A Risk Minimization Approach to Messaging Intervention for Physical Activity

This paper presents a just-in-time adaptive message intervention framework to promote long-term physical activity in young adults with insufficient activity levels. The framework personalizes interventions by approximating individual activity patterns using real-time smartwatch data. Then, a Risk-Sensitive Shrinking Horizon Model Predictive Control (MPC) is employed and reformulated as a Mixed-Integer Linear Programming (MILP) problem to optimize intervention design. To enable real-time adaptive decision-making on smartphones with limited computational resources, a neural network is trained offline using MILP-generated data, providing an efficient online control policy. Results from TryAIM clinical trial indicate that individuals respond differently to interventions, and for many, the models suggest that selecting the right time and message can effectively enhance physical activity levels.