The Effect of Cognitive-Motor Training on Physical Literacy and Cognitive Function of Children with ADHD
Bibliographic record
Abstract
Introduction: Understanding the key factors that influence physical literacy (PL) and cognitive function in children with ADHD is crucial.Objective: This research seeks to explore how cognitive-motor training (CMT) influence both the PL and cognitive abilities of children diagnosed with ADHD.Methods: A quasi-experimental design featuring a pretest-posttest framework with a control group was implemented in this research. The study involved 54 male children aged 7 to 10 years; all diagnosed with ADHD. Participants were split into two equal groups. The intervention group participated in a CMT for eight weeks, attending three sessions per week. To assess the research variables, the Canadian Assessment of Physical Literacy Development, the Numerical Span Subscale of the Revised Wechsler Intelligence Scale for Children, and the Corsi Block-Tapping Task were utilized, with data analysis conducted using ANCOVA. Results: Body mass index (BMI) in both groups was similar, again showing no significant differences (P>0.05). The analysis performed at the end of the intervention period showed significant differences across all groups for the evaluated parameters, including both PL and cognitive function dimensions (P<0.001). These findings suggest that CMT successfully enhanced PL and cognitive function in male children diagnosed with ADHD.Conclusion: By intentionally merging physical exercises with cognitive tasks, there is potential for children with ADHD to experience improvements in both their PL and cognitive functions, which are often areas of difficulty for them. Nonetheless, further research is essential to explore this intersection more thoroughly.
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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 | 0.001 |
| 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 | 0.000 | 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".