A Virtual Reality Game (The Secret Trail of Moon) for Treating Attention-Deficit/Hyperactivity Disorder: Development and Usability Study
Bibliographic record
Abstract
BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) affects between 4% and 8% of children worldwide. The treatment of choice is multimodal treatment. Multimodal interventions for ADHD may be improved by incorporating new treatments, such as treatment via serious video games. The Secret Trail of Moon (TSTM) is a virtual reality serious video game that was designed for cognitive training related to core ADHD symptoms and executive dysfunction. OBJECTIVE: We aimed to describe the development and usability of TSTM. METHODS: The usability study included 37 children and adolescents who tested TSTM during the early usability stage (preinclusion) of a randomized controlled clinical trial for testing the effectiveness of TSTM. Chi-square tests were performed to compare patients with ADHD (ADHD combined subtype vs inattentive subtype) and to compare frequent and infrequent video game players in the second study. We used SPSS version 20 for Macintosh (IBM Corporation). RESULTS: A total of 31/37 (86%) and 30/37 (83%) of participants liked playing TSTM and wanted to continue playing TSTM, respectively. Further, 5/37 (14%) of participants reported that they experienced either perceived dizziness or virtual reality motion sickness. We found no statistically significant differences after comparing the ADHD combined subtype to the inattentive subtype and frequent video game players to infrequent video game players. CONCLUSIONS: Serious video games, such as TSTM, may complement the current multimodal approach for treating ADHD. TRIAL REGISTRATION: ClinicalTrials.gov NCT04355065; https://clinicaltrials.gov/ct2/show/NCT04355065.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".