Cardiorespiratory and metabolic responses associated with children’s physical activity during self-paced games
Bibliographic record
Abstract
The aim of this study was to explore the possibility of identifying clusters of children's games based on estimated energy expenditures and (or) intensity when performed in a guided active play format. The study also investigated whether the identified active play game clusters were repeatable when the games were performed on different days. Children (9.7 ± 1.1 years; n = 12) were assessed for oxygen consumption, heart rate, energy expenditure (EE), and metabolic equivalent (MET) on a treadmill (at 4, 6, and 8 km·h(-1) (0% grade)). HR and ActiGraph GT1M accelerometer (ACC) generated linear regression equations were used to estimate EE. The ACC (3 s epochs) were used for estimating METs in assigning percent time at medium-vigorous physical activity (%MVPA) of 10 self-paced games. The results showed a consistent range of EEs (ACC-equation) from 13.57 kcal·(5 min)(-1) to 25.00 kcal·(5 min)(-1) (p < 0.05); EEs (HR-equation) from 29.72 to 42.49 kcal·(5 min)(-1) (p < 0.05); and %MVPA from 10% to 34% (p < 0.05) (from ACC equations) across all games. These were reproducible from day to day (p > 0.05). This study confirms the existence of active play children's game clusters that might be useful in formatting guided active play in a dose-response manner for children.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".