Many health conditions are caused by unhealthy lifestyles and can be improved by behavior change. Traditional behavior-change methods (e.g., weight-loss clinics; personal trainers) have bottlenecks in providing expert personalized day-to-day support to large populations for long periods. There is a pressing need to extend the reach and intensity of existing successful health behavior change approaches in areas such as diet and fitness. Smartphone platforms provide an excellent opportunity for projecting maximally effective interventions for behavior change into everyday life at great economies of scale. Smartphones also provide an excellent opportunity for collecting rich, fine-grained data necessary for understanding and predicting behavior-change dynamics in people going about their everyday lives. The challenge posed by these opportunities for detailed measurement and intervention is that current theory is not equally fine-grained and predictive.
This interdisciplinary project investigates theory and methods to support fine-grained behavior-change modeling and intervention integrated via smartphone into the daily lives of individuals and groups. Fittle+ develops a new and transformative form of smartphone-delivered Ecological Momentary Intervention (EMI) for improving diet and physical activity. This approach will provide social support and autonomously planned and personalized coaching that builds on methods from mobile sensing, cognitive tutoring, and evidence-based social design. The foundation for this new approach will require new predictive computational theories of health behavior change. Current coarse-grained conceptual theories of individual health behavior change will be refined into fine-grained predictive computational models. These computational models will be capable of tracking moment-by-moment human context, activity, and social patterns based on mobile sensing and interaction data. Using these monitoring capabilities, Fittle+'s computational models will support assessment of, and predictions about, individual users and groups based on underlying motivational, cognitive, and social mechanisms. These predictive models will also be used to plan and optimize coaching actions including detailed diagnostics, individualized goals, and contextually and personally adapted interventions.
The collaborative team of researchers works with weight-loss interventionists at one of nation's largest health organization's facility in Hawaii. The team includes expertise in mobile sensing, artificial intelligence, computational cognition, social psychology, human computer interaction, computer tutoring, and measurement theory.
