Predicting motor competence in school-aged children using biological, contextual and behavioural data.

Area of Science:

• Pediatric Health
• Motor Development
• Physical Activity

Background:

• Motor competence (MC) is crucial for children's physical and cognitive development.
• Identifying predictors of MC can inform targeted interventions.
• Previous research has explored various factors influencing MC, but cluster-based analyses are less common.

Purpose of the Study:

• To identify distinct clusters of motor competence in school-aged children.
• To examine biological, contextual, and behavioral predictors of these motor competence clusters.

Main Methods:

• A total of 880 children aged 6-10 years participated.
• Motor competence was assessed using the Motor Competence Assessment (MCA).
• Two-Step Cluster Analysis identified three MC groups: Higher, Intermediate, and Lower. Multinomial logistic regression analyzed predictors in a subsample.

Main Results:

• Cardiorespiratory fitness, measured by the 20-m shuttle run, was the strongest predictor of higher MC.
• Older age and higher body mass index were associated with lower odds of belonging to the Higher MC group.
• The model significantly predicted cluster membership, with cardiorespiratory fitness showing a strong association (p < .001).

Conclusions:

• Cardiorespiratory fitness and age are significant predictors of motor competence in children.
• Higher body mass index is linked to lower motor competence.
• Promoting physical activity and movement opportunities is vital for supporting children's motor development and overall health.