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Machine learning derived physical activity in preschool children with developmental coordination disorder
Author(s) -
Letts Elyse,
KingDowling Sara,
Kwan Matthew Y. W.,
Obeid Joyce,
Cairney John,
Trost Stewart G.
Publication year - 2025
Publication title -
developmental medicine and child neurology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.658
H-Index - 143
eISSN - 1469-8749
pISSN - 0012-1622
DOI - 10.1111/dmcn.16186
Subject(s) - typically developing , physical activity , ambulatory , analysis of variance , raw score , motor coordination , motor skill , movement assessment , psychology , developmental psychology , physical therapy , medicine , raw data , machine learning , statistics , mathematics , computer science , autism , psychiatry
Abstract 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.