Sitting time and all-cause mortality risk in 222 497 Australian adults.
ABSTRACT Prolonged sitting is considered detrimental to health, but evidence regarding the independent relationship of total sitting time with all-cause mortality is limited. This study aimed to determine the independent relationship of sitting time with all-cause mortality.
We linked prospective questionnaire data from 222 497 individuals 45 years or older from the 45 and Up Study to mortality data from the New South Wales Registry of Births, Deaths, and Marriages (Australia) from February 1, 2006, through December 31, 2010. Cox proportional hazards models examined all-cause mortality in relation to sitting time, adjusting for potential confounders that included sex, age, education, urban/rural residence, physical activity, body mass index, smoking status, self-rated health, and disability.
During 621 695 person-years of follow-up (mean follow-up, 2.8 years), 5405 deaths were registered. All-cause mortality hazard ratios were 1.02 (95% CI, 0.95-1.09), 1.15 (1.06-1.25), and 1.40 (1.27-1.55) for 4 to less than 8, 8 to less than 11, and 11 or more h/d of sitting, respectively, compared with less than 4 h/d, adjusting for physical activity and other confounders. The population-attributable fraction for sitting was 6.9%. The association between sitting and all-cause mortality appeared consistent across the sexes, age groups, body mass index categories, and physical activity levels and across healthy participants compared with participants with preexisting cardiovascular disease or diabetes mellitus.
Prolonged sitting is a risk factor for all-cause mortality, independent of physical activity. Public health programs should focus on reducing sitting time in addition to increasing physical activity levels.
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ABSTRACT: Objective: To describe the total and domain-specific daily sitting time among a sample of Australian office-based employees. Methods: In April 2010, paper-based surveys were provided to desk-based employees (n=801) in Victoria, Australia. Total daily and domain-specific (work, leisure-time and transport-related) sitting time (minutes/day) were assessed by validated questionnaires. Differences in sitting time were examined across socio-demographic (age, sex, occupational status) and lifestyle characteristics (physical activity levels, body mass index [BMI]) using multiple linear regression analyses. Results: The median (95% confidence interval [CI]) of total daily sitting time was 540 (531- 557) minutes/day. Insufficiently active adults (median=578 minutes/day, [95%CI: 564-602]), younger adults aged 18-29 years (median=561 minutes/day, [95%CI: 540-577]) reported the highest total daily sitting times. Occupational sitting time accounted for almost 60% of total daily sitting time. In multivariate analyses, total daily sitting time was negatively associated with age (unstandardised regression coefficient [B]=-1.58, p<0.001) and overall physical activity (minutes/week) (B=-0.03, p<0.001) and positively associated with BMI (B=1.53, p=0.038). Conclusions: Desk-based employees reported that more than half of their total daily sitting time was accrued in the work setting. Implications: Given the high contribution of occupational sitting to total daily sitting time among desk-based employees, interventions should focus on the work setting.Australian and New Zealand Journal of Public Health 12/2014; · 1.64 Impact Factor
- European Journal of Ageing 09/2013; 11(3):205-212. · 1.27 Impact Factor
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ABSTRACT: Research on the correlates of physical activity (PA) and sedentary behaviour (SB) to date has used independent prediction equations for each behaviour, without considering that they are both part of the same continuum of movement. This assumption of independence might lead to inaccurate estimates because common underlying latent variables may simultaneously influence the propensity to engage in PA and SB. This study tests empirically the interdependent nature of PA and SB by comparing independent equations (current approach in the literature), and joint estimators (a novel but unexplored approach). Using Health Survey for England 2008 data, accelerometry-accessed PA and SB were separately modelled (using ordinary least squared regressions - OLS) and then jointly (using seemingly unrelated regressions -SUR). We tested for diagonality, specification, and goodness of fit. The best fit models were the ones that allowed for interdependence of the two movement-related behaviours (rho = -0.156; p < 0.001). The SUR showed more favourable properties compared to OLS models; producing lower standard errors and more consistent and efficient coefficients The efficiency gain was more pronounced in the SB equation (Chi2 = 92.75; p < 0.001). Evidence from a large national population-wide accelerometry study suggests that accounting for the interdependent nature of PA and SB in prediction equations leads to more efficient modelling estimates. Further research using different samples is, however, required to fully understand the magnitude of efficiency gains accruable from using the joint estimators.BMC Research Notes 12/2014; 7(1):921.