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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.
Insights
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.
Aim:
To compare the device-measured physical activity behaviours of preschool children with typical motor development to those with probable developmental coordination disorder (pDCD) and at risk for developmental coordination disorder (DCDr).
Method:
A total of 497 preschool children (4-5 years) in the Coordination and Activity Tracking in CHildren (CATCH) study completed repeated motor assessments and wore an ActiGraph GT3X on the right hip at baseline for 1 week. We calculated physical activity metrics from raw accelerometer data using a validated random forest classification machine learning model for preschool-age children. Analysis of variance (ANOVA) and linear regression models compared physical activity between typically developing children, children at risk for DCDr, and those with pDCD identified based on motor scores at baseline and averaged over time, accounting for age, sex, and accelerometer wear time.
Results:
We found no differences in daily time spent sedentary, in light physical activity, or moderate-to-vigorous physical activity between typically developing children, children at risk for DCDr, and those with pDCD. However, children in the DCD groups spent less time doing ambulatory activities (walking/running) than typically developing children. Analysis of variance: baseline classification, DCDr to typically developing, run: F = 5.34, p = 0.005, classification averaged over time, DCDr to typically developing, walk: F = 5.82, p = 0.003. Regressions: DCDr compared to typically developing for walk: B = -3.47 (standard error 1.05), p < 0.001, pDCD compared to typically developing for run: B = -1.82 (standard error 0.62), p = 0.004.
Interpretation:
Designing interventions for preschool children with motor difficulties targeting specific physical activity types (walk/run) may help mitigate physical activity intensity differences observed later in childhood.
