METHODS: We systematically reviewed articles published from January 2010 to December 2023, examining breast cancer risk factors in Asian women. Data were described narratively, estimates pooled, and prevalence and attributable proportions compared across Asian populations.
RESULTS: Of the 128 studies reviewed, 103 reported adjusted effect sizes for meta-analysis. Lifestyle and reproductive factors were predictive of breast cancer risk in Asian women, with varying impacts on premenopausal and postmenopausal women. Relative risks were similar within Asian populations and in comparison to European populations, except for menarche, menopause, and hormone receptor therapy. However, risk factor distributions differed across populations. While alcohol intake (21%) and oral contraceptive use (20%) emerged as the most attributable modifiable risk factors in Europeans, passive smoking (24%) and higher BMI (17%, ≥24 kg/m2 among postmenopausal women) were predominant in Asians.
CONCLUSIONS: Our study shows that while the effects of lifestyle and reproductive breast cancer risk factors are largely similar across different populations, their distributions vary.
IMPACT: Our analysis underscores the importance of considering population-specific risk factor distributions when developing risk prediction tools for Asian populations.
METHODS: Bedtime was recorded based on self-reported habitual time of going to bed in 112,198 participants from 21 countries in the Prospective Urban Rural Epidemiology (PURE) study. Participants were prospectively followed for 9.2 years. We examined the association between bedtime and the composite outcome of all-cause mortality, non-fatal myocardial infarction, stroke and heart failure. Participants with a usual bedtime earlier than 10PM were categorized as 'earlier' sleepers and those who reported a bedtime after midnight as 'later' sleepers. Cox frailty models were applied with random intercepts to account for the clustering within centers.
RESULTS: A total of 5633 deaths and 5346 major cardiovascular events were reported. A U-shaped association was observed between bedtime and the composite outcome. Using those going to bed between 10PM and midnight as the reference group, after adjustment for age and sex, both earlier and later sleepers had a higher risk of the composite outcome (HR of 1.29 [1.22, 1.35] and 1.11 [1.03, 1.20], respectively). In the fully adjusted model where demographic factors, lifestyle behaviors (including total sleep duration) and history of diseases were included, results were greatly attenuated, but the estimates indicated modestly higher risks in both earlier (HR of 1.09 [1.03-1.16]) and later sleepers (HR of 1.10 [1.02-1.20]).
CONCLUSION: Early (10 PM or earlier) or late (Midnight or later) bedtimes may be an indicator or risk factor of adverse health outcomes.
METHODS: From July 2020 to August 2021, we surveyed 16 461 adults across 29 countries who self-reported changes in 18 lifestyle factors and 13 health outcomes due to the pandemic. Three networks were generated by network analysis for each country: lifestyle, health outcome, and bridge networks. We identified the variables with the highest bridge expected influence as central or bridge variables. Network validation included nonparametric and case-dropping subset bootstrapping, and centrality difference tests confirmed that the central or bridge variables had significantly higher expected influence than other variables within the same network.
RESULTS: Among 87 networks, 75 were validated with correlation-stability coefficients above 0.25. Nine central lifestyle types were identified in 28 countries: cooking at home (in 11 countries), food types in daily meals (in one country), less smoking tobacco (in two countries), less alcohol consumption (in two countries), less duration of sitting (in three countries), less consumption of snacks (in five countries), less sugary drinks (in five countries), having a meal at home (in two countries), taking alternative medicine or natural health products (in one country). Six central health outcomes were noted among 28 countries: social support received (in three countries), physical health (in one country), sleep quality (in four countries), quality of life (in seven countries), less mental burden (in three countries), less emotional distress (in 13 countries). Three bridge lifestyles were identified in 19 countries: food types in daily meals (in one country), cooking at home (in one country), overall amount of exercise (in 17 countries). The centrality difference test showed the central and bridge variables had significantly higher centrality indices than others in their networks (P
METHODS: We combined data from 21 prospective cohorts across six continents (N = 31,680) and conducted cohort-specific Cox proportional hazard regression analyses in a two-step individual participant data meta-analysis.
RESULTS: A one-standard-deviation increase in LIBRA score was associated with a 21% higher risk for dementia. The association was stronger for Asian cohorts compared to European cohorts, and for individuals aged ≤75 years (vs older), though only within the first 5 years of follow-up. No interactions with sex, education, or socioeconomic position were observed.
DISCUSSION: Modifiable risk and protective factors appear relevant for dementia risk reduction across diverse geographical and sociodemographic groups.
HIGHLIGHTS: A two-step individual participant data meta-analysis was conducted. This was done at a global scale using data from 21 ethno-regionally diverse cohorts. The association between a modifiable dementia risk score and dementia was examined. The association was modified by geographical region and age at baseline. Yet, modifiable dementia risk and protective factors appear relevant in all investigated groups and regions.
METHODS: A parallel, open-label, 2-arm prospective, pilot randomised controlled trial was conducted at a long-term stroke service at a university based primary care clinic. All stroke caregivers aged ≥ 18 years, proficient in English or Malay and smartphone operation were invited. From 147 eligible caregivers, 76 participants were randomised to either SRA™ intervention or conventional care group (CCG) after receiving standard health counselling. The intervention group had additional SRA™ installed on their smartphones, which enabled self-monitoring of modifiable and non-modifiable stroke risk factors. The Stroke Riskometer app (SRATM) and Life's Simple 7 (LS7) questionnaires assessed stroke risk and lifestyle practices. Changes in clinical profile, lifestyle practices and calculated stroke risk were analysed at baseline and 3 months. The trial was registered in the Australia-New Zealand Clinical Trial Registry, ACTRN12618002050235.
RESULTS: The demographic and clinical characteristics of the intervention and control group study participants were comparable. Better improvement in LS7 scores were noted in the SRA™ arm compared to CCG at 3 months: Median difference (95% CI) = 0.88 (1.68-0.08), p = 0.03. However, both groups did not show significant changes in median stroke risk and relative risk scores at 5-, 10-years (Stroke risk 5-years: Median difference (95% CI) = 0.53 (0.15-1.21), p = 0.13, 10-years: Median difference (95% CI) = 0.81 (0.53-2.15), p = 0.23; Relative risk 5-years: Median difference (95% CI) = 0.84 (0.29-1.97), p = 0.14, Relative risk 10-years: Median difference (95% CI) = 0.58 (0.36-1.52), p = 0.23).
CONCLUSION: SRA™ is a useful tool for familial stroke caregivers to make lifestyle changes, although it did not reduce personal or relative stroke risk after 3 months usage.
TRIAL REGISTRATION: No: ACTRN12618002050235 (Registration Date: 21st December 2018).
METHOD: This systematic review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines. A literature search was conducted across three scientific databases (Scopus, PubMed, and Web of Science), targeting original articles published in English between 2014 and 2024. The quality of the eligible articles was assessed using the Joanna Briggs Institute Critical Appraisal tools. The findings were synthesised through content analysis.
RESULTS: A total of 17 studies were included, identifying both the direct and indirect effects of illness perception variables as a whole or in their respective dimensions. The illness perception variable has demonstrated a significant positive and negative relationships with the physical activity domain.
LIMITATION: The majority of the included studies had a cross-sectional design. Therefore, the evidence quality was relatively low and exhibited a high risk of bias. Furthermore, there was language bias as only English-language publications were selected.
CONCLUSION: The findings of this review will serve as a guide for healthcare providers in enhancing physical activity adherence among patients with non-communicable diseases through an illness perception approach. This approach can be integrated into clinic consultations and intervention programmes. Future studies are warranted to evaluate the effectiveness of the illness perception approach in promoting physical activity adherence.