Motor Competence, Physical Activity, and Fitness across Early Childhood
Bibliographic record
Abstract
OBJECTIVES: To examine if the associations between motor competence and physical activity and musculoskeletal fitness change over time, whether motor competence is associated with longitudinal trajectories of physical activity and fitness, and mediating pathways among these constructs across early childhood. METHODS: Four hundred and eighteen children 3 to 5 yr of age (210 boys; age, 4.5 ± 1.0 yr) were recruited and completed three annual assessments as part of the Health Outcomes and Physical activity in Preschoolers study. Motor competence was assessed using the Bruininks-Oseretsky Test of Motor Proficiency Second Edition-Short Form. Musculoskeletal fitness (short-term muscle power) was evaluated using a modified 10-s Wingate protocol on a cycle ergometer. Physical activity was measured over 7 d using accelerometers. RESULTS: At baseline, the cross-sectional relationship between motor competence and vigorous physical activity was not significant; however, a significant, weak positive association emerged across time. Results from longitudinal mixed-effect models found motor competence to be a significant positive predictor of musculoskeletal fitness and vigorous physical activity and to be associated with steeper increases in physical activity across time. Motor competence was independently associated with musculoskeletal fitness and physical activity during this early childhood period. CONCLUSIONS: Motor competence is an important independent predictor of physical activity and musculoskeletal fitness levels across early childhood. Motor competence may be an important target for early interventions to improve both physical activity and fitness in the early years.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".