OBJECTIVE: To evaluate the longitudinal relationship between repeated measures of alcohol consumption and risk of developing fatty liver.
PATIENTS AND METHODS: This study includes 5407 men and women from a British population-based cohort, the Whitehall II study of civil servants, who self-reported alcohol consumption by questionnaire over approximately 30 years (1985-1989 through to 2012-2013). Drinking typologies during midlife were linked to measures of fatty liver (the fatty liver index, FLI) when participants were in older age (age range 60-84 years) and adjusted for age, socio-economic position, ethnicity, and smoking.
RESULTS: Those who consistently drank heavily had two-fold higher odds of increased FLI compared to stable low-risk moderate drinkers after adjustment for covariates (men: OR = 2.04, 95%CI = 1.53-2.74; women: OR = 2.24, 95%CI = 1.08-4.55). Former drinkers also had an increased FLI compared to low-risk drinkers (men: OR = 2.09, 95%CI = 1.55-2.85; women: OR = 1.68, 95%CI = 1.08-2.67). There were non-significant differences in FLI between non-drinkers and stable low-risk drinkers. Among women, there was no increased risk for current heavy drinkers in cross sectional analyses.
CONCLUSION: Drinking habits among adults during midlife affect the development of fatty liver, and sustained heavy drinking is associated with an increased FLI compared to stable low-risk drinkers. After the exclusion of former drinkers, there was no difference between non-drinkers and low-risk drinkers, which does not support a protective effect on fatty liver from low-risk drinking. Cross-sectional analyses among women did not find an increased risk of heavy drinking compared to low-risk drinkers, thus highlighting the need to take a longitudinal approach.
We estimated calorie intake from alcohol in Canada, overall and by gender, age, and province, and provide evidence to advocate for mandatory alcohol labelling requirements. Annual per capita (aged 15+) alcohol sales data in litres of pure ethanol by beverage type were taken from Statistics Canada's CANSIM database and converted into calories. The apportionment of consumption by gender, age, and province was based on data from the Canadian Tobacco, Alcohol and Drug Survey. Estimated energy requirements (EER) were from Canada's Food Guide. The average drinker consumed 250 calories, or 11.2% of their daily EER in the form of alcohol, with men (13.3%) consuming a higher proportion of their EER from alcohol than women (8.2%). Drinkers consumed more than one-tenth of their EER from alcohol in all but one province. By beverage type, beer contributes 52.7% of all calories derived from alcohol, while wine (20.8%); spirits (19.8%); and ciders, coolers, and other alcohol (6.7%) also contribute substantially. The substantial caloric impact of alcoholic drinks in the Canadian diet suggests that the addition of caloric labelling on these drinks is a necessary step.
BACKGROUND: Unhealthy alcohol use (UAU) is one of the major causes of preventable morbidity, mortality, and associated behavioral risks worldwide. Although mobile health (mHealth) interventions can provide consumers with an effective means for self-control of UAU in a timely, ubiquitous, and cost-effective manner, to date, there is a lack of understanding about different health outcomes brought by such interventions. The core components of these interventions are also unclear.
OBJECTIVE: This study aimed to systematically review and synthesize the research evidence about the efficacy of mHealth interventions on various health outcomes for consumer self-control of UAU and to identify the core components to achieve these outcomes.
METHODS: We systematically searched 7 electronic interdisciplinary databases: Scopus, PubMed, PubMed Central, CINAHL Plus with full text, MEDLINE with full text, PsycINFO, and PsycARTICLES. Search terms and Medical Subject Headings "mHealth," "text message," "SMS," "App," "IVR," "self-control," "self-regulation," "alcohol*," and "intervention" were used individually or in combination to identify peer-reviewed publications in English from 2008 to 2017. We screened titles and abstracts and assessed full-text papers as per inclusion and exclusion criteria. Data were extracted from the included papers according to the Consolidated Standards of Reporting Trials-EHEALTH checklist (V 1.6.1) by 2 authors independently. Data quality was assessed by the Mixed Methods Appraisal Tool. Data synthesis and analyses were conducted following the procedures for qualitative content analysis. Statistical testing was also conducted to test differences among groups of studies. RESULTS: In total, 19 studies were included in the review. Of these 19 studies, 12 (63%) mHealth interventions brought significant positive outcomes in improving participants' health as measured by behavioral (n=11), physiological (n=1), and cognitive indicators (n=1). No significant health outcome was reported in 6 studies (6/19, 32%). Surprisingly, a significant negative outcome was reported for the male participants in the intervention arm in 1 study (1/19, 5%), but no change was found for the female participants. In total, 5 core components reported in the mHealth interventions for consumer self-control of UAU were context, theoretical base, delivery mode, content, and implementation procedure. However, sound evidence is yet to be generated about the role of each component for mHealth success. The health outcomes were similar regardless of types of UAU, deployment setting, with or without nonmobile cointervention, and with or without theory.
CONCLUSIONS: Most studies reported mHealth interventions for self-control of UAU appeared to be improving behavior, especially the ones delivered by short message service and interactive voice response systems. Further studies are needed to gather sound evidence about the effects of mHealth interventions on improving physiological and cognitive outcomes as well as the optimal design of these interventions, their implementation, and effects in supporting self-control of UAU.
BACKGROUND: Prevention aiming at smoking, alcohol consumption, and BMI could potentially bring large gains in life expectancy (LE) and health expectancy measures such as Healthy Life Years (HLY) and Life Expectancy in Good Perceived Health (LEGPH) in the European Union. However, the potential gains might differ by region.
METHODS: A Sullivan life table model was applied for 27 European countries to calculate the impact of alternative scenarios of lifestyle behavior on life and health expectancy. Results were then pooled over countries to present the potential gains in HLY and LEGPH for four European regions.
RESULTS: Simulations show that up to 4 years of extra health expectancy can be gained by getting all countries to the healthiest levels of lifestyle observed in EU countries. This is more than the 2 years to be gained in life expectancy. Generally, Eastern Europe has the lowest LE, HLY, and LEGPH. Even though the largest gains in LEPGH and HLY can also be made in Eastern Europe, the gap in LE, HLY, and LEGPH can only in a small part be closed by changing smoking, alcohol consumption, and BMI.
CONCLUSION: Based on the current data, up to 4 years of good health could be gained by adopting lifestyle as seen in the best-performing countries. Only a part of the lagging health expectancy of Eastern Europe can potentially be solved by improvements in lifestyle involving smoking and BMI. Before it is definitely concluded that lifestyle policy for alcohol use is of relatively little importance compared to smoking or BMI, as our findings suggest, better data should be gathered in all European countries concerning alcohol use and the odds ratios of overconsumption of alcohol.