2026-10-06T05:21:05Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34029648 2026-09-30T04:45:46Z category_16 portal_316 item_type_3 month_year_09_2026

Table 1_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.docx Xuqingfeng Xu (25141476) Jihe Zhang (25141479) Jianlu Liao (25141482) Physiology biomechanical asymmetry collegiate athletes explainable machine learning functional movement screen inertial measurement unit injury prediction prospective cohort SHAP <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 — including peak tibial acceleration asymmetry, surrogate vertical loading rate, and pelvis–trunk stability indices — 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: 0.61–0.79), 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.</p> 2026-09-30T04:45:46Z Dataset Dataset 10.3389/fphys.2026.1920109.s001 https://figshare.com/articles/dataset/Table_1_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_docx/34029648 CC BY 4.0