Medication vs. Movement in ADHD: Interaction Between Medication and Physical Activity on Neurocognitive Functioning
Bibliographic record
Abstract
Background/Objectives: Movement during attention-demanding tasks may help compensate for cortical under-arousal in pediatric ADHD patients. However, the influence of medication during movement is unknown. This study assessed the impact of concurrent movement during executive functioning tasks on dorsolateral prefrontal cortex (DLPFC) activation and inhibitory control, with a particular focus on the influence of medication status. Methods: Twenty-six children with ADHD (15 medicated; 11 unmedicated) and 24 children without ADHD performed a Stroop task under two conditions: while remaining seated (Stationary condition) and while pedalling on a desk cycle (Movement condition). Functional near-infrared spectroscopy (fNIRS) was used to measure changes in oxygenated and deoxygenated hemoglobin levels in the left DLPFC. Results: Sixty-four percent of unmedicated children with ADHD showed greater left DLPFC activity while desk-cycling compared to remaining stationary. Only 37% of medicated children with ADHD showed the same pattern, with 63% showing greater left DLPFC activation when remaining stationary during executive functioning. Children without ADHD had similar DLPFC patterns as unmedicated ADHD children, with 65% showing increased activation during movement. Unmedicated ADHD children who were able to desk-cycle during the Stroop task had higher overall and incongruent accuracy scores; no Stroop differences were found between conditions for children with ADHD who were medicated or for controls. Conclusions: Medicated ADHD children did not benefit from physical activity during tasks requiring executive control, yet unmedicated ADHD children showed significantly greater DLPFC activation and inhibitory control when engaging in movement. If medication is not suitable for children with ADHD due to adverse side effects, movement during executive functioning may help mimic the benefit of medications and similarly support attention.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".