Inducing an Anxiety Response Using a Contaminated Virtual Environment: Validation of a Therapeutic Tool for Obsessive–Compulsive Disorder
Bibliographic record
Abstract
Obsessive-compulsive disorder (OCD) is characterized by the presence of unwanted and repetitive thoughts triggering significant anxiety, as well as the presence of ritual behaviours or mental acts carried out in response to obsessions to reduce the associated distress. In the contamination subtype, individuals are scared of germs and bacteria, are excessively concerned with cleaning, fear contamination and the spread of disease, and may have a very strong aversion to bodily secretions. A few studies on virtual reality have been conducted with people suffering from OCD, but they all focus on the subtype characterized by checking rituals. The goal of this study is to confirm the potential of a “contaminated” virtual environment in inducing anxiety in 12 adults suffering from contamination-subtype OCD compared to 20 adults without OCD (N = 32) using a within-between protocol. Subjective (questionnaire) and objective (heart rate) measurements were compiled. Participants were immersed in a control virtual environment (empty and clean room) and a “contaminated” virtual environment (filthy public restroom) designed for the treatment of OCD. Immersions were conducted in a 6-wall CAVE-like system. As hypothesized, the results of repeated-measures ANCOVAs revealed the significant impact of immersion in a filthy public restroom for participants suffering from OCD on both measures. Presence was correlated with anxiety in OCD participants and no difference in presence was observed between groups. Unwanted negative side effects induced by immersions in virtual reality were higher in the OCD group. The clinical implications of the results and directions for further studies are discussed.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".