In the Pursuit of the Optimal Path to Motor Competence: A Decision Tree-Based Approach

Area of Science:

• Pediatric Health
• Motor Development Research
• Behavioral Science

Background:

• Motor competence (MC) is crucial for physical activity, health, and well-being in children.
• The complex, non-linear interactions influencing MC development are not fully understood.
• Identifying predictive profiles for MC levels is essential for targeted interventions.

Purpose of the Study:

• To identify multivariate profiles predicting motor competence (MC) levels in primary school children.
• To utilize a decision tree-based classification model to analyze the interplay of MC determinants.
• To uncover key factors associated with both low and high motor competence.

Main Methods:

• A cross-sectional study involving 676 children aged 8-12 years from Valencia, Spain.
• Assessment of MC, physical fitness, anthropometry, motivation, perceived MC, physical literacy, social support, and sedentary behavior.
• Application of a two-stage hierarchical decision tree model for classification.

Main Results:

• Cardiovascular endurance and self-determined motivation were primary discriminators for low MC.
• Low cardiovascular endurance was a significant barrier, even with positive psychological factors.
• High MC correlated with good fitness, motivation, controlled sedentary behavior, and supportive environments.

Conclusions:

• Decision tree models effectively capture the nonlinear, multidimensional nature of MC development.
• Interventions should address clusters of risk and protective factors for optimal motor development.
• Promoting physical literacy requires a holistic approach considering physical, psychological, and environmental influences.