Virtual reality-based exposure with applied biofeedback for social anxiety disorder
Bibliographic record
Abstract
Introduction Social Anxiety Disorder (SAD) is considered the most prevalent anxiety disorder with the highest disease burden amongst anxiety disorders. Despite available effective treatment with Cognitive Behavioral Therapy, a majority of individuals with SAD do not seek treatment and many drop out when confronted with elements of exposure. Several studies highlight the many advantages virtual reality exposure holds over in vivo exposure. In this study, we investigate the added effect of real-time biofeedback during virtual reality exposure. Objectives The current study is part of a large scale study called VR8. The current study aims to develop and evaluate the feasibility of a VR-biofeedback-intervention for adults with mild to severe social anxiety disorder, before continuing randomized controlled trials. Methods Data from semi-structured interviews and surveys will be compared to biodata collected during VR exposure. Participants include a minimum of (n=10) patients and (n=10) clinicians from the Mental Health Services in the Region of Southern Denmark. Surveys include questionnaires used for assessment of anxiety symptoms, usability of technology, and presence in the virtual environment. Collected biodata includes heart rate variability and electrodermal activity. Behavioral markers include eye-gaze. The findings will be analyzed and discussed in a mixed methods design. Results The study is ongoing. Preliminary results will be available at presentation. Conclusions Successful development and implementation of a biofeedback-informed virtual reality exposure intervention may provide increased reach for patients and individuals who would have otherwise not sought- or dropped out of regular treatment, as well as inform the clinician on how to proceed during virtual exposure. Conflict of interest Prof. Stephané Bouchard is consultant to and own equity in Cliniques et Développement In Virtuo, which develops virtual environments, and conflicts of interests are managed according to UQO’s conflict of interests policy; however, Cliniques et Développeme
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".