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Background: Mental health disorders are prevalent and costly to workplaces and individuals in Australia. Work-life interference is thought to contribute negatively. The interplay between work-life interference, depressive symptoms and sleep has not been explored to date in population data. The aims of this study were to establish whether sleep duration moderates the relationship between work-life interference and depressive symptoms, and whether this is expressed differentially in male and female respondents. Methods: Data were drawn from the North West Adelaide Health Study (NWAHS) longitudinal, representative population-based cohort study. Working members of the cohort were invited to participate in a telephone survey about their work conditions, with an 86.7% response rate achieved. Data from 823 respondents were analysed after employing purposeful selection of covariates, using multivariable regression analysis. Results: Sleep duration was found to moderate the relationship between work-life interference and depressive symptoms (F7,815 = 26.60, p < 0.001), and accounted for 19% of the variance observed in depressive symptoms. The strongest effect of work-life interference on depressive symptoms was observed in habitual short sleepers, with the effect weakening as sleep duration increased. The relationship was observed in male and female respondents, but was stronger in females. Conclusions: Supporting and educating workers about the benefits of sleep for managing the relationship between work-life interference and depressive symptoms may offer a novel strategy for improving worker well-being, particularly when negative facets of work-life interference are not easily remedied or 'reduced'. There is a need for education and support strategies around sleep in Australian workplaces. [ABSTRACT FROM AUTHOR]

Purpose: To investigate if sociodemographic characteristics increase the adverse effects of cardiovascular diseases (CVD) and cardiometabolic risk factors (CMRF) on health-related quality of life (HRQoL).Methods: Cross-sectional, face-to-face survey investigating 2379 adults living in South Australia in 2015 (57.1 ± 14 years; 51.7% females). Questions included diagnosis of CMRF (obesity, diabetes, hypertension, dyslipidaemia) and CVD. Physical and mental HRQoL were assessed using the SF-12v1 questionnaire. Multiple linear regression models including confounders (sociodemographic, lifestyle, use of preventive medication) and interaction terms between sociodemographic variables and cardiometabolic conditions were used in adjusted analysis.Results: The prevalence of CMRF (one or more) was 54.6% and CVD was 13.0%. The physical HRQoL reduced from 50.8 (95%CI 50.2-51.4) in healthy individuals to 45.1 (95%CI 44.4-45.9) and 39.1 (95%CI 37.7-40.5) among those with CMRF and CVD, respectively. Adjustment for sociodemographic variables reduced these differences in 33%, remaining stable after controlling for lifestyle and use of preventive medications (p < 0.001). Differences in physical HRQoL according to cardiometabolic conditions were twice as high among those with lower educational level, or if they were not working. Among unemployed, having a CMRF or a CVD had the same impact on the physical HRQoL (9.7 lower score than healthy individuals). The inverse association between cardiometabolic conditions and mental HRQoL was subtle (p = 0.030), with no evidence of disparities due to sociodemographic variables.Conclusions: A lower educational level and unemployment increase the adverse effects of cardiometabolic conditions on the physical HRQoL. Targeted interventions for reducing CMRF and/or CVD in these groups are necessary to improve HRQoL. [ABSTRACT FROM AUTHOR]

Purpose: To examine the validity of using the same scoring coefficients across countries for the SF-12.Methods: We test the equality of scoring coefficients derived for a contraction of the SF-36, the Short Form 12 (SF-12), using a large international database drawn from nine countries, to test equality between Australia and twelve other country/language groups. First, we checked that the theoretical structure of the SF-12 as set out by Ware and colleagues, but including a correlation between physical and mental health, provided an adequate fit to the data for each country/language group in a confirmatory factor analysis. We then compared Australia to all of these country/language groups in multiple-group models to assess whether a model producing common factor score coefficients provided an adequate fit to the data. We also derived Chi-squared tests for the differences between the restricted and unrestricted models, to test the equality of the factor score coefficients across countries.Results: We found that the theoretical structure of the SF-12, with a correlation between physical and mental health, provides an adequate fit to the data for all country/language groups except Hungary. Further, all the unrestricted multiple-group models provide an adequate fit to the data. In contrast, none of the multiple-group models restricted to common parameters provide an adequate fit to the data. The significance tests confirm that the constraints on parameter values produce significantly different models to the unrestricted models.Conclusions: We conclude that researchers should derive their own country-specific scoring coefficients for physical and mental health summary scores. [ABSTRACT FROM AUTHOR]

Purpose: To compare the measurement properties of the physical component summary (PCS) and mental component summary (MCS) scores of the SF-36 and SF-12 based on the traditional orthogonal scoring algorithms with the performance of the PCS and MCS scored based on structural equation model coefficients from a correlated model. Methods: This study used three large-scale representative population studies to compare the measurement properties of the PCS and MCS scores of the SF-36 and SF-12 with the performance of the PCS and MCS scores based on structural equation models producing coefficients from a correlated model. We assessed the relationships of these scores with selected important mental health measures and chronic conditions from three representative Australian population studies that address clinical conditions of high prevalence and health service importance. Results: Structural equation model scoring methods produced summary scores with higher correlations than the recommended orthogonal methods across a range of disease and health conditions. The problem experienced in using the orthogonal methods is that negative scoring coefficients are applied to negative z-scores for sub-scales, inflating the resulting summary scores. Effect sizes over a half of a standard deviation were common. Conclusions: If health policy or investment decisions are made based on the results of studies employing the recommended orthogonal scoring methods then the expected outcome of such decisions or investments may not be achieved. [ABSTRACT FROM AUTHOR]