulated exercise test (PRET) 101-102, 104, 394
Performance-Oriented Mobility Assessment (POMA) 363
periodization, in resistance training 193, 195-196, 205-208, 211
personalized exercise programs 145-155
persuasive technology 75-76
perturbation-based balance training 369
Peterson, E.L. 41
Petrella, R. 118, 119, 395
photoplethysmography (PPG) 73-74, 115
physical activity. See also exercise programs; moderate-intensity physical activity; vigorous-intensity physical activity
aging and 24-25
balance and 364-365, 366t
blood pressure and 7f, 12, 143
for children and adolescents 5t, 6, 22, 297
cognitive performance and 25-26
in disease prevention 3-4, 3f, 6-8, 17-18, 21-22
dose-response relationship and 7-8, 8f, 24
energy expenditure and 293-294, 293t
Exercise and Physical Activity Pyramid 9-10, 9f
flexibility and 311-312
leisure-time 6, 21, 24, 25, 284
lipid profiles and 15-16, 143
recommendations for 4-5t, 4-10
stretching prior to 338-339
technology in promotion of 71-76, 72t
trends in 1-2, 4, 11, 67
in weight management 8-9, 288, 297, 299t
in workplace 2, 8-10
Physical Activity Guidelines for Americans 5t, 297, 338, 349
Physical Activity Guide to Healthy Active Living (Health Canada) 7
physical activity level (PAL) 294, 297
physical activity logs 294, 436
Physical Activity Readiness Medical Examination (PARmed-X+) 31, 33, 384-385
Physical Activity Readiness Questionnaire for Everyone (PAR-Q+) 31, 83, 376-379
physical examinations 36
physical fitness, defined 56
physical fitness testing 56-64
administration and interpretation of 63-64
components of 56
objectivity of 58
prediction equation evaluation in 59-63, 59t, 61-62f
pretest instructions 63
purposes of 57
reliability of 58
testing order and environment 57
validity of 57-58, 58f
piezoelectric pedometers 72
Pilates 346, 364, 365, 369
plantar flexor flexibility exercises 447
plaque formation 10, 14
plateau phenomenon 79-80, 82, 85-86, 216, 220
plyometrics 56, 65, 198
PNF. See proprioceptive neuromuscular facilitation (PNF)
P90X 199
Pollock, M.L. 61, 139-140, 145, 169, 251, 260, 269, 304, 305
POMA (Performance-Oriented Mobility Assessment) 363
population-specific prediction equations 60, 92, 92t, 247-248, 261, 270
Porszasz, J. 94-95
positive energy balance 284, 286
POST (people, objectives, strategies, technology) approach 76
postural stress test 356-357
posture, blood pressure measurement and 46
power. See muscular power
869
PPG (photoplethysmography) 73-74, 115
precontemplation stage of motivational readiness 69
prediabetes 17
prediction equations
for anthropometric assessment 270, 271t
for bioelectrical impedance analysis 261-262, 263t, 264
for blood pressure cuff size 48
in cardiorespiratory fitness assessment 92-93, 92t, 95, 109t, 113, 114
for DXA 244, 246
for energy expenditure 285, 285t, 291-293
evaluation of 59-63, 59t, 61-62f
generalized 60, 92, 92t, 248, 261, 270
for HRmax 133-134
for maximal heart rate 101
for maximum oxygen uptake 57
in muscular fitness assessment 178-179, 181
population-specific 60, 92, 92t, 247-248, 261, 270
for residual volume 237, 422
for skinfold method 59-61, 59t, 247-249, 249t, 250f, 252-253
validity of 61-62, 61f
pregnancy, body composition assessment and 246
preliminary health screening 29-52
blood chemistry profile in 36-37, 36-37t
blood pressure measurement in 38-49
components of 29-30, 30t
disease risk classification 33-35, 34t, 382-383
electrocardiogram in 50-52, 51-52f
graded exercise tests in 38
heart rate measurement in 49-52, 51-52f
informed consent for 35, 83, 389-390
lifestyle evaluation in 35, 148, 386-388
medical clearance and 30-31, 31t, 33, 36
medical history questionnaires for 32, 145-146, 380-381
PARmed-X+ in 31, 33, 384-385
PAR-Q+ in 31, 83, 376-379
physical examination in 36
purpose of 29
resting blood pressure in 37, 39, 40
step-by-step procedures for 32
preparation stage of motivational readiness 69
PRET (perceptually regulated exercise test) 101-102, 104, 394
PR interval 50
professional organizations and institutes 391
progression of exercise 66-67, 137-139, 200
progression principle 64, 66, 203
proprioceptive neuromuscular facilitation (PNF) 332-334, 332t, 334f, 336-337, 340
proprioceptive training 364
prostate cancer 16, 21
protein supplements 217, 218
pull-ups 172-173, 215
pulmonary disease 3f, 38
pulmonary ventilation 135
pulse pressure 37
pulse rate measurement 49-50, 110, 112
push-ups 173, 174t, 180, 215-216
P wave 50
pyramiding 195
Q
QRS complex 50
quadriceps flexibility exercises 442
Quality Seal Protocol (German Hypertension League) 42-45
Queens College submaximal step test 108, 109t, 394
R
race and ethnicity. See also specific racial and ethnic groups
body composition and 231, 232t, 248, 249t
body mass index and 271-272
cardiovascular disease and 10
diabetes mellitus and 17
hypertension and 12
obesity and 19, 283
osteoporosis and 22
waist circumference and 273
radiography 318, 324
ramp protocols 85-87, 94-95, 94t, 116
range of motion (ROM). See also flexibility
assessment of 312-314, 317-320
average values for adults 317t
870
improvement of 331-337
joint structure and 309-310
limitations in 327-328
ratings of perceived exertion (RPE)
Borg scales of 83
in cycle ergometer tests 98, 104
exercise modality comparisons 128-129
in graded exercise tests 82, 83
intensity of exercise and 133t, 134-135
maximum oxygen uptake and 82
in older adults 66, 210
OMNI scales of 83, 83t, 400-403
in treadmill tests 94, 101-102
RDA (Recommended Daily Allowance) profile 435
reactance (Xc) 261, 262
reactive balance 350, 356-357, 367
reciprocal inhibition 336
Recommended Daily Allowance (RDA) profile 435
recreational activities 9f, 10
recumbent stepper tests 100, 111, 395
REE (resting energy expenditure) 285, 291-294, 301-302
reference (criterion) methods of testing 57, 59-60, 59t
reflex model of balance 350
regression line 58, 58f, 60, 61
relative body fat. See percent body fat
Relative Risk Chart 35
relative strength 167, 168-170t, 173, 180
relative V̇O2max 80
reliability
of arm curl test 182
of balance assessments 354-357, 359, 361, 363
of Bod Pod 240, 242
of cardiorespiratory fitness tests 103, 116
of countermovement jump 176
of flexibility assessments 314, 318-319
intertester 58, 251, 259, 318
of LeanScreen app 269
of Myotest accelerometer 167
of physical fitness tests 58
of sit-and-reach tests 58
of 6 min walking test 120
of step tests 119, 121-122
of Tendo system 167
of 30 sec chair stand test 183, 185
of 3D body surface scanners 246
of ultrasound devices 259
of V-sit test 165
reliability coefficient 58
repetition maximum (RM) 190, 191. See also one-repetition maximum
repetitions 190, 191, 191t, 338
RER (respiratory exchange ratio) 82
residual scores 58
residual volume (RV) 233-237, 234f, 422
resistance (R) 260-263, 267, 268
resistance bands 212, 216
resistance index (ht2/R) 261
resistance training 189-226
abdominal training in 212
aerobic activities and 216-217
application of training principles to 202-203
balance and 210, 364, 366-367, 366t
biochemical effects of 221-223
body fat and 305-306
bone health and 23-24, 65, 214, 221-222
calisthenic-type exercises in 215-216
for children and adolescents 23-24, 193, 208-209
cholesterol levels and 15-16
client concerns regarding 215-218
core stability and functional training 198-200
development of 202-211
dynamic 190-198, 415-420
extreme conditioning 199-201
fast-velocity 210
flexibility and 216, 338
hypertrophy and 193, 202, 216-220, 222, 306
isokinetic 201-202, 201t
isometric 190, 190t
joint stability and 221-222
kettlebell training in 215
morphological effects of 219-222
871
muscle soreness resulting from 198, 201, 215, 224-226
for muscular endurance 56
neurological effects of 221, 223-224
for older adults 203, 204, 209-211, 214, 220-221
periodization in 193, 195-196, 205-208, 211
plateau phenomenon in 216, 220
recommendations for 4, 9, 9f, 191, 191-192t
supplements and 217-218
transcriptome signature of 220-221
weight and fat loss from 180, 216, 300-302
whole-body vibration in 213-214, 226
respiratory exchange ratio (RER) 82
resting energy expenditure (REE) 285, 291-294, 301-302
resting metabolic rate (RMR) 285-286, 291-294, 301
resting oxygen consumption (V̇O2rest) 130-131
rest intervals, in resistance training 191-192t, 193, 194, 194t
reverse linear periodization (RLP) 196, 211
reversibility principle 64, 203
Riek, S. 336-337
RLP (reverse linear periodization) 196, 211
RM. See repetition maximum (RM)
RMR (resting metabolic rate) 285-286, 291-294, 301
Robergs, R.A. 111-112
ROM. See range of motion (ROM)
Romberg tests 353-354
rope-skipping activities 129-130
rowing ergometer submaximal test 111, 112f, 395
RPE. See ratings of perceived exertion (RPE)
RPE-clamped protocol 98
RPM program 142
running
distance run tests 109t, 112-114, 395
jogging 103, 113-114, 139-140, 150, 152, 395
in treadmill tests 88, 88t, 89, 103
RV (residual volume) 233-237, 234f, 422
S
SAD (sagittal abdominal diameter) 269, 275
safety considerations. See also preliminary health screening
in DXA scans 246
in flexibility programs 332, 332t, 337-339
in functional training 199
for older adult assessment 119-121, 182, 183, 185
in termination of testing 84, 85
sagittal abdominal diameter (SAD) 269, 275
Sahrmann Core Stability Test 173-174, 175t, 345
Santos, T.M. 137
sarcopenia 209, 214, 223-224, 300
SAT. See subcutaneous adipose tissue (SAT)
SBP. See systolic blood pressure (SBP)
Schroeder, E.T. 266
Scientific Committee on Emerging and Newly Identified Health Risks (SCENIHR) 43
SCORE system 35
screening. See preliminary health screening
sedentarism and sedentary behaviors
cognitive performance and 25
defined 3, 26
flexibility and 311-312
frequency of exercise and 66
health risks from 3, 3f, 8, 11, 18, 21-22
heart rate and 49
pedometer-based classification of 72t
SEE. See standard error of estimate (SEE)
Selassie, M. 287-288
self-determination theory 70
self-efficacy 63, 68-69, 71
self-paced cycle ergometer maximal test 98
self-paced stepping protocol 118, 119
self-paced treadmill protocols 93-94
Senior Fitness Test Battery
for balance assessment 360
for cardiorespiratory fitness 119
for flexibility 326
for muscular fitness 182-185, 182f, 183-184t, 184f
seniors. See older adults
sensitivity, in test validity 58
sets
compound 195
872
defined 190
guidelines for 191, 191t
multiple-set programs 193-195, 211-212
pyramiding 195
single-set programs 193, 194, 211-212
supersetting 195
tri-sets 195, 206-208
variations for 194-195
Sharman, M.J. 336-337
Short, K.R. 143-144
Short Physical Performance Battery 351
shoulder flexibility exercises 449-450
shoulder pull 411
side bridge exercise 343, 344, 459
side bridge test 165-166, 326
Simon, L. 41
simplified skin distraction test 324-325, 325f
simulation technology 75
single-leg extensions 344, 458
Sinha, A.C. 287-288
Siri equation 231, 242, 249
Sisson, S.B. 143-144
SIT (sprint interval training) 143, 144
sit-and-reach tests 319-327
back-saver 323-324, 323f
chair 326-327, 326t, 327f
modified 321-322, 322f, 323t
modified back-saver 324, 324f
standard test 320-321, 321t
validity and reliability of 58, 59, 319-320, 322-324, 327
V sit-and-reach 321, 322t
sit-stand workstations 2
sit-ups 171t, 302-303, 457
6 min walking test 119-120, 120t
skeletal anthropometers 276, 276f
skeletal diameter (D) 269, 275-277
skin distraction test 324-325, 325f
skinfold (SKF) method 247-256
assumptions in 247
calipers for 253-254, 253-254f, 255t
client interaction in 252
comparison with ultrasound method 259-260
error sources in 250-256, 255t
hydration state and 254, 256
Jackson’s skinfold equations 248-249, 249t, 250f, 428
obesity and 256
prediction equations in 59-61, 59t, 247-249, 249t, 250f, 252-253
racial and ethnic considerations in 248, 249t
relationship with body density 247, 248, 248f
sites for 251, 423-428, 424-427f
technician skill in 249-252
Smith, L.L. 225
smoking 16
social cognitive model 68-69
social networking 76
sodium intake 37
soreness. See muscle soreness
Sorensen test 165
specificity, in test validity 58
specificity principle 64, 65, 202-203, 331
sphygmomanometers 39, 41-44
Spinning classes 144-145
split routines 193, 195, 206-208, 215
sports and recreational activities 9f, 10
spot-reduction exercises 302-303
spotting 166-168
spring-loaded dynamometers 162-164, 162f, 164t
sprint interval training (SIT) 143, 144
squat tests 167, 409
SRC (stretch-return-contract) technique 333
SRP (steep ramp cycling protocol) 116
stability balls 212
stage 1 and stage 2 hypertension 11, 12, 34t, 37
stages of exercise progression 137-139
stages of motivational readiness for change model 69
stair climbing 110-111, 140, 395
standard error of estimate (SEE) 58-61, 58f, 59t, 114, 237, 241
standing long jump 177
standing McKenzie exercise 460
star excursion balance test 361-363, 362f, 362t
static balance 350-357
static flexibility
aging and 311
873
defined 309
muscle tension and 310
static jump test 174, 176
static muscle action 160, 160f
static strength testing 162-164, 162t, 164t
static stretching 310-312, 332, 332t, 334-335, 337-340
steady-state heart rate 100, 128-129, 129-130f
steep ramp cycling protocol (SRP) 116
step count goals 72-73, 72t
step ergometry 140
StepMania Endless 74
Step Test and Exercise Prescription (STEP) tool protocol 108-109, 109t, 395
step tests
as field tests 114-115
metabolic equation for 88t
for older adults 118, 119, 121-122, 121t
protocols for 398-399
stethoscopes 39, 49
strength. See bone strength; muscular strength
stress relaxation 310, 335, 336
stretching 332-337
active 332, 335-336
balance and 351
ballistic 311, 332, 332t, 334-335
in cardiorespiratory exercise 126
dynamic 311, 312, 332, 332t, 339
excessive 312
guidelines for 341
muscle performance and 339
passive 332, 335-336
prior to physical activity 338-339
proprioceptive neuromuscular facilitation 332-334, 332t, 334f, 336-337, 340
static 310-312, 332, 332t, 334-335, 337-340
vibration-aided 339-340
stretch-return-contract (SRC) technique 333
stretch tolerance 311, 331-333, 338, 340
stroke
physical activity and 6, 7, 12
prevalence of 10
risk factors for 11, 14, 16, 17, 21, 282
ST segment 50
subcutaneous adipose tissue (SAT) 247, 287, 303, 305
submaximal exercise tests 100-112
assumptions of 100-101
bench stepping 108-110, 109t
cycle ergometer 104-107f, 104-108
elliptical cross-trainer 111-112
graded 81-82, 84, 86
recumbent stepper 111
rowing ergometer 111, 112f
stair climbing 110-111
treadmill 101-103
super circuit resistance training 145
supersetting 195
SuperTracker (U.S. Department of Agriculture) 295
supplements 217
suspension training 212
Swain cycle ergometer submaximal test 106-107, 107f, 394
swimming tests 115
Swinburn, B.A. 287, 288
switch mats 174
systolic blood pressure (SBP) 11, 12, 37, 45-48
T
tachycardia 49
Tae Bo 142
tai chi 56, 130, 364, 365, 366t, 367-368
talk tests 135-136
Tanita analyzers 261-266, 262f
tare weight 234f, 235
TBW. See total body water (TBW)
TC. See total cholesterol (TC)
TE (total error) 59t, 61, 61f
technician skill 177-178, 250-252, 258, 268, 277
technology. See also dynamometers
accelerometers 73, 74, 167, 176, 286, 301
active video games 74-75
activity trackers 286, 301
874
blood pressure smartphone apps 39
calipers 253-254, 253-254f, 255t
electrocardiogram 50-52, 51-52f
goniometers 312-318, 313-314f, 315-316t, 318f
GPS 74, 116
heart rate monitors 50, 73-74, 115
pedometers 72-73, 72t, 137
persuasive 75-76
social networking 76
telemonitoring scales 288
ultrasound 256-257f, 256-260
virtual reality 75, 365-366
wearable 72-74, 72t, 301, 317
for workplace physical activity 2
TEE. See total energy expenditure (TEE)
Tegenkamp, M.H. 242
telemonitoring scales 288
telomeres 24-25
Tendo Weightlifting Analyzer System 167
terminal digit bias 44
test anxiety 57
testosterone 222, 300, 306
TGV (thoracic gas volume) 239, 241, 242
theory of planned behavior 70
theory of reasoned action 69-70
thigh extensions 412
30 sec chair stand test 183-185, 184f, 184t
Thompson, M. 359
thoracic gas volume (TGV) 239, 241, 242
three-dimensional (3D) body surface scanners 246
thyroxine 286
timed one-leg stance test 354-355, 354t
timed up and go tests 359-360t, 359-361, 361f
TLC (total lung capacity) 236, 237
TMS (transcranial magnetic stimulation) 223
tobacco use 16
tonic vibration reflex 213-214
Toomey, C. 258-259
total body water (TBW) 231, 237, 260-261, 267, 268
total cholesterol (TC) 14, 15, 34t, 36, 36t. See also cholesterol
total energy expenditure (TEE) 87-88, 96, 285, 285t, 291, 294-295
total error (TE) 59t, 61, 61f
total lung capacity (TLC) 236, 237
training volume 64, 190, 193, 196, 203
transcranial magnetic stimulation (TMS) 223
transcriptome signature of resistance exercise 220-221
transtheoretical model 69
treading workouts 144-145
treadmill desks 2
treadmills 87f
treadmill tests
Balke protocol for 91-92t, 91f, 92, 93f, 103, 394
Bruce protocol for 90f, 91-92t, 92-93, 93f, 94t, 102, 394
for children and adolescents 115-116, 117t
duration of 85
maximal 87-95
metabolic equations for 87-89, 88t, 92, 92t, 93, 102
modified Balke protocol for 116, 117t, 119, 394
modified Bruce protocol for 91f, 91t, 93, 116, 394
multistage model 102
Naughton protocol for 90f, 91-92t, 394
oxygen uptake in 87-89, 88t, 92t, 93f
ramp protocols for 94-95, 94t
running/jogging 88, 88t, 89, 103
self-paced protocols for 93-94
single-stage model 103
submaximal 101-103
unit conversions in 89
walking 88-89, 88t, 103
triaxial joints 310, 310t
triceps extension 171t, 411
triglycerides
guidelines on 36t, 37
physical activity and 7, 7f
tri-sets 195, 206-208
trunk curl tests 173
trunk endurance tests 165-166
trunk extensor flexibility exercises 448-449
trunk flex exercise 343, 456
trunk flexor flexibility exercises 446, 449-450
T wave 50
875
12 min cycling test 115
12 min run test 109t, 113, 395
12 min swimming test 115
twelve-lead electrocardiogram 50-52, 51-52f
20 m shuttle run test 114, 117, 395
two-component model of body composition 231, 232t
2 min step test 121-122, 121t
type A, B, C, and D aerobic activities 127-128, 132, 139, 142-143, 150
type 1 diabetes 17
type 2 diabetes 3, 6, 7, 13t, 17-18, 21
U
ultrasound 256-257f, 256-260
undercuffing 47
underwater weight (UWW) 233-236, 234f
underweight. See also weight management
BMI classification of 270-271, 272t
defined 282
health risks related to 281, 282
undulating periodization (UP) 196, 206-208, 211
uniaxial joints 310, 310t
unipedal stance test 354-355, 354t
unit conversions 89
United States
cancer in 21
cardiovascular disease in 10, 11
diabetes mellitus in 17
dietitian and nutritionist regulation in 297, 298t
falls and fall risk in 349
hypertension in 12
kettlebell training in 215
metabolic syndrome in 20-21
obesity in 18, 19, 282-283, 287
osteoporosis in 22
physical activity trends in 1, 4, 67
preferred test modalities in 87
tobacco use in 16
universal goniometers 312-314, 313f, 315-316t, 318
UP (undulating periodization) 196, 206-208, 211
upper body analyzers 263-265
upper body obesity 284
USDHHS (U.S. Department of Health and Human Services) 4-6, 5t, 126, 299t
UWW (underwater weight) 233-236, 234f
V
validity
of arm curl test 182
of balance assessments 354, 355, 357, 363
of cardiorespiratory fitness tests 103, 105, 107, 113-116
of countermovement jump 176
of flexibility assessments 314, 318
of Myotest accelerometer 167
of OMNI RPE scales 83
of physical fitness tests 57-58, 58f
of prediction equations 61-62, 61f
of sit-and-reach tests 58, 59, 319-320, 322-324, 327
of 6 min walking test 120
of step tests 119, 121-122
of Tendo system 167
of 30 sec chair stand test 183, 185
validity coefficient 57-58, 61, 62
variable-resistance exercise 166-171, 168-171t
variable-resistance muscle action 160-161, 160f
VAT. See visceral adipose tissue (VAT)
ventilatory threshold (VT) 135
Vera-Garcia, F.J. 213
verification bout 80, 82
Vertec device 174, 176
vertical jump test 174-176, 175t
vertical whole-body analyzers 266
very low-density lipoprotein (VLDL) 14, 15
vibration-aided static stretching 339-340
video games 74-75
vigorous-intensity physical activity (VIPA)
for cardiorespiratory fitness 126
duration of 136
examples of 8, 74-75, 127
frequency of 66, 136
health benefits of 6, 7
876
recommendations for 2, 4, 6, 8, 22, 66
for weight loss 302
virtual reality 75, 365-366
visceral adipose tissue (VAT)
estimation of 243, 246-247, 275
exercise and 302, 304, 305
genetic factors related to 287
waist-to-hip ratio and 284
viscoelastic creep 335
viscoelastic properties 310, 336
viscous deformation 310
VLDL (very low-density lipoprotein) 14, 15
V̇O2. See oxygen uptake
V̇O2max. See maximum oxygen uptake
V̇O2peak 80, 82, 86, 101-102, 111, 116-117
%V̇O2R (percent V̇O2max reserve) 130-131, 133t
V̇O2reserve (V̇O2R) 131-132, 132f
V̇O2rest (resting oxygen consumption) 130-131
Volek, J.S. 300
volume of exercise 137
V sit-and-reach test 321, 322t
V-sit test 165
VT (ventilatory threshold) 135
W
Wagner, D.R. 256-258, 259-260
waist circumference 20, 269, 272-273, 429
waist-to-height ratio (WHTR) 274-275
waist-to-hip ratio (WHR) 269, 273-274, 274f, 274t, 284
walking
ACSM walking equation 88-89, 88t
in aerobic training 139-140
gait velocity 363
metabolic equation for 88t
pedometers and 72-73, 72t, 137
6 min walking test 119-120, 120t
in treadmill tests 88-89, 88t, 103
Walsh, G.S. 365
Wang, J-G. 264, 266
Ward, A. 61
Ware, R. 91f, 92
warm-ups 126, 141, 312, 338-339
water-based exercise 141-142
WBV (whole-body vibration) 213-214, 226
wearable technology 72-74, 72t, 301, 317
Webb step test 109-110, 395
weight gain programs 288, 303-304
weight loss programs 294-303
activity trackers in 301
caloric intake in 288, 294
designing 296-297
exercise prescription in 288, 294, 295, 297-303, 299t
spot-reduction in 302-303
weight management 288-307. See also obesity; overweight; underweight; weight loss programs
body composition improvement 304-307
body weight goals in 288-290
caloric intake in 20, 288, 290-291, 294, 303
energy balance assessment in 284-286, 290-294
physical activity in 8-9, 288, 297, 299t
prevention of weight gain and regain 297-299, 302
principles of 288, 289
trends in 283-284
weight gain programs 288, 303-304
weight-to-height indices 269
Whaley, M.H. 94
whey protein supplements 217, 218
white coat hypertension 46-47
white populations
body composition assessment of 231, 232t, 248, 249t, 271t
tobacco use in 16
WHO. See World Health Organization (WHO)
whole-body BIA method 261, 263, 265, 266
whole-body vibration (WBV) 213-214, 226
877
WHR. See waist-to-hip ratio (WHR)
WHTR (waist-to-height ratio) 274-275
Wii balance board 353
Wii Fit 75, 353
Wii Sports 75
Wilmore, J.H. 169
wobble boards 213
women. See also gender differences
anorexia nervosa among 282
body composition assessment for 237, 256, 265, 267-268
bone health in 221-222
menstrual cycles in 237, 256, 267-268
workload
in bench stepping 98-99
blood pressure and 48
in continuous vs. discontinuous exercise tests 85, 87
in cycle ergometer tests 94-95, 98, 104-107, 106-107f
duration of 82
heart rate and 100, 107f
in recumbent stepper test 100
in treadmill tests 87-88, 88t, 90-91f, 92, 94-95, 94t
workplace, physical activity in 2, 8-10
World Health Organization (WHO)
BMD T-score criterion from 23
Global Action Plan developed by 1, 3
Global Health Observatory data from 283
on hypertension 11
on obesity and overweight 18, 19, 271
tobacco use statistics from 16
on waist-to-hip ratio 273
wrist blood pressure devices 44-46
X
Xbox Kinect system 75
Y
Y balance test 362-363
YMCA
bench press test 169, 179
cycle ergometer submaximal test protocol 105-106, 105-106f, 111, 394
sit-and-reach test 321, 322t
yoga 56, 364, 365, 366t, 368-369
youth. See children and adolescents
Yo-Yo Intermittent Recovery Level 1 test (YYIR1C) 118, 395
Yuen, P.Y. 324
Z
Z (impedance) 260-261, 263, 267-268
Zumba 139, 140
878
About the Authors
Ann L. Gibson, PhD, FACSM, is an associate professor and researcher in exercise science at
the University of New Mexico, with research interests in body composition and physiological
responses to exercise. She developed the ancillary materials for the sixth edition of Advanced
Fitness Assessment and Exercise Prescription in addition to coauthoring the seventh edition.
Gibson has presented internationally in the area of obesity research and has published
original research in journals such as Medicine & Science in Sports & Exercise, American Journal
of Clinical Nutrition, International Journal of Sport Nutrition & Exercise Metabolism, Research
Quarterly for Exercise and Sport, and Journal of Bone and Joint Surgery. She is a member of the
American College of Sports Medicine, National Strength and Conditioning Association, and
the Clinical Exercise Physiology Association.
Gibson resides in New Mexico, where she enjoys spending time outdoors hiking, biking,
snowshoeing, cross-country skiing, and gardening.
Dale R. Wagner, PhD, EPC, ACSM-CEP, CSCS, is a professor of exercise physiology at

Utah State University (USU). His research interests include body composition assessment
and exercise physiology at high altitude. He has been an active researcher for 20 years and has
authored over 60 peer-reviewed research publications. He is a coauthor of Applied Body
Composition Assessment (Human Kinetics, 2004) with Vivian Heyward.
Wagner is a past president of the Southwest Chapter of the American College of Sports
Medicine (SWACSM) and of the American Society of Exercise Physiologists. He is a
research council member of the Wilderness Medical Society and a member of the National
Strength and Conditioning Association, the International Society for Mountain Medicine,
and the International Society for Body Composition Research.
In his spare time, Wagner enjoys mountaineering, cycling (both road and mountain), and
international travel.
Vivian H. Heyward, PhD, is a regents’ professor emerita at the University of New Mexico,
where she taught physical fitness assessment and exercise prescription courses for 26 years. In
addition to the previous editions of this book, she has authored two editions of Applied Body
Composition Assessment (Human Kinetics, 1996, 2004) as well as numerous articles in research
879
and professional journals dealing with various aspects of physical fitness assessment and
exercise prescription. Heyward has received many professional awards, including the
SWACSM Recognition Award for distinguished professional achievement and the
Distinguished Alumni Award from the University of Illinois and the State University of New
York at Cortland.
In her free time, she enjoys hiking, nature photography, golfing, and snowshoeing.
Heyward resides in Albuquerque, New Mexico.
880
881

9781492561347

Uploaded by

Copyright:

Available Formats

9781492561347

Uploaded by

Document Information

Copyright

Available Formats

Share this document

Share or Embed Document

Sharing Options

Did you find this document useful?

Is this content inappropriate?

Copyright:

Available Formats

9781492561347

Uploaded by

Copyright:

Available Formats

Check Out the Online Video!

Follow these steps to purchase access to the online video:

Advanced Fitness Assessment

Ann L. Gibson, PhD

Dale R. Wagner, PhD

Vivian H. Heyward, PhD

ISBN: 978-1-4925-6134-7 (print)

Copyright © 2019 by Ann L. Gibson, Dale R. Wagner, and Vivian H. Heyward

Copyright © 2014 by Vivian H. Heyward and Ann L. Gibson

Copyright © 2010, 2006, 2002, 1998, 1991 by Vivian H. Heyward

Copyright © 1984 by Burgess Publishing Company

Senior Acquisitions Editor: Michelle Maloney; Senior Developmental Editor: Cynthia

We thank the Exercise Physiology Laboratory at the University of New Mexico,

Printed in the United States of America

The paper in this book is certified under a sustainable forestry program.

In the United States, email info@hkusa.com or call 800-747-4457.

In Canada, email info@hkcanada.com.

Chapter 1 Physical Activity, Health, and Chronic Disease

Physical Activity, Health, and Disease: An Overview

Chapter 2 Preliminary Health Screening and Risk Classification

Preliminary Health Evaluation

Chapter 3 Principles of Assessment, Prescription, and Exercise Program

Physical Fitness Testing

Chapter 4 Assessing Cardiorespiratory Fitness

Chapter 5 Designing Cardiorespiratory Exercise Programs

The Exercise Prescription

Chapter 6 Assessing Muscular Fitness

Chapter 7 Designing Resistance Training Programs

Types of Resistance Training

Chapter 8 Assessing Body Composition

Classification and Uses of Body Composition Measures

Chapter 9 Designing Weight Management and Body Composition Programs

Obesity, Overweight, and Underweight: Definitions and Trends

Chapter 10 Assessing Flexibility

Chapter 11 Designing Programs for Flexibility and Low Back Care

Chapter 12 Assessing Balance and Designing Balance Programs

Definitions and Nature of Balance

Appendix A Health and Fitness Appraisal

A.1 Physical Activity Readiness Questionnaire for Everyone (PAR-Q+)

Appendix B Cardiorespiratory Assessments

Appendix C Muscular Fitness Exercises and Norms

C.1 Standardized Testing Protocols for Digital Handheld Dynamometry

Appendix D Body Composition Assessments

D.1 Prediction Equations for Residual Volume

Appendix E Energy Intake and Expenditure

E.1 Food Record and RDA Profile

Appendix F Flexibility and Low Back Care Exercises

F.1 Selected Flexibility Exercises

About the Authors

Video 2.1 Resting BP measurement

Video 4.2 Safely mounting a treadmill

NCCA affiliates NBFE affiliates

National Academy of Sports Medicine (NASM)

Physical Activity, Health, and Chronic Disease

How does physical inactivity differ from sedentarism?

How does physical activity improve health?

How much physical activity is needed for improved health benefits?

USING TECHNOLOGY TO INCREASE PHYSICAL ACTIVITY AT WORK

PHYSICAL ACTIVITY, HEALTH, AND DISEASE: AN OVERVIEW

coronary artery disease;