Dog-Assisted Physical Activity Intervention in Children with Autism Spectrum Disorder: A Feasibility and Efficacy Exploratory Study
Bibliographic record
Abstract
Integrating a therapy dog into physical activity sessions may help children with autism spectrum disorder (ASD) to increase physical activity and gain-related health benefits. This exploratory intervention assessed the feasibility of integrating a therapy dog into exercise sessions and its efficacy to improve physical activity outcomes in children with ASD. After two familiarization sessions, we randomly assigned 18 children with ASD (mean age = 10.1, SD = 2.5) into two groups (n = 9). We used a crossover design and randomized groups to attend a weekly physical activity session with or without a therapy dog for four weeks. Each group had two sessions with the presence of 1–2 therapy dogs and two sessions without a therapy dog. We assessed feasibility by measuring participant attendance to the crossover sessions and retention in the intervention. We measured efficacy by recording light physical activity, moderate to vigorous physical activity (MVPA), number of bone-impacts, and sedentary time using activity monitors (accelerometers) in each session. We compared physical activity outcomes between the crossover sessions with and without a therapy dog using repeated measures MANOVA. Attendance at the sessions was 92% and the retention rate was 90%. Participants had 13% more minutes of light physical activity (mean difference = 3.5 min; 95% CI: 1.2, 5.8 min) and 22% less sedentary minutes (–2.4; –4.3, –0.1) in the sessions with a therapy dog. MVPA and the number of bone-impacts did not differ between the sessions (p > 0.05). Our results suggest that integrating therapy dogs into physical activity sessions is feasible and it increases light physical activity and decreases sedentary time 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 | 0.004 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".