BACKGROUND: We aimed to understand the factors shaping alcohol consumption patterns in middle-aged women (45-64), and to identify participant-driven population- and policy-level strategies that may be used to addresses alcohol consumption and reduce breast cancer risk.
METHODS: Semi-structured interviews (n = 35) were conducted with 'middle-aged' women conversant in English and living in South Australia with no history of breast cancer diagnosis. Data were deductively coded using a co-developed framework including variables relevant to our study objectives. Women were asked about their current level of awareness of the association between alcohol and breast cancer risk, and their personal recommendations for how to decrease consumption in middle-aged Australian women.
RESULTS: Women discussed their previous efforts to decrease consumption, which we drew on to identify preliminary recommendations for consumption reduction. We identified a low level of awareness of alcohol and breast cancer risk, and confusion related to alcohol as a risk for breast cancer, but not always causing breast cancer. Participants suggested that education and awareness, through various means, may help to reduce consumption.
CONCLUSIONS: Participants' description of strategies used to reduce their own consumption lead us to suggest that campaigns might focus on the more salient and immediate effects of alcohol (e.g. on physical appearance and mental health) rather than longer-term consequences. Critical considerations for messaging include addressing the personal, physical and social pleasures that alcohol provides, and how these may differ across socio-demographics.
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.
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.