Perspectives on Physical Activity and Learning from Children With and Without ADHD
Bibliographic record
Abstract
(1) Background: Children with attention-deficit hyperactivity disorder (ADHD) engage in significantly less physical activity than their peers. While ample research has shown the beneficial effect of physical activity on ADHD management, we have little to no knowledge of how children with ADHD experience physical activity, which may ultimately undermine the utility of prescribed physical activity programming. This study compared experiences and perspectives of physical activity in school and non-school settings, between children with and without ADHD. (2) Methods: In this study, 23 children with ADHD and 24 children without ADHD participated in semi-structured interviews, sharing their views on physical activity in school and non-school settings. (3) Results: Inductive content analysis revealed that, compared to children without ADHD, children with ADHD reported lower physical activity levels, more often emphasized the benefits of movement for improving mood and focus during learning, viewed classroom-based desk cycling as a helpful tool to focus their attention, and expressed a desire to use desk cycling during classroom learning. (4) Conclusions: This study emphasizes key differences in the physical activity experiences and preferences between children with and without ADHD; it also offers insight into how classroom learning may be enhanced by offering optional physical activity outlets for children who identify as benefiting from movement during learning.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".