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Daily Sitting Time and All-Cause Mortality: A Meta-Analysis

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      Abstract

      Objective

      To quantify the association between daily total sitting and all-cause mortality risk and to examine dose-response relationships with and without adjustment for moderate-to-vigorous physical activity.

      Methods

      Studies published from 1989 to January 2013 were identified via searches of multiple databases, reference lists of systematic reviews on sitting and health, and from authors’ personal literature databases. We included prospective cohort studies that had total daily sitting time as a quantitative exposure variable, all-cause mortality as the outcome and reported estimates of relative risk, or odds ratios or hazard ratios with 95% confidence intervals. Two authors independently extracted the data and summary estimates of associations were computed using random effects models.

      Results

      Six studies were included, involving data from 595,086 adults and 29,162 deaths over 3,565,569 person-years of follow-up. Study participants were mainly female, middle-aged or older adults from high-income countries; mean study quality score was 12/15 points. Associations between daily total sitting time and all-cause mortality were not linear. With physical activity adjustment, the spline model of best fit had dose-response HRs of 1.00 (95% CI: 0.98-1.03), 1.02 (95% CI: 0.99-1.05) and 1.05 (95% CI: 1.02-1.08) for every 1-hour increase in sitting time in intervals between 0-3, >3-7 and >7 h/day total sitting, respectively. This model estimated a 34% higher mortality risk for adults sitting 10 h/day, after taking physical activity into account. The overall weighted population attributable fraction for all-cause mortality for total daily sitting time was 5.9%, after adjusting for physical activity.

      Conclusions

      Higher amounts of daily total sitting time are associated with greater risk of all-cause mortality and moderate-to-vigorous physical activity appears to attenuate the hazardous association. These findings provide a starting point for identifying a threshold on which to base clinical and public health recommendations for overall sitting time, in addition to physical activity guidelines.

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      Most cited references 31

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      Measuring inconsistency in meta-analyses.

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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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          Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group.

           T Sipe,  D Rennie,  D Stroup (2000)
          Because of the pressure for timely, informed decisions in public health and clinical practice and the explosion of information in the scientific literature, research results must be synthesized. Meta-analyses are increasingly used to address this problem, and they often evaluate observational studies. A workshop was held in Atlanta, Ga, in April 1997, to examine the reporting of meta-analyses of observational studies and to make recommendations to aid authors, reviewers, editors, and readers. Twenty-seven participants were selected by a steering committee, based on expertise in clinical practice, trials, statistics, epidemiology, social sciences, and biomedical editing. Deliberations of the workshop were open to other interested scientists. Funding for this activity was provided by the Centers for Disease Control and Prevention. We conducted a systematic review of the published literature on the conduct and reporting of meta-analyses in observational studies using MEDLINE, Educational Research Information Center (ERIC), PsycLIT, and the Current Index to Statistics. We also examined reference lists of the 32 studies retrieved and contacted experts in the field. Participants were assigned to small-group discussions on the subjects of bias, searching and abstracting, heterogeneity, study categorization, and statistical methods. From the material presented at the workshop, the authors developed a checklist summarizing recommendations for reporting meta-analyses of observational studies. The checklist and supporting evidence were circulated to all conference attendees and additional experts. All suggestions for revisions were addressed. The proposed checklist contains specifications for reporting of meta-analyses of observational studies in epidemiology, including background, search strategy, methods, results, discussion, and conclusion. Use of the checklist should improve the usefulness of meta-analyses for authors, reviewers, editors, readers, and decision makers. An evaluation plan is suggested and research areas are explored.
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            Author and article information

            Affiliations
            [1 ]Prevention Research Collaboration, School of Public Health, University of Sydney, Sydney, Australia
            [2 ]Physical Activity Research Group (UCL-PARG), Division of Population Health, Department of Epidemiology and Public Health, University College London, London, United Kingdom
            [3 ]Centre for Research on Exercise, Physical Activity and Health, School of Human Movement Studies, University of Queensland, Brisbane, Australia
            [4 ]Nutritional Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, United States of America
            [5 ]Department of Public and Occupational Health, VU University Medical Center, EMGO Institute for Health and Care Research, Amsterdam, The Netherlands
            Geisel School of Medicine at Dartmouth College, United States of America
            Author notes

            Competing Interests: The authors have declared that no competing interests exist.

            Conceived and designed the experiments: JYC HPVDP AEB WJB. Analyzed the data: TC. Wrote the manuscript: JYC ACG TC HPVDP ES WJB AEB CEM. Designed search protocol with input from all co-authors: JYC. Performed data extraction and studied quality appraisal: JYC ACG. Made spaghetti plots: ACG. Interpreted data, critically revised manuscript for intellectual content and approved final manuscript: JYC ACG TC HPVDP ES WJB AEB CEM.

            Contributors
            Role: Editor
            Journal
            PLoS One
            PLoS ONE
            plos
            plosone
            PLoS ONE
            Public Library of Science (San Francisco, USA )
            1932-6203
            2013
            13 November 2013
            : 8
            : 11
            3827429 PONE-D-13-19234 10.1371/journal.pone.0080000

            This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

            Funding
            This research is supported by funding from Australian National Health and Medical Research Council Program Grant (No. 569940; to WJB and AEB). ES is funded by the National Institute for Health Research (UK) through a Career Development Fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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            Research Article

            Uncategorized

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