Virtual reality exposure therapy in panic disorder: a pilot study
Bibliographic record
Abstract
Aims To ascertain if virtual reality exposure therapy (VRET) is an effective add-on tool in the treatment of Panic Disorder (PD). Background The exposure to virtual stimuli has been studied as a useful treatment for PD. However, the studies with PD are still scarce and use dissimilar protocols, with effectiveness varying according to the protocol applied. Method Eight PD patients received VRET as an add-on treatment to pharmacotherapy. The treatment protocol consisted of eight sessions. The first session is for the patient to understand the treatment and to answer the questionnaires. The second and third sessions were to prepare the patients for exposures with breathing training using diaphragmatic breathing and others breathing techniques to manage anxiety. From the fourth to eighth sessions, the patients followed a hierarchy of tasks during virtual reality exposure. Clinicians rated the Clinical Global Impression Scale (CGI) and the Panic Disorder Severity Scale (PDSS). The patients rated the Diagnostic Symptom Questionnaire (DSQ); the Mobility Inventory (MI), the Anxiety Sensibility Index (ASI-R), the Beck Depression Inventory (BDI), the Beck Anxiety Inventory (BAI) and the WHOQOL-BREF before and after the protocol. After all exposures, the Igroup Presence Questionnaire (IPQ) was applied to measure the sense of presence experienced in the virtual environment. The virtual environment simulated the subway of Rio de Janeiro. Result There were no statistically significant improvements in the CGI-S, PDSS, BAI, MI or WHOQOL. There was a significant improvement in the BDI scores (P = 0.033). There was a trend towards improvement of anxiety measured by the ASI-R (P = 0.084) and of panic symptoms measured by the DSQ (P = 0.081) scores. There was also a significant improvement of sense of presence (IPQ – general presence) through the exposure sessions. Conclusion Our study demonstrated that VRET as an add-on to pharmacological therapy could benefit PD patients. Despite the lack of significant differences in the means, the dispersion of PDSS and BAI scores were smaller after treatment compared to before treatment, suggesting that patients with more severe anxiety, panic and agoraphobia symptoms benefited more of the VRET protocol so, at the end of the treatment, differences were found in important measures of panic. Randomized controlled clinical trials are warranted to confirm the efficacy of VRET. This study was funded by the Brazilian National Council for Scientific Development (Cnpq). The authors report no conflicts of interest.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".