Virtual Reality–Based Assessment of Attention-Deficit/Hyperactivity Disorder and Comorbid Symptoms in Children: Framework Development and Standardization Study
Bibliographic record
Abstract
Background As virtual reality (VR) technology becomes increasingly prevalent, its potential for collecting objective behavioral data in psychiatric settings has been widely recognized. However, the lack of standardized methodologies limits reproducibility and data integration across studies, particularly in assessing attention-deficit/hyperactivity disorder (ADHD) and associated behaviors, such as irritability and aggression. Objective This study examines the use of VR-based movement data to operationalize core ADHD symptoms (hyperactivity and inattention) and comorbid disruptive behaviors (irritability and aggression), aiming to identify reproducible and clinically actionable metrics and evaluate their explanatory power for each symptom domain to assess the overall use of these variables. Methods A total of 45 children (mean age 9.06, SD 2.11 years; n=14/45, 31% female) participated in the study and were divided into 2 groups: 28 (62%) diagnosed with ADHD and 17 (38%) controls. Seven VR-derived movement variables were analyzed: average speed, acceleration, total distance, area occupied, distance between the hands and head, frequency of movement, and time spent still. Correlation and stepwise regression analyses identified which variables best predicted ADHD symptoms and comorbid behaviors. Results Among the 7 VR-derived variables, average speed (mean r=0.460, SD 0.097) and total distance (mean r=0.442, SD 0.116) showed the broadest associations, each correlating with 8 measures. In contrast, frequency of movement was related only to hyperactivity (r=0.416; P=.004), suggesting strong but narrow predictive value. Stepwise regression identified total distance as the sole and strongest predictor of hyperactivity (R2=0.411) and, except for participant-reported irritability, yielded significant models for all other measures (mean R2=0.282, SD 0.064; all P<.05). Conclusions This study provides empirical evidence on VR-derived movement variables that can inform the development of standardized methodologies for ADHD and comorbid behavior assessment. The identified metrics and their predictive patterns offer a basis for integrating VR-based measures into future research and clinical applications.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".