Precision Physical Activity Prescription via Reinforcement Learning for Functional Actions
Physical activity (PA) plays an important role in maintaining and improving health. Daily steps have been a key PA measure that is easily accessible with common wearable devices. However, methods are lacking to recommend a personalized optimal distribution of daily steps over a period of time for the best of certain health biomarkers. In this paper, we fill this void based on the data from the All of Us Research Program which includes months of step counts as well as repeated measurements of key health biomarkers. We develop a new offline reinforcement learning (RL) algorithm to learn personalized and optimal PA distributions associated with cardiometabolic risk, where the action is a fun...
| Authors | Gefei Lin, Rui Miao, Jennifer Sacheck et al. |
|---|---|
| Primary category | stat.AP |
| Published | 19 May 2026 |
| Last updated at source | 23 June 2026 |
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