Experiences of Patients and Therapists Testing a Virtual Reality Exposure App for Symptoms of Claustrophobia: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: The effectiveness of virtual reality exposure (VRE) in the treatment of anxiety disorders is well established. Several psychological mechanisms of VRE have been identified, whereby both emotional processing and the sense of presence play a key role. However, there are only few studies that contribute to our knowledge of examples of implementation in the case of VRE for claustrophobia based on patients' experiences and the perspective of therapists. OBJECTIVE: This study asks for key elements of a VRE app that are necessary for effective exposure for people with claustrophobic symptoms. METHODS: A mixed methods design was applied in which patients (n=15) and therapeutic experts (n=15) tested a VRE intervention of an elevator ride at 5 intensity levels. Intensity was varied by elevator size, duration of the elevator ride, and presence of virtual humans. Quantitative measures examined self-reported presence with the Igroup Presence Questionnaire (IPQ) ranging from 0 to 6 and 15 Likert-scaled evaluation items that had been developed for the purpose of this study, ranging from 1 to 5. In both measures, higher scores indicate higher levels of presence or agreement. Think-aloud protocols of the patients and semistructured interviews posttreatment of all participants were conducted to gain in-depth perspectives on emotional processes. RESULTS: The intervention induced a feeling of presence in patients and experts, posttreatment scores showed a high IPQ presence score (mean 3.84, SD 0.88), with its subscores IPQ spatial presence (mean 4.53, SD 1.06), IPQ involvement (mean 3.83, SD 1.22), and IPQ experienced realism (mean 2.75, SD 1.02). Patients preferred a setting in the presence of a therapist (mean 4.13, SD 0.83) more than the experts did (mean 3.33, SD 1.54). Think-aloud protocols of the patients revealed that presence and anxiety both were achieved. Qualitative interviews of patients and experts uncovered 8 topics: feelings and emotions, personal story, telepresence, potential therapeutic effects, barriers, conditions and requirements, future prospects, and realization. The intensity levels were felt to appropriately increase in challenge, with ambivalent results regarding the final level. Virtual humans contributed to feelings of fear. CONCLUSIONS: Key elements of a VRE app for claustrophobic symptoms should include variation of intensity by adding challenging cues in order to evoke presence and anxiety. Virtual humans are a suitable possibility to make the intervention realistic and to provide a sense of closeness; however, some of the fears might then be related to symptoms of social phobia or agoraphobia. Patients may need the physical presence of a therapist, though not all of them share this view. A higher degree of sophistication in the intensity levels is needed to deliver targeted help for specific symptoms of anxiety.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".