Evidence map›Paper›PMID 40163491›Full record

ArticlePLOS digital health2025

Effectiveness and user experience of a virtual reality intervention in a cohort of patients with chronic musculoskeletal pain syndromes.

Tiffany Prétat, Pedro Ming Azevedo, Chris Lovejoy, Thomas Hügle

Abstract read
In one paragraph

Article in PLOS digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Tiffany PrétatDepartment of Rheumatology, University Hospital Lausanne (CHUV) and University of Lausanne, Lausanne, Switzerland.ORCID https://orcid.org/0009-0009-8076-1769
Pedro Ming AzevedoDepartment of Rheumatology, University Hospital Lausanne (CHUV) and University of Lausanne, Lausanne, Switzerland.
Chris LovejoyDepartment of Rheumatology, University Hospital Lausanne (CHUV) and University of Lausanne, Lausanne, Switzerland.
Thomas HügleDepartment of Rheumatology, University Hospital Lausanne (CHUV) and University of Lausanne, Lausanne, Switzerland.ORCID https://orcid.org/0000-0002-3276-9581

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chronic musculoskeletal pain (CMP) syndromes, including fibromyalgia, present diverse physical and psychological symptoms often resistant to pharmacological treatment. To retrospectively evaluate the effectiveness and user experience of Virtual Reality (VR) in reducing pain and anxiety in CMP patients and identify predictors of positive response. Data from 91 CMP patients in a 2-week interdisciplinary pain program were analyzed (78% met fibromyalgia criteria). Pain and anxiety were assessed using Numerical Rating Scales (NRS 0-10) before and after VR. Follow-up interviews were conducted after one month. An unsupervised machine learning model explored response patterns. VR led to a moderate but significant short-term reduction in anxiety and pain (median NRS -1.0, p < 0.001). A reduction of ≥3 NRS points occurred in 25% (anxiety) and 14% (pain). High baseline anxiety (NRS ≥ 7) correlated with greater pain reduction (median -2.0, p = 0.01). After one month, half of the patients reported sustained benefits. Catastrophizing and benzodiazepine use were linked to improved anxiety outcomes. Machine learning identified a most responsive cluster, characterized by patients with nociplastic pain, alexithymia, and anxiety. VR provided moderate short- and mid-term benefits for anxiety and pain in CMP patients, particularly in those with nociplastic pain and high baseline anxiety.

Identifiers

PMID40163491
PMCPMC11957290

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.