Examining Associations Between Physical Activity and Academic Performance in a Large Sample of Ontario Students: The Role of Inattention and Hyperactivity
Bibliographic record
Abstract
BACKGROUND: Participation in physical activity (PA) is a modifiable factor that contributes to academic success, yet the optimal dose (ie, frequency) and mechanisms underlying the effect require further exploration. METHODS: Using data from 19,886 elementary and 11,238 secondary school students across Ontario, Canada, this study examined associations between PA participation frequency, academic achievement, and inattention and hyperactivity. RESULTS: Among elementary students, there was a positive association between PA frequency and academic achievement. Participating in 1 to 2 days per week of PA related to higher academic achievement compared with no days, whereas 7 days per week had the largest associations. For secondary students, a minimum of 3 to 4 days per week was associated with higher academic achievement with no significant benefit of additional days. Indirect effects of inattention and hyperactivity were found for both groups, suggesting that the benefits of PA on academic achievement may be partly explained by reductions in inattention and hyperactivity, especially for secondary school students. CONCLUSION: Students may experience academic benefits from PA even if they are not meeting the guidelines of exercising daily. These benefits may occur, in part, through reductions in inattention and hyperactivity. Further work is needed to determine the temporality and mechanism of these associations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".