Associations Between Physical Activity, Bone Mineral Density, and Low Bone Mass in U.S. Adolescents Aged 12–19 Years: A SHAP‑Based Machine‑Learning Study of NHANES 2011–2018

Associations Between Physical Activity, Bone Mineral Density, and Low Bone Mass in U.S. Adolescents Aged 12–19 Years: A SHAP‑Based Machine‑Learning Study of NHANES 2011–2018 | 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 Associations Between Physical Activity, Bone Mineral Density, and Low Bone Mass in U.S. Adolescents Aged 12–19 Years: A SHAP‑Based Machine‑Learning Study of NHANES 2011–2018 ZhenYi Zhao, Yunhan Jiang, Hong Ye, Jing Mi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6776023/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 Background: Adolescence is a critical period for skeletal development and peak bone mass (PBM) acquisition. Low bone mineral density (BMD) during this stage increases the risk of osteoporosis and fractures later in life. While physical activity (PA) is known to benefit bone health, its dose-response relationship and predictive potential remain insufficiently explored. Methods: This study utilized data from the 2011–2018 National Health and Nutrition Examination Survey (NHANES), including 3,411 U.S. adolescents aged 12–19 years. Associations between PA (measured in MET-min/wk), BMD, and low bone mass were assessed using multivariate regression, restricted cubic spline (RCS) analysis, and machine learning approaches (LASSO, Boruta, AdaBoost). Results: Higher PA levels were nonlinearly associated with increased lumbar spine, pelvic, and total BMD (plateauing at ~ 1,440 MET-min/wk) and linearly associated with reduced risk of low bone mass (10.6% risk reduction per 1,000 MET-min/wk). Subgroup analyses revealed stronger protective effects among adolescents with BMI 25–30 and in certain racial/ethnic groups. AdaBoost outperformed other models in prediction performance, while SHAP (based on the Random Forest model) and LIME identified age, BMI, creatinine, and PA as key predictors. Conclusions: Moderate-to-vigorous PA is associated with improved BMD and reduced risk of low bone mass among adolescents. These findings support individualized, stratified PA interventions and provide a data-driven foundation for adolescent bone health promotion strategies. Biological sciences/Physiology/Bone Health sciences/Health care/Disease prevention Health sciences/Health care/Public health Bone mineral density Physical activity Adolescents Low bone mass Machine learning Dose-response NHANES Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx 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-6776023","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":468188003,"identity":"033cbf02-0e09-45e1-abd9-7180cb5637ad","order_by":0,"name":"ZhenYi Zhao","email":"","orcid":"","institution":"Beijing Sport University","correspondingAuthor":false,"prefix":"","firstName":"ZhenYi","middleName":"","lastName":"Zhao","suffix":""},{"id":468188004,"identity":"804098a2-5a6d-4e03-9e3d-026a7858192b","order_by":1,"name":"Yunhan Jiang","email":"","orcid":"","institution":"The state university of New York at Buffalo","correspondingAuthor":false,"prefix":"","firstName":"Yunhan","middleName":"","lastName":"Jiang","suffix":""},{"id":468188006,"identity":"628eb73b-0ddb-41f0-a714-aaf3ba37627d","order_by":2,"name":"Hong Ye","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYHACNiC2qe9nZj78gBQtaYwz29nSDEjRcphxw3keBQmi1Mv3nzF7+LPtMLPxYR4GA4Yam2iCWhgbzpgb87als5kd5j3wgOFYWm4DIS3MjD1m0oxt1jxmh/kSDBgbDhPWwsbMYyb5s41ZwriZx0CCKC08bDxmErxtzgYGzMRqkeBhKzfmOZeWIHEYGMgJxPhFvv/wtoc/ymwS+PsPH37wocaGsBYGBg6kCEwgrBwE2B8Qp24UjIJRMApGLgAAORM3qfH/Sa8AAAAASUVORK5CYII=","orcid":"","institution":"Beijing Sport University","correspondingAuthor":true,"prefix":"","firstName":"Hong","middleName":"","lastName":"Ye","suffix":""},{"id":468188007,"identity":"0f941337-c3aa-4d80-bd88-55e41e8bfe93","order_by":3,"name":"Jing Mi","email":"","orcid":"","institution":"Beijing Sport University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Mi","suffix":""}],"badges":[],"createdAt":"2025-05-29 11:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6776023/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6776023/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94629818,"identity":"62f20b94-7e2d-4717-a112-b3cea8aa4ac8","added_by":"auto","created_at":"2025-10-29 05:53:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1077207,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6776023/v1_covered_de606b10-87fa-4a0f-b74f-6d7913a338d3.pdf"},{"id":84230886,"identity":"1355e010-f9c2-4f45-a067-8621f8a048fd","added_by":"auto","created_at":"2025-06-09 13:52:24","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":232976,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6776023/v1/91c679f2e86c95e05e82b19c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations Between Physical Activity, Bone Mineral Density, and Low Bone Mass in U.S. Adolescents Aged 12–19 Years: A SHAP‑Based Machine‑Learning Study of NHANES 2011–2018","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bone mineral density, Physical activity, Adolescents, Low bone mass, Machine learning, Dose-response, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-6776023/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6776023/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eAdolescence is a critical period for skeletal development and peak bone mass (PBM) acquisition. Low bone mineral density (BMD) during this stage increases the risk of osteoporosis and fractures later in life. While physical activity (PA) is known to benefit bone health, its dose-response relationship and predictive potential remain insufficiently explored.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eThis study utilized data from the 2011\u0026ndash;2018 National Health and Nutrition Examination Survey (NHANES), including 3,411 U.S. adolescents aged 12\u0026ndash;19 years. Associations between PA (measured in MET-min/wk), BMD, and low bone mass were assessed using multivariate regression, restricted cubic spline (RCS) analysis, and machine learning approaches (LASSO, Boruta, AdaBoost).\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eHigher PA levels were nonlinearly associated with increased lumbar spine, pelvic, and total BMD (plateauing at ~\u0026thinsp;1,440 MET-min/wk) and linearly associated with reduced risk of low bone mass (10.6% risk reduction per 1,000 MET-min/wk). Subgroup analyses revealed stronger protective effects among adolescents with BMI 25\u0026ndash;30 and in certain racial/ethnic groups. AdaBoost outperformed other models in prediction performance, while SHAP (based on the Random Forest model) and LIME identified age, BMI, creatinine, and PA as key predictors.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eModerate-to-vigorous PA is associated with improved BMD and reduced risk of low bone mass among adolescents. These findings support individualized, stratified PA interventions and provide a data-driven foundation for adolescent bone health promotion strategies.\u003c/p\u003e","manuscriptTitle":"Associations Between Physical Activity, Bone Mineral Density, and Low Bone Mass in U.S. Adolescents Aged 12–19 Years: A SHAP‑Based Machine‑Learning Study of NHANES 2011–2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-09 13:44:19","doi":"10.21203/rs.3.rs-6776023/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"15fd21d0-284e-4ece-806d-f8896a66a35c","owner":[],"postedDate":"June 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49706683,"name":"Biological sciences/Physiology/Bone"},{"id":49706684,"name":"Health sciences/Health care/Disease prevention"},{"id":49706685,"name":"Health sciences/Health care/Public health"}],"tags":[],"updatedAt":"2025-10-29T05:53:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-09 13:44:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6776023","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6776023","identity":"rs-6776023","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}