Virtual Reality and Neurofeedback for Management of Cancer Symptoms: A Feasibility Pilot
Bibliographic record
Abstract
Background: Evidence suggests the usefulness of complementary and alternative medicine approaches, like neurofeedback and virtual reality, for the management of cancer-related pain and mood. It is not well-understood whether neurofeedback delivered through virtual reality is feasible and acceptable to patients actively undergoing cancer treatment. Objective: The purpose of this study was to explore the feasibility and acceptability of a nature-based virtual reality combined with neurofeedback as a non-pharmacologic strategy for managing cancer-related pain and anxiety. Methods: This study utilized a mixed-methods approach. Participants included 15 cancer patients undergoing treatment. Patients engaged in a 22-minute nature-based virtual reality experience, wearing a virtual reality headset with a Brainlink headband measuring EEG activity. Participants were asked to complete the Edmonton Symptom Assessment System revised version (ESAS-r) before (T1) and after (T3) the experience to measure pain and anxiety. They were asked their level of pain midway through the experience (T2) and completed a follow-up interview afterward. Results: This study revealed feasible delivery of a virtual reality intervention combined with neurofeedback for patients seeking cancer treatment. All participants (100%) completed the intervention experience. Patients report this is an acceptable approach to managing cancer-related pain and anxiety. Comparisons between patients’ pain scores at T1, T2, and T3 reveal statistically significant reductions in pain (p .001). Patients also report decreased depression and anxiety. Conclusion: This is the first study examining virtual reality combined with neurofeedback as a non-pharmacologic intervention for managing cancer symptoms during treatment. The study reveals it is a promising for managing cancer-symptoms.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".