ChatGPT-Delivered Physical Activity Intervention for Children With Autism Spectrum Disorder: Pre-Post Feasibility Study
Bibliographic record
Abstract
Background: The use of digital technologies, such as mobile apps, Zoom (Zoom Communications), virtual reality, and video games, to promote physical activity in individuals with autism spectrum disorder (ASD) has been increasing. However, there are no studies using ChatGPT (OpenAI), a popular tool in recent years, for promoting physical activity in children with ASD. Objective: This study aimed to evaluate the feasibility and potential effectiveness of ChatGPT-delivered physical activity interventions in children with ASD. Methods: A total of 26 families (parent-child dyads) participated in the study. Families were randomly assigned to an application group (n=13) and a control group (n=13). In the application group, parents implemented physical activities recommended by ChatGPT for their children with ASD. Data were collected using the Leisure Time Exercise Questionnaire (LTEQ) and a feasibility questionnaire. Results: Parents reported that ChatGPT-delivered physical activities were a feasible intervention to increase physical activity levels in children with ASD. They also found the activity content suggested by ChatGPT to be interesting and useful. LTEQ measurements corroborated these findings, showing a significant increase in the physical activity levels of children in the intervention group after the intervention. Conclusions: The results suggest that ChatGPT-delivered physical activities could be a promising intervention to enhance physical activity in children with ASD. Further investigation is warranted.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".