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Record W4416202206 · doi:10.1002/ejp.70166

Comparison of Experimental Pain Modulation by Hypnosis, Virtual Reality and Virtual Reality Hypnosis in Healthy Individuals

2025· article· en· W4416202206 on OpenAlexafffund
Floriane Rousseaux, Émilie Giguère, Mathieu Landry, Mathieu Piché, Maedeh Mostanadi, Pierre Augier, Jessica Simon, Aminata Bicego, Marie-Élisabeth Faymonville, Anne‐Sophie Nyssen, David Ogez, Pierre Rainville, Audrey Vanhaudenhuyse

Bibliographic record

VenueEuropean Journal of Pain · 2025
Typearticle
Languageen
FieldNeuroscience
TopicPain Management and Placebo Effect
Canadian institutionsUniversité du Québec à Trois-RivièresMcGill UniversityInstitut Universitaire de Gériatrie de MontréalUniversité de MontréalHôpital Maisonneuve-Rosemont
FundersFonds de Recherche du Québec - SantéFonds Spéciaux de RechercheFondazione Europea Ricerca BiomedicaKoning BoudewijnstichtingUniversité de LiègeMitacsAstraZeneca FoundationFonds De La Recherche Scientifique - FNRSFondation contre le CancerAstraZeneca
KeywordsVirtual realityHypnosisPerceptionInterference (communication)Pain perceptionModulation (music)

Abstract

fetched live from OpenAlex

BACKGROUND: Hypnosis (H) and virtual reality (VR) are effective behavioural interventions to influence acute pain perception. Hypnotic suggestions have also been shown to modulate the nociceptive flexion reflex (NFR), suggesting the activation of descending modulatory mechanisms affecting spinal nociceptive activity. The combination of these techniques, virtual reality hypnosis (VRH), may reduce pain, but research on their comparative efficacy and mechanisms requires further experimental investigation. This study compared the effects of relaxation hypnosis, VR and VRH on pain perception and nociceptive physiological responses. METHODS: Twenty-four healthy participants were tested at baseline followed by three experimental conditions (relaxation hypnosis, VR, VRH) in a counterbalanced order. Pain intensity and unpleasantness, as well as NFR amplitude evoked by noxious transcutaneous electrical stimulation, were measured. Bayesian statistics assessed evidence for analgesic effects on each variable. RESULTS: The strength of evidence in favour of our hypotheses was categorised as follow: BF = 1-3: anecdotal evidence; BF = 3-10: moderate evidence and BF > 10: strong evidence. For NFR values, Bayesian paired-sample T-tests provided anecdotal support for the efficacy of relaxation hypnosis (BF + 0 = 2.11) and stronger evidence for VR (BF + 0 = 10.94) and VRH (BF + 0 = 14). For pain intensity, moderate evidence supported reductions with relaxation hypnosis (BF + 0 = 9.18), while strong evidence was found for VR (BF + 0 = 27.99) and low to moderate for VRH (BF + 0 = 5.88). Similarly, unpleasantness showed anecdotal reduction with hypnosis (BF + 0 = 1.9), and moderate evidence supported VR (BF + 0 = 4.86) and VRH (BF + 0 = 7.18). Across all measures, no significant differences were found between hypnosis, VR and VRH. CONCLUSION: These findings suggest that these techniques did not differentially affect NFR, pain intensity, or unpleasantness. SIGNIFICANCE STATEMENT: The strength of this fundamental study is to directly compare hypnosis, VR, and VRH on both pain perception and physiological responses. It shows that VR alone is effective, while adding hypnosis does not always lead to better results and the combination could even create interference in some cases. This article helps to nuance the existing literature and common assumptions about the tool's use. These findings help clarify how VRH works and to propose guidance for clinical practices and further VRH development.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.047
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0470.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.054
GPT teacher head0.342
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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