Machine learning applications in the analysis of sedentary behavior and associated health risks - review

Aug 5, 2026

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Rapid technological progress has increased sedentary lifestyles, elevating public health risks associated with prolonged inactivity. This review examines how machine learning (ML) can analyze sedentary patterns using large datasets, extract meaningful trends in physical activity and inactivity, and inform intervention strategies. Objectives focus on ML’s role, optimization of techniques to improve predictive accuracy for sedentary behavior, and evaluation of approaches to strengthen algorithm effectiveness. A 2004–2024 PubMed and Scopus search produced 46 included articles.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Introduction

Objectives

Methods

Results

Conclusion

TYPE Review PUBLISHED 18 June 2025 DOI 10.3389/frai.2025.1538807 OPEN ACCESS EDITED BY Amelia Zafra, University of Cordoba, Spain REVIEWED BY Pradeep Paraman, SEGi University, Malaysia Nejmeddine Ouerghi, Hôpital La Rabta, Tunisia *CORRESPONDENCE Maha Al-Asmakh [maha.alasmakh@qu.edu.qa](maha.alasmakh@qu.edu.qa) RECEIVED 03 December 2024 ACCEPTED 26 May 2025 PUBLISHED 18 June 2025 CITATION Hammad AS, Tajammul A, Dergaa I and Al-Asmakh M (2025) Machine learning applications in the analysis of sedentary behavior and associated health risks. Front. Artif. Intell. 8:1538807. doi: 10.3389/frai.2025.1538807 COPYRIGHT © 2025 Hammad, Tajammul, Dergaa and Al-Asmakh. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Machine learning applications in the analysis of sedentary behavior and associated health risks Ayat S Hammad 1,2, Ali Tajammul 1, Ismail Dergaa3,4,5 and Maha Al-Asmakh 1,2* 1 Department of Biomedical Sciences, College of Health Sciences, QU Health, Qatar University, Doha, Qatar, 2 Biomedical Research Center, Qatar University, Doha, Qatar, 3 High Institute of Sport and Physical Education of Ksar Saïd, University of Manouba, Manouba, Tunisia, 4 Physical Activity Research Unit, Sport and Health (UR18JS01), National Observatory of Sports, Tunis, Tunisia, 5 High Institute of Sport and Physical Education of El Kef, University of Jendouba, Jendouba, Tunisia Background: The rapid advancement of technology has brought numerous benefits to public health but has also contributed to a rise in sedentary lifestyles, linked to various health issues. As prolonged inactivity becomes a growing public health concern, researchers are increasingly utilizing machine learning (ML) techniques to examine and understand these patterns. ML offers powerful tools for analyzing large datasets and identifying trends in physical activity and inactivity, generating insights that can support effective interventions. Objectives: This review aims to: (i) examine the role of ML in analyzing sedentary patterns,(ii) explore how different ML techniques can be optimized to improve the accuracy of predicting sedentary behavior, and (iii) assess strategies to enhance the effectiveness of ML algorithms. Methods: A comprehensive search was conducted in PubMed and Scopus, targeting peer-reviewed articles published between 2004 and 2024. The search included the subject terms “sedentary behavior,”“sedentary lifestyle health,”and “machine learning sedentary lifestyle,” combined with the keywords“physical inactivity” and “diseases” using Boolean operators (AND, OR) . Articles were included if they addressed the health impacts of sedentary behavior or employed ML techniques for its analysis. Exclusion criteria involved studies older than 20 years or lacking direct relevance. After screening 33 core articles and identifying 13 more through citation tracking, 46 articles were included in the final review. Results: This narrative review describes the characteristics of sedentary behavior, associated health risks, and the applications of ML in this context. Based on the reviewed literature, sedentary behavior was consistently associated with cardiovascular disease, metabolic disorders, and mental health conditions. Thereview highlights the utility of various ML approaches in classifying activity levels and significantly improving the prediction of sedentary behavior, offering a promising approach to address this widespread health issue. Conclusion: ML algorithms, including supervised and unsupervised models, show great potential in

FAQs

What is the main purpose of this review?

To examine the role of machine learning in analyzing sedentary patterns, optimize ML techniques for better prediction of sedentary behavior, and assess strategies to improve ML algorithm effectiveness.

How were the studies selected for the review?

The review searched PubMed and Scopus for peer-reviewed articles from 2004 to 2024, applied inclusion criteria related to health impacts or ML analysis of sedentary behavior, and excluded studies older than 20 years or lacking direct relevance; 46 articles were included after screening and citation tracking.

What health outcomes are linked to sedentary behavior according to the review?

Sedentary behavior is consistently associated with cardiovascular disease, metabolic disorders, and mental health conditions, as reported across the included literature.

Aug 5, 2026

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