709317

research-article2017
HEBXXX10.1177/1090198117709317Health Education & BehaviorBaruth et al.

Original Article
Health Education & Behavior

Effects of a Self-Directed Nutrition 2018, Vol. 45(1) 61­–67

© 2017 Society for Public
Health Education
Intervention Among Adults With Reprints and permissions:
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Chronic Health Conditions DOI: 10.1177/1090198117709317

https://doi.org/10.1177/1090198117709317
journals.sagepub.com/home/heb

Meghan Baruth, PhD1, Sara Wilcox, PhD2,

Danielle E. Jake-Schoffman, PhD3, Rebecca A. Schlaff, PhD1,
and Tatum M. Goldufsky, BS4

Abstract
Chronic diseases are common among adults. A healthy diet may be beneficial for managing the consequences of such
conditions. The purpose of this study was to evaluate the effects of a self-directed nutrition program on dietary behaviors
among adults with chronic health conditions. As part of a larger trial examining the effects of a self-directed exercise
program, participants with arthritis were randomized to a 12-week self-directed exercise or nutrition intervention. Self-
reported fruit and vegetable consumption, fat- and fiber-related behaviors were assessed at baseline, 12 weeks, and 9
months. Repeated measures analyses of covariance examined Group × Time changes in dietary behaviors. Effect sizes were
computed. Participants (n = 321) were, on average, 56.5 ± 10.5 years old, had a mean body mass index of 32.9 ± 8.3 kg/m2,
and had 2.0 ± 1.0 chronic health conditions; 88% were female, 65% White, 88% had at least some college education, and 62%
married. There were significant Group × Time interactions favoring the nutrition group at 12 weeks for all dietary behaviors
(p < .05) but not at 9 months. Between-group effect sizes were small at 12 weeks and decreased at 9 months. Within-group
effect sizes were larger for the nutrition group (small to medium) than the exercise group (none to small) at both time points.
A self-directed nutrition intervention can result in meaningful improvements in dietary behaviors among adults with chronic
health conditions in the short term.

Keywords
chronic disease, dietary behaviors, intervention, nutrition, public health, self-directed

Chronic diseases are a large and growing public health prob- reduces the risk of diseases such as coronary heart disease,
lem. Seven of the top 10 leading causes of death in the United hypertension, stroke, cancer, and overweight, among others
States are chronic diseases (Murphy, Kochanek, Xu, & Arias, (Boeing et al., 2012). The evidence regarding lowering satu-
2015). About half of all adults in the United States have at rated fat intake is mixed, although it has been associated with
least one chronic health condition, and many adults (espe- reduced risk of coronary heart disease (Astrup et al., 2011).
cially middle to older) have multiple chronic diseases (Ward Additionally, increased intake of dietary fiber has been shown
& Schiller, 2013). Approximately one third of those 45 to 64 to reduce the risk of cardiovascular disease, diabetes, obesity,
years of age and over 60% of those 65+ years of age have breast cancer, and colon cancer (Timm & Slavin, 2007).
multiple chronic health conditions (Ward & Schiller, 2013). There is also evidence suggesting that dietary factors/behav-
Obesity was not included as a chronic disease in these esti- iors can help manage many chronic health conditions, includ-
mates; therefore, the prevalence of multiple chronic diseases ing diabetes (Pastors, Warshaw, Daly, Franz, & Kulkarni,
is likely to be even higher.
Contributors to these high rates of chronic disease include
1
unhealthy diet, physical inactivity, and tobacco use (Dietz, Saginaw Valley State University, University Center, MI, USA
2
University of South Carolina, Columbia, SC, USA
Douglas, & Brownson, 2016). These factors are modifiable 3
University of Massachusetts Medical School, Worcester, MA, USA
and are commonly referred to as the leading causes of actual 4
Michigan State University, East Lansing, MI, USA
death (Mokdad, Marks, Stroup, & Gerberding, 2004). A num-
Corresponding Author:
ber of specific dietary factors can help prevent the develop-
Meghan Baruth, Saginaw Valley State University, 7400 Bay Road,
ment of comorbid chronic health conditions. There is evidence University Center, MI 48710, USA.
suggesting that increased fruit and vegetable consumption Email: mbaruth@svsu.edu
62 Health Education & Behavior 45(1)

2002), hypertension (Appel et al., 2006), and obesity (Makris Table 1.  Eligibility and Ineligibility Criteria.
& Foster, 2011). Participants were eligible if they:
Unfortunately, a majority of American adults do not meet • Were told by a health care professional that they have some
dietary guidelines (Krebs-Smith, Guenther, Subar, form of arthritis
Kirkpatrick, & Dodd, 2010). Lifestyle interventions focusing • Reported at least one symptom of arthritis (joint pain,
on healthy eating may be one means for preventing chronic stiffness, tenderness, decreased range of motion, redness and
diseases and/or improving the health of those with chronic warmth, deformity, crackling or grating, fatigue)
diseases. A number of dietary interventions focused on pre- • Were ≥18 years of age
venting or managing chronic diseases have been conducted; • Were the only one in their household participating in the
study
however the effectiveness of these programs on dietary
• Were not planning to move out of the area in the next 9
adherence outcomes is mixed (Desroches et al., 2013). A months
majority of these interventions to date are group-based or • Were able to read and write in English
individualized with a health professional (e.g., nurse, dieti- • Were not participating in another research study (unless
cian; Desroches et al., 2013). In an effort to combat this sig- it was an observational study without an intervention or
nificant public health problem, there is a need for low-cost medication)
programs that are easily accessible and can reach a large Participants were ineligible if they:
number of people. One such intervention approach may be •• Endorsed an item on the Physical Activity Readiness
computer-tailored interventions, which have shown promise Questionnaire (Adams, 1999):
for improving dietary outcomes (Broekhuizen, Kroeze, van ♦ Note: Participants were not excluded if they took
medication for hypertension and their blood pressure was
Poppel, Oenema, & Brug, 2012). Self-directed print dietary controlled
programs may also be appealing, as they require very few • Had a fall in the past year that required medical assistance
resources in terms of staff time and equipment, making large- • Were pregnant, breastfeeding, or planning to become
scale implementation feasible and potentially effective. pregnant in the next year
Unfortunately, self-directed programs that can successfully • Were diabetic and taking insulin
improve dietary behaviors that do not do not require the • Could not walk longer than 3 minutes without a rest
Internet or phone contact (Fries et al., 2005; Kristal, Curry, • Could not stand without assistance for more than 2 minutes
Shattuck, Feng, & Li, 2000) are rare. • Could not sit in chair without arms for more than 5 minutes
As part of a larger trial designed to evaluate the effective- • Were already physically active (aerobic activities ≥3 days/week
ness of a self-directed exercise program (First Step to Active for ≥30 minutes/day or strength training ≥2 days/week for ≥20
Health®; Wilcox et al., 2015), a self-directed nutrition pro- minutes/day)
gram (i.e., Steps to Healthy Eating) was developed to serve as
the attention control condition. Results from the main trial
have been published elsewhere (Wilcox et al., 2015). Briefly, 2012). A number of strategies to recruit participants were
participants in the exercise condition showed greater increases used; the most successful were e-mails sent to worksite list-
in physical activity than those in the nutrition group, and servs and advertisements in newspapers.
weight significantly decreased in the nutrition group at 9
months (~2 lbs), whereas there was no change in the exercise Procedure
group. Although evaluating the effects of this program was not
a main aim of the trial (Wilcox et al., 2015), the purpose of the Participants deemed eligible were scheduled for a measure-
secondary analyses conducted in this article were to evaluate ment session that was held at the University of South
the effectiveness of the self-directed nutrition program on fruit Carolina. At the session, informed consent was obtained, and
and vegetable consumption and fat- and fiber-related behav- participants turned in their survey (completed prior to the
iors among adults with chronic health conditions. session) and completed physical and functional measure-
ments. At the end of the session, each participant was ran-
domized to a self-directed exercise program (First Step to
Method Active Health®) or to an attention control self-directed nutri-
tion program (Steps to Healthy Eating). Participants were
Participant Recruitment
oriented to their program by study staff. The same measure-
Participants with self-reported doctor diagnosed arthritis and ment procedures were used at each follow-up session (i.e.,
who met other eligibility criteria (Table 1) based on a tele- 12 weeks and 9 months). Participants received a monetary
phone screening interview were eligible to take part. This incentive for taking part in each measurement session and for
validated case definition of arthritis has been used in the returning logs (described below). This study was approved
National Health Interview Survey and the Behavioral Risk by the institutional review board at the University of South
Factor Surveillance System (Hootman, Helmick, & Brady, Carolina.
Baruth et al. 63

Interventions 2 into their routine (while continuing on with Step 1), and so
on. Although progression through the program was self-
First Step to Active Health® paced, participants were encouraged to incorporate all four
Participants randomized to the intervention group received steps by the end of 12 weeks.
the First Step to Active Health® program (i.e., exercise
group), which is a 12 week, self-directed multicomponent Measures
progressive exercise program. The four “Steps” were as fol-
lows: (1) cardiovascular activities, (2) flexibility, (3) strength, Measures were completed at baseline, 12 weeks, and 9
and (4) balance. Dietary information was not included in this months. Measurement staff were blinded to group assign-
program. More details of the exercise intervention have been ment at all visits.
reported elsewhere (Wilcox et al., 2015).
Demographic/Health-Related Variables. Participants reported
their age, gender, education, race, and marital status. Self-
Steps to Healthy Eating reported presence of hypertension, high cholesterol, cancer,
A self-directed nutrition program (Steps to Healthy Eating) and osteoporosis was also obtained. Height to the nearest
was developed and used as the attention control group in the quarter inch and weight to the nearest tenth of a pound were
overall trial. The program was based on the U.S. Department obtained via trained measurement staff and body mass index
of Agriculture MyPyramid approach (which has since been (kg/m2) was calculated.
replaced with MyPlate) and modeled to parallel the First
Step to Active Health® kit. Participants received a Steps to Fruit and Vegetable Intake.  The National Cancer Institute Fruit
Healthy Eating kit and a folder containing weekly self-mon- and Vegetable all-day screener measured fruit and vegetable
itoring logs, postage paid return envelopes (for the logs), and consumption (cups/day) in the past month (National Cancer
a study expectations calendar. The program includes compo- Institute, 2000). Nine of the original 10 items were used
nents of the social cognitive theory (Bandura, 1986), with a (French fry consumption was excluded; Thompson et al.,
particular emphasis on self-regulation and self-efficacy 1999). A higher score indicated higher fruit and vegetable
(Michie, Abraham, Whittington, McAteer, & Gupta, 2009). consumption.
Participants were encouraged to plan, set goals, and self-
monitor their dietary intake for each of the four steps. Fat and Fiber Intake. The Fat- and Fiber-Related Behavior
Furthermore, in an effort to enhance self-efficacy, an indi- Questionnaire (Shannon, Kristal, Curry, & Beresford, 1997)
vidualized, stepped approach was used (i.e., participants assessed fat- (27 items) and fiber-related (14 items) dietary
were instructed to move to the next “Step” when they were behaviors over the past 3 months. All questions used a
consistently meeting recommendations at the current Step), 4-point scale, and a lower score indicated more favorable fat-
described in more detail below. and fiber-related behaviors.
The Steps to Healthy Eating kit contained a program man-
ual that included tools to help participants assess their food
Statistical Analyses
intake, set goals, customize their program, enhance motiva-
tion, and ensure food safety, and four nutrition “Steps.” This study included participants with at least one dietary out-
Participants were instructed to progress through the four come variable at 12 weeks and/or 9 months. Basic descrip-
nutrition steps during the 12-week study: (1) fruits, (2) vege- tive statistics were conducted on demographic and key
tables, (3) grains, and (4) meat and beans. Each step discussed survey variables. Repeated measures analyses of covariance
the benefits of the food group, food(s) included in the group, examined intervention Group × Time differences in fruit and
how much they should consume, examples of what foods vegetable consumption and fat- and fiber-related behaviors
“count” toward the recommendation, where to start (based on at 12 weeks and 9 months. All models controlled for age,
their personal assessment), goals, and tips for meeting recom- gender, education, and marital status. To determine the mag-
mendations. Using the Flesch–Kincaid Grade Level readabil- nitude of change (within group) and the magnitude of the
ity assessment tool, the readability of the nutrition kit was 6.7 difference in change (between groups), effect sizes were
(~7th-grade reading level). A MyPyramid pocket card was computed for each dietary variable. The within-group effect
included that reminded participants of serving sizes for foods size was calculated for each intervention group at 12 weeks
in these four groups along with age and gender recommenda- and 9 months as d = (post adjusted mean − baseline adjusted
tions for servings per day. Although MyPyramid and MyPlate mean)/unadjusted baseline standard deviation. The between-
have five food groups, only four were included to be consis- group effect size was calculated at 12 weeks and 9 months as
tent with the number of steps in the exercise program d = (post adjusted mean − baseline adjusted mean for the
described below (dairy not included). Once participants were nutrition group) − (post adjusted mean − baseline adjusted
consistently meeting Step 1, they were instructed to add Step mean for the exercise group)/unadjusted pooled baseline
64 Health Education & Behavior 45(1)

Table 2.  Demographic and Health-Related Characteristics of Participants Enrolled in a Self-Directed Nutrition or Exercise Program
(n = 321).

Nutrition group Exercise group

Characteristic n M (SD) or % n M (SD) or %

Age, years 164 56.2 (11.0) 157 56.8 (9.9)
BMI, kg/m2 164 33.3 (8.0) 157 32.5 (8.6)
Chronic diseasesa 164 2.0 (1.1) 157 2.0 (1.0)
Gender
 Male 20 12.2 20 12.7
 Female 144 87.8 137 87.3
Race
 White 108 66.3 100 63.7
 Non-White 55 33.7 57 36.3
Marital status
 Married/partnered 96 58.5 102 65.0
  Not married 68 41.5 55 35.0
Education
  High school graduate or less 19 11.6 18 11.5
  At least some college 145 88.4 139 88.5
Arthritis, % reporting 164 100.0 157 100.0
Overweight/obesity (BMI ≥ 25), % yes 145 88.4 127 80.9
High blood pressure, % reporting 78 47.6 79 50.0
High cholesterol % reporting 67 40.9 68 43.6
Osteoporosis, % reporting 21 12.8 21 13.5
Cancer, % reporting 16 9.8 19 12.3

Note. BMI = body mass index.

a
Sum of arthritis, overweight/obesity, high blood pressure, high cholesterol, osteoporosis, cancer.

standard deviation. Using Cohen’s effect sizes (Cohen, (n = 162), 98.2% made it to Step 1 fruit, 92.6% made it to
1988), d = 0.2 was considered a small effect, d = 0.5 a Step 2 vegetables, 81.5% made it to Step 3 grains, and 64.1%
medium effect, and d = 0.8 a large effect. made it to Step 4 meat and beans. Among those returning a
log in week 12 (n = 143), 2.8% were on Step 1, 5.6% were on
Step 2, 12.6% were on Step 3, and 79.0% were on Step 4.
Results
Table 3 shows the adjusted baseline, 12-week, and
Of the 401 participants randomized, 321 had 12-week and/or 9-month means for dietary behaviors, and the within- and
9-month data and were included in this study (80%). More between-group effect sizes. There was a significant Group ×
women than men were retained at either time point (p = .04), Time interaction for fruit and vegetable consumption. There
and those retained also had higher fruit and vegetable con- was a significant increase from baseline to 12 weeks but not
sumption at baseline (p = .02). On average, participants were baseline to 9 months for the nutrition group. There was no
56.5 ± 10.5 years of age, had a body mass index of 32.9 ± 8.3 change at either time point for the exercise group. The nutri-
kg/m2, and had 2.0 ± 1.0 chronic health conditions. A major- tion group had significantly higher fruit and vegetable con-
ity were women (88%), White (65%), and married (62%) sumption than the exercise group at 12 weeks, but there was
and had at least some college education (88%). Chronic dis- no difference between groups at 9 months. The effect sizes
eases were prevalent; 85% of participants were overweight show that the magnitude of reductions were greater for the
or obese, 100% reported arthritis, 49% high blood pressure, nutrition than the exercise group.
42% high cholesterol, 13% osteoporosis, and 11% cancer; There was a significant Group × Time interaction for fat-
94% had at least 2 chronic health conditions, and 67% had at related behaviors. There was a significant decrease from
least 3. Table 2 shows the health-related characteristics of the baseline to 12 weeks and baseline to 9 months for both the
sample by intervention group assignment. There were no sig- nutrition and exercise groups. The nutrition group had sig-
nificant baseline differences between intervention groups on nificantly lower scores than the exercise group at 12 weeks,
any of these factors. but there was no difference between groups at 9 months. The
Participants in the nutrition group returned, on average, effect sizes show that the magnitude of reductions were
11.0 ± 2.6 (out of 12) logs. Among those returning any logs greater for the nutrition than the exercise group.
Baruth et al. 65

Table 3.  Changes in Nutrition-Related Behaviors Among Participants in a Self-Directed Nutrition or Exercise Program, Adjusted Mean
(SE) Unless Otherwise Noted.

Intervention versus control Adjusted group ×

Behavior Nutrition Exercise effect size (d) time (p)
Fruit and vegetable, cups/day .001
 Baseline 0.9 (0.1) 0.8 (0.1)  
  12 weeks 1.2 (0.1)a 0.8 (0.1)b  
  12-week effect size, d 0.26 −0.05 0.33
  9 months 0.8 (0.1)a 0.8 (0.1)a  
  9-month effect size, d −0.06 −0.01 −0.06  
Fat-related behaviors .004
 Baseline 2.7 (.01) 2.7 (0.1)  
  12 weeks 2.5 (0.1)a 2.6 (0.1)b  
  12-week effect size, d −0.32 −0.13 −0.22
  9 months 2.5 (0.1)a 2.6 (0.1)a  
  9-month effect size, d −0.29 −0.19 −0.12  
Fiber-related behaviors <.0001
 Baseline 3.0 (0.1) 3.0 (0.1)  
  12 weeks 2.8 (0.1)a 2.9 (0.1)b  
  12-week effect size, d −0.45 −0.12 −0.35
  9 months 2.9 (0.1)a 2.9 (0.1)a  
  9-month effect size, d −0.24 −0.12 −0.14  

Note. Differing superscripts indicates significant between-group differences at 12 weeks or 9 months; boldface indicates significant within-group change
from baseline; for fat- and fiber-related behaviors, scores can range from 1 to 4; lower scores indicate healthier dietary behaviors; models adjusted for
age, gender, education, and marital status; d = 0.2 small effect, d = .5 medium effect, d = .8 large effect.

There was a significant Group × Time interaction for nutrition group specifically, fat-related behaviors were main-
fiber-related behaviors. There was a significant decrease tained at 9 months, and although fiber-related behaviors
from baseline to 12 weeks and baseline to 9 months for both were not fully maintained, there was still a small effect at 9
the nutrition and exercise groups. The nutrition group had months. However, increases in fruit and vegetable consump-
significantly lower scores than the exercise group at 12 tion were not maintained at 9 months. Furthermore, differ-
weeks, but there was no difference between groups at 9 ences in dietary behaviors between groups no longer existed
months. The effect sizes show that the magnitude of reduc- at 9 months. Although the intervention was low in intensity,
tions were greater for the nutrition than the exercise group. participants were asked to complete and return (weekly)
daily logs, and compliance was high. Logs were not required
during the maintenance period of the study (i.e., after 12
Discussion weeks), and interestingly, the corresponding dietary out-
The public health burden of chronic diseases in terms of both comes during this time period were less impressive. The lit-
prevalence and cost is significant (Gerteis et al., 2014). erature shows that self-monitoring is key for behavior
Although dietary interventions may be one means for reduc- change. A meta-regression analysis found that across 122
ing the burden of chronic diseases, self-directed dietary pro- evaluations of physical activity and dietary change interven-
grams that can be widely disseminated at a low cost and that tions, those that combined self-monitoring with at least one
do not require Internet or phone contact are rare. Findings other technique related to self-regulation (e.g., planning,
from this study show promise in that a low-cost, low-resource goal setting) were significantly more successful than inter-
(i.e., staff and materials), self-directed dietary intervention ventions not including these techniques (Michie et al., 2009).
that incorporates evidence-based behavioral strategies Efforts to continue self-monitoring after the active interven-
(Artinian et al., 2010) can improve dietary behaviors in tion is complete (i.e., during the follow-up period) should be
adults with chronic diseases in the short term. Although made, as it may improve maintenance of dietary changes.
between-group effect sizes were small, small changes across Maintenance of behavior change, including dietary behav-
a large number of people have a greater public health impact iors, is challenging, and it is unclear how to best achieve it
than large changes across a small number of people (Greaves et al., 2011). Many of the dietary interventions con-
(Dzewaltowski, Estabrooks, & Glasgow, 2004; Estabrooks ducted to date have limited follow-up evaluation (Ammerman,
& Gyurcsik, 2003). Lindquist, Lohr, & Hersey, 2002; Desroches et al., 2013).
While the short-term effects of this intervention are some- Therefore, little is known about the long-term effectiveness of
what promising, the long-term effects are less clear. In the dietary interventions or how to prevent relapse (Ammerman
66 Health Education & Behavior 45(1)

et al., 2002), particularly among those with chronic health health problem, there is a need for low-cost programs that are
conditions. Follow-up measures and extending the follow-up easily accessible, can reach a large number of people, and are
period even further (i.e., beyond 1 year) may be necessary to effective at producing behavior change. Self-directed pro-
better understand the long-term effectiveness of these types grams are one approach that could be used. These types of
of interventions (Ammerman et al., 2002). programs do not require substantial resources in terms of
This self-directed nutrition program is appealing, as it did staff and equipment, making the potential to disseminate
not require following a strict diet (e.g., vegetarian/vegan, widely feasible. Although there was evidence that a self-
Mediterranean), fasting, or excluding certain foods. Such directed dietary intervention can produce changes in dietary
diets may be unrealistic from a public health perspective, as behaviors, the long-term effectiveness of such programs is
it may be difficult for the average person to understand and less clear. Continued work is needed to identify effective
follow these diets in the long term. This intervention was ways to promote sustained behavior change, even after the
based on the U.S. dietary guidelines, incorporated behavior active intervention is over (i.e., prevent relapse). Such pro-
change strategies consistent with social cognitive theory grams and approaches are critical for efforts aimed at pre-
(Bandura, 1986; i.e., self-monitoring, goal setting, enhancing venting and/or managing this large, burdensome U.S. public
self-efficacy), and was designed to be low-cost and easily health problem.
adapted to meet individual needs, making it more desirable
and feasible for use at a population level. Authors’ Note
Given the importance of physical activity in the preven- The findings and conclusions in this report are those of the authors
tion and management of chronic diseases (Durstine, Gordon, and do not necessarily represent the official position of the Centers
Wang, & Luo, 2013), public health interventions may want for Disease Control and Prevention or the Department of Health &
to consider including a dietary and a physical activity com- Human Services.
ponent. A self-directed exercise intervention delivered in the
same manner as this intervention was shown to be effective Declaration of Conflicting Interests
in increasing physical activity (Wilcox et al., 2015). The authors declared no potential conflicts of interest with respect
Interestingly, and unexpectedly, participants in the exercise to the research, authorship, and/or publication of this article.
group also showed improvements in fat- and fiber-related
behaviors, albeit very small in magnitude, suggesting that an
Funding
exercise intervention may also elicit some changes in dietary
behaviors. Previous research has suggested that physical The authors disclosed receipt of the following financial support for
the research, authorship, and/or publication of this article: This
activity may be a “gateway behavior” to engaging in other
study is registered with ClinicalTrials.gov: NCT01172327. This
healthy behaviors (e.g., healthy diet; Blakely, Dunnagan, research is supported by the Centers for Disease Control and
Haynes, Moore, & Pelican, 2004; Jayawardene, Torabi, & Prevention’s National Center for Chronic Disease Prevention and
Lohrmann, 2016). Regardless, targeting and changing both Health Promotion by Cooperative Agreement Number
behaviors (physical activity and diet) simultaneously may U48-DP-001936, Special Interest Project 09-028.
result in more powerful and meaningful effects (King et al.,
2013). Although the availability of such programs is the first References
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