Virtual Reality-Based Assessment of Attention-Deficit/Hyperactivity Disorder and Comorbid Symptoms in Children:...

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.

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.

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.

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).

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.