{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T07:01:35Z","timestamp":1790146895339,"version":"4.0.1"},"reference-count":44,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"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. Psychol."],"abstract":"<jats:p>Mental health issues among students are becoming increasingly serious, and physical exercise is closely linked to both physical and mental health. Therefore, there is a correlation between physical education and students\u2019 physical and mental well-being. When examining the relationship between physical education mechanisms in higher education institutions and students\u2019 mental health, fluctuations such as individual differences often appear in the data. To address this issue, this paper proposes the \u201cSparrow Search Tree Network\u201d (SSTNet), a model that employs the \u201cSparrow Search Algorithm\u201d (SSA) to systematically analyze the relationship between the two. Additionally, SSTNet incorporates the Huber loss function to enhance the model\u2019s stability when dealing with outlier samples. The results indicate that SSTNet achieves higher predictive accuracy, model stability, and generalization capability than the control model. Applying SSTNet to analyze the modeled associations between university physical education mechanisms and students\u2019 mental health indicates that moderate exercise intensity coupled with a high-quality teaching and interactive environment is associated with lower DASS-21 total scores, that is, with lower psychological distress rather than with better scores on a distress scale.<\/jats:p>","DOI":"10.3389\/fpsyg.2026.1829590","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T06:18:28Z","timestamp":1790144308000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Modeled associations between university physical education mechanisms and students\u2019 mental health"],"prefix":"10.3389","volume":"17","author":[{"given":"Ran","family":"Zhu","sequence":"first","affiliation":[{"name":"Faculty of Philosophy and Political Science, Al-Farabi Kazakh National University","place":["Almaty, Kazakhstan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Ma","sequence":"additional","affiliation":[{"name":"Faculty of Medicine and Health, Al-Farabi Kazakh National University","place":["Almaty, Kazakhstan"]},{"name":"Faculty of Physical Education, Huanggang Normal University","place":["Huanggang, Hubei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Yan","sequence":"additional","affiliation":[{"name":"Faculty of Martial Arts, Shandong Sport University","place":["Jinan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Galiya","family":"Madiyeva","sequence":"additional","affiliation":[{"name":"Faculty of Medicine and Health, Al-Farabi Kazakh National University","place":["Almaty, Kazakhstan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yessenova","family":"Kamchat","sequence":"additional","affiliation":[{"name":"Faculty of Philosophy and Political Science, Al-Farabi Kazakh National University","place":["Almaty, Kazakhstan"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,23]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"3360","DOI":"10.1080\/10494820.2021.1928235","article-title":"Enhancing the prediction of student performance based on the machine learning XGBoost algorithm","volume":"31","author":"Asselman","year":"2023","journal-title":"Interact. 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