用强化学习为不同人群定制最优每日步数分布，提升心血管代谢健康。
Precision Physical Activity Prescription via Reinforcement Learning for Functional Actions
- 基于离线强化学习，将每日步数分布作为连续动作策略优化
- 在All of Us数据上验证，可显著改善心血管代谢风险指标
- 支持按血糖、体重、年龄等细分群体的个性化建议
体力活动对维持和改善健康至关重要。日常步数是通过常见可穿戴设备易于获取的关键活动指标。然而，目前缺乏针对特定健康生物标志物，推荐个体化每日步数随时间最优分布的方法。本文基于美国全民研究计划（All of Us Research Program）的数据，包含数月步数记录及多次重复测量的关键健康生物标志物，提出一种新的离线强化学习算法，学习与心血管代谢风险相关的个性化最优体力活动分布。其中，动作定义为一段时间内的每日步数分布函数。模拟研究表明，该方法优于现有连续动作强化学习方法。基于All of Us数据学习到的最优策略普遍建议增加每日步数并保持更稳定的活动模式，同时可为血糖水平、身体质量指数、血压、年龄和性别等亚组提供定制化建议。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。