{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T05:10:08Z","timestamp":1789017008373,"version":"build-2803163510"},"reference-count":25,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T00:00:00Z","timestamp":1788998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Physical inactivity among children and adolescents represents a critical global public health challenge, imposing an annual economic burden exceeding $27 billion on healthcare systems worldwide. Despite this pressing concern, the complex, cross-level relationships between structured environmental cues-spanning household, school, and community domains-and youth sports consumption remain inadequately understood, particularly across the heterogeneous urbanrural continuum characteristic of rapidly developing economies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study employed a longitudinal survey (China Health and Nutrition Survey, CHNS) comprising 4,240 child-wave observations nested within 940 households and 182 communities. We benchmarked conventional multilevel regression against three machine learning approaches-gradient boosting, random forest, and LSTM sequence models-using temporal and spatial holdout validation. Feature-level and interaction-level interpretability was recovered via SHAP decomposition to quantify the magnitude and breakdown of urbanrural interplay in multi-layered environmental factors shaping youth sport consumption.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The gradient boosting model significantly outperformed the multilevel baseline, achieving a holdout\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      of 0.49 versus 0.31 (RMSE = 1.08 vs. 1.42,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.001), representing a 0.18\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      unit improvement. Community recreational infrastructure emerged as the most influential predictor (mean |SHAP| = 0.42). SHAP dependence analysis revealed a compensatory pattern wherein the marginal predicted contribution of community infrastructure was three times greater for low-income households (0.181) compared to high-income households (0.046). School-based physical education provision demonstrated stronger effects among younger adolescents (mean |SHAP| = 0.39 vs. 0.22), with a thresholdpeak urbanization effect identified at index level 40 for rural communities.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>This study provides novel evidence that public space recreation infrastructure yields substantially greater behavioral returns in resource-limited communities, challenging assumptions that uniform investment strategies are optimal across socioeconomic strata. The findings offer a new methodological and intervention paradigm for promoting youth physical activity in socioeconomically diverse, rapidly urbanizing regions, emphasizing targeted infrastructure investment in disadvantaged communities as a high-return public health strategy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1916559","type":"journal-article","created":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T04:30:54Z","timestamp":1789014654000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Structured environmental cues and youth sports consumption: an AI-powered behavioral modeling approach"],"prefix":"10.3389","volume":"14","author":[{"given":"Xingyu","family":"Cheng","sequence":"first","affiliation":[{"name":"Capital University of Physical Education and Sports","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Physical Education, Jiangsu Normal University","place":["Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,10]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1479","DOI":"10.1038\/s41591-026-04237-5","article-title":"Physical activity for public health in the 21st century","volume":"32","author":"Salvo","year":"2026","journal-title":"Nat Med"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"e32","DOI":"10.1016\/S2214-109X(22)00464-8","article-title":"The cost of inaction on physical inactivity to public health-care systems: a population-attributable fraction analysis","volume":"11","author":"Santos","year":"2023","journal-title":"Lancet Glob 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