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Machine learning derived physical activity in preschool children with developmental coordination disorder.
Elyse Letts1, Sara King-Dowling2,3, Matthew Y W Kwan2,4
1Child Health & Exercise Medicine Program, Department of Pediatrics, McMaster University, Hamilton, Canada.
Developmental Medicine and Child Neurology
|December 10, 2024
Summary
Preschool children with probable developmental coordination disorder (pDCD) and at risk for DCD (DCDr) spent less time walking and running than typically developing peers. Interventions should target these specific ambulatory activities.
Area of Science:
- Pediatric physical activity and motor development.
- Childhood developmental disorders and their impact on physical behavior.
Background:
- Developmental Coordination Disorder (DCD) affects motor skills in children.
- Understanding physical activity patterns in preschool children with DCD is crucial for early intervention.
Purpose of the Study:
- To compare device-measured physical activity behaviors in preschool children with typical motor development versus those with probable DCD (pDCD) and at risk for DCD (DCDr).
Main Methods:
- 497 preschool children (4-5 years) from the CATCH study wore accelerometers for 1 week.
- Physical activity metrics were derived using a machine learning model on accelerometer data.
- ANOVA and regression analyses compared activity levels between typically developing children and DCD groups, controlling for covariates.
Main Results:
- No significant differences were found in sedentary time, light, or moderate-to-vigorous physical activity.
- Children with DCD (pDCD and DCDr) demonstrated significantly less time spent in ambulatory activities (walking/running) compared to typically developing peers.
Conclusions:
- Preschool children with DCD exhibit reduced participation in walking and running activities.
- Targeted interventions focusing on ambulatory activities may help address physical activity intensity differences in children with motor difficulties.
