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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
5R01HL125440-04
Application #
9389525
Study Section
Biomedical Computing and Health Informatics Study Section (BCHI)
Program Officer
Nicastro, Holly L
Project Start
2015-12-01
Project End
2019-11-30
Budget Start
2017-12-01
Budget End
2018-11-30
Support Year
4
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Kaiser Foundation Health Plan of Washington
Department
Type
DUNS #
078198520
City
Seattle
State
WA
Country
United States
Zip Code
98101
Hekler, Eric B; Klasnja, Predrag; Riley, William T et al. (2016) Agile science: creating useful products for behavior change in the real world. Transl Behav Med 6:317-28
Nahum-Shani, Inbal; Smith, Shawna N; Spring, Bonnie J et al. (2016) Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support. Ann Behav Med :
Liao, Peng; Klasnja, Predrag; Tewari, Ambuj et al. (2016) Sample size calculations for micro-randomized trials in mHealth. Stat Med 35:1944-71
Dempsey, Walter; Liao, Peng; Klasnja, Pedja et al. (2015) Randomised trials for the Fitbit generation. Signif (Oxf) 12:20-23
Klasnja, Predrag; Hekler, Eric B; Shiffman, Saul et al. (2015) Microrandomized trials: An experimental design for developing just-in-time adaptive interventions. Health Psychol 34S:1220-8