{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T04:31:00Z","timestamp":1786681860955,"version":"build-2736575974"},"reference-count":49,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T00:00:00Z","timestamp":1786665600000},"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. Physiol."],"abstract":"<jats:p>Track-and-field performance fluctuates week to week under the joint influence of training load, recovery, autonomic regulation, and psychological readiness, and wearables now enable continuous physiological monitoring; however, whether bodily signals and readiness markers contribute differently to performance prediction across technical and physical events remains unclear. In a prospective longitudinal study, 132 competitive track-and-field athletes (71 physical-event, 61 technical-event) were monitored over a planned 14-week athlete-week design. One-week lagged morning LnRMSSD, resting heart rate, sleep duration and quality, session-RPE load, cognitive anxiety, and self-efficacy were used to predict subsequent standardized weekly performance change. Extreme gradient boosting (XGBoost) models were evaluated with athlete-level grouped cross-validation. SHapley Additive exPlanations (SHAP) summarized feature contributions, and OLS interaction models with athlete-clustered robust standard errors tested moderation by event type. The integrated XGBoost model showed the highest overall prediction among the evaluated feature sets (mean outer-fold R\u00b2 = 0.510, RMSE\u00a0=\u00a00.505, MAE\u00a0=\u00a00.406; within-athlete centered sensitivity R\u00b2 = 0.177\u20130.306), and readiness measures added a consistent increment beyond monitoring-only features (mean \u0394R\u00b2 = 0.088). SHAP identified the ordinal competitive-level code as the largest single contribution (23.4%), followed by lagged physiological and self-efficacy features. Interaction analyses gave partial evidence of event-sensitive moderation, mainly for psychological readiness in technical events, whereas aggregated physiological and load interactions were non-significant. Sensitivity analyses showed that predictive performance decreased after removing the competitive-level code (R\u00b2 = 0.366) and in within-athlete centered models, indicating that the headline R\u00b2 reflected both stable athlete-level stratification and week-to-week monitoring signals. Overall, integrated wearable physiological and psychological monitoring can help predict and characterize week-to-week performance change in track-and-field athletes, with the most consistent event-sensitive signal involving psychological readiness, particularly self-efficacy, in technical events, while not implying causal mechanisms.<\/jats:p>","DOI":"10.3389\/fphys.2026.1912453","type":"journal-article","created":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T04:23:28Z","timestamp":1786681408000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting athletic performance in track and field athletes based on wearable physiological and psychological indicators: an interpretable machine learning study"],"prefix":"10.3389","volume":"17","author":[{"given":"Pengfei","family":"Zhang","sequence":"first","affiliation":[{"name":"Faculty of Education, Languages, Psychology and Music, SEGi University","place":["Kuala Lumpur, Malaysia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,14]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1590\/1982-4327e3412","article-title":"Relationships between self-efficacy and high-performance sport: A systematic review","volume":"34","author":"Aizava","year":"2024","journal-title":"Paid\u00e9ia (Ribeir\u00e3o Preto)"},{"key":"B2","doi-asserted-by":"publisher","first-page":"217","DOI":"10.2165\/00007256-199826040-00002","article-title":"Statistical methods for assessing measurement error (reliability) in variables relevant to sports medicine","volume":"26","author":"Atkinson","year":"1998","journal-title":"Sports Med."},{"key":"B3","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1123\/pes.2023-0150","article-title":"Physical determinants of sprint and long jump performance in male youth track-and-field athletes with differing maturity statuses","volume":"37","author":"Baker","year":"2025","journal-title":"Pediatr. 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