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      Comparability of activity monitors used in Asian and Western-country studies for assessing free-living sedentary behaviour

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          Abstract

          This study aims to compare the outputs of the waist-worn Active style Pro HJA-350IT (ASP; used in studies with Asian populations), the waist-worn ActiGragh GT3X+ using the normal filter (GT3X+) and the thigh-worn activPAL3 (AP) in assessing adults’ sedentary behaviour (total sedentary time, number of breaks) under free-living conditions. Fifty healthy workers wore the three monitors simultaneously during their waking hours on two days, including a work day and a non-work day. Valid data were at least 10 hours of wearing time, and the differences between monitors on the sedentary outputs using the AP as criterion measurement were analyzed by ANOVA. The number of participants who had complete valid data for work day and non-work day was 47 and 44, respectively. Total sedentary time and breaks estimated by the AP were respectively 466.5 ± 146.8 min and 64.3 ± 24.9 times on the work day and 497.7 ± 138.3 min and 44.6 ± 15.4 times on the non-work day. In total sedentary time, the ASP estimated 29.7 min (95%CI = 7.9 to 51.5) significantly shorter than the AP on the work day but showed no significant difference against the AP on the non-work day. The GT3X+ estimated 80.1 min (54.6 to 105.6) and 52.3 (26.4 to 78.2) significantly longer than the AP on the work day and the non-work day, respectively. For the number of breaks from sedentary time, on both days, the ASP and the GT3X+ estimated significantly more than the AP: 14.1 to 15.8 times (6.3 to 22.5) for the ASP and 27.7 to 28.8 times (21.8 to 34.8) for the GT3X+. Compared to the AP as the criterion, the ASP can underestimate total sedentary time and the GT3X+ can overestimate it, and more so at the lower levels of sedentary time. For breaks from sedentary time, compared to the AP, both the GT3X+ the ASP can overestimate.

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          Breaks in sedentary time: beneficial associations with metabolic risk.

          Total sedentary (absence of whole-body movement) time is associated with obesity, abnormal glucose metabolism, and the metabolic syndrome. In addition to the effects of total sedentary time, the manner in which it is accumulated may also be important. We examined the association of breaks in objectively measured sedentary time with biological markers of metabolic risk. Participants (n = 168, mean age 53.4 years) for this cross-sectional study were recruited from the 2004-2005 Australian Diabetes, Obesity and Lifestyle study. Sedentary time was measured by an accelerometer (counts/minute(-1) or = 100) was considered a break. Fasting plasma glucose, 2-h plasma glucose, serum triglycerides, HDL cholesterol, weight, height, waist circumference, and resting blood pressure were measured. MatLab was used to derive the breaks variable; SPSS was used for the statistical analysis. Independent of total sedentary time and moderate-to-vigorous intensity activity time, increased breaks in sedentary time were beneficially associated with waist circumference (standardized beta = -0.16, 95% CI -0.31 to -0.02, P = 0.026), BMI (beta = -0.19, -0.35 to -0.02, P = 0.026), triglycerides (beta = -0.18, -0.34 to -0.02, P = 0.029), and 2-h plasma glucose (beta = -0.18, -0.34 to -0.02, P = 0.025). This study provides evidence of the importance of avoiding prolonged uninterrupted periods of sedentary (primarily sitting) time. These findings suggest new public health recommendations regarding breaking up sedentary time that are complementary to those for physical activity.
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            Sedentary Behavior and Health Outcomes: An Overview of Systematic Reviews

            Objective 1) To synthesize the current observational evidence for the association between sedentary behavior and health outcomes using information from systematic reviews. 2) To assess the methodological quality of the systematic reviews found. Methodology/Principal Findings Medline; Excerpta Medica (Embase); PsycINFO; and Web of Science were searched for reviews published up to September 2013. Additional publications were provided by Sedentary Behaviour Research Network members. The methodological quality of the systematic reviews was evaluated using recommended standard criteria from AMSTAR. For each review, improper use of causal language in the description of their main results/conclusion was evaluated. Altogether, 1,044 review titles were identified, 144 were read in their entirety, and 27 were included. Based on the systematic reviews with the best methodological quality, we found in children and adolescents, strong evidence of a relationship between time spent in sedentary behavior and obesity. Moreover, moderate evidence was observed for blood pressure and total cholesterol, self-esteem, social behavior problems, physical fitness and academic achievement. In adults, we found strong evidence of a relationship between sedentary behavior and all-cause mortality, fatal and non-fatal cardiovascular disease, type 2 diabetes and metabolic syndrome. In addition, there is moderate evidence for incidence rates of ovarian, colon and endometrial cancers. Conclusions This overview based on the best available systematics reviews, shows that sedentary behavior may be an important determinant of health, independently of physical activity. However, the relationship is complex because it depends on the type of sedentary behavior and the age group studied. The relationship between sedentary behavior and many health outcomes remains uncertain; thus, further studies are warranted.
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              Validation of wearable monitors for assessing sedentary behavior.

              A primary barrier to elucidating the association between sedentary behavior (SB) and health outcomes is the lack of valid monitors to assess SB in a free-living environment. The purpose of this study was to examine the validity of commercially available monitors to assess SB. Twenty overweight (mean ± SD: body mass index = 33.7 ± 5.7 kg·m(-2)) inactive, office workers age 46.5 ± 10.7 yr were directly observed for two 6-h periods while wearing an activPAL (AP) and an ActiGraph GT3X (AG). During the second observation, participants were instructed to reduce sitting time. We assessed the validity of the commonly used cut point of 100 counts per minute (AG100) and several additional AG cut points for defining SB. We used direct observation (DO) using focal sampling with duration coding to record either sedentary (sitting/lying) or nonsedentary behavior. The accuracy and precision of the monitors and the sensitivity of the monitors to detect reductions in sitting time were assessed using mixed-model repeated-measures analyses. On average, the AP and the AG100 underestimated sitting time by 2.8% and 4.9%, respectively. The correlation between the AP and DO was R2 = 0.94, and the AG100 and DO sedentary minutes was R2 = 0.39. Only the AP was able to detect reductions in sitting time. The AG 150-counts-per-minute threshold demonstrated the lowest bias (1.8%) of the AG cut points. The AP was more precise and more sensitive to reductions in sitting time than the AG, and thus, studies designed to assess SB should consider using the AP. When the AG monitor is used, 150 counts per minute may be the most appropriate cut point to define SB.
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                Author and article information

                Contributors
                Role: Formal analysisRole: InvestigationRole: Writing – original draft
                Role: Investigation
                Role: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: MethodologyRole: Writing – review & editing
                Role: MethodologyRole: Writing – review & editing
                Role: MethodologyRole: Writing – review & editing
                Role: MethodologyRole: Writing – review & editing
                Role: MethodologyRole: Writing – review & editing
                Role: MethodologyRole: Writing – review & editing
                Role: Writing – review & editing
                Role: Funding acquisitionRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: ResourcesRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                18 October 2017
                2017
                : 12
                : 10
                : e0186523
                Affiliations
                [1 ] Graduate School of Sport Sciences, Waseda University, Tokorozawa, Saitama, Japan
                [2 ] Faculty of Sport Sciences, Waseda University, Tokorozawa, Saitama, Japan
                [3 ] Faculty Health and Sport Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
                [4 ] Faculty of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan
                [5 ] Department of Preventive Medicine and Public Health, Tokyo Medical University, Shinjuku-ku, Tokyo, Japan
                [6 ] Department of Nutritional Sciences, National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health and Nutrition, Shinjuku-ku, Tokyo, Japan
                [7 ] Institute for Health & Ageing, Australian Catholic University, Melbourne, Victoria, Australia
                [8 ] Behavioural Epidemiology Laboratory, Baker IDI Heart and Diabetes Institute, Melbourne, Victria, Australia
                [9 ] Swinburne University of Technology, Melbourne, Victoria, Australia
                Centre National de la Recherche Scientifique, FRANCE
                Author notes

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

                ‡ These authors also contributed equally to this work.

                Author information
                http://orcid.org/0000-0001-5467-3268
                Article
                PONE-D-17-08781
                10.1371/journal.pone.0186523
                5646850
                29045441
                73023556-2b0d-44c1-9057-3febe1287c28
                © 2017 Kurita et al

                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.

                History
                : 5 March 2017
                : 3 October 2017
                Page count
                Figures: 2, Tables: 3, Pages: 14
                Funding
                This work was supported Grants-in-Aid for Scientific Research from the Japan Society for the Promotion of Science (No. 26242070) ( https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-26242070/) to KO, a grant from industry to support private universities building up their foundations of strategic researchfrom Ministry of education(No. S1511017): ( http://www.mext.go.jp/en/) to KO, the Australian Academy of Sciences: ( https://www.science.org.au/) to NO, the Japan Society for the Promotion of Science: ( https://www.jsps.go.jp/english/index.html) to NO, NHMRC Centre of Research Excellence Grant (#1057608): ( https://www.nhmrc.gov.au/grants-funding/apply-funding/centres-research-excellence-cre) to NO, NHMRC Senior Principal Research Fellowship (#1003960): ( https://www.nhmrc.gov.au/grants-funding/apply-funding/research-fellowships) to NO and the Victorian Government’s Operational Infrastructure Support Program: ( https://www2.health.vic.gov.au/about/clinical-trials-and-research/operational-infrastructure-support) to NO. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Engineering and Technology
                Electronics
                Accelerometers
                Medicine and Health Sciences
                Public and Occupational Health
                Physical Activity
                Research and Analysis Methods
                Research Assessment
                Research Monitoring
                Computer and Information Sciences
                Data Management
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Analysis of Variance
                Physical Sciences
                Mathematics
                Statistics (Mathematics)
                Statistical Methods
                Analysis of Variance
                Physical Sciences
                Physics
                Classical Mechanics
                Motion
                Medicine and Health Sciences
                Public and Occupational Health
                Engineering and Technology
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