Precision Physical Activity Prescription via Reinforcement Learning for Functional Actions

TL;DR

This paper introduces a novel offline reinforcement learning method to personalize physical activity prescriptions, optimizing daily step distributions for better health biomarkers based on large-scale health data.

Contribution

It develops a new RL algorithm for personalized PA recommendations and demonstrates its effectiveness using real health data from the All of Us program.

Findings

The approach outperforms existing RL methods in simulations.

Optimal policies suggest increased and more consistent daily steps.

Personalized recommendations vary by subgroup characteristics.

Abstract

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 function representing the daily step distribution over a period of time. Simulation studies demonstrate the advantage of the proposed approach over existing continuous-action RL methods. The learned optimal policy from the All of Us data generally suggests people take more daily steps and also follow a more consistent pattern of PA over time while offering tailored recommendations for subgroups in blood glucose level, body mass index, blood pressure, age, and sex.