• Energy Research

<b><i>Introduction:</i></b> Evidence suggests an association between perceived alcohol-related norms and personal consumption. These perceptions develop over years of observation and exposure to alcohol, likely beginning in early childhood, and likely differing by sex. Understanding the early development of perceptions of drinking may provide insight into the development of gendered drinking practices. The aim of this study was to explore boys’ and girls’ perceptions about men and women’s alcohol consumption and whether and how these change over time as children age. <b><i>Methods:</i></b> 329 children (aged 4–6 years at baseline) completed the Dutch electronic Appropriate Beverage Task annually for three consecutive years (2015 [baseline], 2016, 2017). Regression models were used to examine whether perceptions of consumption varied as a function of the gender of the adult, the participants’ sex, and any changes over time. <b><i>Results:</i></b> In illustrated pictures, children perceived that men (39%) drank alcoholic beverages more often than women (24%). Men were perceived to drink alcohol more frequently than women at baseline and this difference increased with age. Girls were more likely to perceive men drinking at baseline (aged 4–6), but there were few sex differences by time point three (aged 6–8). <b><i>Discussion/Conclusion:</i></b> From a young age, children perceive that men drink more than women. These perceptions strengthen as children grow older, with young girls perceiving these gender differences at earlier ages than boys. Understanding children’s perceptions of gendered drinking norms and their development over time can enable targeted prevention efforts.

BackgroundResearch on alcohol consumption mostly relies on self‐reported data, which are subject to recall bias. Wearable transdermal alcohol concentration (TAC) monitors address this limitation by continuously measuring the ethanol excreted via the skin. This systematic review aims to provide an overview of TAC monitors’ reliability to detect alcohol consumption and methods to estimate breath alcohol concentration (BrAC) and number of standard drinks consumed in a given time frame.MethodsThe databases MEDLINE, PsycINFO, SCOPUS, Engineering Village, and CINAHL were systematically searched to identify 1,048 empirical research papers published from 2013 onwards, of which 13 were included after full‐text screening. The selected studies included 3 TAC monitors: SCRAM™, WristTAS™, and Skyn™.ResultsTAC measures of SCRAM, WrisTAS, and Skyn are found to be positively correlated with BrAC (r = 0.56 to 0.79) and/or self‐reports (r = 0.62). Using the AMS criteria for detection results in low sensitivity, adjusted criteria can increase the sensitivity of the SCRAM from 39.9 to 68.5%. The WrisTAS and an early prototype of the Skyn showed high failure rates (17 to 38%). Recent advances toward transforming the TAC data into more clinically relevant measures have led to the development of mathematical models and the BrAC Estimator Software. Using TAC data, both approaches produce estimates explaining 70 to 82% of actual BrAC and self‐reported drinking or to highly correlate with the actual BrAC measures (β = 0.90 to 0.91).ConclusionsTransdermal alcohol monitors offer an opportunity to measure alcohol consumption in a valid and continuous way with mathematical models and software estimating BrAC values improving interpretation of TAC data. However, the SCRAM seems unable to detect low‐to‐moderate drinking levels using the thresholds and criteria set by the manufacturer. Moreover, the WrisTAS and the Skyn prototype show a high failure rate, raising questions about reliability. Future studies will assess the validity of new‐generation wristbands, including the next Skyn generations.

Limited research is available on children's alcohol-related knowledge and alcohol-related norms, yet a better comprehension of these factors may be crucial in explaining alcohol use later in life. This study provides insights into alcohol-related knowledge and alcohol-related norms in 4- to 6-year-olds.Participating children (N = 329; 48.9% boys) were shown, on a tablet, 18 drawings depicting 72 male and female adults and/or children in various situations, and were asked to indicate what the depicted persons drank by touching 1 of 12 depicted beverages (4 alcoholic; 8 nonalcoholic). Subsequently, the children were asked to name the beverages and indicate whether they contained alcohol.Children identified 30.7% of the alcoholic beverages (i.e., beer, champagne, red wine, and white wine) correctly by name, and they identified 41.6% of the alcoholic beverages correctly as alcohol containing. Children more often correctly identified the name and nonalcoholic content of nonalcoholic beverages compared to the name and alcoholic content of alcoholic beverages. No sex differences emerged in the correct identification of the name and the content of both alcoholic beverages and nonalcoholic beverages. However, alcohol-related knowledge was age graded. Alcoholic beverages were more often assigned to male adults (39.2%) than to female adults (24.8%) or to children (13.2%). Additionally, alcoholic beverages were more often assigned to adults depicted in the presumably more appropriate situations (e.g., "when having an indoor party": 37.0%) than to those depicted in the presumably more inappropriate situations (e.g., "when driving a car": 28.6%).Four- to 6-year-olds already have knowledge about alcohol and its norms in adult culture. Insight into the development of children's alcohol-related knowledge and alcohol-related norms over time is required to investigate the transitions to alcohol expectancies, drinking motives, and alcohol initiation often occurring in adolescence.

AbstractIntroduction and AimsDrinks consumed in real life are diverse, in terms of beverage type, container size and alcohol by volume. To date, most ecological momentary assessment studies have assessed drinking amounts with ‘standard’ drinks, although their event‐level design allows for more advanced assessment schemes. The purpose of this empirical study is to compare participants' estimates of alcoholic drink characteristics, assessed using drink‐specific questions, with estimates generated by annotators based on pictures of the same drinks.Design and MethodsOn weekend nights, 186 young adults took 1484 close‐up pictures of their drinks using a custom‐built smartphone application. Participants reported the beverage type, drink size and alcohol by volume. Annotators described the beverage type, container size and filling level. Correspondence between participants’ and annotators' estimates was explored using descriptive statistics, difference tests and correlations.ResultsAnnotators were unable to precisely identify the beverage types in most pictures of liqueurs, spirits and mixed drinks. Participants' drink size estimates converged with annotators' estimates of the container size for beer (41 cl corresponding to 16 g of pure alcohol) and mixed drinks (28 cl/35 g), and of the content size for wine (10 cl/9 g). However, annotators estimated larger sizes for liqueur/fortified wine (12 cl/14 g vs. 7 cl/9 g) and spirits (8 cl/26 g vs. 4 cl/10 g) than participants.Discussion and ConclusionsAnnotations of pictures should be considered as a complement to participants' reports rather than a substitute. Except for wine, real‐life drinks vary largely and often exceed 10 g ‘standard’ drinks.

No description supplied 

AbstractBackgroundSeeing alcohol in media has been demonstrated to increase alcohol craving, impulsive decision‐making, and hazardous drinking. Due to the exponential growth of (social) media use it is important to develop algorithms to quantify alcohol exposure efficiently in electronic images. In this article, we describe the development of an improved version of the Alcoholic Beverage Identification Deep Learning Algorithm (ABIDLA), called ABIDLA2.MethodsABIDLA2 was trained on 191,286 images downloaded from Google Image Search results (based on search terms) and Bing Image Search results. In Task‐1, ABIDLA2 identified images as containing one of eight beverage categories (beer/cider cup, beer/cider bottle, beer/cider can, wine, champagne, cocktails, whiskey/cognac/brandy, other images). In Task‐2, ABIDLA2 made a binary classification between images containing an “alcoholic beverage” or “other”. An ablation study was performed to determine which techniques improved algorithm performance.ResultsABIDLA2 was most accurate in identifying Whiskey/Cognac/Brandy (88.1%) followed by Beer/Cider Can (80.5%), Beer/Cider Bottle (78.3%), and Wine (77.8%). Its overall accuracy was 77.0% (Task‐1) and 87.7% (Task‐2). Even the identification of the least accurate beverage category (Champagne, 64.5%) was more than five times higher than random chance (12.5% = 1/8 categories). The implementation of balanced data sampler to address class skewness and the use of self‐training to make use of a large, secondary, weakly labeled dataset particularly improved overall algorithm performance.ConclusionWith extended capabilities and a higher accuracy, ABIDLA2 outperforms its predecessor and enables the screening of any kind of electronic media rapidly to estimate the quantity of alcohol exposure. Quantifying alcohol exposure automatically through algorithms like ABIDLA2 is important because viewing images of alcoholic beverages in media tends to increase alcohol consumption and related harms.

AbstractIntroductionPrevious studies validating the transdermal alcohol concentration (TAC) as measured by the Secure Continuous Remote Alcohol Monitors Continuous Alcohol Monitoring (SCRAM‐CAM) have tested the monitor against self‐reports or breath alcohol concentration (BrAC). This study aims to provide further evidence of the reliability of the SCRAM‐CAM testing two monitors in parallel.MethodsParticipants (N = 21) received four standard drinks in a laboratory session while wearing SCRAM‐CAMs simultaneously on their left and right ankles. The SCRAM‐CAMs sampled TAC every 30 min and participants were monitored for at least 2–3 h after their BrAC levels reached zero. Weight and height measures were taken to calculate body mass index (BMI).ResultsThere was a positive correlation between the TAC measurements from the left and right SCRAM‐CAM (r = 0.718), a cross‐correlation model revealed that this correlation was not significantly different for sex or BMI. Area under the TAC curve (AUC) and peak TAC values as measured by the left and right SCRAM‐CAM also show positive correlations (r = 0.554 and r = 0.579, respectively). Cross‐correlation models show a significant effect of BMI on the relationship between left and right peak TAC values, which may be due to outlier effects. No further effects were significant for on both peak and AUC values.Discussion and ConclusionsResults show that TAC measured by SCRAM‐CAMs worn on the left and right showed a good correlation, with correlations between AUC and peak TAC values considered to be fair. TAC monitors show promise for use in research settings; however, work is needed testing the reliability of TAC as measured by two TAC monitors.