Blended Mobile-Based Interventions With Integrated Virtual Reality Exposure Therapy for Anxiety Disorders: Thematic Analysis of Patient Perspectives
Bibliographic record
Abstract
BACKGROUND: Guided mobile-based interventions may mitigate symptoms of anxiety disorders such as panic disorder, agoraphobia, or social anxiety disorder. With exposure therapy being efficacious in traditional treatments for these disorders, recent advancements have introduced 360° videos to deliver virtual reality exposure therapy (VRET) within mobile-based interventions. OBJECTIVE: Despite ongoing trials evaluating the treatment's efficacy, research examining patient perceptions of this innovative approach is still scarce. Therefore, this study aimed to explore patient opinions on specific treatment aspects of mobile-based interventions using mobile VRET and psychotherapeutic guidance for anxiety disorders. METHODS: A total of 11 patients diagnosed with panic disorder, agoraphobia, or social anxiety disorder who had previously taken part in the experimental conditions of 2 randomized controlled trials for a mobile intervention including mobile VRET participated in cross-sectional, retrospective interviews. Using a semistructured interview format, patients were asked to reflect on their treatment experiences; personal changes; helpful and hindering aspects; their motivation levels; and their encounters with the mobile-based intervention, manualized treatment sessions, and the mobile VRET. RESULTS: Thematic analysis led to the formation of 14 themes in four superordinate categories: (1) perceived treatment outcomes, (2) aspects of the mobile intervention, (3) experiences with mobile VRET, and (4) contextual considerations. Patients offered their insights into factors contributing to treatment success or failure, delineated perceived treatment outcomes, and highlighted favorable aspects of the treatment while pointing out shortcomings and suggesting potential enhancements. Most strikingly, while using a blended app-based intervention, patients highlighted the role of psychotherapeutic guidance as a central contributing factor to their symptom improvement. CONCLUSIONS: The findings of the thematic analysis and its diverse patient perspectives hold the potential to guide future research to improve mobile-based treatment options for anxiety disorders. Insights from these patient experiences can contribute to refining mobile-based interventions and optimizing the integration of VRET in accordance with patients' preferences, needs, and expectations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".