The Effects of Physical Activity and Exergaming on Motor Skills and Executive Functions in Children with Autism Spectrum Disorder
Bibliographic record
Abstract
Objective: This study aims at investigating the effects of two types of interventions, Sports, Play and Active Recreation for Kids (SPARK) and exergaming (Kinect), on motor skills (MS) and executive functions (EF) in children with autism spectrum disorder (ASD). Materials and Methods: Sixty children, aged 6–10 years were randomly assigned to SPARK (n = 20), Kinect (n = 20), or a control group (n = 20). Children's MS and EF were assessed before and after the intervention. The SPARK and Kinect groups participated in an 8-week intervention; the control group received treatment as usual. Intention-to-treat repeated-measures ANOVA was used to examine the effects of the intervention. Results: For MS, a significant group X time interaction was observed for aiming and catching skills [F(2, 53) = 4.12, P < 0.05]; the SPARK group improved significantly from pre- to post-test compared with the other groups. For EF, a main effect of group was found for correct responses [F(2, 53) = 5.43, P < 0.01]. The Kinect group showed more correct responses than the SPARK and control groups. A main effect of time was significant for conceptual responses [F(1, 53) = 10.61, P < 0.01] and perseverative errors [F(1, 53) = 14.31, P < 0.01]. Conclusion: This study suggests that structured physical activity (PA) interventions that target specific MS improve motor function in children with ASD and exergaming could be effective for improving EF. Future research is needed to untangle the interaction between the type of exercise, traditional PA versus exergaming, and the dose associated with improvements in MS and EF in children with ASD.
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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) | 0.001 | 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".