{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T05:01:30Z","timestamp":1790744490622,"version":"4.1.0"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"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>Lower-limb load-related injuries account for a disproportionate share of time loss in collegiate sport, yet the biomechanical and movement-quality factors distinguishing injured from uninjured athletes remain incompletely characterized. This prospective cohort study followed 161 collegiate athletes for one competitive season, integrating Functional Movement Screen task-level scores with wearable inertial measurement unit-derived features \u2014 including peak tibial acceleration asymmetry, surrogate vertical loading rate, and pelvis\u2013trunk stability indices \u2014 within an explainable machine learning framework. Four classifiers were compared across FMS-only, wearable-only, and fused configurations under nested five-fold cross-validation with embedded LASSO feature selection. Thirty-seven load-related time-loss injuries were recorded (23.0% incidence). The fused XGBoost model achieved an out-of-fold AUC of 0.70 (95% CI: 0.61\u20130.79), outperforming FMS-only (AUC 0.62) and wearable-only (AUC 0.64) configurations by statistically significant margins. Peak tibial acceleration asymmetry and surrogate loading rate on the non-dominant limb were the strongest biomechanical predictors; in-line lunge and active straight-leg raise task scores contributed most among FMS inputs. Biomechanical features added an independent AUC increment of 0.07 beyond previous-injury history. SHAP analysis identified an interaction between tibial impact asymmetry and in-line lunge score that met the pre-specified directional stability criterion. These findings establish a physiologically interpretable template for biomechanical phenotyping of injury risk, supporting the combined use of wearable sensing and task-level movement screening in collegiate athletic populations.<\/jats:p>","DOI":"10.3389\/fphys.2026.1920109","type":"journal-article","created":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T04:47:26Z","timestamp":1790743646000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Biomechanical and movement-quality predictors of lower-limb load-related injury in collegiate athletes: a prospective cohort study using functional movement screening, wearable inertial sensing, and explainable machine learning"],"prefix":"10.3389","volume":"17","author":[{"given":"Xuqingfeng","family":"Xu","sequence":"first","affiliation":[{"name":"Department of Aviation Sports, Aviation University of Air Force","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jihe","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Aviation Sports, Aviation University of Air Force","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianlu","family":"Liao","sequence":"additional","affiliation":[{"name":"Department of Aviation Sports, Aviation University of Air Force","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,30]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/j.jbmt.2020.06.008","article-title":"Concurrent validity and within-session reliability of gait kinematics measured using an inertial motion capture system with repeated calibration","volume":"24","author":"Berner","year":"2020","journal-title":"J. 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