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

Motor competence (MC) is a multifaceted construct influenced by physical, behavioral, emotional, and environmental factors. Its development plays a pivotal role in physical activity participation, health, and well-being. However, the complex, non-linear interplay among these determinants remains underexplored. This study aimed to identify multivariate profiles according to multiple correlates that predict levels of MC in primary school children using a decision tree-based classification model. A cross-sectional sample of 676 children aged 8-12 from five public schools in Valencia, Spain, was assessed on a comprehensive array of variables, including actual MC, physical fitness, anthropometry, motivation, perceived MC, physical literacy, social support, and sedentary behavior. A two-stage hierarchical decision tree model was applied to classify children based on their MC levels. The initial tree identified key predictors of low MC, with cardiovascular endurance and self-determined motivation as dominant discriminators. A secondary tree further refined classifications among ambiguous profiles. Notably, low cardiovascular endurance emerged as a major limiting factor, with few children overcoming its influence despite positive psychological attributes. Conversely, high MC was associated with positive combinations of fitness, motivation, controlled sedentary behavior, and supportive environments. Findings underscore the utility of decision tree models in capturing the nonlinear, multidimensional nature of MC development. Tailored interventions addressing clusters of risk or protective factors are essential to promote motor development and physical literacy in diverse child populations.