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Record W4412616388 · doi:10.3390/sports13080240

Perspectives on Physical Activity and Learning from Children With and Without ADHD

2025· article· en· W4412616388 on OpenAlexafffund
Beverly-Ann Hoy, Barbara Fenesi

Bibliographic record

VenueSports · 2025
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsWestern University
FundersCanada Foundation for Innovation
KeywordsPhysical activityPsychologyAttention deficit hyperactivity disorderDeskMoodDevelopmental psychologyClinical psychologyMedicinePhysical therapy

Abstract

fetched live from OpenAlex

(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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.301
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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