Experiences of Gamified and Automated Virtual Reality Exposure Therapy for Spider Phobia: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Virtual reality exposure therapy is an efficacious treatment of anxiety disorders, and recent research suggests that such treatments can be automated, relying on gamification elements instead of a real-life therapist directing treatment. Such automated, gamified treatments could be disseminated without restrictions, helping to close the treatment gap for anxiety disorders. Despite initial findings suggesting high efficacy, very is little is known about how users experience this type of intervention. OBJECTIVE: The aim of this study was to examine user experiences of automated, gamified virtual reality exposure therapy using in-depth qualitative methods. METHODS: Seven participants were recruited from a parallel clinical trial comparing automated, gamified virtual reality exposure therapy for spider phobia against an in vivo exposure equivalent. Participants received the same virtual reality treatment as in the trial and completed a semistructured interview afterward. The transcribed material was analyzed using thematic analysis. RESULTS: Many of the uncovered themes pertained directly or indirectly to a sense of presence in the virtual environment, both positive and negative. The automated format was perceived as natural and the gamification elements appear to have been successful in framing the experience not as psychotherapy devoid of a therapist but rather as a serious game with a psychotherapeutic goal. CONCLUSIONS: Automated, gamified virtual reality exposure therapy appears to be an appealing treatment modality and to work by the intended mechanisms. Findings from the current study may guide the next generation of interventions and inform dissemination efforts and future qualitative research into user experiences.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".