Dynamic Physical Activity Recommendation on Personalised Mobile Health Information Service - A Deep Reinforcement Learning Approach

Aug 4, 2026

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Mobile health (mHealth) information services help users manage healthcare by increasing physical activity and supporting better health outcomes, yet personalization gaps, adherence issues, and uncertainty in future effects can reduce recommendation impact. This paper proposes an efficient, data-driven real-time interaction model that selects optimal exercise plans for individuals, explicitly accounting for time-varying behavior to maximize long-term health utility. A personalised AI module integrates scientific exercise knowledge, deep learning for behavior inference from time-series data, and asynchronous advantage actor-critic reinforcement learning to learn optimal policies. Validated on real-world runner data against fixed plans, the method adapts to changing behavior, improves exercise performance, and supports a Pareto-optimal healthy lifestyle.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages34

Reading Time1 h 26 min

AccessFree to download

Abstract

Introduction

Mobile health information services and wearable tracking

Challenges in personalization, adherence, and long-term uncertainty

Proposed approach (personalised AI module and DRL framework)

Dynamic physical activity recommendation on personalised mobile health information service: A deep reinforcement learning approach Ji Fang 1,2, Vincent CS Lee2, Haiyan Wang 1,* 1School of Economics and Management, Southeast University, Nanjing, 210096, P R China 2Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University (Clayton Campus), Melbourne, VIC, 3800, Australia 1First author: [ji.fang@monash.edu](ji.fang@monash.edu) [2](2Second author: vincent.cs.lee@monash.edu)[Second author: ](2Second author: vincent.cs.lee@monash.edu)[vincent.cs.lee@monash.edu](2Second author: vincent.cs.lee@monash.edu) 1,* [Corresponding and third author: ](Corresponding and third author: hywang@seu.edu.cn)[hywang@seu.edu.cn](Corresponding and third author: hywang@seu.edu.cn) Abstract Mobile health (mHealth) information service makes healthcare management easier for users, who want to increase physical activity and improve health. However, the differences in activity preference among the individual, adherence problems, and uncertainty of future health outcomes may reduce the effect of the mHealth information service. The current health service system usually provides recommendations based on fixed exercise plans that do not satisfy the user’s specific needs. This paper seeks an efficient way to make physical activity recommendation decisions on physical activity promotion in personalised mHealth information service by establishing data-driven model. In this study, we propose a real-time interaction model to select the optimal exercise plan for the individual considering the time-varying characteristics in maximising the long-term health utility of the user. We construct a framework for mHealth information service system comprising a personalised AI module, which is based on the scientific knowledge about physical activity to evaluate the individual's exercise performance, which may increase the awareness of the mHealth artificial intelligence system. The proposed deep reinforcement learning (DRL) methodology combining two classes of approaches to improve the learning capability for the mHealth information service system. A deep learning method is introduced to construct the hybrid neural network combing long-short term memory (LSTM) network and deep neural network (DNN) techniques to infer the individual exercise behavior from the time series data. A reinforcement learning method is applied based on the asynchronous advantage actor-critic algorithm to find the optimal policy through exploration and exploitation. We tested our DRL methodology using real-world data from a runner program, and our policy was validated by comparison to other fixed exercise plans. The results show that the proposed physical activity recommendation system for the personalised mHealth information service, represented as a personalized AI module, can adapt to the users' changing behaviour, boost the users' exercise performance, and promote a pareto optimal healthy lifestyle. Keywords: OR in health services; mHealth information service system; personalised practice process optimization; real-time interaction model; A deep reinforcement learning methodology 1. Introduction Mobile Health(mHealth) information services combine the users' physiology data and daily exercise information with their fitness demands, such as Fitbit, Pelton. These services provide recommendations of short-term physical activity plans for their users, improving their performance of the physical activities and enabling them to live a healthy life. For decades, wearable activity trackersand fitness applications have been widely adopted in health self-management, making it low cost and convenient for users to exercise under the guidance of a scientific training system (Liu & Avello, 2020; Nahum-shani et al., 2014) . Typically, mHealth information services record and visualize the individual exercise activities through wearable trackers

FAQs

What components are used to recommend exercises in real time?

The framework uses a personalised AI module that evaluates exercise performance using scientific knowledge, infers individual behavior from time-series data via a hybrid LSTM+DNN model, and learns optimal action policies with the asynchronous advantage actor-critic algorithm.

Aug 4, 2026

Upload by Jiven