Research Article
No access
Published Online: 3 June 2020

Factors Associated with Older Adults' Long-Term Use of Wearable Activity Trackers

Publication: Telemedicine and e-Health
Volume 26, Issue Number 6


Background: Wearable activity trackers (WATs) have the potential to improve older adults' health; yet, many adopters of WATs are not able to use them on a long-term basis.
Methods: A survey was conducted with an online panel of adults 65 and older (N = 214) to explore factors associated with long-term use of WATs, including initial adoption motivations, usage patterns, as well as differences in sociodemographic factors, health status, and activity levels.
Results: Results from the logistic regression analysis indicated that being a long-term WAT user was significantly associated with using a wider variety of WAT functions, wearing WAT every day, being female, exercising more frequently, having higher education, not engaging in step count competition, and not having chronic conditions.
Conclusions: Understanding long-term use of WATs among older adults is important given that this technology is prone to be abandoned quickly after initial adoption and such abandonment negates its potential in supporting long-term health behavior change. Findings of this study will inform innovative WAT designs that afford long-term use and offer helpful strategies for future interventions using WATs among older adults.

Get full access to this article

View all available purchase options and get full access to this article.


1. Mercer K, Giangregorio L, Schneider E, Chilana P, Li M, Grindrod K. Acceptance of commercially available wearable activity trackers among adults aged over 50 and with chronic illness: A mixed-methods evaluation. JMIR MHealth UHealth 2016;4:e7.
2. Fritz T, Huang EM, Murphy GC, Zimmermann T. Persuasive technology in the real world: A study of long-term use of activity sensing devices for fitness. New York, NY: ACM Press, 2014;487–496.
3. Randriambelonoro M, Chen Y, Pu P. Can fitness trackers help diabetic and obese users make and sustain lifestyle changes? Computer 2017;50:20–29.
4. Clawson J, Pater JA, Miller AD, Mynatt ED, Mamykina L. No longer wearing: Investigating the abandonment of personal health-tracking technologies on craigslist. New York, NY: ACM Press, 2015;647–658.
5. Epstein DA, Kang JH, Pina LR, Fogarty J, Munson SA. Reconsidering the device in the drawer: Lapses as a design opportunity in personal informatics. New York, NY: ACM Press, 2016;829–840.
6. Lazar A, Koehler C, Tanenbaum J, Nguyen DH. Why we use and abandon smart devices. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. UbiComp’15. New York: ACM, 2015;635–646.
7. Ledger D, McCaffrey D. Inside wearables: How the science of human behavior change offers the secret to long-term engagement-part 2. 2014. Available at (last accessed September 3, 2019).
8. Shih PC, Han K, Poole ES, Rosson MB, Carroll JM. Use and adoption challenges of wearable activity trackers. IConference 2015 Proc. 2015. Available at (last accessed December 29, 2016).
9. Karapanos E, Gouveia R, Hassenzahl M, Forlizzi J. Wellbeing in the making: Peoples' experiences with wearable activity trackers. Psychol Well-Being 2016;6:4.
10. Maher C, Ryan J, Ambrosi C, Edney S. Users' experiences of wearable activity trackers: A cross-sectional study. BMC Public Health 2017;17:880.
11. U.S Department of Health and Human Services. Physical activity guidelines advisory committee report, 2008. 2008. Washington, DC. Available at (last accessed July 10, 2019).
12. Cadmus-Bertram LA, Marcus BH, Patterson RE, Parker BA, Morey BL. Randomized trial of a Fitbit-based physical activity intervention for women. Am J Prev Med 2015;49:414–418.
13. O'Brien T, Troutman-Jordan M, Hathaway D, Armstrong S, Moore M. Acceptability of wristband activity trackers among community dwelling older adults. Geriatr Nur (Lond) 2015;36:S21–S25.
14. Lyons EJ, Lewis ZH, Mayrsohn BG, Rowland JL. Behavior change techniques implemented in electronic lifestyle activity monitors: A systematic content analysis. J Med Internet Res 2014;16:e192.
15. Kononova A, Li L, Kamp K, et al. The use of wearable activity trackers among older adults: Focus group study of tracker perceptions, motivators, and barriers in the maintenance stage of behavior change. JMIR MHealth UHealth 2019;7:e9832.
16. Shin G, Feng Y, Jarrahi MH, Gafinowitz N. Beyond novelty effect: A mixed-methods exploration into the motivation for long-term activity tracker use. JAMIA Open 2019;2:62–72.
17. Meyer J, Wasmann M, Heuten W, El Ali A, Boll SCJ. Identification and classification of usage patterns in long-term activity tracking. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. CHI’17. New York: ACM, 2017;667–678.
18. Ding Y, Chai KH. Emotions and continued usage of mobile applications. Ind Manag Data Syst 2015;115:833–852.
19. Huang TC-K, Wu I-L, Chou C-C. Investigating use continuance of data mining tools. Int J Inf Manag 2013;33:791–801.
20. Rieder A, Lehrer C, Jung R. Understanding the habitual use of wearable activity trackers. 14th International Conference on Wirtschaftsinformatik. Siegen, Germany, 2019;1002–1016.
21. Jarrahi MH, Gafinowitz N, Shin G. Activity trackers, prior motivation, and perceived informational and motivational affordances. Pers Ubiquitous Comput 2018;22:433–448.
22. Gui X, Chen Y, Caldeira C, Xiao D, Chen Y. When fitness meets social networks: Investigating fitness tracking and social practices on WeRun. New York, NY: ACM Press, 2017;1647–1659.
23. Alley S, Schoeppe S, Guertler D, Jennings C, Duncan MJ, Vandelanotte C. Interest and preferences for using advanced physical activity tracking devices: Results of a national cross-sectional survey. BMJ Open 2016;6:e011243.
24. Macridis S, Johnston N, Johnson S, Vallance JK. Consumer physical activity tracking device ownership and use among a population-based sample of adults. PLoS One 2018;13:e0189298.
25. Kadylak T, Cotten SR. Factors that influence wearable activity tracker adoption and use among U.S. older adults. Innov Aging 2018;2(Suppl 1):41.
26. Tedesco S, Barton J, O'Flynn B. A review of activity trackers for senior citizens: Research perspectives, commercial landscape and the role of the insurance industry. Sensors 2017;17:1277.
27. McFadden D. Quantitative methods for analyzing travel behavior of individuals: some recent developments. 1977. Institute of Transportation Studies, University of California. Available at (last accessed September 18, 2017).
28. Lally P, Gardner B. Promoting habit formation. Health Psychol Rev 2013;7(Suppl 1):S137–S158.
29. Soontornwat A, Funilkul S, Supasitthimethee U. Essential social attributes and Habit in fitness mobile applications usage to motivate a physical activity. Chiangmai: 2016 International Computer Science and Engineering Conference (ICSEC). 2016;1–6.
30. Limayem M, Hirt SG, Cheung CM. How habit limits the predictive power of intention: The case of information systems continuance. MIS Q 2007;31:705–737.
31. Peng W, Crouse J. Playing in parallel: The effects of multiplayer modes in active video game on motivation and physical exertion. Cyberpsychol Behav Soc Netw 2013;16:423–427.
32. Lin JJ, Mamykina L, Lindtner S, Delajoux G, Strub HB. Fish'N'Steps: Encouraging physical activity with an interactive computer game. Proceedings of the 8th International Conference on Ubiquitous Computing. UbiComp’06. Berlin, Heidelberg: Springer-Verlag, 2006;261–278.
33. Peng W, Kanthawala S, Yuan S, Hussain SA. A qualitative study of user perceptions of mobile health apps. BMC Public Health 2016;16:1158.
34. Fan C, Forlizzi J, Dey A. Considerations for technology that support physical activity by older adults. Proceedings of the 14th International ACM SIGACCESS Conference on Computers and Accessibility. Boulder, CO: ACM, 2012;33–40. Available at (last accessed June 28, 2017).
35. Abril EP. Tracking myself: Assessing the contribution of mobile technologies for self-trackers of weight, diet, or exercise. J Health Commun 2016;21:638–646.
36. Carroll JK, Moorhead A, Bond R, LeBlanc WG, Petrella RJ, Fiscella K. Who uses mobile phone health apps and does use matter? A secondary data analytics approach. J Med Internet Res 2017;19:e125.
37. Levine DM, Lipsitz SR, Linder JA. Trends in seniors' use of digital health technology in the United States, 2011–2014. JAMA 2016;316:538–540.
38. Phillips SM, Cadmus-Bertram L, Rosenberg D, Buman MP, Lynch BM. Wearable technology and physical activity in chronic disease: Opportunities and challenges. Am J Prev Med 2018;54:144–150.
39. Elsden C, Kirk DS, Durrant AC. A quantified past: Toward design for remembering with personal informatics. Hum Comput Interact 2016;31:518–557.

Information & Authors


Published In

cover image Telemedicine and e-Health
Telemedicine and e-Health
Volume 26Issue Number 6June 2020
Pages: 769 - 775
PubMed: 31553281


Published online: 3 June 2020
Published in print: June 2020
Published ahead of print: 25 September 2019
Accepted: 19 July 2019
Revision received: 16 July 2019
Received: 9 March 2019


Request permissions for this article.




Department of Media and Information, Michigan State University, East Lansing, Michigan, USA.
Department of Media and Information, Michigan State University, East Lansing, Michigan, USA.
Anastasia Kononova
Department of Advertising and Public Relations, Michigan State University, East Lansing, Michigan, USA.
Marie Bowen
College of Communication Arts and Sciences, Michigan State University, East Lansing, Michigan, USA.
Shelia R. Cotten
Department of Media and Information, Michigan State University, East Lansing, Michigan, USA.


Address correspondence to: Lin Li, MA, Department of Media and Information, Michigan State University, Rm 409, 404 Wilson Road, East Lansing, MI 48823, USA [email protected]
Wei Peng, PhD, Department of Media and Information, Michigan State University, Rm 409, 404 Wilson Road, East Lansing, MI 48823, USA [email protected]

Disclosure Statement

No competing financial interests exist.

Funding Information

We thank the Michigan State University Science and Society at State (S3) program for providing funding for this work.

Metrics & Citations



Export citation

Select the format you want to export the citations of this publication.

View Options

Get Access

Access content

To read the fulltext, please use one of the options below to sign in or purchase access.

Society Access

If you are a member of a society that has access to this content please log in via your society website and then return to this publication.

Restore your content access

Enter your email address to restore your content access:

Note: This functionality works only for purchases done as a guest. If you already have an account, log in to access the content to which you are entitled.

View options


View PDF/ePub

Full Text

View Full Text







Copy the content Link

Share on social media

Back to Top