An examination of the prospective association between physical activity and academic achievement in youth at the population level
Bibliographic record
Abstract
Exercise has significant benefits for brain health and this may have downstream learning benefits for youth. However existing studies looking at links between physical activity and academic achievement are limited by relatively small sample sizes and/or cross-sectional designs. The objective of this study is to determine the direction and magnitude of the association between physical activity and academic achievement in a large prospective sample of adolescents. Linear mixed models with random intercepts and multinomial ordinal generalized estimating equations were employed to analyze the prospective relationship between measures of physical activity and academic achievement from the COMPASS study (N = 9,898 linked participant data cases from year 2 (2013-2014) to year 4 (2015-2016)). The linear relationships between minutes of moderate to vigorous physical activity and academic achievement (English: r = -.047, p < .000; Math: r = -.026, p = .008) as well as meeting the national physical activity guidelines and academic achievement (English: est = -.052, p = .004; Math: est = -.052, p = .028) were negative and trivial in magnitude. Organized sport participation showed slight positive associations with academic achievement indices, but these were also of trivial magnitude. In conclusion, the relationship between physical activity and academic achievement was effectively null in this population sample. Advocacy for physical activity programming for adolescent populations may best be undertaken with reference to lack of net academic achievement cost, rather than presence of benefit, or simply with reference to the many other physical and mental health benefits for youth.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".