Motivation and User Engagement in Fitness Tracking: Heuristics for Mobile Healthcare Wearables
- What is the impact of self-efficacy and health technology factors on users’ attitudes toward mobile fitness tracking apps?
- Which specific areas of UX directly impact motivation and self-efficacy?
- What are the design implications and requirements to improve fitness trackers and other m-health applications?
2. Fitness Tracking, UX and Self-Determination Theory
3. Methods and Study Design
4.1. Descriptive Statistics
4.2. Regression Analysis
4.3. Qualitative Data Findings
“I can do exercise without Fitbit, but the actions would be less engaging with partial success—the feedback from Fitbit motivates and guides me to do better and keep going” (USA).“Each morning at look at the villages below and above my lodge to plan exercise and route” (Kenya).
“This helps me try and use my Fitbit more and as much as I can, this only helps me to get better. The stats help me to see where I am in a bunch of areas - activity, sleep, food, calories, etc. These features are really inspiring and will help me to do better (USA).”
“I found that the sleep tracker really makes me want to sleep more and the fact that it is very accurate is quite good. Some days I wake up tired and never know why, but the Fitbit now tells me how much time I've been restless or awake, even if I am semi-conscious. I find that my reasons for being tired are really monitored well. Right now I'm having a very hard time sleeping so I know Fitbit will be tracking it as if I am awake!” (UK).
“Really happy to see my weekly average of steps going up. Can’t say that it is the prime motivator but it is NICE reinforcement!” (USA)“I have been a little stressed lately and my friend gave me these flowers to make me feel better and motivate me” (USA).
5. UX Heuristics for Fitness Trackers
- Level of personalization: Default goal-setting for most users/most occasions; let the user decide what is desirable without making necessary restrictions imposing a hinder for the desired outcome/activity performance level.
- Navigation/input: Provide a starting point for personalization features; a clear way to show that there are options/further ways of personalizing single functions. Gamification of the process of navigating and personalizing is critical.
- Positive Feedback: Provide feedback that motivation and/or self-efficacy level has changed through user-defined ratings and questionnaires; system to provide new goals based on the user reported or system-defined motivation level; provide boundaries for motivation and self-efficacy to support users in their activity and needs; expose users to positive and constructive feedback that seems to promote greater motivation—a finding contrary to  study.
- Multi-activity motivation analysis: Users expressed a desire for features that enable them to better analyse relations between data/information—activities and motivation/self-efficacy behaviour, e.g., between sleep/diet and high or low motivation. Users may be able to categorize activities based on the motivation or self-efficacy improvements they see, as well as to explore behaviours that promote higher motivation or increased self-efficacy.
- Context integration: Capturing reflections on life events and emotional  or social interactions during fitness tracking may be an important facilitator of motivation and self-efficacy. This can create an added sense of sociability  or social UX known to drive healing, motivation behaviour change in healthcare .
- Provide intelligence to encourage more targeted behaviour change: Giving users a means to explore their gathered data to increase their self-efficacy and fitness levels, can make the experience more meaningful. Interpreted data can be helpful (like SmartCoach in the Jawbone app) but making sense of activity trends and patterns and tying those to “victories” or self-defined goals might improve self-efficacy.
- Sustain user motivation by leveraging intrinsic motivation into a playful experience: Use game elements and small rewards to support different stages of self-monitoring; thus it is possible to meet user needs for autonomy, competence, and relatedness that support the development of intrinsic motivation .
Conflicts of Interest
Diary study questions
- Team sports
- Nothing—I was too busy
- Other (please specify)
- Mobile (app on phone/tablet)
- Both phone and mobile
- Other (please specify)
- An older or different model
- Other (please specify)
- Extremely motivated
- Somewhat motivated
- Not motivated at all
- Seeing how I rank (among others)
- if I met my goal
- information tips and suggestions
- Other (please specify)
- Not sure
- Other (please specify)
- 1–3 months
- 4–6 months
- 7–12 months
- 13–24 months
- 2 years or more
Healthcare Technology Self-efficacy (HTSE) Questions
- General Self-Efficacy
- I can solve most problems if I invest the necessary effort.
- When facing difficult tasks, I am certain that I will accomplish them.
- I believe I can succeed at most any endeavor to which I set my mind.
- I will be able to successfully overcome many challenges.
- I am confident that I can perform effectively on many different tasks.
- I feel insecure about my ability to do things. (R)
- I give up easily. (R)
- Computer Self-Efficacy
- I have the ability to understand common operational problems with a computer.
- I am very unsure of my abilities to use computers. (R)
- I rely heavily on instructions and manuals to help me use a computer.
- I am very confident in my abilities to use computers.
- I find it difficult to get computers to do what I want them to.
- At times I find working with computers very confusing.
- Health Technology Self-Efficacy (Technology)
- It is easy for me to use health technology.
- I have the capability to use health technology
- I do not feel comfortable using health technology (R) Adapted from
- When using health technology, I worry I might press the wrong button and risk my health.
- Health Technology Self-Efficacy (Service)
- It is easy for me to receive service that uses health technology. Adapted from
- I feel uncomfortable to receive service that uses health technology because the device can be risky. (R)
- I am very confident in my abilities to receive service that uses health technology.
- I would have difficulties receiving service that uses health technology
- Health Technology Self-Efficacy (Web)
- It is easy for me to use internet health services.
- I feel uncomfortable to use internet health services. (R)
- I am very confident in my abilities to use internet health services.
- I would be able to use internet health services without much effort.
- Attitude toward Health Technology
- Using health technology is a good idea.
- Using health technology may be harmful to my health.
- Using health technology improve quality of my health
- I believe that health technology is responsible for improving quality of healthcare.
- Using health technology is risky.
- Age18 to 2930 to 3940 to 4950 to 5960 and over
- EducationHigh schoolCollegeBachelor’s degreeMaster’s degreeDoctorateOther
- How would you rate your computer experience?NoneVery littleAverageextensiveVery extensive
- How would you rate your health technology experience?NoneVery littleAverageextensiveVery extensive
- Li, I.; Dey, A.K.; Forlizzi, J. A stage-based model of personal informatics systems. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’10), Atlanta, GA, USA, 10–15 April 2010; pp. 557–566.
- Cafazzo, J.A.; Casselman, M.; Hamming, N.; Katzman, D.K.; Palmert, M.R. Design of an mHealth app for the self-management of adolescent type 1 diabetes: A pilot study. J. Med. Internet Res. 2012, 14, e70. [Google Scholar] [CrossRef] [PubMed]
- Ben-Zeev, D.; Kaiser, S.M.; Brenner, C.J.; Begale, M.; Duffecy, J.; Mohr, D.C. Development and usability testing of FOCUS: A smartphone system for self-management of schizophrenia. Psychiatr. Rehabil. J. 2013, 36, 289–296. [Google Scholar] [CrossRef] [PubMed]
- Kumar, S.; Nilsen, W.J.; Abernethy, A.; Patrick, K.; Pavel, M.; Riley, W.T.; Shar, A.; Spring, B.; Spruijt-Metz, D.; Hedeker, D. Mobile health technology evaluation: The mHealth evidence workshop. Am. J. Prev. Med. 2013, 45, 228–236. [Google Scholar] [CrossRef] [PubMed]
- Nylander, S.; Tholander, J.; Mueller, F.; Marshall, J. HCI and sports. In Proceedings of CHI 2014, Toronto, ON, Canada, 26 April–1 May 2014; pp. 115–118.
- Albinali, F.; Intille, S.; Haskell, W.; Rosenberger, M. Using wearable activity type detection to improve physical activity energy expenditure estimation. In Proceedings of the 12th ACM International Conference on Ubiquitous Computing, Copenhagen, Denmark, 26–29 September 2010; pp. 311–320.
- Gupta, N.; Jilla, S. Digital Fitness Connector: Smart Wearable System. In Proceedings of 2011 First International Conference on Informatics and Computational Intelligence (ICI), Bandung, Indonesia, 12–14 December 2011; pp. 118–121.
- Buttussi, F.; Chittaro, L.; Nadalutti, D. Bringing mobile guides and fitness activities together: A solution based on an embodied virtual trainer. In Proceedings of the 8th Conference on Human–Computer Interaction with Mobile Devices and Services, Helsinki, Finland, 12–15 September 2006; pp. 29–36.
- Lin, J.J.; Mamykina, L.; Lindtner, S.; Delajoux, G.; Strub, H.B. Fish’n’Steps: Encouraging Physical Activity with an Interactive Computer Game. In Proceedings of the International Conference on Ubiquitous Computing, Orange County, CA, USA, 17–21 September 2006; pp. 261–278.
- Spillers, F.; Asimakopoulos, S. Help me Relax! Biofeedback and Gamification to improve interaction design in healthcare. In Proceedings of 8th International Design and Emotion Conference, London, UK, 11–14 September 2012.
- Rooksby, J.; Rost, M.; Morrison, A.; Chalmers, M.C. Personal tracking as lived informatics. In Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems, Toronto, ON, Canada, 26 April–1 May 2014; pp. 1163–1172.
- Aitken, M.; Gauntlett, C. Patient Apps for Improved Healthcare: From Novelty to Mainstream; IMS Institute for Healthcare Informatics: Parsipanny, NJ, USA, 2013. [Google Scholar]
- Deci, E.L.; Ryan, R.M. Intrinsic Motivation and Self-Determination in Human Behaviour; Springer US: New York, NY, USA, 1985. [Google Scholar]
- Ryan, R.M.; Deci, E.L. Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am. Psychol. 2000, 55, 68–78. [Google Scholar] [CrossRef] [PubMed]
- Deci, E.G.; Ryan, R.M. Handbook of Self-Determination Research; University Rochester Press: Rochester, NY, USA, 2002. [Google Scholar]
- Rahman, M.S.; Ko, M.; Warren, J.; Carpenter, D. Healthcare Technology Self-Efficacy (HTSE) and its influence on individual attitude: An empirical study. Comput. Hum. Behav. 2016, 58, 12–24. [Google Scholar] [CrossRef]
- Berg Insight. Shipments of Connected Wearables will Reach 168 Million in 2019. Available online: http://www.berginsight.com/News.asp (accessed on 28 April 2016).
- Rabbani, S. ABI Research Predicts We’ll Buy 90 Million Wearable Devices This Year. Available online: http://www.androidheadlines.com/2014/02/abi-research-predicts-well-buy-90-million-wearable-devices-year.html (accessed on 11 June 2016).
- Lamkin, P. Fitness Tracker Market to Top $5bn by 2019. Available online: http://www.wareable.com/fitness-trackers/fitness-tracker-market-to-top-dollar-5-billion-by-2019-995 (accessed on 13 October 2016).
- Ledger, D. Inside Wearables—Part 2. Endeavour Partners. Available online: http://endeavourpartners.net/assets/Endeavour-PartnersInside-Wearables-Part-2-July-2014.pdf (accessed on 9 May 2016).
- Shih, P.C.; Han, K.; Poole, E.S.; Rosson, M.B.; Carroll, J.M. Use and Adoption Challenges of Wearable Activity Trackers. Available online: https://www.ideals.illinois.edu/handle/2142/73649 (accessed on 13 October 2016).
- Gouveia, R.; Karapanos, E.; Hassenzahl, M. How do we engage with activity trackers? A longitudinal study of Habito. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Osaka, Japan, 7–11 September 2015.
- Ledger, D.; McCaffrey, D. How The Science of Human Behavior Change Offers The Secret to Long-Term Engagement. Available online: http://endeavourpartners.net/assets/Endeavour-Partners-Wearables-White-Paper-20141.pdf (accessed on 21 September 2016).
- Harrison, D.; Marshall, P.; Bianchi-Berthouze, N.; Bird, J. Activity tracking: Barriers, workarounds and customization. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Osaka, Japan, 7–11 September 2015.
- Maher, C.; Lewis, L.; Ferrar, K.; Marshall, S.; De Bourdeaudhuij, I. Are health behavior change interventions that use online social networks effective? A systematic review. J. Med. Int. Res. 2014, 16, e40. [Google Scholar] [CrossRef] [PubMed]
- Spillers, F.; Asimakopoulos, S. Does Social User Experience Improve Motivation for Runners? In Design, User Experience, and Usability. User Experience Design Practice; Springer International Publishing: Cham, Switzerland, 2014; pp. 358–369. [Google Scholar]
- Bort-Roig, J.; Gilson, N.; Puig-Ribera, A.; Contreras, R.; Trost, S. Measuring and influencing physical activity with smartphone technology: A systematic review. Sports Med. 2014, 44, 671–686. [Google Scholar] [CrossRef] [PubMed]
- Herz, J.C. Wearables are Totally Failing the People Who Most Need Them. Available online: http//www.wired.com/2014/11/where-fitness-trackers-fail/ (accessed on 8 June 2016).
- Karapanos, E.; Gouveia, R.; Hassenzahl, M.; Forlizzi, J. Wellbeing in the Making: Peoples’ Experiences with Wearable Activity Trackers. Psychol. Well-Being 2016, 6, 1–17. [Google Scholar] [CrossRef] [PubMed]
- Bandura, A. Social cognitive theory: An agentic perspective. Annual review of psychology. Ann. Rev. Psychol. 2001, 52, 1–716. [Google Scholar] [CrossRef] [PubMed]
- Shih, P.C.; Han, K.; Poole, E.S.; Rosson, M.B.; Carroll, J.M. Use and adoption challenges of wearable activity trackers. Available online: https://www.ideals.illinois.edu/handle/2142/73649 (accessed on 18 August 2016).
- Axelrod, L.; Fitzpatrick, G.; Balaam, M.; Mawson, S.; Burridge, J.; Ricketts, I.; Smith, P.P.; Rodden, T. A toolkit to explore lived experience of motivation: When words are not enough. In Proceedings of 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), Dublin, Ireland, 23–26 May 2011; pp. 32–39.
- Donker, T.; Petrie, K.; Proudfoot, J.; Clarke, J.; Birch, M.R.; Christensen, H. Smartphones for smarter delivery of mental health programs: A systematic review. J. Med. Int. Res. 2013, 15, 239–251. [Google Scholar] [CrossRef] [PubMed]
- Meyer, J.; Fortmann, J.; Wasmann, M.; Heuten, W. Making Lifelogging Usable: Design Guidelines for Activity Trackers. In International Conference on Multimedia Modeling; Springer International Publishing: Cham, Switzerland, 2015. [Google Scholar]
- Fritz, T.; Huang, M.E.; Murphy, C.G.; Zimmermann, T. Persuasive Technology in the Real World: A Study of Long-Term Use of Activity Sensing Devices for Fitness. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Toronto, ON, Canada, 26 April–1 May 2014.
- Ryan, R.M.; Rigby, C.S.; Przybylski, A. The Motivational Pull of Video Games: A Self-Determination Theory Approach. Motiv. Emot. 2006, 30, 344–360. [Google Scholar] [CrossRef]
- Nardi, B. My Life as a Night Elf Priest: An Anthropological Account of World of Warcraft; University of Michigan Press: Ann Arbor, MI, USA.
- Pavlas, D.A. Model of Flow and Play in Game-based Learning: The Impact of Game Characteristics, Player Traits, and Player States. Ph.D. Thesis, University of Central Florida, Orlando, FL, USA, 2010. [Google Scholar]
- Consolvo, S.; Everitt, K.; Smith, I.; Landay, J. Design requirements for technologies that encourage physical activity. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Montréal, QC, Canada, 22–27 April 2006; pp. 457–466.
- Lee, J.; Finkelstein, J. Consumer Sleep Tracking Devices: A Critical Review. In Digital Healthcare Empowering Europeans: Proceedings of MIE2015; IOS Press: Amsterdam, The Netherlands, 2015; pp. 458–460. [Google Scholar]
- Carter, S.; Mankoff, J. When participants do the capturing: The role of media in diary studies. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’05), Portland, OR, USA, 2–7 April 2005; pp. 899–908.
- Downey, J.P.; Rainer, R.K., Jr.; Bartczak, S.E. General and specific computer self-efficacy: An empirical comparison of their strength in predicting general and specific outcomes. In Computational Advancements in End-User Technologies: Emerging Models and Frameworks; Clarke, S., Ed.; IGI Global: Hershey, PA, USA, 2009; pp. 176–192. [Google Scholar]
- Marakas, G.M.; Yi, M.Y.; Johnson, R.D. The multilevel and multifaceted character of computer self-efficacy: Toward clarification of the construct and an integrative framework for research. Inf. Syst. Res. 1998, 9, 126–163. [Google Scholar] [CrossRef]
- Marakas, G.M.; Johnson, R.D.; Clay, P.F. The evolving nature of the computer self-efficacy construct: An empirical investigation of measurement construction, validity, reliability and stability over time. J. Assoc. Inf. Syst. 2007, 8, 16–46. [Google Scholar]
- Agarwal, R.; Sambamurthy, V.; Stair, R.M. Research report: The evolving relationship between general and specific computer self-efficacy—An empirical assessment. Inf. Syst. Res. 2000, 11, 418–430. [Google Scholar] [CrossRef]
- Hollis, V.; Konrad, A.; Whittaker, S. Change of Heart: Emotion Tracking to Behavior Change. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, Seoul, Korea, 18–23 April 2015.
- Spillers, F. Emotion as a Cognitive Artifact and the Implications for Products that are Perceived as Pleasurable. In Proceedings of the Fourth Bi-annual Conference on Design and Emotion, Ankara, Turkey, 12–14 July 2004.
- Hassenzahl, M. User experience (UX): Towards an experiential perspective on product quality. In Proceedings of the 20th Conference on l'Interaction Homme-Machine, Metz, France, 2–5 September 2008.
- Hassenzahl, M. Experience design: Technology for all the right reasons. Synth. Lect. Hum.-Cent. Inform. 2010, 3, 1–95. [Google Scholar] [CrossRef]
- Rapp, A. Designing interactive systems through a game lens: An ethnographic approach. Comput. Hum. Behav. 2015, in press. [Google Scholar] [CrossRef]
- Cho, J. The impact of post-adoption beliefs on the continued use of health apps. Int. J. Med. Inform. 2016, 87, 75–83. [Google Scholar] [CrossRef] [PubMed]
|Participant Demographics||Frequency||Percentage Frequency|
|18 to 29||9||26.47|
|30 to 39||8||23.53|
|40 to 49||13||38.24|
|50 to 59||3||8.82|
|60 and over||1||2.94|
|Health technology experience|
|General Self-efficacy (GSE)||34||5.85||1.11||2.85||7|
|Computer Self-efficacy (CSE)||34||4.20||0.63||2.66||6|
|Health Technology Self-Efficacy Technology (HTSET)||34||4||0.98||2||7|
|Health Technology Self-Efficacy Services (HTSES)||34||4.9||0.76||3||5.75|
|Health Technology Self-Efficacy Web (HTSEW)||34||6.19||1.10||3||7|
|Attitude toward Health Technology (AHT)||34||4.6||0.93||2.2||7|
|Linear Regression Pooled Sample||Number of Obs||34|
|Prob > F||0.0000|
|ahtmean||Coef.||Robust Std. Err.||t||p > |t||
|Health technology experience||−0.10158||0.0988504||−1.03||0.315|
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Asimakopoulos, S.; Asimakopoulos, G.; Spillers, F. Motivation and User Engagement in Fitness Tracking: Heuristics for Mobile Healthcare Wearables. Informatics 2017, 4, 5. https://doi.org/10.3390/informatics4010005
Asimakopoulos S, Asimakopoulos G, Spillers F. Motivation and User Engagement in Fitness Tracking: Heuristics for Mobile Healthcare Wearables. Informatics. 2017; 4(1):5. https://doi.org/10.3390/informatics4010005Chicago/Turabian Style
Asimakopoulos, Stavros, Grigorios Asimakopoulos, and Frank Spillers. 2017. "Motivation and User Engagement in Fitness Tracking: Heuristics for Mobile Healthcare Wearables" Informatics 4, no. 1: 5. https://doi.org/10.3390/informatics4010005