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Why they do not move: An explainable machine learning analysis of physical activity barriers in obese adolescents and tool translation

2026Artigo científico Acesso aberto
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datacite.subject.fosCiências Sociais::Ciências da Educação
datacite.subject.sdg04:Educação de Qualidade
datacite.subject.sdg03:Saúde de Qualidade
dc.contributor.authorChen, Cheng
dc.contributor.authorChen, Kai
dc.contributor.authorDu, Wenqian
dc.contributor.authorLi, Shengtao
dc.contributor.authorGou, Wenling
dc.contributor.authorYang, Jing
dc.contributor.authorForte, Pedro
dc.contributor.authorZhang, Xiaoran
dc.contributor.authorShangguan, Yuwen
dc.contributor.authorHuang, Yongyu
dc.contributor.authorZhang, Hao
dc.contributor.authorZhang, Xiaofei
dc.contributor.authorLin, Zhiyi
dc.contributor.authorYao, Xiaolin
dc.contributor.authorLi, Huan
dc.date.accessioned2026-08-04T14:11:50Z
dc.date.available2026-08-04T14:11:50Z
dc.date.issued2026
dc.description.abstractDespite the well-documented benefits of physical activity, insufficient activity remains highly prevalent among adolescents with obesity. This study is the first to apply interpretable machine learning methods to identify the barriers, facilitators, and U-shaped determinants of physical activity in this population. Methods: We analyzed data from 1,041 adolescents with obesity from the China Education Panel Survey. A range of personal, family, and school-level variables were incorporated to construct six machine learning models for predicting physical activity attainment. The Shapley Additive Explanations (SHAP) method, a game-theoretic approach for explainable artificial intelligence, was used to identify key predictive factors and quantify their relative contributions. Results: The Random Forest model demonstrated the best performance, achieving an accuracy of 85.30% and an AUC of 0.720 on the test set. SHAP analysis revealed several key factors associated with physical activity. Positive facilitators included parents with an education level beyond high school (≥ 3.27 for mothers, ≥ 3.51 for fathers), higher school rankings (≥ 3.77), adequate school sports facilities (≥ 1.44), and a personal interest in sports. Negative barriers included excessive screen time (≥ 5.51 h) and school location in central urban areas (≥ 4.23). Notably, U-shaped relationships were identified for academic workload, sleep problems, and self-perceived appearance. Specifically, moderate levels of these factors were associated with lower physical activity, whereas both low and high extremes promoted activity. Conclusion: This study demonstrates that physical activity among adolescents with obesity is shaped by a complex interplay of individual, family, and school-level factors, with parental education emerging as the strongest predictor. The identification of specific risk thresholds (e.g., screen time ≥ 5.51 h) and U-shaped relationships offers precise, actionable targets for intervention. These findings underscore the need for multi-level strategies: families should prioritize fostering cultural capital, schools should ensure facility accessibility beyond regular hours, and policymakers must address environmental constraints in urban settings. To facilitate the translation of these insights into practice, a web-based tool has been deployed to help physical education teachers identify at-risk students and design targeted interventions.eng
dc.description.sponsorshipThis study was supported by the Applied Basic Research Project of Changzhou (CJ20252030)
dc.identifier.citationChen, Cheng; Chen, Kai; Du, Wenqian; Li, Shengtao; Gou, Wenling; Yang, Jing; Forte, Pedro; Zhang, Xiaoran; Shangguan, Yuwen; Huang, Yongyu; Zhang, Hao; Zhang, Xiaofei; Lin, Zhiyi; Yao, Xiaolin; Li, Huan (2026). Why they do not move: An explainable machine learning analysis of physical activity barriers in obese adolescents and tool translation. BMC Public Health. ISSN 1471-2458. 26:1, p. 1-16
dc.identifier.doi10.1186/s12889-026-27050-8
dc.identifier.issn1471-2458
dc.identifier.urihttp://hdl.handle.net/10198/37066
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Science and Business Media LLC
dc.relationCJ20252030
dc.relation.ispartofBMC Public Health
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectAdolescent obesity
dc.subjectPhysical activity
dc.subjectMachine learning
dc.subjectWeight management
dc.subjectAcademic workload
dc.titleWhy they do not move: An explainable machine learning analysis of physical activity barriers in obese adolescents and tool translationeng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage16
oaire.citation.issue1
oaire.citation.startPage1
oaire.citation.titleBMC Public Health
oaire.citation.volume26
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameForte
person.givenNamePedro
person.identifier.ciencia-id351B-B16B-79C7
person.identifier.orcid0000-0003-0184-6780
relation.isAuthorOfPublication3ecc6d1b-07a4-40d7-81f4-df6fd7b3d5b0
relation.isAuthorOfPublication.latestForDiscovery3ecc6d1b-07a4-40d7-81f4-df6fd7b3d5b0

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