An exploratory randomized controlled trial of virtual reality as a non-pharmacological adjunctive intervention for adults with chronic cancer-related pain
Bibliographic record
Abstract
Abstract Background The growing popularity and affordability of immersive virtual reality (VR), as adjunctive non-pharmacological interventions (NPIs) for chronic pain, has resulted in increasing research, with mixed results of its effectiveness reported. This randomized controlled superiority trial explored the effects of a home-based adjunctive 3D VR NPI for chronic pain in cancer patients, compared to the same NPI experienced through a two-dimensional (2D) medium. Methods The NPI used four different applications experienced for 30 min for six days a week at home for four weeks using established cognitive distraction and mindfulness meditation techniques. Participants were randomly assigned ( N = 110) into two arms: a VR group ( n = 57) where the NPI was delivered through a VR system, and a control group ( n = 53) which used a computer screen for delivery. Participants were blinded to which arm of the study they were in, and sequence of the NPI applications experienced was randomized. Primary outcomes of daily pre/post/during exposure, and weekly average pain scores were assessed via the Visual Analog Scale (VAS) and the Short Form McGill Pain Questionnaire (SF-MPQ), and secondary outcomes of weekly Quality of Life (SF-12), and sleep quality (Pittsburgh Sleep Quality Index) were measured. Results Findings indicate VR applications were not significantly superior to the 2D group, but both VR and control NPIs provided clinically important pain reduction for participants when experiencing significant daily pain of a VAS ≥ 4. No significant adverse effects were encountered, although many of the participants in the VR group reported some cybersickness in certain applications (VR group n = 46 vs 2D group n = 28). Conclusions Overall, VR did not provide superiority as an NPI for pain relief compared to 2D computer-based applications. For those experiencing significant pain, cognitive distractive applications appeared superior for VR-based pain reduction during exposure, whilst meditative applications supplied better pain relief post-exposure. Findings from this trial support some clinical efficacy of home-based VR immersive experiences as NPIs for chronic cancer-related pain but in this context the 2D computer-based applications demonstrated similar value. Trial registration Clinicaltrials.gov, identifier NCT02995434, registered 2017–07-31.
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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 | 0.013 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".