Examining the Use of Virtual Reality to Support Mindfulness Skills Practice in Mood and Anxiety Disorders: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Virtual reality (VR) has been proposed as a technology to support mindfulness practice through promoting increased engagement and presence. The proposed benefits of this technology have been largely unexamined with clinical populations. Further research is required to understand its clinical potential and utility in improving and managing mental health symptoms. OBJECTIVE: This study aims to investigate the proximal impacts of a single, brief, VR-supported mindfulness practice for individuals with a mood or anxiety disorder and to understand user experiences, which may affect the acceptability and efficacy of VR mindfulness for this population. METHODS: This mixed methods study recruited 28 participants with a primary diagnosis of major depressive disorder, bipolar disorder, or anxiety disorder. Participants completed a mindfulness practice wearing a VR headset that was presenting an omnidirectional video of a forest scene, which was overlaid with a guided audio voiceover. Before and after the practice, measures were completed assessing state mindfulness (Toronto Mindfulness Scale), affect (Positive and Negative Affect Schedule), and anxiety (State-Trait Anxiety Inventory Y-1; n=27). Semistructured interviews were then held inquiring about the user experience and were analyzed using thematic analysis (n=24). RESULTS: After completing the VR-supported mindfulness practice, both measures of state mindfulness on the Toronto Mindfulness Scale, mean curiosity and decentering, increased significantly (Cohen d=1.3 and 1.51, respectively; P<.001). Negative affect on the Positive and Negative Affect Schedule (Cohen d=0.62; P=.003) and State-Trait Anxiety Inventory Y-1 state anxiety (Cohen d=0.84; P<.001) significantly reduced. There was no significant change in positive affect (Cohen d=0.29; P=.08). Qualitative analysis of interviews identified 14 themes across 5 primary theme categories. The results suggested that being mindful during the use of the app was experienced as relatively effortless because of the visual and immersive elements. It was also experienced as convenient and safe, including when compared with prior traditional experiences of mindfulness. Participants also identified the uses for VR-supported mindfulness in managing emotions and symptoms of mental illness. CONCLUSIONS: The results provide preliminary evidence that VR-supported mindfulness can improve emotional states and manage mental health symptoms for those with mood or anxiety disorders. It offers some potential clinical applications for those with mood or anxiety disorders for exploration within future research.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".