Design and Evaluation of an Exergaming System for Children With Autism Spectrum Disorder: The Children’s and Families’ Perspective
Bibliographic record
Abstract
Children with autism spectrum disorder (ASD) have lower levels of physical activity than their typically developing peers. Barriers to participation include deficits in motor function and in social interaction, both of which reduce opportunities to engage in leisure activities that incorporate physical exertion. Because children with ASD also have higher than average levels of media use, exergames—video games that require bodily interaction to play—are a promising form of exercise. While studies have examined exergaming interventions for children with ASD, to date there has been little research on exergames that have been specifically designed for children with neurodevelopmental disorders, or qualitative analysis of players’ and families’ experience with exergaming programs. In this paper we present Liberi , an exergaming system involving kinaesthetic interaction within a virtual world, and designed explicitly for children with neurodevelopmental disorders. We report the results of a 6-week study where Liberi was played from the home by five children with ASD. The paper describes those aspects of the design that were successful and unsuccessful; how children and parents viewed the exergames; how the games were incorporated into the children’s lives; and how parents envisaged exergames could be best deployed for 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".