Efficacy of Immersive Virtual Reality Combined With Multisensor Biofeedback on Chronic Pain in Fibromyalgia: A Pilot Randomized Controlled Trial
Bibliographic record
Abstract
OBJECTIVE: Fibromyalgia (FM) is a syndrome marked by chronic pain, fatigue, and mood disorders. Nonpharmacologic strategies are recommended to avoid overuse of opioids or nonsteroidal anti-inflammatory drugs, but current approaches often provide limited relief. This study aimed to preliminarily assess the efficacy and feasibility of a new combined intervention of immersive virtual reality with multisensor biofeedback (IVR-BF) in FM management. METHODS: In this single-center, pilot, open-label, randomized controlled trial, adult patients with FM were randomly assigned 1:1 to either the treatment (TR) group, receiving IVR-BF immediately, or a waitlist control (WL) group, receiving IVR-BF after the TR group completed treatment. The primary outcome was reduction in visual analog scale (VAS) pain scores in the TR group, after five IVR-BF sessions, compared to the WL group, after the waiting period. Secondary outcomes included improvements in FM impact (FM Impact Questionnaire [FIQ] score) and qualitative aspect of pain (Short-form McGill Pain Questionnaire [SF-MPQ] score). A longitudinal analysis was conducted across all patients to examine the trends in VAS pain, SF-MPQ, and FIQ score during the trial. RESULTS: Fifty patients were screened, and 20 female patients (10 TR and 10 WL) completed the trial and were analyzed. Those in the TR group showed significantly lower VAS pain scores compared to those in the WL group (P = 0.011), along with significant improvement in the FIQ score (P = 0.018). The longitudinal analysis revealed progressive improvements in VAS pain, SF-MPQ, and FIQ score, supported by physiologic improvements (heart rate variability, respiratory rate, skin conductance). No significant safety concerns were reported. Patients expressed a high level of satisfaction with the IVR experience. CONCLUSION: IVR-BF is a feasible treatment that shows potential in reducing pain and improving quality of life in patients with FM, supporting the need for larger trials to further evaluate its efficacy.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".