Predictive Model of Psychological Factors Influencing University Students' Intentions and Behaviour Regarding Physical Exercise—Based on Machine Learning Algorithms

Predictive Model of Psychological Factors Influencing University Students' Intentions and Behaviour Regarding Physical Exercise—Based on Machine Learning Algorithms | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Predictive Model of Psychological Factors Influencing University Students' Intentions and Behaviour Regarding Physical Exercise—Based on Machine Learning Algorithms Lihong Zhu, Kong Fanjing, Liu Tianzhuo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8043331/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Insufficient physical exercise has become a significant risk factor affecting university students' physical and mental health, with participation behaviour influenced by the complex interplay of multidimensional psychological and physiological variables. This study aims to utilise machine learning algorithms to construct predictive models for university students' physical exercise participation behaviour, identify key influencing factors, and validate the diversity gradient hypothesis. Based on questionnaire survey data from university students across multiple provinces nationwide, five machine learning algorithms—Multi-Layer Perceptron (MLP), XGBoost, Random Forest, Gradient Boosting Decision Tree (GBDT), and Decision Tree—were employed for modelling and comparative analysis. Results indicate that among the five models, GBDT exhibits optimal classification performance. Feature importance analysis identifies weight and anxiety as core variables influencing model predictions. Propensity score propensity score maps further reveal marginal effects of different feature value ranges on predictions, particularly evident in assessing tendencies towards high-intensity exercise. The decision tree model features resistance to temptation as its root node, with healthy habits and impulse control as sub-nodes. Integrating the Multi-Process Action Control (M-PAC) model with stress process theory, this study elucidates at the mechanistic level how the interplay between physical and mental health, self-control capacity, and emotion regulation strategies collectively influences physical exercise behaviour. It proposes that systematic health intervention and educational planning strategies can promote the development of exercise habits among university students. Health sciences/Health care Physical sciences/Mathematics and computing Biological sciences/Psychology Social science/Psychology physical activity influencing factors machine learning predictive models Full Text Additional Declarations No competing interests reported. Supplementary Files dateresearch.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8043331","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":554410980,"identity":"fd46a336-a223-49ad-a3a0-d53ee4a613a9","order_by":0,"name":"Lihong Zhu","email":"","orcid":"","institution":"Northeast Normal University","correspondingAuthor":false,"prefix":"","firstName":"Lihong","middleName":"","lastName":"Zhu","suffix":""},{"id":554410983,"identity":"ea3eb5b2-7f5a-40f5-812f-4721d5b977df","order_by":1,"name":"Kong 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