A Machine Learning Framework for Personalized Exercise Prescription Based on BMI and Physical Fitness Assessment

A Machine Learning Framework for Personalized Exercise Prescription Based on BMI and Physical Fitness Assessment | 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 A Machine Learning Framework for Personalized Exercise Prescription Based on BMI and Physical Fitness Assessment Ming Mo, Buxi Li, Ye Yang, Peng Kang, Jun Wang, Wanhong Luo, Tianshuo Jiao, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7810535/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract BACKGROUND: This study proposes a hybrid machine learning framework that integrates one-dimensional convolutional neural networks (1D-CNN) with multi-head attention and Light Gradient Boosting Machines (LightGBM) to model the relationship between physical fitness and body mass index (BMI), thereby generating personalized exercise prescriptions. METHODS: The dataset consists of 6,698 male students aged 18–20 years, including BMI measurements alongside four standardized fitness indicators: 3,000-meter run (aerobic capacity), pull-ups (muscular strength), sit-ups (muscular endurance), and shuttle run (anaerobic capacity). RESULTS: The 1D-CNN + Attention module effectively captures both local and global temporal patterns, while LightGBM significantly enhances classification accuracy through gradient-boosted decision trees. The proposed hybrid architecture achieved state-of-the-art performance in BMI classification, with an accuracy of 94.5% (Cohen’s κ = 0.91) and an F1 score of 0.93, outperforming traditional classifiers by 12.3% to 19.1%. Model interpretability is ensured through SHapley Additive exPlanations (SHAP), which supports dynamic prescription adjustments aimed at improving muscular strength, cardiorespiratory endurance, speed, agility, and flexibility. A 12-week randomized trial demonstrated the clinical efficacy of this framework, yielding a 23.5% reduction in overweight and obesity prevalence, a 15.2% increase in pull-up performance, and a 9.8% improvement in 30×2 shuttle run results. With an inference time of less than 0.8 milliseconds per sample and robust clinical outcomes, this framework provides a scalable real-time solution for data-driven health optimization. CONCLUSIONS: It’s well-suited for both clinical and mobile healthcare applications, addressing the growing demand for personalized exercise interventions among young adults. Biological sciences/Computational biology and bioinformatics Health sciences/Health care Physical sciences/Mathematics and computing Health sciences/Medical research personalized exercise prescription data processing dynamic adjustment machine learning health informatics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 05 Jan, 2026 Reviews received at journal 02 Jan, 2026 Reviews received at journal 17 Nov, 2025 Reviewers agreed at journal 13 Nov, 2025 Reviewers agreed at journal 07 Nov, 2025 Reviewers invited by journal 07 Nov, 2025 Submission checks completed at journal 05 Nov, 2025 First submitted to journal 05 Nov, 2025 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. 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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-7810535","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":546506781,"identity":"6c198ec8-064a-4a8f-bde0-f8b95d6e96d5","order_by":0,"name":"Ming Mo","email":"","orcid":"","institution":"Changsha Aeronautical Vocational and Technical College","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Mo","suffix":""},{"id":546506782,"identity":"ebbe536b-c91a-4c0e-9dd7-0b1d5883e8ea","order_by":1,"name":"Buxi Li","email":"","orcid":"","institution":"Hunan Hongtian Publishing and Distribution Co., 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Assessment","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"personalized exercise prescription, data processing, dynamic adjustment, machine learning, health informatics","lastPublishedDoi":"10.21203/rs.3.rs-7810535/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7810535/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBACKGROUND:\u003c/h2\u003e\u003cp\u003eThis study proposes a hybrid machine learning framework that integrates one-dimensional convolutional neural networks (1D-CNN) with multi-head attention and Light Gradient Boosting Machines (LightGBM) to model the relationship between physical fitness and body mass index (BMI), thereby generating personalized exercise prescriptions.\u003c/p\u003e\u003ch2\u003eMETHODS:\u003c/h2\u003e\u003cp\u003eThe dataset consists of 6,698 male students aged 18\u0026ndash;20 years, including BMI measurements alongside four standardized fitness indicators: 3,000-meter run (aerobic capacity), pull-ups (muscular strength), sit-ups (muscular endurance), and shuttle run (anaerobic capacity).\u003c/p\u003e\u003ch2\u003eRESULTS:\u003c/h2\u003e\u003cp\u003eThe 1D-CNN\u0026thinsp;+\u0026thinsp;Attention module effectively captures both local and global temporal patterns, while LightGBM significantly enhances classification accuracy through gradient-boosted decision trees. The proposed hybrid architecture achieved state-of-the-art performance in BMI classification, with an accuracy of 94.5% (Cohen\u0026rsquo;s \u003cem\u003eκ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.91) and an F1 score of 0.93, outperforming traditional classifiers by 12.3% to 19.1%. Model interpretability is ensured through SHapley Additive exPlanations (SHAP), which supports dynamic prescription adjustments aimed at improving muscular strength, cardiorespiratory endurance, speed, agility, and flexibility. A 12-week randomized trial demonstrated the clinical efficacy of this framework, yielding a 23.5% reduction in overweight and obesity prevalence, a 15.2% increase in pull-up performance, and a 9.8% improvement in 30\u0026times;2 shuttle run results. With an inference time of less than 0.8 milliseconds per sample and robust clinical outcomes, this framework provides a scalable real-time solution for data-driven health optimization.\u003c/p\u003e\u003ch2\u003eCONCLUSIONS:\u003c/h2\u003e\u003cp\u003eIt\u0026rsquo;s well-suited for both clinical and mobile healthcare applications, addressing the growing demand for personalized exercise interventions among young adults.\u003c/p\u003e","manuscriptTitle":"A Machine Learning Framework for Personalized Exercise Prescription Based on BMI and Physical Fitness Assessment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 08:01:24","doi":"10.21203/rs.3.rs-7810535/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-05T11:58:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-02T08:10:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-17T19:05:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"272323524663260460000651101718418594292","date":"2025-11-13T13:30:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"325845657273775691759275130232954250246","date":"2025-11-07T07:08:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-07T07:04:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-05T16:31:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-11-05T16:19:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"824f62b1-577d-4fd1-b128-d0d063ebc81b","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":58138930,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":58138931,"name":"Health sciences/Health care"},{"id":58138932,"name":"Physical sciences/Mathematics and computing"},{"id":58138933,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-03-16T16:03:42+00:00","versionOfRecord":{"articleIdentity":"rs-7810535","link":"https://doi.org/10.1038/s41598-026-42405-2","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-03-13 15:58:02","publishedOnDateReadable":"March 13th, 2026"},"versionCreatedAt":"2025-11-18 08:01:24","video":"","vorDoi":"10.1038/s41598-026-42405-2","vorDoiUrl":"https://doi.org/10.1038/s41598-026-42405-2","workflowStages":[]},"version":"v1","identity":"rs-7810535","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7810535","identity":"rs-7810535","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}