Virtual reality interventions for the treatment of anxiety disorders: A scoping review
Bibliographic record
Abstract
BACKGROUND: & Objectives: Virtual Reality (VR) refers to an artificial, immersive three-dimensional environment with interactive sensory stimuli. VR is typically incorporated into the psychotherapeutic process as a means of providing exposure therapy. The objectives of this scoping review were to synthesize the most up-to-date evidence on the outcomes, acceptability, and side effects of VR interventions for treating anxiety disorders in adults. METHODS: This scoping review is grounded in the methodological framework of Arksey and O'Malley (2005). The databases searched were PubMed, EMBASE, Web of Science, PsycINFO, and ProQuest Dissertations and Theses. RESULTS: The search process identified 112 unique citations. 52 (46%) of the eligible articles examined participants with specific phobias, 25 (22%) with PTSD, 21 (19%) with social anxiety disorder, 12 (10%) with panic disorder with or without agoraphobia, and 3 (3%) with generalized anxiety disorder. VR interventions often led to statistically significant and meaningful reductions in symptoms for people with anxiety disorders. Additionally, they were acceptable to clients and associated with minimal side effects for all types of anxiety disorders, except for Combat-Related PTSD in Vietnam veterans. LIMITATIONS: Limitations included the fact that the studies in this review were of varying quality, and that articles in languages other than English and French were excluded. CONCLUSION: VR interventions appeared to be a viable alternative to conventional exposure therapy. Future research should include more male participants and have a stronger emphasis on acceptability and side effects. Increased traction for VR interventions for generalized anxiety disorder and panic disorder is also important.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".