The Virtual Mind Study: Virtual Reality (VR) Guided Mindfulness for Chronic Pain in Cancer Survivors: Protocol for a Single-group Feasibility Trial (Preprint)
Bibliographic record
Abstract
UNSTRUCTURED Introduction: Chronic cancer-related pain (CRP) has a significant negative impact on quality of life. Mindfulness is hypothesized to mitigate chronic CRP by regulating both physical and emotional resistance to pain. In recent years, there has been an interest in the use of virtual reality (VR) to deliver mindfulness meditation. Virtual reality provides an immersive and engaging environment which may enhance one’s focused attention to present moment experiences, potentially making mindfulness less effortful and more efficacious for individuals with chronic pain. There has been little research in this area for people with a history of cancer. The aim of this mixed-methods study is to evaluate the feasibility of a virtual reality guided mindfulness (VRGM) intervention offered to adult cancer survivors with chronic CRP. Methods and analysis: This mixed-methods feasibility study will employ a single-arm, pretest–posttest design with semi-structured interviews. Fifteen cancer survivors will be enrolled in a 6-week, home-based intervention that consists of 10-15 minutes of daily VRGM practice. The primary outcome is feasibility as assessed by accrual rates, retention in the study, intervention adherence, questionnaire completion and side-effect rates. Participants will be assessed on psychosocial outcome measures (i.e., pain, sleep, depressive and anxiety symptoms, fatigue, quality of life, mindfulness) before and after the intervention, and 6 weeks post-intervention (follow-up). Factors with possible moderation or mediation effects (i.e., levels of immersion and presence in the virtual environment, trait mindfulness, amount of VRGM practice) will be assessed in relation to changes in pain. Qualitative information will provide subjective detail on participants’ experience with VRGM to complement quantitative data. Ethics and dissemination: This study has been approved by the Health Research Ethics Board of Alberta Cancer Committee [HREBA.CC-20-0411]. Study findings will be disseminated through open access publications, traditional conference presentations, professional cancer organizations, and social media platforms.
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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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.048 | 0.010 |
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".