The impact of virtual reality cognitive behavioral therapy on mental disorders among children and youth: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Cognitive behavioral therapy (CBT) is an effective treatment for mental disorders, however, it can be associated with limited patient engagement, low adherence, and stigma among younger populations. Virtual reality (VR) environments can facilitate innovative approaches to enhance CBT implementation in a controlled and immersive way. Objectives This study evaluates the impact of VR-CBT interventions on mental disorders in children and youth through a systematic review and meta-analysis. Methods A search was conducted in PsycINFO, PubMed, EMBASE, Scopus, and Web of Science. Studies compared VR-CBT interventions to traditional therapy or control conditions. Extracted data included post-intervention means, standard deviations, and 95% confidence intervals. Pooled effect sizes were calculated using Hedges’ g and analyzed with a random-effects model. Risk of bias was evaluated using the Cochrane risk-of-bias (RoB) 2 tool and the JBI Critical Appraisal Tool. Results In total, 20 studies were included in the systematic review, with 85% (n = 17) utilizing virtual reality exposure therapy (VRET), and 15% (n = 3) implementing broader VR-CBT frameworks. VR technologies included wearable head-mounted displays (70%, n = 14), with 30% (n = 6) relying on non-wearable systems, and 15% (n = 3) incorporating gamification elements. Seven studies were included in a meta-analysis, which showed that VR-CBT was associated with a small to moderate reduction in mental disorder symptoms in full-scale studies (pooled Hedge’s g = -0.46 (95% CI: [-0.84], [-0.09]). Conclusions VR-CBT interventions demonstrate potential for addressing mental disorders in children and youth, particularly when traditional therapy alone is insufficient and/or inaccessible.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.034 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".