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Updated: May 26, 2026

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity
Published on: March 7, 2019
A Risk Minimization Approach to Messaging Intervention for Physical Activity
Zhengxing Li1, Sahand Kiani1, Constantino M Lagoa1
1Department of Electrical and Computer Engineering, Pennsylvania State University, State College, PA 16801, USA.
Abstract:
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.
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