Machine learning derived physical activity in preschool children with developmental coordination disorder
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".