Effectiveness of Virtual Reality–Based Cognitive Control Training Game for Children With Attention-Deficit/Hyperactivity Disorder Symptoms: Preliminary Effectiveness Study
Bibliographic record
Abstract
Background: Recent advancements in digital technologies hold promise for psychological interventions. Virtual reality (VR) has emerged as a particularly innovative tool, and its application expanded during the COVID-19 pandemic period. A recent study combining material and psychological rewards within a VR platform showed that this approach effectively improves attention-deficit behaviors in children with attention-deficit/hyperactivity disorder (ADHD), enhancing their inhibitory control abilities. Objective: This study aimed to evaluate the effectiveness of a newly developed VR-based cognitive control training game for children with ADHD symptoms. Specifically, it examined the sustainability of the training effects through a 3-month follow-up assessment. In addition, the study analyzed training response patterns and influential factors using a clustering method. Methods: A total of 29 children and adolescents (21 males and 8 females) aged 10-14 years participated in the study, with a mean IQ of 94 (SD 16.53). For 20 consecutive days, participants self-administered the training on a daily basis using the VR app. The following assessments were administered face-to-face: the Korean Wechsler Intelligence Scale for Children, Fourth Edition; the Stroop test; the Color Trails test; and the Flanker test from the National Institutes of Health toolbox. In addition, the parent-completed Korean Child Behavior Checklist was used to identify behavioral problems in the children. Participants engaged in at least 20 minutes of daily training for 20 consecutive days, with assessments conducted at baseline, posttraining, and follow-up. Results: Repeated measures ANOVA revealed significant main effects in the Stroop Color-Word test (F2,56=4.97; P=.001; ηp2=0.151), Child Behavior Checklist (CBCL) Total Problems (F2,56=21.0; P<.001; ηp2=0.429), CBCL Attention Problems (F2,56=11.7; P<.001; ηp2=0.294), and CBCL ADHD (F2,56=3.46; P=.004; ηp2=0.110). K-means clustering identified 2 distinct clusters that did not differ significantly in IQ variables but showed significant differences in game-related behavioral variables, including mean correct response time (t27=-2.56; P=.02) and the correct response ratio (t27=2.60; P=.02). Conclusions: The findings indicate that the VR-based training effectively improved cognitive control on the Stroop test and ADHD-related symptoms as measured by the CBCL. However, no significant training effects were observed on other attentional measures, namely the Color Trails test and the Flanker test from the National Institutes of Health toolbox. This VR-based approach shows promise as a potential therapeutic intervention for children with ADHD symptoms.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".