Abstract
Adaptive interventions are an emerging class of behavioral interventions that allow for individualized tailoring of intervention components over time to a person’s evolving needs. The purpose of this study was to evaluate an adaptive step goal + reward intervention, grounded in Social Cognitive Theory delivered via a smartphone application (Just Walk), using a mixed modeling approach. Participants (N = 20) were overweight (mean BMI = 33.8 ± 6.82 kg/m2), sedentary adults (90% female) interested in participating in a 14-week walking intervention. All participants received a Fitbit Zip that automatically synced with Just Walk to track daily steps. Step goals and expected points were delivered through the app every morning and were designed using a pseudo-random multisine algorithm that was a function of each participant’s median baseline steps. Self-report measures were also collected each morning and evening via daily surveys administered through the app. The linear mixed effects model showed that, on average, participants significantly increased their daily steps by 2650 (t = 8.25, p < 0.01) from baseline to intervention completion. A non-linear model with a quadratic time variable indicated an inflection point for increasing steps near the midpoint of the intervention and this effect was significant (t2 = −247, t = −5.01, p < 0.001). An adaptive step goal + rewards intervention using a smartphone app appears to be a feasible approach for increasing walking behavior in overweight adults. App satisfaction was high and participants enjoyed receiving variable goals each day. Future mHealth studies should consider the use of adaptive step goals + rewards in conjunction with other intervention components for increasing physical activity.
Similar content being viewed by others
References
Adams, M. A., Hurley, J. C., Todd, M., Bhuiyan, N., Jarrett, C. L., Tucker, W. J., et al. (2017). Adaptive goal setting and financial incentives: A 2 × 2 factorial randomized controlled trial to increase adults’ physical activity. BMC Public Health, 17, 286.
Adams, M. A., Sallis, J. F., Norman, G. J., Hovell, M. F., Hekler, E. B., & Perata, E. (2013). An adaptive physical activity intervention for overweight adults: A randomized controlled trial. PLoS ONE, 8, e82901.
Almirall, D., Nahum-Shani, I., Sherwood, N. E., & Murphy, S. A. (2014). Introduction to SMART designs for the development of adaptive interventions: With application to weight loss research. Translational Behavioral Medicine, 4, 260–274.
Bowen, D. J., Kreuter, M., Spring, B., et al. (2009). How we design feasibility studies. American Journal of Preventive Medicine, 36, 452–457.
Collins, L. M., Murphy, S. A., & Bierman, K. L. (2004). A conceptual framework for adaptive preventive interventions. Prevention Science, 5, 185–196.
Craig, C. L., Marshall, A. L., Sjöström, M., Bauman, A. E., Booth, M. L., Ainsworth, B. E., et al. (2003). International physical activity questionnaire: 12-country reliability and validity. Medicine and Science in Sports and Exercise, 35, 1381–1395.
Dallery, J., Cassidy, R. N., & Raiff, B. R. (2013). Single-case experimental designs to evaluate novel technology-based health interventions. Journal of Medical Internet Research, 15, e22.
Direito, A., Carraça, E., Rawstorn, J., Whittaker, R., & Maddison, R. (2017). mHealth technologies to influence physical activity and sedentary behaviors: Behavior change techniques, systematic review and meta-analysis of randomized controlled trials. Annals of Behavioral Medicine, 51(2), 226–239.
Evenson, K. R., Goto, M. M., & Furberg, R. D. (2015). Systematic review of the validity and reliability of consumer-wearable activity trackers. International Journal of Behavioral Nutrition and Physical Activity, 12, 159.
Ferguson, T., Rowlands, A. V., Olds, T., & Maher, C. (2015). The validity of consumer-level, activity monitors in healthy adults worn in free-living conditions: a cross-sectional study. International Journal of Behavioral Nutrition and Physical Activity, 12, 42.
Freigoun, M. T., Martín, C. A., Magann, A. B., Rivera, D. E., Phatak, S. S., Korinek, E. V., et al. (2017). System identification of just walk: A behavioral mhealth intervention for promoting physical activity. In Proceedings of the 2017 American control conference, May 24–26, Seattle, WA (in press).
Hekler, E. B., Buman, M. P., Poothakandiyil, N., Rivera, D. E., Dzierzewski, J. M., Morgan, A. A., et al. (2013). Exploring behavioral markers of long-term physical activity maintenance a case study of system identification modeling within a behavioral intervention. Health Education and Behavior, 40, 51S–62S.
Hochberg, I., Feraru, G., Kozdoba, M., Mannor, S., Tennenholtz, M., & Yom-Tov, E. (2016). A reinforcement learning system to encourage physical activity in diabetes patients. arXiv:1605.04070
Hurley, J. C., Hollingshead, K. E., Todd, M., Jarrett, C. L., Tucker, W. J., Angadi, S. S., et al. (2015). The walking interventions through texting (WalkIT) trial: Rationale, design, and protocol for a factorial randomized controlled trial of adaptive interventions for overweight and obese, inactive adults. JMIR Research Protocols, 4, e108.
Kazemi, D. M., Borsari, B., Levine, M. J., Li, S., Lamberson, K. A., & Matta, L. A. (2017). A systematic review of the mhealth interventions to prevent alcohol and substance abuse. Journal of Health Communication, 22(5), 413–432.
Lin, J. J., Mamykina, L., Lindtner, S., Delajoux, G., & Strub, H. B. (2006). Fish’n’Steps: Encouraging physical activity with an interactive computer game. In International conference on ubiquitous computing (pp. 261–278). Berlin: Springer.
Ljung, L. (1999). System identification: Theory for the use (2nd ed.). Upper Saddle River, NJ: Prentice Hall.
Martín, C. A., Deshpande, S., Hekler, E. B., & Rivera, D. E. (2015a). A system identification approach for improving behavioral interventions based on social cognitive theory. In IEEE 2015 American control conference (ACC) (pp. 5878–5883).
Martín, C. A., Rivera, D. E., & Hekler, E. B. (2015b). Design of informative identification experiments for behavioral interventions. In Proceedings of the 17th IFAC Symposium on system identification, Beijing, China (Vol. 48, pp. 1325–1330).
Martín, C. A., Rivera, D. E., Riley, W. T., Hekler, E. B., Buman, M. P., Adams, M. A., et al., (2014). A dynamical systems model of social cognitive theory. In IEEE 2014 American control conference (pp. 2407–2412).
Patrick, K., Hekler, E. B., Estrin, D., Mohr, D. C., Riper, H., Crane, D., et al. (2016). The pace of technologic change: Implications for digital health behavior intervention research. American Journal of Preventive Medicine, 51, 816–824.
Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., & R Core Team, (2016). nlme: Linear and nonlinear mixed effects models. R package version 3.1-128. http://CRAN.R-project.org/package=nlme
Poirier, J., Bennett, W. L., Jerome, G. J., Shah, N. G., Lazo, M., Yeh, H. C., et al. (2016). Effectiveness of an activity tracker-and internet-based adaptive walking program for adults: A randomized controlled trial. Journal of medical Internet research, 18, e34.
Riley, W. T., Martin, C. A., Rivera, D. E., et al. (2015a). Behav. Med. Pract. Policy Res.. doi:10.1007/s13142-015-0356-6
Riley, W. T., Serrano, K. J., Nilsen, W., & Atienza, A. A. (2015b). Mobile and wireless technologies in health behavior and the potential for intensively adaptive interventions. Current Opinion in Psychology, 5, 67–71.
Rivera, D. E. (2012). Optimized behavioral interventions: What does system identification and control engineering have to offer? IFAC Proceedings Volumes, 45, 882–893.
Rivera, D. E., Pew, M. D., & Collins, L. M. (2007). Using engineering control principles to inform the design of adaptive interventions: A conceptual introduction. Drug and Alcohol Dependence, 88, S31–S40.
Rivera, D. E., Pew, M. D., Collins, L. M., & Murphy, S. A. (2005). Engineering control approaches for the design and analysis of adaptive, time-varying interventions. The Methodology Center Technical Report, 05–73.
Schneider, P. L., Bassett, D. R., Jr., Thompson, D. L., Pronk, N. P., & Bielak, K. M. (2006). Effects of a 10,000 steps per day goal in overweight adults. The American Journal of Health Promotion, 21, 85–89.
Shiffman, S., Stone, A. A., & Hufford, M. R. (2008). Ecological momentary assessment. Annual Review of Clinical Psychology, 4, 1–32.
Spruijt-Metz, D., Hekler, E., Saranummi, N., Intille, S., Korhonen, I., Nilsen, W., et al. (2015a). Building new computational models to support health behavior change and maintenance: New opportunities in behavioral research. Translational Behavioral Medicine, 5, 335–346.
Spruijt-Metz, D., Wen, C. K. F., O’Reilly, G., Li, M., Lee, S., Emken, B. A., et al. (2015b). Innovations in the use of interactive technology to support weight management. Current Obesity Reports, 4, 510–519.
Stajkovic, A. D., & Luthans, F. (1979). Social cognitive theory and self-efficacy: Implications for motivation theory and practice. In R. M. Steers, L. W. Porter, & G. A. Bigley (Eds.), Motivation and Work Behavior (pp. 126–140). Boston: MA. McGraw-Hill.
Timms, K. P., Rivera, D. E., Collins, L. M., & Piper, M. E. (2014). A dynamical systems approach to understanding self-regulation in smoking cessation behavior change. Nicotine & Tobacco Research, 16, S159–S168.
Troiano, R. P., Berrigan, D., Dodd, K. W., Masse, L. C., Tilert, T., & McDowell, M. (2008). Physical activity in the United States measured by accelerometer. Medicine and Science in Sports and Exercise, 40, 181.
Tudor-Locke, C., Hatano, Y., Pangrazi, R. P., & Kang, M. (2008). Revisiting “how many steps are enough?”. Medicine and Science in Sports and Exercise, 40, S537.
Acknowledgements
Funding was provided by National Science Foundation (Grant Number IIS-1449751).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
Elizabeth V. Korinek, Sayali S. Phatak, Cesar A. Martin, Mohammad T. Freigoun, Daniel E. Rivera, Marc A. Adams, Pedja Klasnja, Matthew P. Buman, and Eric B. Hekler declare that they have no conflicts of interest.
Human and animal rights and Informed consent
All procedures followed were in accordance with ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. Informed consent was obtained from all patients for being included in the study.
Rights and permissions
About this article
Cite this article
Korinek, E.V., Phatak, S.S., Martin, C.A. et al. Adaptive step goals and rewards: a longitudinal growth model of daily steps for a smartphone-based walking intervention. J Behav Med 41, 74–86 (2018). https://doi.org/10.1007/s10865-017-9878-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10865-017-9878-3