[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120572-en":3,"doc-seo-120572-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120572,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Predicting College Students’ Exercise Dependence - A Machine Learning Approach","Exercise dependence behavior among college students is a key concern in sports psychology because maladaptive, withdrawal-like patterns may emerge as training intensity and frequency rise. The study analyzes data from 2,745 students using three standardized questionnaires on exercise dependence, psychological characteristics, and demographics. Four machine learning models are trained and combined with an ensemble stacking strategy, reaching a mean AUC of 0.96 for risk identification. Results highlight behavioral and psychological drivers and support AI-enabled early monitoring.","TYPE Original Research PUBLISHED 29 January 2026 DOI 10.3389/fpsyg.2026.1743725  \nOPEN ACCESS  \nEDITED BY  \nDavid Manzano Sánchez, University of Almeria, Spain  \nREVIEWED BY  \nYuer Yang,  \nThe University of Hong Kong, Hong Kong SAR, China  \nSeungbak Lee,  \nState University of New York at Fredonia, United States  \n*CORRESPONDENCE  \nYi Lin Ren  \n [yilinren@jnu.edu.cn](yilinren@jnu.edu.cn)  \nRECEIVED 11 November 2025  \nREVISED 15 December 2025  \nACCEPTED 09 January 2026  \nPUBLISHED 29 January 2026  \nCITATION  \nDeng Y, Lan W, Si M and Ren YL (2026) Predicting college students’ exercise dependence: a machine learning approach.  \nFront. Psychol. 17:1743725 .  \ndoi: 10.3389/fpsyg.2026.1743725  \nCOPYRIGHT  \n© 2026 Deng, Lan, Si and Ren. 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.  \nPredicting college students’exercise dependence: a machine learning approach  \nYihang Deng 1, Wei Lan 2, Mingda Si3 and Yi Lin Ren4*  \n1Department of Physical Education, Neijiang Normal University, Neijiang, China, 2Embodied Media Laboratory, Graduate School of Media Design, Keio University, Hiyoshi Campus, Yokohama, Japan, 3National Institute of Education, Nanyang Technological University, Singapore, Singapore, 4Zhuhai Research Center for Women and Children’s Sports Culture, College of Sports, Jinan University Zhuhai Campus, Zhuhai, Guangdong, China  \nExercise dependence behavior among college students is a critical issue in sports psychology that deserve closer examination, and artificial intelligence offer a useful ways to explore its mechanisms and predicting associated risks. In this study, data were collected from 2,745 college students using three standardized questionnaires, covering (i) exercise dependence behavior, (ii) psychological characteristics (e. g., exercise identity, weight biases), and (iii) basic demographic information. We used four widely used machine learning algorithms: logistic regression, random forest, extreme gradient boosting (XGBoost), and multilayer perceptron, and their outputs were further integrated through an ensemble learning techniques to further enhance the robustness and predictive power of the models. The stacking ensemble model achieved a mean AUC of 0.96 in identifying exercise dependence risk among college students, demonstrating that integrating multiple machine learning approaches can yield robust and highly accurate risk prediction in this setting. Among the variables, the most influential predictors of exercise dependence behavior included prolonging exercise to obtain the desired effect, allocating most leisure time on exercise, experiencing difficulty in reducing exercise frequency, and actual exercise time longer than originally planned. These findings uncovers the key psychological and behavioral mechanisms underlying in exercise dependence among college students and show that artificial intelligence methods can be effectively applied to support risk monitoring in sport and psychological health contexts.  \nKEYWORDS  \ncollege student, ensemble learning (EN), exercise dependence behavior, machine learning, risk prediction  \nHighlights  \n• This study applies advanced machine learning algorithms to predict exercise dependence among Chinese college students.  \n• Deviation regulation theory and exercise psychology provide the theoretical foundation for identifying key psychological predictors.  \n• Exercise identity and weight bias are found to be significant psychological variables influencing exercise dependence.  \n• The use of data-driven methods improves prediction accuracy compared to traditional statistical a","cbCaiuMx2ComoFmo","https://ap.wps.com/l/cbCaiuMx2ComoFmo","pdf",2084365,1,17,"English","en",105,"# Introduction\n## Exercise dependence and health context\n## College-student risk factors in China\n# Methods\n## Participants and questionnaire measures\n## Machine learning models and ensemble strategy\n# Results\n## Prediction performance\n## Key influential predictors\n# Discussion\n## Psychological and behavioral mechanisms\n## Implications for monitoring and intervention","[{\"question\":\"How is exercise dependence defined and why is it important for college students?\",\"answer\":\"Exercise dependence is described as a dysfunctional behavior pattern featuring withdrawal-like effects (e.g., anxiety or depression) when exercise is stopped, along with an uncontrollable drive to increase workout intensity or frequency. It is important because rising exercise loads can shift benefits toward maladaptive or addictive behavior.\"},{\"question\":\"Which machine learning methods and ensemble approach were used to predict risk?\",\"answer\":\"The study applies logistic regression, random forest, extreme gradient boosting (XGBoost), and multilayer perceptron, then integrates their outputs using a stacking ensemble technique to strengthen robustness and predictive power.\"},{\"question\":\"What factors were identified as the most influential predictors?\",\"answer\":\"The most influential predictors include prolonging exercise to achieve desired effects, allocating most leisure time to exercise, difficulty reducing exercise frequency, and actual exercise time longer than originally planned.\"}]","Predicting College Students’ Exercise Dependence - A Machine Learning Approach | PDF",1785730714,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predicting-college-students-exercise-dependence-a-machine-learning-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-college-students-exercise-dependence-a-machine-learning-approach/120572/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How is exercise dependence defined and why is it important for college students?","Question",{"text":75,"@type":76},"Exercise dependence is described as a dysfunctional behavior pattern featuring withdrawal-like effects (e.g., anxiety or depression) when exercise is stopped, along with an uncontrollable drive to increase workout intensity or frequency. It is important because rising exercise loads can shift benefits toward maladaptive or addictive behavior.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning methods and ensemble approach were used to predict risk?",{"text":80,"@type":76},"The study applies logistic regression, random forest, extreme gradient boosting (XGBoost), and multilayer perceptron, then integrates their outputs using a stacking ensemble technique to strengthen robustness and predictive power.",{"name":82,"@type":73,"acceptedAnswer":83},"What factors were identified as the most influential predictors?",{"text":84,"@type":76},"The most influential predictors include prolonging exercise to achieve desired effects, allocating most leisure time to exercise, difficulty reducing exercise frequency, and actual exercise time longer than originally planned.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]