Enhancing Digital Health Services: A Machine Learning Approach to Personalized Exercise Goal Setting

TL;DR

This paper presents a deep reinforcement learning method that dynamically personalizes exercise goals in digital health services by analyzing user behavior and health data, aiming to improve user engagement and health outcomes.

Contribution

It introduces a novel deep reinforcement learning algorithm that adapts exercise goals in real-time based on behavioral and biometric data, addressing limitations of static goal-setting approaches.

Findings

The proposed method effectively personalizes exercise goals.

It outperforms traditional static goal-setting strategies.

The approach adapts to changing user health conditions.

Abstract

The utilization of digital health has increased recently, and these services provide extensive guidance to encourage users to exercise frequently by setting daily exercise goals to promote a healthy lifestyle. These comprehensive guides evolved from the consideration of various personalized behavioral factors. Nevertheless, existing approaches frequently neglect the users dynamic behavior and the changing in their health conditions. This study aims to fill this gap by developing a machine learning algorithm that dynamically updates auto-suggestion exercise goals using retrospective data and realistic behavior trajectory. We conducted a methodological study by designing a deep reinforcement learning algorithm to evaluate exercise performance, considering fitness-fatigue effects. The deep reinforcement learning algorithm combines deep learning techniques to analyse time series data and infer user exercise behavior. In addition, we use the asynchronous advantage actor-critic algorithm for reinforcement learning to determine the optimal exercise intensity through exploration and exploitation. The personalized exercise data and biometric data used in this study were collected from publicly available datasets, encompassing walking, sports logs, and running. In our study, we conducted The statistical analyses/inferential tests to compare the effectiveness of machine learning approach in exercise goal setting across different exercise goal setting strategies.