Age and Gender Differences in Youth Physical Activity
Bibliographic record
Abstract
PURPOSE: To investigate whether observed gender differences in objectively measured physical activity (PA) in children (8-13 yr) are confounded by physical maturity differences. METHODS: Four hundred and one children (194 boys and 207 girls) volunteered for this study. An Actigraph accelerometer was used to obtain seven consecutive days of minute-by-minute PA data for each participant. Minutes of moderate to vigorous PA per day (MVPA), continuous minutes of MVPA per day (CMVPA), and minutes of vigorous PA per day (VPA) were derived from the accelerometer data. Age at peak height velocity (APHV), an indicator of somatic maturity, was predicted in all individuals. Gender differences in the PA variables were analyzed using a two-way (gender x age) ANOVA. RESULTS: Levels of PA decreased with increasing chronological age in both genders (P < 0.05). When aligned on chronological age, boys had a higher MVPA at 10-13 yr, a higher CMVPA at 9-12 yr, and a higher VPA at 9-13 yr (P < 0.05). When aligned on biological age, PA declined with increasing maturity (P < 0.05); however, gender differences between biological age groups disappeared. CONCLUSION: The observed age-related decline in adolescent boys and girls PA is antithetical to public health goals; as such, it is an important area of research. To fully understand gender disparities in PA, consideration must be given to the confounding effects of physical maturity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".