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Enhancing digital health services: A machine learning approach to personalized exercise goal setting by Ji Fang; Vincent CS Lee; Hao Ji; Haiyan Wang is a Health Professions article available to read on EtoBox.

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## Background 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. ## Objective 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. ## Methods 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 analyze time series data and infer user's 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 use

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Author

Ji Fang; Vincent CS Lee; Hao Ji; Haiyan Wang

Publisher

SAGE Publications

Published

2024

Language

EN

Field

Health Professions (Health Sciences)