The impact of an immersive digital therapeutic tool on experimental pain: a pilot randomized within-subject experiment with an active control condition
Bibliographic record
Abstract
Background: Pain is a complex and multifaced sensory and emotional experience. Virtual reality (VR) has shown promise in reducing experimental pain and chronic pain. This study examines an immersive VR environment initially designed for endometriosis patients, which demonstrated short-term analgesic effects. The research aims to determine the impact of the VR environment on experimental pain intensity and unpleasantness both during and after VR exposure (3D with binaural beats), while using an active control condition (2D with no binaural beats). Additionally, a secondary objective of the study was to identify the psychological and psychophysical factors that predict the analgesic effects of the immersive digital therapeutic tool. Methods: The study involved twenty-one healthy individuals and used a within-subject design, comparing a VR treatment with an active control condition. Continuous heat stimulation was applied to the left forearm with a Peltier thermode. Pain ratings were collected for immediate and short-term effects. Results: < 0.001), with a 27.2% pain reduction. In the Control condition, there were no significant differences in pain unpleasantness during and after exposure. Furthermore, no psychological and psychophysical factors predicted the analgesic effects. Discussion: The study investigated how a VR environment affected experimentally induced pain in healthy volunteers. It showed that VR reduced pain unpleasantness during exposure but had no lasting impact. The VR environment mainly influenced the emotional aspect of pain, possibly due to its inclusion of binaural beats and natural stimuli. The study suggests that the VR environment should be tested in chronic pain population with high distress levels. Registration number clinicaltrialsgov: NCT06130267.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".