School Physical Activity Intervention Effect on Adolescents’ Performance in Mathematics
Bibliographic record
Abstract
PURPOSE: The primary aim of this study was to test the effect of a school-based physical activity intervention on adolescents' performance in mathematics. A secondary aim was to explore potential mechanisms that might explain the intervention effect. METHODS: The Activity and Motivation in Physical EDucation intervention was evaluated using a two-arm cluster randomized controlled trial in 14 secondary schools located in low socioeconomic areas of Western Sydney, Australia. Study participants (n = 1173) were grade 8 students (mean age = 12.94 yr, SD = 0.54). The multicomponent intervention was designed to help teachers maximize students' opportunities for moderate-to-vigorous physical activity (MVPA) during physical education (PE) and enhance students' motivation toward PE. Mathematics performance was assessed as part of national testing in grade 7, which was the year before the trial began and then again in grade 9. Potential mediators were: (i) proportion of PE lesson time that students spent in MVPA and leisure time MVPA (%), measured using Actigraph GT3X+ accelerometers, and (ii) students' self-reported engagement (behavioral, emotional, and cognitive) during mathematics lessons. Mediators were assessed at baseline (grade 8) and follow-up (grade 9, 14-15 months after baseline). RESULTS: The effect of the intervention on mathematics performance was small-to-medium (β = 0.16, P < 0.001). An intervention effect was observed for MVPA% in PE (β = 0.59, P < 0.001), but not for leisure time MVPA or any of the engagement mediators. There were no significant associations between changes in potential mediators and mathematics performance. CONCLUSIONS: The Activity and Motivation in Physical EDucation intervention had a significant positive effect on mathematics performance in adolescents. However, findings should be interpreted with caution as the effect was small and not associated with changes in hypothesized mediators.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".