The Coordination and Activity Tracking in CHildren (CATCH) study: rationale and design
Bibliographic record
Abstract
BACKGROUND: Past studies have found that children with Developmental Coordination Disorder (DCD) engage in less physical activity than typically developing children. This "activity deficit" may result in children with DCD being less physically fit and more likely to be overweight or obese, potentially increasing later risk for poor cardiovascular health. Unfortunately, the majority of DCD research has been limited to cross-sectional designs, leading to questions about the complex relationship among motor ability, inactivity and health-related fitness. Of the few longitudinal studies on the topic, determining precedence amongst these factors is difficult because study cohorts typically focus on mid to late childhood. By this age, both decreased physical fitness and obesity are often established. The Coordination and Activity Tracking in CHildren (CATCH) study will examine the pathways connecting DCD, physical activity, physical fitness, and body composition from early to middle childhood. METHODS: The CATCH study is a prospective cohort study. We aim to recruit a cohort of 600 children aged 4 to 5 years (300 probable DCD [pDCD] and 300 controls) and test them once a year for 4 years. At Phase 1 of baseline testing, we assess motor skills, cognitive ability (IQ), basic anthropometry, flexibility and lower body muscle strength, while parents complete an interview and questionnaires regarding family demographics, their child's physical activity, and behavioural characteristics. Children who move on to Phase 2 (longitudinal cohort) have their body fat percentage, foot structure, aerobic and anaerobic fitness assessed. An accelerometer to measure physical activity is then given to the child and interested family members. The family also receives an accelerometer logbook and 3-day food dairy. At years 2 to 4, children in the longitudinal cohort will have all baseline assessments repeated (excluding the IQ test), and complete an additional measure of perceived self-efficacy. Parents will complete an ADHD index twice within the follow-up period. To assess the association between DCD, fitness and adiposity, our primary analysis will involve longitudinal growth models with fixed effects. DISCUSSION: The CATCH study will provide a clearer understanding of pathways between DCD and health-related fitness necessary to determine the types of interventions children with DCD require.
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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 | 0.027 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".