MazeOut Adaptive Serious Game: Evaluation of Performance and Usability for Motor Rehabilitation in Individuals with Autism Spectrum Disorder
Bibliographic record
Abstract
Objective: To evaluate the effectiveness of MazeOut , an adaptive serious game for motor rehabilitation, in individuals with autism spectrum disorder (ASD), by comparing their performance and usability with that of individuals with typical development (TD) and assessing the impact of adaptive (AG) versus nonadaptive gameplay on task performance. Materials and Methods: A mixed-design study with 30 participants (15 ASD, 15 TD), aged 8 to 40 years, had each participant experience both adaptive and nonadaptive interventions in randomized order, allowing within- and between-subject comparisons. Performance was measured using overall scores (based on maze navigation speed and coin collection), and usability was assessed with the System Usability Scale (SUS). Data analysis was conducted using R software, with performance trends evaluated through segmented regression and the Kruskal–Wallis test. Results: The TD group outperformed the ASD group across all conditions (TD median score: 27.54; ASD median score: 23.79, P < 0.001). Notably, participants in both groups achieved significantly better performance when AG was introduced first (ASD: 24.04 vs. 19.1, P < 0.001; TD: 30.2 vs. 24.31, P = 0.005), suggesting that the adaptation facilitates initial task learning. ASD participants reported slightly higher usability (mean SUS = 77.2) than TD participants (74.6), with the highest scores among younger users (81.9). Conclusions: Adaptive serious games can enhance motor performance, particularly for individuals with ASD. The findings suggest that early exposure to AG may improve task performance. Future studies with larger samples and longer interventions are needed to assess long-term benefits.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".