Intensity- and Domain-Specific Levels of Physical Activity and Sedentary Behavior in 10- to 14-Year-Old Children
Bibliographic record
Abstract
BACKGROUND: to investigate levels of physical activity (PA) and sedentary behavior (SB) in 10- to 14-year-olds and to determine PA differences between week-weekend days, genders and school stages. METHODS: 241 children were recruited from 15 primary and 15 secondary schools. PA was assessed for 7 days using the SenseWear Mini Armband and an electronic diary. Week-weekend and gender differences were determined using 2-way repeated-measures ANOVA. Combined intensity- and domain-specific PA differences between genders and school stages were examined using 2-way ANOVA. RESULTS: Weekdays were more active compared with weekend days. Physical activity level (PAL) of boys was higher compared with girls. Boys showed more moderate (+15 min/day) and vigorous PA (+9 min/day), no differences were found for SB and light PA. Secondary school children showed more SB (+111 min/day), moderate (+8 min/day) and vigorous (+9 min/day) PA and less light PA (-66 min/day) compared with primary school children. No difference was found for PAL. The results of the combined intensity- and domain-specific parameters revealed more nuanced differences between genders and school stages. CONCLUSIONS: Our results demonstrate the complexity of PA and SB behavior of children, indicating the need for a multidimensional and differentiated approach in PA promotion.
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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 | 0.001 | 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".