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      Office workers' objectively assessed total and prolonged sitting time: Individual-level correlates and worksite variations

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          Abstract

          Sedentary behavior is highly prevalent in office-based workplaces; however, few studies have assessed the attributes associated with this health risk factor in the workplace setting. This study aimed to identify the correlates of office workers' objectively-assessed total and prolonged (≥ 30 min bouts) workplace sitting time. Participants were 231 Australian office workers recruited from 14 sites of a single government employer in 2012–13. Potential socio-demographic, work-related, health-related and cognitive-social correlates were measured through a self-administered survey and anthropometric measurements. Associations with total and prolonged workplace sitting time (measured with the activPAL3) were tested using linear mixed models. Worksites varied significantly in total workplace sitting time (overall mean [SD]: 79% [10%] of work hours) and prolonged workplace sitting time (42% [19%]), after adjusting for socio-demographic and work-related characteristics. Organisational tenure of 3–5 years (compared to tenure > 5 years) was associated with more time spent in total and prolonged workplace sitting time, while having a BMI categorised as obese (compared to a healthy BMI) was associated with less time spent in total and prolonged workplace sitting time. Significant variations in sitting time were observed across different worksites of the same employer and the variation remained after adjusting for individual-level factors. Only BMI and organisational tenure were identified as correlates of total and prolonged workplace sitting time. Additional studies are needed to confirm the present findings across diverse organisations and occupations.

          Highlights

          • We examined the correlates of objectively measured workplace sitting time.

          • Tenure and BMI were associated with total and prolonged bouts of sitting at work.

          • There was significant variation in sitting time between different worksites.

          • Worksite variation remained after adjusting for individual-level factors.

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          Most cited references26

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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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            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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              Risk factors for work-related musculoskeletal disorders: A systematic review of recent longitudinal studies.

              This systematic review was designed and conducted in an effort to evaluate the evidence currently available for the many suggested risk factors for work-related musculoskeletal disorders. To identify pertinent literature we searched four electronic databases (Cinahl, Embase, Medline, and The Cochrane Library). The search strategies combined terms for musculoskeletal disorders, work, and risk factors. Only case-control or cohort studies were included. A total of 1,761 non-duplicated articles were identified and screened, and 63 studies were reviewed and integrated in this article. The risk factors identified for the development of work-related musculoskeletal disorders were divided and organized according to the affected body part, type of risk factor (biomechanical, psychosocial, or individual) and level of evidence (strong, reasonable, or insufficient evidence). Risk factors with at least reasonable evidence of a causal relationship for the development of work-related musculoskeletal disorders include: heavy physical work, smoking, high body mass index, high psychosocial work demands, and the presence of co-morbidities. The most commonly reported biomechanical risk factors with at least reasonable evidence for causing WMSD include excessive repetition, awkward postures, and heavy lifting. Additional high methodological quality studies are needed to further understand and provide stronger evidence of the causal relationship between risk factors and work-related musculoskeletal disorders. The information provided in this article may be useful to healthcare providers, researchers, and ergonomists interested on risk identification and design of interventions to reduce the rates of work-related musculoskeletal disorders. 2009 Wiley-Liss, Inc.
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                Author and article information

                Contributors
                Journal
                Prev Med Rep
                Prev Med Rep
                Preventive Medicine Reports
                Elsevier
                2211-3355
                15 June 2016
                December 2016
                15 June 2016
                : 4
                : 184-191
                Affiliations
                [a ]Baker IDI Heart and Diabetes Institute, Melbourne, VIC, Australia
                [b ]School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
                [c ]The University of Queensland, School of Public Health, Brisbane, QLD, Australia
                [d ]School of Physiotherapy and Exercise Science, Curtin University, Perth, Australia
                [e ]Melbourne School of Population & Global Health, The University of Melbourne, Melbourne, VIC, Australia
                [f ]Department of Medicine, Monash University, Melbourne, VIC, Australia
                [g ]Cancer Council Victoria, Cancer Epidemiology Centre, Melbourne, VIC, Australia
                [h ]Centre for Population Health Research, School of Health & Social Development, Deakin University, Geelong, VIC, Australia
                [i ]School of Exercise and Nutrition Sciences, Deakin University, Burwood, VIC, Australia
                [j ]School of Sport Science, Exercise and Health, The University of Western Australia, Perth, WA, Australia
                [k ]Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC, Australia
                Author notes
                [* ]Corresponding author at: 99 Commercial Rd, Melbourne, Victoria 3004, Australia.99 Commercial RdMelbourneVictoria3004Australia nyssa.hadgraft@ 123456bakeridi.edu.au
                Article
                S2211-3355(16)30061-4
                10.1016/j.pmedr.2016.06.011
                4929063
                27413681
                3b5bc0a0-604a-4473-81f3-7f924696666f
                © 2016 The Authors

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

                History
                : 20 January 2016
                : 6 June 2016
                : 12 June 2016
                Categories
                Regular Article

                workplace,sedentary behavior,determinants
                workplace, sedentary behavior, determinants

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