A SHAP-based machine learning approach to decoding the relationship between running posture features and tibial load...

This study systematically investigates the relationship between running posture and tibial load in novice runners using machine learning methods from the field of artificial intelligence. The aim is to provide scientific evidence for the precise prevention and personalized rehabilitation of Medial Tibial Stress Syndrome (MTSS).

A total of 174 novice runners diagnosed with MTSS were included in the study. Kinematic and kinetic data were collected during their running trials, and tibial load was subsequently calculated. Three machine learning models-Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Regression (SVR)-were employed, combined with the SHapley Additive exPlanations (SHAP) framework, to analyze the relationship between running posture and tibial load in this population.

The predictive performance of the XGBoost model outperformed both the RF and SVR models. SHAP analysis revealed that tibial load predicted by the model significantly increased when the vertical center of mass (COM) position was below 927 mm, the ankle inversion angle exceeded 2.2°, and the knee internal rotation angle surpassed 2.8°.

For novice MTSS runners, a lower COM position, greater ankle inversion, and increased knee internal rotation were associated with elevated tibial loading and showed clear nonlinear relationships with identifiable threshold effects. These findings highlight complex nonlinear interactions among multiple biomechanical factors and demonstrate the value of SHAP in quantifying such effects. Clinically, optimizing vertical COM control and strengthening the ankle evertor and knee external rotator musculature may help reduce tibial loading during running and lower the risk of MTSS development or recurrence.

The entire study protocol was approved by the Medical Ethics Committee of East China Normal University (Approval No. 2024-28).