How much do schools help? The contribution of school to children’s physical activity levels
Bibliographic record
Abstract
Most children do not engage in enough physical activity and spend a significant portion of their school days in sedentary behaviours (SB). We explored how different time segments of the day contribute to daily levels of SB, light-intensity physical activity (LPA), and moderate-to-vigorous physical activity (MVPA) among Canadian school children. Students (n = 193; 50.3% male, average age 9.3 years) from three elementary schools wore accelerometers for 7-8 days. School schedules were used to estimate behaviours during specific time segments: Before school (06:00-08:44), in-school time (08:45-15:04), after school (15:05-16:59), and evening (17:00-21:59). Children engaged in an average of 581.0±74.8 minutes/day of SB (65.5% of the day), 200.4±48.7 minutes/day of LPA (22.5% of the day), and 107.4±45.5 minutes/day of MVPA (12.0% of the day). Children spent 63.4% of the school day in SB, 23.3% in LPA, and 13.3% in MVPA. However, in-school time accounted for 38.0% of daily SB, 40.7% of LPA, and 44.7% of MVPA. The shorter after-school period was the most active segment but contributed only 16.2% of daily MVPA, 16.7% of LPA, and 12.0% of SB. The evening segment contributed to 29.6% of daily MVPA, 28.6% of LPA, and 27.4% of SB. In conclusion, given the amount of time spent at school, its impact on children's MVPA could be optimized. The results highlight the need for tailored theory-based interventions for the school and after-school periods aiming at empowering children to move more and sit less.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".