Combining virtual reality and hypnosis to alleviate chronic pain in elderly with hand arthritis: protocol for a randomised phase II clinical trial
Bibliographic record
Abstract
INTRODUCTION: Chronic pain is a common health condition that significantly impacts the quality of life of those affected, affecting one in five people in Canada. The prevalence of this condition tends to increase with age, making it a major health issue given the ageing population. However, its management remains inadequate and requires significant mobilisation of healthcare professionals as well as the development of multiple therapeutic solutions. Among these, non-pharmacological interventions such as hypnosis and virtual reality have proven effective. Nevertheless, while the existing literature seems promising, it presents methodological limitations. Therefore, this study aims to assess the effectiveness of an intervention combining virtual reality and hypnosis in an ageing population suffering from a widespread chronic pain condition, that is, hand arthritis. METHODS AND ANALYSIS: This study will be a single-centre randomised clinical trial. Participants will be randomly assigned to one of two conditions: one receiving an intervention combining virtual reality and hypnosis, and the other receiving only virtual reality. The effectiveness of the intervention on current perceived pain before and after the intervention (primary outcome) will be evaluated. Secondary outcomes will include anxiety and depressive symptoms, quality of life, relaxation and fatigue. Exploratory analyses will also be conducted to contribute to the emerging literature by examining physiological variables such as heart rate variability, respiratory rate and electrodermal activity during the intervention, and their relationship with primary and secondary outcomes. ETHICS AND DISSEMINATION: The project was approved by the Research Ethical Committee of the Hospital Maisonneuve-Rosemont (Project no 2024-3539). Participants will be asked to provide written consent for their participation. Results from this study will be shared through peer-reviewed publications, as well as oral and poster presentations at scientific events. The protocol for this study was preregistered on Open Science Framework and raw anonymised data will be available on this platform (https://osf.io/vbh72/?view_only=1d17c5708f894faab6669d85e1fde75d). TRIAL REGISTRATION NUMBER: NCT06833905.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.031 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.081 | 0.015 |
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".