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      Associations of mutually exclusive categories of physical activity and sedentary time with markers of cardiometabolic health in English adults: a cross-sectional analysis of the Health Survey for England

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          Abstract

          Background

          Both physical activity and sedentary behaviour have been individually associated with health, however, the extent to which the combination of these behaviours influence health is less well-known. The aim of this study was to examine the associations of four mutually exclusive categories of objectively measured physical activity and sedentary time on markers of cardiometabolic health in a nationally representative sample of English adults.

          Methods

          Using the 2008 Health Survey for England dataset, 2131 participants aged ≥18 years, who provided valid accelerometry data, were included for analysis and grouped into one of four behavioural categories: (1) ‘Busy Bees’: physically active & low sedentary, (2) ‘Sedentary Exercisers’: physically active & high sedentary, (3) ‘Light Movers’: physically inactive & low sedentary, and (4) ‘Couch Potatoes’: physically inactive & high sedentary. ‘Physically active’ was defined as accumulating at least 150 min of moderate-to-vigorous physical activity (MVPA) per week. ‘Low sedentary’ was defined as residing in the lowest quartile of the ratio between the average sedentary time and the average light-intensity physical activity time. Weighted multiple linear regression models, adjusting for measured confounders, investigated the differences in markers of health across the derived behavioural categories. The associations between continuous measures of physical activity and sedentary levels with markers of health were also explored, as well as a number of sensitivity analyses.

          Results

          In comparison to ‘Couch Potatoes’, ‘Busy Bees’ [body mass index: −1.67 kg/m 2 ( p < 0.001); waist circumference: −1.17 cm ( p = 0.007); glycated haemoglobin: −0.12 % ( p = 0.003); HDL-cholesterol: 0.09 mmol/L ( p = 0.001)], ‘Sedentary Exercisers’ [body mass index: −1.64 kg/m 2 ( p < 0.001); glycated haemoglobin: −0.11 % ( p = 0.009); HDL-cholesterol: 0.07 mmol/L ( p < 0.001)] and ‘Light Movers’ [HDL-cholesterol: 0.11 mmol/L ( p = 0.004)] had more favourable health markers. The continuous analyses showed consistency with the categorical analyses and the sensitivity analyses indicated robustness and stability.

          Conclusions

          In this national sample of English adults, being physically active was associated with a better health profile, even in those with concomitant high sedentary time. Low sedentary time independent of physical activity had a positive association with HDL-cholesterol.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12889-016-2694-9) contains supplementary material, which is available to authorized users.

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          Most cited references20

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          Calibration of the Computer Science and Applications, Inc. accelerometer.

          We established accelerometer count ranges for the Computer Science and Applications, Inc. (CSA) activity monitor corresponding to commonly employed MET categories. Data were obtained from 50 adults (25 males, 25 females) during treadmill exercise at three different speeds (4.8, 6.4, and 9.7 km x h(-1)). Activity counts and steady-state oxygen consumption were highly correlated (r = 0.88), and count ranges corresponding to light, moderate, hard, and very hard intensity levels were or = 9499 cnts x min(-1), respectively. A model to predict energy expenditure from activity counts and body mass was developed using data from a random sample of 35 subjects (r2 = 0.82, SEE = 1.40 kcal x min(-1)). Cross validation with data from the remaining 15 subjects revealed no significant differences between actual and predicted energy expenditure at any treadmill speed (SEE = 0.50-1.40 kcal x min(-1)). These data provide a template on which patterns of activity can be classified into intensity levels using the CSA accelerometer.
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            Sedentary behaviors and subsequent health outcomes in adults a systematic review of longitudinal studies, 1996-2011.

            To systematically review and provide an informative synthesis of findings from longitudinal studies published since 1996 reporting on relationships between self-reported sedentary behavior and device-based measures of sedentary time with health-related outcomes in adults. Studies published between 1996 and January 2011 were identified by examining existing literature reviews and by systematic searches in Web of Science, MEDLINE, PubMed, and PsycINFO. English-written articles were selected according to study design, targeted behavior, and health outcome. Forty-eight articles met the inclusion criteria; of these, 46 incorporated self-reported measures including total sitting time; TV viewing time only; TV viewing time and other screen-time behaviors; and TV viewing time plus other sedentary behaviors. Findings indicate a consistent relationship of self-reported sedentary behavior with mortality and with weight gain from childhood to the adult years. However, findings were mixed for associations with disease incidence, weight gain during adulthood, and cardiometabolic risk. Of the three studies that used device-based measures of sedentary time, one showed that markers of obesity predicted sedentary time, whereas inconclusive findings have been observed for markers of insulin resistance. There is a growing body of evidence that sedentary behavior may be a distinct risk factor, independent of physical activity, for multiple adverse health outcomes in adults. Prospective studies using device-based measures are required to provide a clearer understanding of the impact of sedentary time on health outcomes. Copyright © 2011 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.
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              Objectively measured sedentary time, physical activity, and metabolic risk: the Australian Diabetes, Obesity and Lifestyle Study (AusDiab).

              We examined the associations of objectively measured sedentary time and physical activity with continuous indexes of metabolic risk in Australian adults without known diabetes. An accelerometer was used to derive the percentage of monitoring time spent sedentary and in light-intensity and moderate-to-vigorous-intensity activity, as well as mean activity intensity, in 169 Australian Diabetes, Obesity and Lifestyle Study (AusDiab) participants (mean age 53.4 years). Associations with waist circumference, triglycerides, HDL cholesterol, resting blood pressure, fasting plasma glucose, and a clustered metabolic risk score were examined. Independent of time spent in moderate-to-vigorous-intensity activity, there were significant associations of sedentary time, light-intensity time, and mean activity intensity with waist circumference and clustered metabolic risk. Independent of waist circumference, moderate-to-vigorous-intensity activity time was significantly beneficially associated with triglycerides. These findings highlight the importance of decreasing sedentary time, as well as increasing time spent in physical activity, for metabolic health.
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                Author and article information

                Contributors
                kb318@le.ac.uk
                +44(0)116 258 8577 , ce95@le.ac.uk
                dhm6@le.ac.uk
                D.Esliger@lboro.ac.uk
                Jason.Gill@glasgow.ac.uk
                Aadil.Kazi@uhl-tr.nhs.uk
                lv24@le.ac.uk
                sinclair.5@btinternet.com
                n.sattar@clinmed.gla.ac.uk
                Stuart.Biddle@vu.edu.au
                kk22@le.ac.uk
                melanie.davies@uhl-tr.nhs.uk
                ty20@le.ac.uk
                Journal
                BMC Public Health
                BMC Public Health
                BMC Public Health
                BioMed Central (London )
                1471-2458
                12 January 2016
                12 January 2016
                2015
                : 16
                : 25
                Affiliations
                [ ]Department of Health Sciences, University of Leicester, Leicester Diabetes Centre, Leicester General Hospital, Gwendolen Road, Leicester, LE5 4PW United Kingdom
                [ ]Diabetes Research Centre, University of Leicester, Leicester Diabetes Centre, Leicester General Hospital, Gwendolen Road, Leicester, LE5 4PW UK
                [ ]National Institute for Health Research (NIHR) Leicester-Loughborough Diet, Lifestyle and Physical Activity Biomedical Research Unit (BRU), Leicester Diabetes Centre, Leicester General Hospital, Gwendolen Road, Leicester, LE5 4PW UK
                [ ]School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, LE11 3TU UK
                [ ]British Heart Foundation Glasgow Cardiovascular Research Centre (BHF GCRC), Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, G12 8TA UK
                [ ]Psychiatry for the Elderly, Department of Health Sciences, University of Leicester, Leicester, LE1 7RH UK
                [ ]Diabetes Frail Ltd, University of Aston, Birmingham, B4 7ET UK
                [ ]Institute of Sport, Exercise & Active Living, Victoria University, Melbourne, Australia
                [ ]National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care – East Midlands (CLAHRC – EM) Leicester Diabetes Centre, Leicester General Hospital, Gwendolen Road, Leicester, LE5 4PW UK
                Article
                2694
                10.1186/s12889-016-2694-9
                4709945
                26753523
                aa757f16-d5d3-44ca-93b5-f95c8e7a5243
                © Bakrania et al. 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 20 October 2015
                : 6 January 2016
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000265, Medical Research Council (GB);
                Award ID: MR/K025090/1
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2016

                Public health
                accelerometry,objective,physical activity,sedentary,cardiometabolic health
                Public health
                accelerometry, objective, physical activity, sedentary, cardiometabolic health

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