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      Handgrip strength and health outcomes: Umbrella review of systematic reviews with meta-analyses of observational studies

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          Highlights

          • We carried out an umbrella review of systematic reviews with meta-analyses of observational studies on handgrip strength and all health outcomes.

          • Three outcomes (lower all-cause mortality, lower cardiovascular mortality, and lower risk of disability) were found to have highly suggestive evidence.

          • One outcome (chair rise performance over time) was found to have suggestive evidence.

          • Five outcomes (walking speed, inability to balance, hospital admissions, cardiac death, and mortality in those with chronic kidney disease) were found to have weak evidence.

          Abstract

          Purpose

          The aim of the present study was to assess both the credibility and strength of evidence arising from systematic reviews with meta-analyses of observational studies on handgrip strength and health outcomes.

          Methods

          An umbrella review of systematic reviews with meta-analyses of observational studies was conducted. We assessed meta-analyses of observational studies based on random-effect summary effect sizes and their p values, 95% prediction intervals, heterogeneity, small-study effects, and excess significance. We graded the evidence from convincing (Class I) to weak (Class IV).

          Results

          From 504 articles returned in a search of the literature, 8 systematic reviews were included in our review, with a total of 11 outcomes. Overall, nine of the 11 of the outcomes reported nominally significant summary results ( p < 0.05), with 4 associations surviving the application of the more stringent p value ( p < 10 −6). No outcome presented convincing evidence. Three associations showed Class II evidence (i.e., highly suggestive): (1) higher handgrip values at baseline were associated with a minor reduction in mortality risk in the general population ( n = 34 studies; sample size = 1,855,817; relative risk = 0.72, 95% confidence interval (95%CI): 0.67–0.78), (2) cardiovascular death risk in mixed populations ( n = 15 studies; relative risk = 0.84, 95%CI: 0.78–0.91), and (3) incidence of disability ( n = 7 studies; relative risk = 0.76, 95%CI: 0.66–0.87).

          Conclusion

          The present results show that handgrip strength is a useful indicator for general health status and specifically for early all-cause and cardiovascular mortality, as well as disability. To further inform intervention strategies, future research is now required to fully understand mechanisms linking handgrip strength scores to these health outcomes.

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          Most cited references47

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          AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both

          The number of published systematic reviews of studies of healthcare interventions has increased rapidly and these are used extensively for clinical and policy decisions. Systematic reviews are subject to a range of biases and increasingly include non-randomised studies of interventions. It is important that users can distinguish high quality reviews. Many instruments have been designed to evaluate different aspects of reviews, but there are few comprehensive critical appraisal instruments. AMSTAR was developed to evaluate systematic reviews of randomised trials. In this paper, we report on the updating of AMSTAR and its adaptation to enable more detailed assessment of systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. With moves to base more decisions on real world observational evidence we believe that AMSTAR 2 will assist decision makers in the identification of high quality systematic reviews, including those based on non-randomised studies of healthcare interventions.
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            Bias in meta-analysis detected by a simple, graphical test.

            Funnel plots (plots of effect estimates against sample size) may be useful to detect bias in meta-analyses that were later contradicted by large trials. We examined whether a simple test of asymmetry of funnel plots predicts discordance of results when meta-analyses are compared to large trials, and we assessed the prevalence of bias in published meta-analyses. Medline search to identify pairs consisting of a meta-analysis and a single large trial (concordance of results was assumed if effects were in the same direction and the meta-analytic estimate was within 30% of the trial); analysis of funnel plots from 37 meta-analyses identified from a hand search of four leading general medicine journals 1993-6 and 38 meta-analyses from the second 1996 issue of the Cochrane Database of Systematic Reviews. Degree of funnel plot asymmetry as measured by the intercept from regression of standard normal deviates against precision. In the eight pairs of meta-analysis and large trial that were identified (five from cardiovascular medicine, one from diabetic medicine, one from geriatric medicine, one from perinatal medicine) there were four concordant and four discordant pairs. In all cases discordance was due to meta-analyses showing larger effects. Funnel plot asymmetry was present in three out of four discordant pairs but in none of concordant pairs. In 14 (38%) journal meta-analyses and 5 (13%) Cochrane reviews, funnel plot asymmetry indicated that there was bias. A simple analysis of funnel plots provides a useful test for the likely presence of bias in meta-analyses, but as the capacity to detect bias will be limited when meta-analyses are based on a limited number of small trials the results from such analyses should be treated with considerable caution.
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              A re-evaluation of random-effects meta-analysis

              Meta-analysis in the presence of unexplained heterogeneity is frequently undertaken by using a random-effects model, in which the effects underlying different studies are assumed to be drawn from a normal distribution. Here we discuss the justification and interpretation of such models, by addressing in turn the aims of estimation, prediction and hypothesis testing. A particular issue that we consider is the distinction between inference on the mean of the random-effects distribution and inference on the whole distribution. We suggest that random-effects meta-analyses as currently conducted often fail to provide the key results, and we investigate the extent to which distribution-free, classical and Bayesian approaches can provide satisfactory methods. We conclude that the Bayesian approach has the advantage of naturally allowing for full uncertainty, especially for prediction. However, it is not without problems, including computational intensity and sensitivity to a priori judgements. We propose a simple prediction interval for classical meta-analysis and offer extensions to standard practice of Bayesian meta-analysis, making use of an example of studies of ‘set shifting’ ability in people with eating disorders.
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                Author and article information

                Contributors
                Journal
                J Sport Health Sci
                J Sport Health Sci
                Journal of Sport and Health Science
                Shanghai University of Sport
                2095-2546
                2213-2961
                19 June 2020
                May 2021
                19 June 2020
                : 10
                : 3
                : 290-295
                Affiliations
                [a ]Department of Geriatric Medicine, Bezmialem Vakif University, Istanbul 34093, Turkey
                [b ]Institute of Neuroscience, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
                [c ]Primary Care Department, Azienda Usl Toscana Sud Est, Grosseto 52100, Italy
                [d ]Division of Psychology and Mental Health, University of Manchester, Manchester M13 9PL, UK
                [e ]College of Health & Human Services, University of North Carolina at Charlotte, Long Beach, CA 90815, USA
                [f ]Department of Cancer Epidemiology and Prevention Research, Alberta Health Services, Holy Cross Centre, AlbertaT5J 3E4, Canada
                [g ]School of Health Sciences, Institute of Mental Health Sciences, Ulster University, Newtownabbey BT15 1ED, UK
                [h ]Research and Development Unit, Sant Joan de Déu Health Park, CIBER of Mental Health (CIBERSAM), Barcelona 08003, Spain
                [i ]ICREA, Pg. Lluis Companys 23, Barcelona 08010, Spain
                [j ]The Queen Elizabeth Hospital King's Lynn NHS Foundation Trust, King's Lynn PE30 4ET, UK
                [k ]Faculty of Sport Sciences, University of Murcia, Murcia 30100, Spain
                [l ]Institute for Evidence in Medicine, Medical Center–University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg 79085, Germany
                [m ]Neuroscience Institute, Aging Branch, National Research Council, Padua 35122, Italy
                [n ]The Cambridge Centre for Sport and Exercise Sciences, Anglia Ruskin University, Cambridge CB1 1PT, UK
                Author notes
                [†]

                These two authors contributed equally to this work.

                Article
                S2095-2546(20)30075-2
                10.1016/j.jshs.2020.06.009
                8167328
                32565244
                083e6d3b-339b-4b10-9556-120723ad9bd0
                © 2020 Published by Elsevier B.V. on behalf of Shanghai University of Sport.

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 17 April 2020
                : 13 May 2020
                : 20 May 2020
                Categories
                Review

                handgrip strength,health outcomes,meta-analysis,umbrella review

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