ABSTRACT
With the widespread use of fitness trackers, predicting the future of individuals’ health is becoming easier. However, little is known about how presenting a prediction of an individual's future impacts one's behavior. In this study, we targeted walking behavior and aimed to clarify the impact of presenting a prediction of the number of steps on one's behavior. We conducted a five-week experiment with 36 participants using the application “StepUp Forecast”, which presents prediction on the basis of past lifelogs. We found that self-efficacy and the number of steps increased significantly when the predictions were presented compared with when only records of steps were shown. This was because people were motivated to exceed the predicted value. Furthermore, when additional steps were presented along with the step prediction, neither self-efficacy nor the number of steps increased. Our findings suggest that the prediction should be an achievable value in which the user can exceed, and that a positive feedback loop could be possible by enhancing self-efficacy through the experience of achievement.
- Maximilian Altmeyer, Pascal Lessel, Tobias Sander, and Antonio Krüger. 2018. Extending a Gamified Mobile App with a Public Display to Encourage Walking. In Proceedings of the 22nd International Academic Mindtrek Conference (Mindtrek '18). ACM, 20–29. https://doi.org/10.1145/3275116.3275135Google ScholarDigital Library
- Apple watch. 2021. Retrieved February 4, 2021 from https://www.apple.com/watch/Google Scholar
- Albert Bandura. 1977. Self-efficacy: Toward a Unifying Theory of Behavioral Change. Psychological review. Vol. 84, No. 2, 191-215. https://doi.org/10.1037/0033-295X.84.2.191Google Scholar
- Albert Bandura and Nancy E. Adams. 1977. Analysis of Self-Efficacy Theory of Behavioral Change. Cognitive therapy and research, 1, 4, 287-310. https://doi.org/10.1007/BF01663995Google Scholar
- Sunny Consolvo, David W. McDonald, Tammy Toscos, Mike Y. Chen, Jon Froehlich, Beverly Harrison, Predrag Klasnja, Anthony LaMarca, Louis LeGrand, Ryan Libby, Ian Smith, and James A. Landay. 2008. Activity Sensing in the Wild: A Field Trial of UbiFit Garden. In Proceedings of the 2008 CHI Conference on Human Factors in Computing Systems. ACM, 1797-1806. https://doi.org/10.1145/1357054.1357335Google Scholar
- Pooja M. Desai, Elliot G. Mitchell, Maria L. Hwang, Matthew E. Levine, David J. Albers, and Lena Mamykina. 2019. Personal health oracle: Explorations of personalized predictions in diabetes self-management. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, 1-13. https://doi.org/10.1145/3290605.3300600Google ScholarDigital Library
- Firebase. 2020. Retrieved February 4, 2021 from https://firebase.google.com/?hl=enGoogle Scholar
- fitbit. 2020. Retrieved February 4, 2021 from https://www.fitbit.com/Google Scholar
- Bryan Gibson, Leah Yingling, Alisa Bednarchuk, Ashwini Janamatti, Ingrid Oakley-Girvan, and Nancy Allen. 2018. An Interactive Simulation to Change Outcome Expectancies and Intentions in Adults With Type 2 Diabetes: Within-Subjects Experiment. JMIR Diabetes 3, 1 (2018), e2. https://doi.org/10.2196/diabetes.8069Google ScholarCross Ref
- Elaine Anne Hargreaves, Nanette Mutrie, and Jade Dallas Fleming. 2016. A Web-Based Intervention to Encourage Walking (StepWise): Pilot Randomized Controlled Trial. JMIR research protocols, 5, 1, e14. https://doi.org/10.2196/resprot.4288Google Scholar
- Victoria Hollis, Artie Konrad, Aaron Springer, Matthew Antoun, Christopher Antoun, Rob Martin, and Steve Whittaker. 2017. What Does All This Data Mean for My Future Mood? Actionable Analytics and Targeted Reflection for Emotional Well-Being. Human–Computer Interaction 32, 5-6 (2017), 208-267. https://doi.org/10.1080/07370024.2016.1277724Google ScholarDigital Library
- Daniel Kahneman, Amos Tversky. 1979. Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47. 2. 263-292. https://doi.org/10.2307/1914185Google ScholarCross Ref
- Gary P. Latham, Edwin A Locke. 1991. Self-regulation through goal setting. Organizational Behavior and Human Decision Processes, 50. 2, 212-247. https://doi.org/10.1016/0749-5978(91)90021-KGoogle ScholarCross Ref
- Gary P. Latham. 2003. Goal Setting: A Five-Step Approach to Behavior Change. Organizational Dynamics, 32. 3, 309–318. https://doi.org/10.1016/S0090-2616(03)00028-7Google ScholarCross Ref
- Kwangyoung Lee, Hyewon Cho, Kobiljon Toshnazarov, Nematjon Narziev, So Young Rhim, Kyungsik Han, YoungTae Noh, and Hwajung Hong. 2020. Toward Future-Centric Personal Informatics: Expecting Stressful Events and Preparing Personalized Interventions in Stress Management. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI '20). ACM, 1–13. https://doi.org/10.1145/3313831.3376475Google ScholarDigital Library
- James J. Lin, Lena Mamykina, Silvia Lindtner, Gregory Delajoux, and Henry B. Strub. 2006. Fish'n’Steps: Encouraging Physical Activity with an Interactive Computer Game. In International conference on ubiquitous computing (UbiComp '06). Springer, 261-278. https://doi.org/ 10.1007/11853565_16Google Scholar
- Edward McAuley, Bryan Blissmer. 2000. Self-Efficacy Determinants and Consequences of Physical Activity. Exerc Sport Sci Rev, 28. 2. 85-88.Google Scholar
- John B. Miner. 2005. Organizational Behavior: Essential theories of motivation and leadership. one. Vol. 1. ME Sharpe.Google Scholar
- Sean A. Munson, Sunny Consolvo. 2012. Exploring Goal-setting, Rewards, Self-monitoring, and Sharing to Motivate Physical Activity. In 2012 6th international conference on pervasive computing technologies for healthcare (pervasivehealth) and workshops. IEEE, 25-32. https://doi.org/ 10.4108/icst.pervasivehealth.2012.248691Google ScholarCross Ref
- Elizabeth L. Murnane, Xin Jiang, Anna Kong, Michelle Park, Weili Shi, Connor Soohoo, Luke Vink, Iris Xia, Xin Yu, John Yang-Sammataro, Grace Young, Jenny Zhi, Paula Moya, and James A. Landay. 2020. Designing Ambient Narrative-Based Interfaces to Reflect and Motivate Physical Activity. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI '20). ACM, 1–14. https://doi.org/10.1145/3313831.3376478Google Scholar
- National Health Promotion Movement in the twenty first century. Ministry of Health, Labour Standards. Retrieved April 5, 2021 from http://www.kenkounippon21.gr.jp/kenkounippon21/about/kakuron/index.htmlGoogle Scholar
- OpenWeatherMap. 2020. Retrieved February 4, 2021 from https://openweathermap.org/Google Scholar
- Josée Poirier, Wendy L Bennett, Gerald J Jerome, Nina G Shah, Mariana Lazo, Hsin-Chieh Yeh, Jeanne M Clark, and Nathan K Cobb. 2016. Effectiveness of an Activity Tracker-and Internet-Based Adaptive Walking Program for Adults: A Randomized Controlled Trial. Journal of medical Internet research, 18, 2, e34. https://doi.org/10.2196/jmir.5295Google ScholarCross Ref
- Pokémon GO. 2020. Retrieved February 4, 2021 from https://www.pokemongo.com/en-us/Google Scholar
- James O. Prochaska, Wayne F. Velicer. 1997. The transtheoretical model of health behavior change. American journal of health promotion, 12, 1, 38-48. https://doi.org/10.4278/0890-1171-12.1.38Google Scholar
- Saeyoung Rho, Injung Lee, Hankyung Kim, Jonghyuk Jung, Hyungi Kim, Bong Gwan Jun, and Youn-kyung Lim. 2017. FutureSelf: What Happens When We Forecast Self-Trackers? Future Health Statuses? In Proceedings of the 2017 Conference on Designing Interactive Systems (DIS '17). ACM, 637–648. https://doi.org/10.1145/3064663.3064676Google ScholarDigital Library
- Pedro F. Saint-Maurice, Richard P. Troiano, David R. Bassett Jr, Barry I. Graubard, Susan A. Carlson, Eric J. Shiroma, Janet E. Fulton, and Charles E. Matthews. 2020. Association of daily step count and step intensity with mortality among US adults. Jama, 323, 12, 1151-1160. https://doi.org/10.1001/jama.2020.1382Google ScholarCross Ref
- scikit-learn. sklearn.ensemble.GradientBoostingRegressor. Retrieved from https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#.Google Scholar
- Toshiki Takeuchi, Takuji Narumi, Kunihiro Nishimura, Tomohiro Tanikawa, and Michitaka Hirose. 2010. Receiptlog Applied to Forecast of Personal Consumption. In 2010 16th International Conference on Virtual Systems and Multimedia. IEEE, 79-83. https://doi.org/10.1109/VSMM.2010.5665961Google ScholarCross Ref
- Geoffrey H. Tison, Robert Avram, Peter Kuhar, Sean Abreau, Greg M. Marcus, Mark J. Pletcher, and Jeffrey E. Olgin. 2020. Worldwide Effect of COVID-19 on Physical Activity: A Descriptive Study. Annals of internal medicine, 173, 9, 767-770. https://doi.org/10.7326/M20-2665Google ScholarCross Ref
- Tammy Toscos, Anne Faber, Shunying An, and Mona Praful Gandhi. 2006. Chick Clique: Persuasive Technology to Motivate Teenage Girls to Exercise. In CHI '06 Extended Abstracts on Human Factors in Computing Systems (CHI EA '06). ACM, 1873–1878. https://doi.org/10.1145/1125451.1125805Google ScholarDigital Library
- U.S. Department of Health and Human Services. 1996. Physical Activity and Health: A Report of the Surgeon General. U.S. Department of Health and Human Services Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion.Google Scholar
- Jane C Walsh, Teresa Corbett, Michael Hogan, Jim Duggan, and Abra McNamara. 2016. An mHealth intervention using a smartphone app to increase walking behavior in young adults: a pilot study. JMIR mHealth and uHealth, 4, 3, e109. https://doi.org/10.4278/0890-1171-12.1.38Google Scholar
- S. L. Williams, D. P. French. 2011. What are the most effective intervention techniques for changing physical activity self-efficacy and physical activity behaviour—and are they the same?, Health Education Research, 26. 2. 308–322. https://doi.org/10.1093/her/cyr005Google ScholarCross Ref
- Mo Zhou, Yonatan Mintz, Yoshimi Fukuoka, Ken Goldberg, Elena Flowers, Philip Kaminsky, Alejandro Castillejo, and Anil Aswani. 2018. Personalizing mobile fitness apps using reinforcement learning. In CEUR workshop proceedings, 2068.Google Scholar
Recommendations
Preliminary Study on Effect of Secretly Increasing or Decreasing Predicted Number of Steps to Promote Walking
UbiComp/ISWC '23 Adjunct: Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable ComputingWith the widespread use of wearable devices, it is becoming easier to predict the future of an individual’s behavior regarding health. However, little is known about how presenting future predictions about an individual’s behavior affects awareness and ...
Patterns of behavior change in students over an academic term
The recent arrival of smartphone-sensing methods has made it possible to objectively track consequential everyday health-related behaviors rather than rely on self-reports. To evaluate the viability of using sensing methods to monitor such behaviors in ...
Integrating population-based patterns with personal routine to re-engage fitbit use
PervasiveHealth '16: Proceedings of the 10th EAI International Conference on Pervasive Computing Technologies for HealthcareIn this paper, we explore user reactions to prototypes that integrate population fitness data with personal practice to bolster motivation and help decrease pragmatic barriers to incorporating exercise in daily life. We conducted a study in a major ...
Comments