research-article

Real-time feedback for improving medication taking

Authors:

Matthew L. Lee,

Anind K. DeyAuthors Info & Claims

CHI '14: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Pages 2259 - 2268

https://doi.org/10.1145/2556288.2557210

Published: 26 April 2014 Publication History

Get Access

Abstract

Medication taking is a self-regulatory process that requires individuals to self-monitor their medication taking behaviors, but this can be difficult because medication taking is such a mundane, unremarkable behavior. Ubiquitous sensing systems have the potential to sense everyday behaviors and provide the objective feedback necessary for self-regulation of medication taking. We describe an unobtrusive sensing system consisting of a sensor-augmented pillbox and an ambient display that provides near real-time visual feedback about how well medications are being taken. In contrast to other systems that focus on reminding before medication taking, our approach uses feedback after medication taking to allow the individual to develop their own routines through self-regulation. We evaluated this system in the homes of older adults in a 10-month deployment. Feedback helped improve the consistency of medication-taking behaviors as well as increased ratings of self-efficacy. However, the improved performance did not persist after the feedback display was removed, because individuals had integrated the feedback display into their routines to support their self-awareness, identify mistakes, guide the timing of medication taking, and provide a sense of security that they are taking their medications well. Finally, we reflect on design considerations for feedback systems to support the process of self-regulation of everyday behaviors.

References

[1]

Bandura, A. Self-efficacy: toward a unifying theory of behavioral change. Psychological review 84, 2 (1977), 191.

Google Scholar

[2]

Bandura, A. Social cognitive theory of self-regulation. Organizational behavior and human decision processes 50, 2 (1991), 248--287.

Google Scholar

[3]

Brus, H., Van de Laar, M., Taal, E., Rasker, J., and Wiegman, O. Determinants of compliance with medication in patients with rheumatoid arthritis. Patient Education and Counseling 36, 1 (1999), 57--64.

Crossref

Google Scholar

[4]

Consolvo, S., Klasnja, P., McDonald, D. W., et al. Flowers or a robot army' encouraging awareness & activity with personal, mobile displays. In Proc. Ubicomp 2008, ACM (2008), 54--63.

Digital Library

Google Scholar

[5]

Consolvo, S., McDonald, D. W., and Landay, J. A. Theory-driven design strategies for technologies that support behavior change in everyday life. In Proc. CHI 2009, ACM (2009), 405--414.

Digital Library

Google Scholar

[6]

Elliott, R. A. Problems with medication use in the elderly: an Australian perspective. Journal of Pharmacy Practice and Research 36, 1 (2006), 58.

Crossref

Google Scholar

[7]

Erickson, T., et al. The dubuque water portal: evaluation of the uptake, use and impact of residential water consumption feedback. In Proc CHI 2012, ACM (2012), 675--684.

Digital Library

Google Scholar

[8]

Fishkin, K. P., Wang, M., and Borriello, G. A ubiquitous system for medication monitoring. In Proc. Pervasive 2004.

Google Scholar

[9]

Friedman, R. H., et al. A Telecommunications System for Monitoring and Counseling Patients With Hypertension. Am J Hypertension 9, 4 (1996), 285--292.

Crossref

Google Scholar

[10]

Gadkari, A. S. and McHorney, C. A. Unintentional non-adherence to chronic prescription medications. BMC Health Services Research 12, 1 (2012), 98.

Crossref

Google Scholar

[11]

Hayes, T. L., Cobbinah, K., Dishongh, T., et al. A study of medication-taking and unobtrusive, intelligent reminding. Telemedicine and e-Health 15, 8 (2009), 770--776.

Google Scholar

[12]

Haynes, R. B., Ackloo, E., Sahota, N., McDonald, H. P., & Yao, X. Interventions for enhancing medication adherence. Cochrane database syst Rev, 2, 2 (2008), 11.

Google Scholar

[13]

Kripalani, S. Interventions to Enhance Medication Adherence in Chronic Medical Conditions. Archives of Internal Medicine 167, 6 (2007), 540.

Crossref

Google Scholar

[14]

Lee, M. L. and Dey, A. K. Reflecting on pills and phone use: supporting awareness of functional abilities for older adults. In Proc. CHI 2011, ACM (2011), 2095--2104.

Digital Library

Google Scholar

[15]

Li, I. Personal informatics & context: Using context to reveal factors that affect behavior. J. Ambient Intell. Smart Environ. 4, 1 (2012), 71--72.

Digital Library

Google Scholar

[16]

Locke, E. A. and Latham, G. P. A theory of goal setting & task performance. Prentice-Hall, Inc, 1990.

Google Scholar

[17]

Lundell, J. et al., Continuous activity monitoring and intelligent contextual prompting to improve medication adherence. In Proc. EMBS, IEEE (2007), pp. 6286--6289.

Google Scholar

[18]

Maes, S. and Karoly, P. Self-Regulation Assessment and Intervention in Physical Health and Illness: A Review. Applied Psychology 54, 2 (2005), 267--299.

Crossref

Google Scholar

[19]

McDonnell, P. J. and Jacobs, M. R. Hospital admissions resulting from preventable adverse drug reactions. The Annals of pharmacotherapy 36, 9 (2002), 1331--1336.

Google Scholar

[20]

McGee-Lennon, M. R., Wolters, M. K., and Brewster, S. User-centred multimodal reminders for assistive living. In Proc. CHI 2011, ACM (2011), 2105--2114.

Digital Library

Google Scholar

[21]

Morris, S. B. Estimating effect sizes from pretest-posttest-control group designs. Organizational Research Methods, 11, 2, (2008), 364--386.

Crossref

Google Scholar

[22]

Nelson, R. O. and Hayes, S. C. Theoretical Explanations for Reactivity in Self-Monitoring. Behavior Modification 5, 1 (1981), 3--14.

Google Scholar

[23]

De Oliveira, R., Cherubini, M., and Oliver, N. MoviPill: improving medication compliance for elders using a mobile persuasive social game. In Proc. Ubicomp 2010. ACM (2010), 251--260.

Digital Library

Google Scholar

[24]

Risser, J., Jacobson, T. A., and Kripalani, S. Development and psychometric evaluation of the Self-efficacy for Appropriate Medication Use Scale (SEAMS) in low-literacy patients with chronic disease. Journal of nursing measurement 15, 3 (2007), 203--219.

Google Scholar

[25]

Rogers, Y., Hazlewood, W. R., Marshall, P., Dalton, N., and Hertrich, S. Ambient influence: can twinkly lights lure and abstract representations trigger behavioral change' In Proc. Ubicomp 2010, ACM (2010), 261--270.

Digital Library

Google Scholar

[26]

Williams, G., Rodin, G., Ryan, R., Grolnick, W. and Deci, E. Autonomous regulation and long-term medication adherence in adult outpatients. Health Psychology, 17, 3 (1998), 269.

Crossref

Google Scholar

[27]

Zhu, L. L., Zhou, Q., Yan, X. F., Zeng, S. Optimal time to take once-daily oral medications in clinical practice. International Journal of Clinical Practice, 62 (2008), 1560.

Crossref

Google Scholar

[28]

http://www.vitality.net/glowcaps.html.

Google Scholar

[29]

http://proteusdigitalhealth.com/.

Google Scholar

Cited By

View all

Arakawa RYakura HGoel M(2024)PrISM-Observer: Intervention Agent to Help Users Perform Everyday Procedures Sensed using a SmartwatchProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676350(1-16)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3654777.3676350
Chin GChow K(2023)Technology-Enabled Interventions for Sustaining Behaviour Change in Adolescents: A Scoping Review for Research GapsProceedings of the ACM on Human-Computer Interaction10.1145/36102117:CSCW2(1-30)Online publication date: 4-Oct-2023
https://dl.acm.org/doi/10.1145/3610211
Bhowmick PStolterman E(2023)Enhancing Social Connectivity: Tangible Peer-Based Check-in Systems for Isolated Older AdultsAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3610764(230-235)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3594739.3610764
Show More Cited By

Index Terms

Real-time feedback for improving medication taking
1. Human-centered computing

Recommendations

A personalized medication management platform (PMMP) to improve medication adherence

Addressed medication non-adherence, an important aspect in healthcare which significantly influences patients health and expenses to patients, healthcare providers and insurance companies.The PMMP platform implemented and tested through a randomized ...
An Activity Recognition System for Taking Medicine Using In-The-Wild Data to Promote Medication Adherence
IUI '21: Proceedings of the 26th International Conference on Intelligent User Interfaces

Nearly half of people prescribed medication to treat chronic or short-term conditions do not take their medicine as prescribed. This leads to worse treatment outcomes, higher hospital admission rates, increased healthcare costs, and increased morbidity ...
Sensor-based observations of daily living for aging in place

Older adults often find it difficult to keep track of their cognitive and functional abilities required for remaining independent in their homes. Healthcare providers need objective, timely, and ecologically valid information about their patient's ...

Reviews

Reviewer: Amos O Olagunju

Elderly patients with multiple lingering medical conditions habitually take several pills every day. How should effective real-time feedback sensor display systems be designed for patients who take many drugs at specific times each day__?__ In an effort to address absentmindedness, negligence, and/or the fear of troublesome side effects, Lee and Dey built a sensor-augmented pillbox for monitoring medicine use behaviors of patients. The seven-day pillbox consists of an accelerometer to trace when it is picked up, spring switches to discover open pillbox doors, and a conventional circuitry with microcontrollers and a wireless modem. The pillbox sensor uses a wireless network to transmit data captured from each patient to a remote server. The server processes the patient behavioral data to ascertain and assess the incidents of medicine consumption, and to provide visual feedback display on how well individual patients take medications, use the phone, and prepare coffee. A focus group experiment was performed to investigate the effective use of a feedback display in helping patients accurately take medicine. Twelve elderly patients with multiple protracted illnesses such as hypertension and diabetes were randomly assigned to feedback and control groups. The feedback group patients viewed the performance of their medication-taking behaviors in real time from a tablet display, whereas the control group patients only received a hard copy performance report for one month. The authors evaluated the impact of a real-time feedback display on medication-taking behaviors. Adherence is the percentage of all pills taken in a time period. Correctness is the rate of accurate pills taken each day. Promptness is the percentage of pills taken on time. Time of day variance "measures how the time of day that medications were taken varied from one day to another." Self-efficacy is the extent to which patients feel confident about surmounting the barriers to taking medications. The feedback display significantly enriched adherence, promptness, correctness, and the variance in the time of day; it had no significant effect on self-efficacy. Although the small number of patients studied limits the generalizability of the experimental results, the authors have developed a valuable tool for monitoring the drug intake behaviors of patients with multiple chronic health problems. Online Computing Reviews Service

Access critical reviews of Computing literature here

Become a reviewer for Computing Reviews.

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

CHI '14: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

April 2014

4206 pages

ISBN:9781450324731

DOI:10.1145/2556288

General Chairs:
Matt Jones
Swansea University, Wales, UK
,
Philippe Palanque
Université Paul Sabatier, France
,
Program Chairs:
Albrecht Schmidt
University of Stuttgart, Germany
,
Tovi Grossman
Autodesk Research, Canada

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 April 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CHI '14

Sponsor:

SIGCHI

CHI '14: CHI Conference on Human Factors in Computing Systems

April 26 - May 1, 2014

Ontario, Toronto, Canada

Acceptance Rates

CHI '14 Paper Acceptance Rate 465 of 2,043 submissions, 23%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

Upcoming Conference

CHI '25

Sponsor:
sigchi

CHI Conference on Human Factors in Computing Systems

April 26 - May 1, 2025

Yokohama , Japan

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

49
Total Citations
View Citations
1,541
Total Downloads

Downloads (Last 12 months)103
Downloads (Last 6 weeks)19

Reflects downloads up to 20 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Arakawa RYakura HGoel M(2024)PrISM-Observer: Intervention Agent to Help Users Perform Everyday Procedures Sensed using a SmartwatchProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676350(1-16)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3654777.3676350
Chin GChow K(2023)Technology-Enabled Interventions for Sustaining Behaviour Change in Adolescents: A Scoping Review for Research GapsProceedings of the ACM on Human-Computer Interaction10.1145/36102117:CSCW2(1-30)Online publication date: 4-Oct-2023
https://dl.acm.org/doi/10.1145/3610211
Bhowmick PStolterman E(2023)Enhancing Social Connectivity: Tangible Peer-Based Check-in Systems for Isolated Older AdultsAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3610764(230-235)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3594739.3610764
Xie YYang HHuang JWang JLi HGuo YCastellano GRiek LCakmak MLeite I(2023)CHIBOCompanion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3568294.3580216(725-729)Online publication date: 13-Mar-2023
https://dl.acm.org/doi/10.1145/3568294.3580216
Bhowmick PStolterman Bergqvist E(2023)Exploring Tangible User Interface Design for Social Connection Among Older Adults: A Preliminary ReviewExtended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544549.3585722(1-9)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544549.3585722
Kwok TKiefer PRaubal M(2023)Unobtrusive interaction: a systematic literature review and expert surveyHuman–Computer Interaction10.1080/07370024.2022.216240439:5-6(380-416)Online publication date: Feb-2023
https://doi.org/10.1080/07370024.2022.2162404
Jung HKim YLee JLee SChoe E(2022)Envisioning the use of in-situ arm movement data in stroke rehabilitation: Stroke survivors’ and occupational therapists’ perspectivesPLOS ONE10.1371/journal.pone.027414217:10(e0274142)Online publication date: 20-Oct-2022
https://doi.org/10.1371/journal.pone.0274142
Nie QMorrow DRogers W(2022)Designing Feedback Visualizations for Anti-Hypertensive Medication Adherence for Older AdultsProceedings of the Human Factors and Ergonomics Society Annual Meeting10.1177/107118132266107666:1(23-27)Online publication date: 27-Oct-2022
https://doi.org/10.1177/1071181322661076
Burgess EKaziunas EJacobs M(2022)Care Frictions: A Critical Reframing of Patient Noncompliance in Health Technology DesignProceedings of the ACM on Human-Computer Interaction10.1145/35551726:CSCW2(1-31)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555172
Mathur NDhodapkar KZubatiy TLi JJones BMynatt E(2022)A Collaborative Approach to Support Medication Management in Older Adults with Mild Cognitive Impairment Using Conversational Assistants (CAs)Proceedings of the 24th International ACM SIGACCESS Conference on Computers and Accessibility10.1145/3517428.3544830(1-14)Online publication date: 23-Oct-2022
https://dl.acm.org/doi/10.1145/3517428.3544830
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

A personalized medication management platform (PMMP) to improve medication adherence

An Activity Recognition System for Taking Medicine Using In-The-Wild Data to Promote Medication Adherence

Sensor-based observations of daily living for aging in place

Reviews

Access critical reviews of Computing literature here