Individual Differences in the Neurocognitive Effect of Movement During Executive Functioning in Children with ADHD: Impact of Subtype, Severity, and Gender
Bibliographic record
Abstract
Background/Objectives: Attention-Deficit Hyperactivity Disorder (ADHD) is an immensely heterogeneous developmental disorder, uniquely impacting each individual. Physical movement is a promising adjunct behavioral treatment that can promote executive functioning in children with ADHD. The current study used neuroimaging and behavioral techniques to investigate the impact of movement during executive functioning on dorsolateral prefrontal cortical (DLPFC) activity and inhibitory control in children with ADHD, with particular focus on key individual difference factors in ADHD, such as subtype, severity, and gender. Methods: Twenty-eight children with ADHD completed a Stroop task while remaining stationary (stationary condition) and while desk cycling (movement condition). Simultaneous functional near-infrared spectroscopy (fNIRS) recorded oxygenated and deoxygenated changes in hemoglobin within the left DLPFC. Participants were categorized into ADHD subtype (hyperactive/impulsive, inattention, combined), ADHD severity (low, moderate, high), and gender (male, female). Results: Those with the hyperactive and combined ADHD subtypes, those with high ADHD severity, and males with ADHD showed greater DLPFC activation when engaging in movement during executive functioning compared to remaining stationary. In contrast, those with the inattentive ADHD subtype, those with low-to-moderate ADHD severity, and females with ADHD showed greater DLPFC activation when remaining stationary during executive functioning compared to engaging in movement. Inhibitory control improved in the stationary condition for females who were predominantly inattentive. Conclusions: This work underscores the importance of considering individual difference factors in ADHD when designing physical activity interventions, as treatment efficacy may vary.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".