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      Relationships between socioeconomic position and objectively measured sedentary behaviour in older adults in three prospective cohorts

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          Abstract

          Objectives

          To investigate whether sedentary behaviour in older adults is associated with a systematic and comprehensive range of socioeconomic position (SEP) measures across the life course. SEP measures included prospective measures of social class, income, educational qualifications and parental social class and contemporaneous measures of area deprivation.

          Setting

          Glasgow and the surrounding (West of Scotland) combined with Edinburgh and the surrounding area (the Lothians).

          Participants

          Community-dwelling adults aged around 79, 83, and 64 years from, respectively, the Lothian Birth Cohort 1936 (LBC1936) (n=271) and the 1930s (n=119) and 1950s (n=310) cohorts of the West of Scotland Twenty-07 study.

          Primary outcome measure

          Sedentary behaviour was measured objectively using an activPAL activity monitor worn continuously for 7 days and used to calculate percentage of waking time spent sedentary.

          Results

          Among retired participants, for most cohort and SEP combinations, greater social disadvantage was associated with increased sedentary time. For example, in the Twenty-07 1930s cohort, those most deprived on the Carstairs measure spent 6.5% (95% CI 0.3 to 12.7) more of their waking time sedentary than the least deprived. However, for employed people, the relationship between SEP and sedentary behaviour was much weaker. For example, in terms of social class differences, among the retired, the most disadvantaged spent 5.7% more waking time sedentary (95% CI 2.6% to 87%), whereas among the employed, there was effectively no difference (−0.5%; 95% CI −9.0 to 8.0).

          Conclusions

          Diverse SEP measures were associated with increased sedentary behaviour among retired people. There was little evidence for a relationship between SEP measures and sedentary behaviour among employed older adults. Prior to retirement, the constraints of the workplace may be masking effects that are only apparent at weekends.

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          Most cited references27

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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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            Measurement of adults' sedentary time in population-based studies.

            Sedentary time (too much sitting) increasingly is being recognized as a distinct health risk behavior. This paper reviews the reliability and validity of self-reported and device-based sedentary time measures and provides recommendations for their use in population-based studies. The focus is on instruments that have been used in free-living, population-based research in adults. Data from the 2003-2006 National Health and Nutrition Examination Survey are utilized to compare the descriptive epidemiology of sedentary time that arises from the use of different sedentary time measures. A key recommendation from this review is that, wherever possible, population-based monitoring of sedentary time should incorporate both self-reported measures (to capture important domain- and behavior-specific sedentary time information) and device-based measures (to measure both total sedentary time and patterns of sedentary time accumulation). Copyright © 2011 American Journal of Preventive Medicine. All rights reserved.
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              Objective vs. Self-Reported Physical Activity and Sedentary Time: Effects of Measurement Method on Relationships with Risk Biomarkers

              Purpose Imprecise measurement of physical activity variables might attenuate estimates of the beneficial effects of activity on health-related outcomes. We aimed to compare the cardiometabolic risk factor dose-response relationships for physical activity and sedentary behaviour between accelerometer- and questionnaire-based activity measures. Methods Physical activity and sedentary behaviour were assessed in 317 adults by 7-day accelerometry and International Physical Activity Questionnaire (IPAQ). Fasting blood was taken to determine insulin, glucose, triglyceride and total, LDL and HDL cholesterol concentrations and homeostasis model-estimated insulin resistance (HOMAIR). Waist circumference, BMI, body fat percentage and blood pressure were also measured. Results For both accelerometer-derived sedentary time ( 50% lower for the IPAQ-reported compared to the accelerometer-derived measure (p<0.0001 for both interactions). The relationships for moderate-to-vigorous physical activity (MVPA) and risk factors were less strong than those observed for sedentary behaviours, but significant negative relationships were observed for both accelerometer and IPAQ MVPA measures with glucose, and insulin and HOMAIR values (all p<0.05). For accelerometer-derived MVPA only, additional negative relationships were seen with triglyceride, total cholesterol and LDL cholesterol concentrations, BMI, waist circumference and percentage body fat, and a positive relationship was evident with HDL cholesterol (p = 0.0002). Regression coefficients for HOMAIR, insulin and triglyceride were 43–50% lower for the IPAQ-reported compared to the accelerometer-derived MVPA measure (all p≤0.01). Conclusion Using the IPAQ to determine sitting time and MVPA reveals some, but not all, relationships between these activity measures and metabolic and vascular disease risk factors. Using this self-report method to quantify activity can therefore underestimate the strength of some relationships with risk factors.
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                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2017
                15 June 2017
                : 7
                : 6
                : e016436
                Affiliations
                [1 ] departmentMRC/CSO Social and Public Health Sciences Unit , University of Glasgow , Glasgow, UK
                [2 ] departmentDepartment of Psychology Centre for Cognitive Ageing and Cognitive Epidemiology , University of Edinburgh , Edinburgh, UK
                [3 ] MRC Lifecourse Epidemiology Unit, University of Southampton , Southampton, UK
                [4 ] departmentInstitute for Applied Health Research, School of Health and Life Sciences , Glasgow Caledonian University , Glasgow, UK
                [5 ] departmentDepartment of Movement and Sports Sciences , Faculty of Medicine and Health Science, Ghent University , Ghent, Belgium
                Author notes
                [Correspondence to ] Dr. Richard John Shaw; dr.richard.shaw@ 123456gmail.com
                Article
                bmjopen-2017-016436
                10.1136/bmjopen-2017-016436
                5541575
                28619784
                13d9a73d-1eda-4ec6-b203-df897f8a4ee9
                © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

                This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/

                History
                : 17 February 2017
                : 03 May 2017
                : 17 May 2017
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Funded by: FundRef http://dx.doi.org/10.13039/501100000629, Age UK;
                Categories
                Public Health
                Research
                1506
                1724
                Custom metadata
                unlocked

                Medicine
                epidemiology,public health,social medicine,sports medicine
                Medicine
                epidemiology, public health, social medicine, sports medicine

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