Objectives To synthesise existing evidence on perinatal health interventions implemented in Australia to improve perinatal outcomes. Study design Scoping review. Methods The scoping review followed Arksey and O'Malley's methodological framework, refined by the Joanna Briggs Institute (JBI), and was reported in accordance with the PRISMA extension for scoping reviews checklist. A systematic search was undertaken in PubMed, Embase, Scopus, Web of Science, and CINAHL for literature published from 01 January 2000 to 30 September 2025, supplemented by grey literature. Structured programs, models of care, and policy initiatives aimed at improving perinatal health outcomes in Australia were included and synthesised narratively. Results Of the 3649 records identified from five databases and grey literature, 59 studies were included. Observational cohort designs were the most common (18; 30.5%). Most studies (31; 52.5%) were conducted in New South Wales, Victoria, and Queensland, and a few (8; 13.6%) in remote settings. Midwifery continuity-of-care models were the most frequent interventions (16; 27.1%), followed by clinical and biomedical risk-targeted interventions (15; 25.4%). Preterm birth (12; 20.3%) was the most reported outcome. In 52.5% of studies, authors reported improvements in maternal and neonatal outcomes. Only (23; 39%) studies were specifically targeted at Aboriginal and Torres Strait Islander women. Implementation and sustainability plans were reported in (30: 50.8%) studies. Conclusions Continuity models of care and risk-targeted interventions were associated with improved perinatal outcomes. However, most evidence comes from metropolitan settings; evidence from remote areas, where the burden is greatest, remains uncertain. Equitable progress will require culturally appropriate and community-led models of care in regions of greatest need.
Objectives Regular exercise improves cardiometabolic health; however, the magnitude of its benefits may vary by time of day. To evaluate whether the timing of supervised exercise training is associated with differences in cardiometabolic outcomes in adults. Study design Systematic review and meta-analysis of randomized controlled trials. Methods PubMed, EMBASE, Cochrane Library, Web of Science, and Scopus were searches from inception to March 2026. Randomized controlled trials (RCTs) of ≥2 weeks comparing supervised exercise performed at different times of day in adults and reporting cardiometabolic outcomes were eligible. Mean difference (MD) with 95% confidence intervals (CIs) were pooled using random-effects models with restricted maximum likelihood estimation. Knapp–Hartung and leave-one-out sensitivity analyses, exploratory subgroup analyses, and GRADE assessment were performed. Results Twelve RCTs (n = 879) were included. Later-day exercise was associated with a small reduction in fasting blood glucose compared with earlier exercise (MD = 0.16 mmol/L, 95% CI 0.03 to 0.29; I2 = 0.0%). This finding remained statistically significant with Knapp–Hartung adjustment. No clear timing-related differences were observed for HbA1c, fasting insulin, HOMA-IR, blood lipids, systolic or diastolic blood pressure, BMI, or fat mass. Systolic blood pressure showed substantial heterogeneity (I2 = 72.5%). Certainty of evidence ranged from very low to moderate across outcomes. Conclusions Later-day exercise was associated with a small reduction in fasting blood glucose, but current evidence does not support a consistent advantage of a specific exercise time for other cardiometabolic outcomes. Larger, well-standardized RCTs are needed before specific exercise-timing recommendations can be made.
Objectives This scoping review examined how artificial intelligence is applied in multidisciplinary and cross-sector public health practice, focusing on public health roles, collaborative implementation, and cross-sector governance and equity risks. Study design Scoping review. Methods Following Arksey and O'Malley's framework and PRISMA-ScR reporting guidance, six databases were searched. Sources were included if they examined artificial intelligence or artificial intelligence-enabled decision support in public, population, or community health contexts and provided information on public health functions, collaboration, implementation, equity, or governance. Data were charted and synthesised narratively by study characteristics, artificial intelligence roles, collaborators, implementation conditions, and governance risks. Results 27 sources published between 2021 and 2026 were included. Most were reviews, evidence syntheses, policy or guidance documents, or conceptual papers, with relatively few empirical or practitioner-oriented studies. Artificial intelligence was mainly used for surveillance and early warning, population risk modelling, health communication, misinformation detection, policy intelligence, participatory co-creation, and routine public health operations. Implementation depended on collaboration among public health practitioners, epidemiologists, data scientists, policymakers, legal and ethics experts, communication specialists, community organisations, and affected populations. Cross-sector governance and equity risks included poor data quality, limited interoperability, workforce gaps, weak real-world evaluation, privacy risks, and unclear accountability. Conclusions Artificial intelligence applications in public health are expanding, but the evidence base remains heterogeneous and empirically limited. Routine operational support and AI readiness, together with surveillance and early warning, were among the more frequently represented roles, while empirical evidence on real-world implementation, community engagement and governance evaluation remained limited. Future research should move beyond model performance to examine workflow fit, equity impact, public trust, accountability and population-level outcomes.
