mlsanityData to 14 Oct 2026

Precision Physical Activity Prescription via Reinforcement Learning for Functional Actions

arXiv 2605.19208 ↗First posted 19 May 2026stat.AP cs.LG stat.MLcited by 1

On the map: Deep reinforcement learning methods → RL for clinical treatment and surgery

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for clinical treatment and surgery, one of 15 directions in Deep reinforcement learning methods. Builds on 9 of its 42 references that are in the map. First posted on arXiv 19 May 2026.

Builds on

9 of 42 references.

References · 9

  1. 2019Batch Policy Learning under Constraintscited by 244
  2. 2022A Review of Off-Policy Evaluation in Reinforcement Learningcited by 95
  3. 2013The Sample-Complexity of General Reinforcement Learningcited by 18
  4. 2018Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Controlcited by 11
  5. 2021Distributional data analysis of accelerometer data from the NHANES database using nonparametric survey regression modelscited by 11
  6. 2023Flexible Functional Treatment Effect Estimationcited by 3
  7. 2019Missingness as Stability: Understanding the Structure of Missingness in Longitudinal EHR data and its Impact on Reinforcement Learning in Healthcarecited by 2
  8. 2023Distributional outcome regression via quantile functions and its application to modelling continuously monitored heart rate and physical activitycited by 2
  9. 2020Exploring Optimal Control With Observations at a Costcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Finite-Sample Theory for Fitted Q-Iteration When Actions Are Functionscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025A Personalized Exercise Assistant using Reinforcement Learning (PEARL): Results from a four-arm Randomized-controlled Trial
  2. 2025Diffusion Policies with Offline and Inverse Reinforcement Learning for Promoting Physical Activity in Older Adults Using Wearable Sensors
  3. 2023Adaptive Interventions with User-Defined Goals for Health Behavior Change
  4. 2025Diabetes Lifestyle Medicine Treatment Assistance Using Reinforcement Learning
  5. 2025Reinforcement Learning Driven Generalizable Feature Representation for Cross-User Activity Recognition
  6. 2026Collaborative Temporal Feature Generation via Critic-Free Reinforcement Learning for Cross-User Sensor-Based Activity Recognition
  7. 2024StepCountJITAI: simulation environment for RL with application to physical activity adaptive intervention
  8. 2026Learning to Compress Time-to-Control: A Reinforcement Learning Framework for Chronic Disease Management