Interpretable prediction of gross motor coordination in children aged 9-10 using machine learning and SHAP: the influence of physical fitness, basic coordination, and executive function.

Lingfeng Mao, Yuan Sui, Xiangyang Ding, Min He, Liqin Deng, Yue Shi, Fei Li

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Article in PeerJ, 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

7 authors.

Lingfeng MaoShanghai University of Sport, School of Athletic Performance, Shanghai, China.

Yuan SuiShanghai University of Sport, School of Athletic Performance, Shanghai, China.

Xiangyang DingShanghai University of Sport, School of Athletic Performance, Shanghai, China.

Min HeShanghai University of Sport, School of Athletic Performance, Shanghai, China.

Liqin DengShanghai University of Sport, Key Laboratory of Exercise and Health Sciences of Ministry of Education, Shanghai, China.

Yue ShiShanghai University of Sport, School of Athletic Performance, Shanghai, China.

Fei LiShanghai University of Sport, School of Athletic Performance, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Gross motor coordination is a fundamental component of children's physical development and motor skill acquisition, closely associated with physical fitness, cognitive function, and overall health. This study aimed to examine the influence of physical fitness, basic coordination, and executive function (EF) on gross motor coordination, and to evaluate the predictive performance of machine learning models compared with traditional multiple linear regression (MLR). Methods: A total of 167 children (85 boys and 82 girls), aged 9-10 years, participated in the study. Gross motor coordination was assessed using the Körperkoordinationtest für Kinder (KTK). Physical fitness ( Results: Among the models, Random Forest Regression (RFR) achieved the highest performance ( Conclusion: Spatial-body integration, physical fitness, and postural control are primary determinants of gross motor coordination in children, while cognitive regulation plays a secondary role. Training programs aiming to enhance gross motor coordination should emphasize spatial orientation, body weight management, balance, and lower-limb strength.

Indexed as

Executive FunctionMachine LearningMotor SkillsPhysical FitnessChildFemaleHumansLinear ModelsMalePostural BalancePredictive Learning ModelsRandom ForestBasic coordination capacityGross motor coordinationMachine learningPhysical fitnessSHAP

Identifiers

PMID41773195

DOI10.7717/peerj.20827

PMCPMC12950188

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