3
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Efficacy of mirror therapy and virtual reality therapy in alleviating phantom limb pain: a meta-analysis and systematic review

      , , ,
      BMJ Military Health
      BMJ

      Read this article at

      ScienceOpenPublisherPubMed
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Introduction

          Amputations result from trauma, war, conflict, vascular diseases and cancer. Phantom limb pain (PLP) is a potentially debilitating form of chronic pain affecting around 100 million amputees across the world. Mirror therapy and virtual reality (VR) are two commonly used treatments, and we evaluated their respective success rates.

          Methods

          A meta-analysis and systematic review was undertaken to investigate mirror therapy and VR in their ability to reduce pain levels. A mean difference (MD) model to compare group pain levels pretreatment and post-treatment via aggregating these results from numerous similar studies was employed. Meta-analysis was conducted using RevMan (V.5.4) and expressed in MD for visual analogue scale (VAS) score.

          Results

          A total of 15 studies met our search criteria; they consisted of eight mirror therapy with 214 participants and seven VR including 86 participants, totalling 300 participants. Mean age ranged from 36 to 63 years, 77% male, of which 61% were lower body amputees. Both led to a VAS reduction (mirror therapy mean reduction VAS score was 2.54, 95% CI 1.42 to 3.66; p<0.001; VR 2.24, 95% CI 1.28 to 3.20; p<0.001). There was no statistically significant difference in pain alleviation between mirror therapy and VR (p=0.69).

          Conclusions

          Mirror therapy and VR are both equally efficacious in alleviating PLP, but neither is more effective than the other. However, due to small sample size and limited number of studies, factors such as gender, cause of amputation, site of limb loss or length of time from amputation, which may influence treatment success, could not be explored.

          Related collections

          Most cited references29

          • Record: found
          • Abstract: not found
          • Article: not found

          Measuring inconsistency in meta-analyses.

            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials

            Flaws in the design, conduct, analysis, and reporting of randomised trials can cause the effect of an intervention to be underestimated or overestimated. The Cochrane Collaboration’s tool for assessing risk of bias aims to make the process clearer and more accurate
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Quantifying heterogeneity in a meta-analysis.

              The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity. Copyright 2002 John Wiley & Sons, Ltd.
                Bookmark

                Author and article information

                Journal
                BMJ Military Health
                BMJ Mil Health
                BMJ
                2633-3767
                2633-3775
                March 25 2022
                April 2022
                April 2022
                January 18 2022
                : 168
                : 2
                : 173-177
                Article
                10.1136/bmjmilitary-2021-002018
                35042760
                00e9bf21-d99b-4f32-a812-c43c25c556b6
                © 2022
                History

                Comments

                Comment on this article