{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T08:43:19Z","timestamp":1778575399717,"version":"3.51.4"},"reference-count":44,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,2,7]],"date-time":"2025-02-07T00:00:00Z","timestamp":1738886400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009103","name":"Education Department of Shaanxi Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100009103","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Physiol."],"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>This study aims to compare the differences in the precompetition status (nutritional, physiological, biochemical, psychological, and sleep statuses) among college track and field athletes with different competition performances and to screen for key indicators of differences affecting athletic performance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Multiple indicators, traditional methods, and machine learning methods are used to detect the exercise load, fatigue index, and precompetition state of athletes with different sports performances.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>(1) Two weeks before the competition, the fat mass in the left upper limb in the BP group was significantly higher than that in the BnP group (<jats:italic>P<\/jats:italic> &amp;lt; 0.05). The absolute values of blood basophils and triglycerides (TGs) in the BnP group were significantly higher than those in the BP group (<jats:italic>P<\/jats:italic> &amp;lt; 0.05). The positive detection rate of urinary leukocytes in the BnP group was higher than that in the BP group, and the positive detection rate of urinary occult blood and vitamin C in the BP group was higher than that in the BnP group. (2) One week before the competition, the blood lactate dehydrogenase (LDH) in the BP group was significantly higher than that in the BnP group (<jats:italic>P<\/jats:italic> &amp;lt; 0.05). The detection rate of positive urinary occult blood in the BnP group was higher than that in the BP group (<jats:italic>P<\/jats:italic> &amp;lt; 0.05). (3) No significant differences were found in the daily dietary intake, energy consumption values, physical activity, sleep efficiency, real-time heart rate, real-time respiratory rate, and real-time heart rate variability between the intensive and reduced periods. (4) The Rosenberg Self-Esteem Scale score of the BnP group was significantly higher than that of the BP group (<jats:italic>P<\/jats:italic> &amp;lt; 0.05).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Precompetition absolute basophil, LDH, TG, white blood cells, creatine kinase, fat mass in the left upper limb, erythrocyte pressure (HCT), and individual failure anxiety can be used as training monitoring indicators that focus on tracking athlete status before the race.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fphys.2025.1429510","type":"journal-article","created":{"date-parts":[[2025,2,7]],"date-time":"2025-02-07T06:49:27Z","timestamp":1738910967000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["The impact of precompetition state on athletic performance among track and field athletes using machine learning"],"prefix":"10.3389","volume":"16","author":[{"given":"Yuting","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengyu","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingmei","family":"Niu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongfeng","family":"Nie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangya","family":"Dou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoqin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,2,7]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1369","DOI":"10.1038\/s42256-023-00748-9","article-title":"Spatially embedded recurrent neural networks reveal widespread links between structural and functional neuroscience findings","volume":"5","author":"Achterberg","year":"2023","journal-title":"Nat. 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