Tracking of physical activity and fitness during the early years
Bibliographic record
Abstract
The early years are characterized by rapid physical growth and the development of behaviours such as physical activity. The objectives of this study were to assess the 12-month changes in and the tracking of physical activity and fitness in 400 preschoolers (201 boys, 4.5 ± 0.9 years of age). Physical activity data, expressed as minutes per day and as the percentage of time spent at various intensities while wearing an accelerometer, were collected in 3-s epochs for 7 days. Short-term muscle power, assessed with a 10-s modified Wingate Anaerobic Test, was expressed as absolute (W) and relative (W/kg) peak power (PP) and mean power (MP). Aerobic fitness, assessed with the Bruce Protocol progressive treadmill test, was expressed as maximal treadmill time and heart rate recovery (HRR). Light physical activity decreased by 3.2 min/day (p < 0.05), whereas vigorous physical activity increased by 3.7 min/day (p < 0.001), from year 1 to year 2. Physical activity exhibited moderate tracking on the basis of Spearman correlations (r = 0.45-0.59, p < 0.001) and fair tracking on the basis of κ statistics (κ = 0.26-0.38). PP and MP increased from year 1 (PP, 94.1 ± 37.3 W; MP, 84.1 ± 30.9 W) to year 2 (PP, 125.6 ± 36.2 W; MP, 112.3 ± 32.2 W) (p < 0.001) and tracked moderately to substantially (PP, r = 0.89, κ = 0.61; MP, r = 0.86, κ = 0.56). Time to exhaustion on the treadmill increased from 9.4 ± 2.3 min to 11.8 ± 2.3 min (p < 0.001) and tracked strongly (r = 0.82, κ = 0.56). HRR was unchanged at 65 ± 14 beats/min (p = 0.297) and tracked fairly (r = 0.52, κ = 0.23). The findings indicate that fitness tracks better than physical activity over a 12-month period during 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 | 0.000 | 0.001 |
| 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".