Evaluating Digital Health Interventions: Key Questions and Approaches

Digital health interventions have enormous potential as scalable tools to improve health and healthcare delivery by improving effectiveness, efficiency, accessibility, safety, and personalization. Achieving these improvements requires a cumulative knowledge base to inform development and deployment of digital health interventions. However, evaluations of digital health interventions present special challenges. This paper aims to examine these challenges and outline an evaluation strategy in terms of the research questions needed to appraise such interventions. As they are at the intersection of biomedical, behavioral, computing, and engineering research, methods drawn from all of these disciplines are required. Relevant research questions include defining the problem and the likely benefit of the digital health intervention, which in turn requires establishing the likely reach and uptake of the intervention, the causal model describing how the intervention will achieve its intended benefit, key components, and how they interact with one another, and estimating overall benefit in terms of effectiveness, cost effectiveness, and harms. Although RCTs are important for evaluation of effectiveness and cost effectiveness, they are best undertaken only when: (1) the intervention and its delivery package are stable; (2) these can be implemented with high fidelity; and (3) there is a reasonable likelihood that the overall benefits will be clinically meaningful (improved outcomes or equivalent outcomes at lower cost). Broadening the portfolio of research questions and evaluation methods will help with developing the necessary knowledge base to inform decisions on policy, practice, and research

What sustains behavioral changes? A dynamical systems approach to improving theories of change in physical exercise

University of Minnesota Ph.D. dissertation. August 2019. Major: Psychology. Advisors: Alexander Rothman, Traci Mann. 1 computer file (PDF); ix, 224 pages.Health behaviors, such as physical exercise, are associated with chronic diseases that top the list of all-cause mortality. Yet, the most healthful lifestyle changes people can (and often want to) make, also tend to be the most challenging to sustain. This dissertation explores how modeling behavior as a dynamical system could improve understanding of psychological processes that sustain behavioral changes. I focus on two classes of processes—motivational and habitual—that may be most pertinent to sustaining changes in physical exercise. A model based on prior theorizing is constructed and simulated (Study 1), and observational data are analyzed (Study 2). Intensive longitudinal data are collected from healthy US-based Fitbit users who recently initiated an increase in exercise. Participants are prospectively observed for two months during which measures of motivation and habit are assessed three days per week, and exercise-as-usual is passively tracked via Fitbit. I find that within-person increases in the automaticity with which exercise is performed in a given week is associated with increases in time spent exercising. Furthermore, differences in the trajectory of automaticity and satisfaction with exercise over time may differentiate those who successfully maintain increases in exercise and those who do not. Results are placed in the context of contemporary theories of behavior change maintenance and suggestions for improvement are forwarded