Objectives The abbreviated World Health Organisation Quality of Life tool (WHOQOL-BREF) is a short-form quality of life (QoL) assessment commonly used worldwide in both healthy and ill populations. Normative data for the Australian general population are limited. The objective of this study was to present normative data for the WHOQOL-BREF based on a general population sample. A secondary aim was to explore sociodemographic factors related to QoL. Design Population-based cross-sectional study. Participants 929 men and 830 women aged 24–94 years participating in the Geelong Osteoporosis Study. Outcome measures The 26-item WHOQOL-BREF. Results Means and SD for each domain are presented by age group and sex. Percentile scores were also generated. Mean scores for WHOQOL-BREF domains were 74.52 (SD=16.22) for physical health, 72.07 (SD=15.35) for psychological, 72.87 (SD=18.78) for social relationships and 79.68 (SD=12.55) for environment. We identified significant associations between sociodemographic factors and WHOQOL-BREF domains. Notably, being married or in a relationship was associated with increased odds for high QoL across all four WHOQOL-BREF domains: physical health (women OR 2.46, 95% CI 1.36 to 4.44, p=0.003), psychological (men OR 2.07, 95% CI: 1.20 to 3.55, p=0.009; women OR 2.15, 95% CI 1.21 to 3.81, p=0.009), social relationships (men OR 2.28, 95% CI 1.29 to 4.04, p=0.005; women OR 2.77, 95% CI 1.42 to 5.41, p=0.003) and environment (women OR 2.07, 95% CI 1.13 to 3.80, p=0.019). Conclusions This study provides population norms for the WHOQOL-BREF based on a representative sample of Australian adults. Our results will be useful to researchers and clinicians who can use these data as a reference point for interpreting WHOQOL-BREF scores.

Accumulation of fat in the liver and skeletal muscle is associated with obesity and poor health outcomes. Liver steatosis is a characteristic of non-alcoholic fatty liver disease (NAFLD) and myosteatosis, of poor muscle quality in sarcopenia. In this study of 403 men (33–96 years), we investigated associations between the fatty liver index (FLI) and muscle density, as markers of fat accumulation in these organs. We also investigated associations between the FLI and parameters of sarcopenia, including DXA-derived appendicular lean mass (ALM) and handgrip strength by dynamometry. Muscle density was measured using pQCT at the radius and tibia. FLI was calculated from BMI, waist circumference, and levels of triglycerides and gamma-glutamyltransferase. There was a pattern of decreasing muscle density across increasing quartiles of FLI. After adjusting for age and lifestyle, mean radial muscle density in Q4 was 2.1% lower than Q1 (p < 0.001) and mean tibial muscle density was 1.8% lower in Q3 and 3.0% lower in Q4, compared to Q1 (p = 0.022 and < 0.001, respectively). After adjusting for age and sedentary lifestyle, participants in the highest FLI quartile were sixfold more likely to have sarcopenia. In conclusion, our results suggest that fat accumulation in the liver co-exists with fat infiltration into skeletal muscle.

Background: To evaluate the impact of the Dementia Care in Hospitals Program (DCHP) on clinical and non-clinical staff job satisfaction, level of confidence and comfort in caring for patients with cognitive impairment (CI). Staff perceptions of how organisational support and hospital environment met the needs of patients with CI were also assessed.Methods: The DCHP was implemented across four acute hospital sites across Australia. Clinical and non-clinical staff received training on CI screening and communication strategies for patients with CI. A staff satisfaction survey was administered pre- and post-implementation of the DCHP.Results: One thousand seven hundred forty-eight staff received DCHP education and 1375 staff participated in the survey. Self-reported confidence and level of comfort in caring for patients with CI significantly improved following implementation. Staff also reported increased job satisfaction and organisational support at all hospital sites.Conclusions: The DCHP implementation within an acute hospital setting was found to show an improvement in staff confidence, comfort, and job satisfaction when caring for patients with CI. This study has significant implications for the improvement of care for patients with CI as well as staff retention and job satisfaction. Further research is required to determine whether these improvements are sustained in the longer term.

Machine Learning (ML) models have been used to predict common mental disorders (CMDs) and may provide insights into the key modifiable factors that can identify and predict CMD risk and be targeted through interventions. This systematic review aimed to synthesise evidence from ML studies predicting CMDs, evaluate their performance, and establish the potential benefit of incorporating lifestyle data in ML models alongside biological and/or demographic-environmental factors. This systematic review adheres to the PRISMA statement (Prospero CRD42023401194). Databases searched included MEDLINE, EMBASE, PsycInfo, IEEE Xplore, Engineering Village, Web of Science, and Scopus from database inception to 28/08/24. Included studies used ML methods with feature importance to predict CMDs in adults. Risk of bias (ROB) was assessed using PROBAST. Model performance metrics were compared. The ten most important variables reported by each study were assigned to broader categories to evaluate their frequency across studies. 117 studies were included (111 model development-only, 16 development and validation). Deep learning methods showed best accuracy for predicting CMD cases. Studies commonly incorporated features from multiple categories (n = 56), and frequently identified demographic-environmental predictors in their top ten most important variables (63/69 models). These tended to be in combination with psycho-social and biological variables (n = 15). Lifestyle data were infrequently examined as sole predictors of CMDs across included studies (4.27 %). Studies commonly had high heterogeneity and ROB ratings. This review is the first to evaluate the utility of diagnostic ML for CMDs, assess their ROB, and evaluate predictor types. CMDs were able to be predicted, however studies had high ROB and lifestyle data were underutilised, precluding full identification of a robust predictor set.

Introduction Alzheimer’s disease has a high prevalence and a substantial impact on society, as well as the individual. Findings from clinical studies to date, suggest that multiple factors are likely to contribute to the variability seen in the progression of Alzheimer’s disease. However, despite this accumulating evidence, current identified factors do not explain the full extent of disease onset. Thus, the role of additional factors needs to be explored further. One such factor is exposure to adverse childhood experiences. However, the degree of this association is unknown. This systematic review will examine the literature investigating the associations between adverse childhood experiences and the risk of Alzheimer’s disease. Methods and analysis Articles investigating associations between exposure to adverse childhood experiences and the risk of Alzheimer’s disease will be identified systematically by searching CINAHL, MEDLINE and PsycInfo using Ebscohost. No restrictions on date of publication will be applied. The search strategy will be built combining the main key elements of the Population, Exposure, Comparator, and Outcomes inclusion criteria. A meta-analysis is planned and statistical methods will be used to identify and control for heterogeneity, if possible. The development of this protocol was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols. Ethics and dissemination Only published data will be used for this study, thus, ethical approval will not be required. Findings of the review will be published in a peer-reviewed scientific journal, and presented at national and international conferences. PROSPERO registration number CRD42020191439.