Effect of cognitively engaging physical activity on executive functions in children
Bibliographic record
Abstract
Purpose Physical activity (PA) has been shown to enhance executive functions (EFs) in children, and PA involving a cognitive component may confer additional benefit. The purpose of this study was to investigate whether cognitively engaging PA impacts EF, and whether affect and fitness act as either mediators or moderators of this relationship. Methods A randomized controlled trial was utilized to assess changes in EFs between a 20-min Dual Task (intervention condition), a PA Task (control condition), and a Cognitive Task (control condition). Children were scheduled for two visits in the INfant and Child Health (INCH) Lab at the University of Toronto. Physical fitness was assessed using the 20 m shuttle run, standing long jump, and grip strength tests. EFs were assessed using the Stroop Task, Trail Making Task (TMT), and Forward Working Memory Task (FWMT). Results 38 children (Mage = 11.95 years, SD = 0.49, 61% female) participated. Repeated measures ANOVA showed main interactions between time on inhibition scores (p < 0.05, ηp2 = 0.489), and positive affect scores (p < 0.01, ηp2 = 0.284). Interaction effects between condition and time were not significant (p = 0.787, ηp2 = 0.014, p = .333, ηp2 = 0.061, p = 0.799, ηp2 = 0.013 for inhibition, switching, and passive working memory, respectively). Results showed no significant mediation effect of affect (95% CI = –0.5915, 2.147), or moderating effect between fitness and EF changes. Conclusion Possible explanations for these findings include inadequate cognitive engagement, lack of EF transfer, and statistical power. Results suggest any of these interventions may be beneficial for improving inhibition and positive affect in children.
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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 | 0.000 |
| 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".