Immersive Virtual Reality–Assisted Therapy for Distressing Voices in Psychosis: Qualitative Study of Participants’ and Therapists’ Experiences in the Challenge Trial
Bibliographic record
Abstract
Background: Immersive virtual reality-assisted therapy (VRT) is a relational therapy for distressing voices in psychosis. Like AVATAR therapy (AT), VRT centers on therapist-facilitated dialogues with a digital avatar representing a voice. Unlike AT, VRT uses immersive virtual reality (VR). While participant experiences of AT have been explored, therapist perspectives remain unexamined, and for VRT, neither participant nor therapist experiences have been studied. Understanding these perspectives is essential to inform optimization of therapy, future research, and implementation. Objective: The objective of this qualitative study was to explore both trial participants' and therapists' experience of VRT in the Challenge trial. Methods: Semistructured interviews were conducted with 10 trial participants and 8 therapists across the 3 Challenge trial sites. Trial participants were purposively sampled to ensure site representation and variation in voice-hearing duration. Individual interviews were conducted with trial participants, while therapists participated in site-based groups with 2-3 in each. Interviews were audio-recorded, transcribed, and subjected to reflexive thematic analysis from a critical realist position. Coding and theme development were inductive. People with lived experience were invited to an initial focus group for topic guide development and a later theme validation workshop. Reporting followed the Standards of Reporting Qualitative Research (SRQR). Results: A total of five overarching themes were generated: (1) using technology to meet the voice, (2) a different approach to voice-hearing and treatment, (3) on a tight schedule, (4) a toolbox for transformation, and (5) a price to pay. Trial participants and therapists generally found VRT acceptable, appropriate, and feasible. Highlights included the acknowledging approach to the voice(s), facilitation of engagement with the voice(s), and opportunity to share the otherwise private experience of voice-hearing. Externalizing and embodying the voice(s) in VR-supported avatar role-plays was seen as a key affordance. Positive outcomes included increased trial participant empowerment and self-worth, enabling or improving voice dialogue, new understanding of voice intentions, and changes in voice frequency or content. Challenges included instances of participant anxiety, exhaustion, or suboptimal sense of (voice) presence; adverse voice reactions; technological malfunctions and limitations to avatar design; measurement insensitivity; tensions between assertiveness and compassion; difficulties with reproducing negative voice content; and the demanding nature of the therapy and the nontraditional skills required of therapists. Conclusions: The study provided comprehensive insights into trial participants' and therapists' experiences of VRT in the Challenge trial. Findings share several similarities with qualitative research on other relational therapies for distressing voices and highlight VRT's potential for positive change. Key considerations for future research and implementation include monitoring anxiety and voice reactions, ensuring operational reliability of hardware and software, and addressing the additional effort required by therapists, which may be unsustainable in routine practice. As a demanding intervention, the successful implementation of VRT will require adequate training, supervision, and structural support.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".