Evidence map›Paper›PMID 42174528›Full record

ArticleBMC musculoskeletal disorders2026

A SHAP-based machine learning approach to decoding the relationship between running posture features and tibial load in novice runners with MTSS: towards accurate and intelligent rehabilitation strategies.

Ting Fan, Xiaomin Wu, Zeyi Zhang, Youping Sun

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Article in BMC musculoskeletal disorders, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

4 authors.

Ting Fan *Institution of Physical Education, Xinjiang Normal University, Urumqi, 830054, China.ORCID http://orcid.org/0000-0001-6265-7209
Xiaomin Wu *Institution of Physical Education, Xinjiang Normal University, Urumqi, 830054, China.ORCID http://orcid.org/0009-0008-0466-5073
Zeyi Zhang *College of Physical Education and Health, East China Normal University, Shanghai, 200241, China.ORCID http://orcid.org/0000-0001-7098-9238
Youping SunInstitution of Physical Education, Xinjiang Normal University, Urumqi, 830054, China. syp1959@163.com.ORCID http://orcid.org/0009-0004-9199-8757

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis 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).

methodsA 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.

resultsThe 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°.

conclusionFor 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.

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

Indexed as

Machine LearningMedial Tibial Stress SyndromePostureRunningTibiaAdultBiomechanical PhenomenaBoosting Machine Learning AlgorithmsFemaleHumansMalePredictive Learning ModelsRandom ForestWeight-BearingYoung AdultMachine learningMedial tibial stress syndromeNovice runnersRunning postureTibial load

Identifiers

PMID42174528
PMCPMC13393863

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.