Many of the risk factors for heart disease are behavioral, such as physical inactivity, smoking, and diets high in saturated and trans-fats. Cardiac rehabilitation and other secondary prevention programs are effective at helping cardiac patients make the initial lifestyle changes needed to reduce their risks, but patients often fail t maintain those changes after the program ends. In this grant, the investigators propose to develop a novel mHealth application for supporting maintenance of physical activity after cardiac rehabilitation. By taking advantage of the frequent interactions that individuals have with their mobile phones throughout the day, the investigators will design and evaluate an adaptive, personalized application that (1) keeps patients reminded of their health goals, increasing the likelihood that they will notice opportunities to engage in physical activity; (2) provides actionable ideas for how patients can be active right now, given their current context; and (3) helps patients plan and reflect on their physical activity to enable creation of robust and sustainable physical-activity habits. In addition, the application will adapt its functioning for ech patient over time in order to minimize user burden while optimizing its ability to encourage physical activity and maintain engagement with the intervention. A user-centered design process will be used to investigate design requirements of mHealth technologies for long-term use, and the system will be evaluated in a year-long study with 60 patients with coronary artery disease who completed phase II cardiac rehabilitation. Study data will be analyzed to understand how the use of different components affect, over time, patients' physical activity levels, perceived burden, and engagement with the system. The project's innovations lie in grounding the proposed intervention in dual-process models of self-regulation, developing new algorithms to enable adaptation and personalization of how the application works over time, and using a micro-randomization study design to enable causal accounts of how the application use affects patients' physical activity, user burden, and engagement over the course of a year. The success of the project will provide cardiac patients with continuously available support for staying physically active in the midst of daily life, and deep understanding of technical and design requirements for behavior-change mHealth technologies for long-term use.
Individuals with heart disease often have difficulties adopting and sustaining lifestyle changes needed to reduce their health risks, such as increasing physical activity and changing their diet. In this project, we will design and evaluate a personalized, adaptive mHealth intervention that leverages frequent interactions that people have with their mobile phones to enable individuals with heart disease to stay focused on their health goals, engage in opportunistic physical activity throughout the day, and build robust and sustainable physical- activity habits that can help reduce-and keep down-their cardiac risks.
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| Dempsey, Walter; McCullagh, Peter (2018) Survival models and health sequences. Lifetime Data Anal 24:550-584 |
| Luers, Brook; Klasnja, Predrag; Murphy, Susan (2018) Standardized Effect Sizes for Preventive Mobile Health Interventions in Micro-randomized Trials. Prev Sci : |
| Nahum-Shani, Inbal; Smith, Shawna N; Spring, Bonnie J et al. (2018) Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support. Ann Behav Med 52:446-462 |
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| Rabbi, Mashfiqui; Philyaw Kotov, Meredith; Cunningham, Rebecca et al. (2018) Toward Increasing Engagement in Substance Use Data Collection: Development of the Substance Abuse Research Assistant App and Protocol for a Microrandomized Trial Using Adolescents and Emerging Adults. JMIR Res Protoc 7:e166 |
| Greenewald, Kristjan; Tewari, Ambuj; Klasnja, Predrag et al. (2017) Action Centered Contextual Bandits. Adv Neural Inf Process Syst 30:5973-5981 |
| Bekiroglu, Korkut; Lagoa, Constantino; Murphy, Suzan A et al. (2017) Control Engineering Methods for the Design of Robust Behavioral Treatments. IEEE Trans Control Syst Technol 25:979-990 |
| Klasnja, Predrag; Hekler, Eric B; Korinek, Elizabeth V et al. (2017) Toward Usable Evidence: Optimizing Knowledge Accumulation in HCI Research on Health Behavior Change. Proc SIGCHI Conf Hum Factor Comput Syst 2017:3071-3082 |
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