Virtual reality-based exposure with 360° video as part of cognitive behavioral therapy for social anxiety disorder: a three-arm randomized controlled trial
Bibliographic record
Abstract
Background Social anxiety disorder (SAD) has a high prevalence and an early onset. It often persists well into adulthood, turning into a chronic disorder. Cognitive behavioral therapy (CBT) is effective in treating SAD, but real-life exposure conducted as part of CBT is often costly and time-consuming, and imaginary exposure might lack realism and intensity. Virtual reality (VR)-based exposure using 360° video offers a promising way to deliver exposure therapy. Objective To develop a complete psychotherapeutic treatment program including CBT with VR-based exposure using 360° videos (CBT-ExpVR) for adult patients with SAD and to test the treatment effect using the Social Interaction Anxiety Scale (SIAS) as the primary outcome. Methods This three-arm randomized controlled trial involved 51 participants who were recruited through self-referral. The interventions took place at Center for Digital Psychiatry in Denmark. Participants were randomized via computer program to CBT-ExpVR, CBT with in vivo exposure (CBT-Exp), or an active control group offered VR relaxation (RlxVR). Afterwards, participants assigned to RlxVR were re-randomized to one of the two CBT interventions. Allocation was not blinded. Results Intention-to-treat analysis showed that participants receiving CBT-ExpVR reported significantly fewer symptoms of social anxiety at post-treatment compared to pre-treatment, β = −14.89, 95% CI (−18.64, −11.14), p < 0.0001. At post-treatment, no difference in treatment effect was found between CBT-ExpVR and CBT-Exp, β = 3.643, 95% CI (−1.727, 9.013), p = 0.1839. However, CBT-ExpVR was more effective than RlxVR, β = −11.537, 95% CI (−16.163, −6.911), p < 0.0001. Dropout rates post-treatment were 16% (CBT-ExpVR), 38% (CBT-Exp), and 20% (RlxVR). No harms were registered during the study. Conclusion CBT-ExpVR represents an effective treatment for SAD comparable to standard CBT treatment. CBT-ExpVR was reported as less costly and requiring less effort by the therapist compared to CBT-Exp. Thus, VR-based exposure might pave the way for a broader implementation of exposure in psychotherapeutic interventions for social anxiety by providing easy and low-cost access to exposure scenarios. Clinical Trials Registration https://clinicaltrials.gov/study/NCT03973541 , identifier NCT03973541.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".