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We assessed the feasibility and acceptability of using BACtrack Skyn wearable alcohol monitors for alcohol research in a college student population. We enrolled n = 5 (Sample 1) and n = 84 (Sample 2) Indiana University undergraduate students to wear BACtrack Skyn devices continuously over a 5-day to 7-day study period. We assessed feasibility in both samples by calculating compliance with study procedures, and by analyzing amount and distributions of device output [e.g., transdermal alcohol content (TAC), temperature, motion]. In Sample 1, we assessed feasibility and acceptability with the Feasibility of Intervention Measure (FIM) scale and the Acceptability of Intervention Measure (AIM) scale. All participants were able to successfully use the alcohol monitors, producing a total of 11,504 h of TAC data. TAC data were produced on 567 days of the 602 total possible days of data collection. The distribution of the TAC data showed between-person variation, as would be expected with between-person differences in drinking patterns. Temperature and motion data were also produced as expected. Sample 1 participants (n = 5) reported high feasibility and acceptability of the wearable alcohol monitors in survey responses with a mean FIM score of 4.3 (of 5.0 possible score) and mean AIM score of 4.3 (of 5.0 possible score). The high feasibility and acceptability we observed underscore the promise of using BACtrack Skyn wearable alcohol monitors to improve our understanding of alcohol consumption among college students, a population at particularly high risk for alcohol-related harms. •BACtrack Skyn wearable alcohol devices were feasible and acceptable for use among college students.•Data were collected over 5–7 days in naturalistic drinking environments.•High feasibility and acceptability were observed in survey responses and operational measures.

There is no intervention that quickly reduces a person's blood alcohol content (BAC). “Sober up before you drive” may prevent road traffic injury (RTI) caused by alcohol consumption. The aim of this study was to analyze the risk of RTI on an hourly basis (1–6 h) after the last drink and the determinants of this risk. This was a case-crossover study of alcohol consumption among 430 patients who suffered an RTI and arrived at an emergency department in a large hospital in Mexico City from January to April 2022. Of the 430 patients studied, 46 reported drinking within 1 h before the RTI, for a risk ratio (RR) of 7.7 (95% CI [5.6, 10.4]). This RR decreased to 2.2 (95% CI [1.3, 3.7]) for the 12 patients drinking in the second hour before the RTI and was null for those drinking earlier (p ≤ 0.001 for the trend). The induction time was 3 h among those with higher BAC and intoxication levels. The RR for an RTI decreases 71% from the first to second hour from the last drink and 32% from the second to third hour. This decrease was similar among those intoxicated and those with increased BACs. In multiple regression models, higher levels of intoxication and higher BACs remained associated with higher RRs. People should avoid driving after drinking. Waiting to drive, walking, or riding after drinking reduces the risk of an RTI, especially for people with higher BAC and intoxication levels. •The risk of TRI was higher 1 h after the last drink and decreased thereafter.•Risk was similar for drivers, pedestrians, and passengers.•The induction time was 1–2 h, extended for those with high BAC or intoxication.•Elevated risk depended on BAC at hospital entry and feeling intoxicated at injury.

Problematic alcohol use is prevalent in Russia and is deleterious for individuals with HIV and Hepatitis C Virus (HCV). Ethyl glucuronide (EtG) and blood alcohol content (BAC) provide objective biomarkers of drinking that can be compared to self-reported alcohol use. This paper describes patterns of alcohol use measured by biomarkers and self-report along with concordance across measures. Participants were Russian women with HIV and HCV co-infection (N = 200; Mean age = 34.9) from two Saint Petersburg comprehensive HIV care centers enrolled in an alcohol reduction intervention clinical trial. Measures were: (a) urine specimen analyzed for EtG; (b) breathalyzer reading of BAC; and (c) self-reported frequency of drinking, typical number of drinks consumed, and number of standard drinks consumed in the past month. At baseline, 64.0% (n = 128) had a positive EtG (> 500 ng/mL) and 76.5% (n = 153) had a positive breathalyzer reading (non-zero reading). There was agreement between EtG and BAC (kappa = 0.66, p < .001; Phi coefficient = 0.69, p < .001); self-reported alcohol measures were positively correlated with positive EtG and BAC (p’s < 0.001). There was concordance between EtG and BAC measures, which have differing alcohol detection windows. Most participants endorsed frequent drinking at high quantities, with very few reporting no alcohol consumption in the past month. Concordance between biomarkers and self-reported alcohol use suggests that underreporting of alcohol use was minimal. Results highlight the need for alcohol screening within HIV care. Implications for alcohol assessment within research and clinical contexts are discussed.

Impairments in gait occur after alcohol consumption, and, if detected in real-time, could guide the delivery of “just-in-time” injury prevention interventions. We aimed to identify the salient features of gait that could be used for estimating blood alcohol content (BAC) level in a typical drinking environment. We recruited 10 young adults with a history of heavy drinking to test our research app. During four consecutive Fridays and Saturdays, every hour from 8 p.m. to 12 a.m., they were prompted to use the app to report alcohol consumption and complete a 5-step straight-line walking task, during which 3-axis acceleration and angular velocity data was sampled at a frequency of 100 Hz. BAC for each subject was calculated. From sensor signals, 24 features were calculated using a sliding window technique, including energy, mean, and standard deviation. Using an artificial neural network (ANN), we performed regression analysis to define a model determining association between gait features and BACs. Part (70%) of the data was then used as a training dataset, and the results tested and validated using the rest of the samples. We evaluated different training algorithms for the neural network and the result showed that a Bayesian regularization neural network (BRNN) was the most efficient and accurate. Analyses support the use of the tandem gait task paired with our approach to reliably estimate BAC based on gait features. Results from this work could be useful in designing effective prevention interventions to reduce risky behaviors during periods of alcohol consumption.

Driving under the influence (DUI) remains an important threat to public health in the United States, and a substantial literature has evaluated the effectiveness of state-mandated penalties. Researchers have overlooked accelerated use of obscured fees and surcharges levied by local and state court systems added to penalties in the past 15 years. We present data regarding DUI penalties for offenders with a blood alcohol content (BAC of 0.08) and the fees and surcharges attached to them in Minnesota, Illinois, Wisconsin, Michigan, and Iowa, and variation in these within Wisconsin at four BAC levels. In all states, surcharges and fees exceed penalty fines substantially. Variation within Wisconsin is also meaningful. Our data suggest that opaque costs in state court systems add a substantial financial burden to DUI penalties, particularly for those with lower incomes. An appraisal of the deterrent role of these added costs is warranted.

Road safety is of increasing concern in developed countries because of the significant number of deaths and large economic losses. One tool commonly used by governments to deal with road accidents is the enactment of stricter policies and regulations. Drunk driving is one of the leading concerns in this field and several European countries have decided to lower their illegal Blood Alcohol Content levels to 0.5 mg/ml over the last decade. This study uses European panel-based data (CARE) for the period 1991-2003 for the first time to evaluate the effectiveness of this transition by applying the differences-in-differences method in a fixed effects estimation that allows for any pattern of correlation (Cluster-Robust). The results show positive policy impacts, particularly on certain groups of victims, such as young males in urban zones. However, there are reasons to expect a short lag in that effectiveness.

Background and Aim: Little evidence is available on whether respondents from divergent sociocultural populations report alcohol consumption in systematically similar ways. Therefore, this study examined whether the validity of self-reported alcohol use differed between Arab and Jewish Israeli pub patrons. Methods: The analytical sample consisted of 227 Arab and 900 Jewish Israeli pub patrons who were approached as they left pubs and asked to record their Breath Alcohol Content (BrAC) value and complete a questionnaire that probed into their alcohol use. Validity of self-reported alcohol use across the 2 groups was examined by testing the discrepancy in concordance between the self-reported number of drinks and BrAC scores through simple Pearson correlations and by performing a multi-group measurement invariance (MI) comparison. Results: The Pearson correlation between the self-reported number of drinks and BrAC by the ethno-cultural group was almost identical across groups (Jews: r = 0.47, p < 0.01, df = 898; Arabs: r = 0.42, p < 0.01, df = 225). MI test results further confirmed that the factor loadings of the 2 drinking measures are similar across the 2 ethno-cultural groups. Conclusions: Self-reported alcohol consumption gives cross-culturally valid and acceptable estimates of alcohol consumption in this sample of Israeli Arabs and Jews.

There are a range of wearable transdermal alcohol sensors that are available and are being developed. These devices have the potential to monitor alcohol consumption continuously over extended periods in an objective manner, overcoming some of the limitations of other alcohol measurement methods (blood, breath, and urine). The objective of our systematic review was to assess wearable transdermal alcohol sensor accuracy. A systematic search of the CINAHL, Embase, Google Scholar, MEDLINE, PsycINFO, PubMed, and Scopus bibliographic databases was conducted in February 2021. In total, 2 team members (EB and SH) independently screened studies for inclusion, extracted data, and assessed the risk of bias. The methodological quality of each study was appraised using the Mixed Methods Appraisal Tool. The primary outcome was transdermal alcohol sensor accuracy. The data were presented as a narrative synthesis. We identified and analyzed 32 studies. Study designs included laboratory, ambulatory, and mixed designs, as well as randomized controlled trials; the length of time for which the device was worn ranged from days to weeks; and the analyzed sample sizes ranged from 1 to 250. The results for transdermal alcohol concentration data from various transdermal alcohol sensors were generally found to positively correlate with breath alcohol concentration, blood alcohol concentration, and self-report (moderate to large correlations). However, there were some discrepancies between study reports; for example, WrisTAS sensitivity ranged from 24% to 85.6%, and specificity ranged from 67.5% to 92.94%. Higher malfunctions were reported with the BACtrack prototype (16%-38%) and WrisTAS (8%) than with SCRAM (2%); however, the former devices also reported a reduced time lag for peak transdermal alcohol concentration values when compared with SCRAM. It was also found that many companies were developing new models of wearable transdermal alcohol sensors. As shown, there is a lack of consistency in the studies on wearable transdermal alcohol sensor accuracy regarding study procedures and analyses of findings, thus making it difficult to draw direct comparisons between them. This needs to be considered in future research, and there needs to be an increase in studies directly comparing different transdermal alcohol sensors. There is also a lack of research investigating the accuracy of transdermal alcohol sensors as a tool for monitoring alcohol consumption in clinical populations and use over extended periods. Although there is some preliminary evidence suggesting the accuracy of these devices, this needs to be further investigated in clinical populations. PROSPERO CRD42021231027; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=231027.

In most studies, the alcohol marker phosphatidylethanol (PEth) was used to differentiate social drinking from alcohol abuse. This study investigates PEth’s potential in abstinence monitoring by performing a drinking study to assess the detection window of PEth after ingesting a defined amount of alcohol. After 2 weeks of abstinence, 16 volunteers ingested a single dose of alcohol, leading to an estimated blood alcohol concentration (BAC) of 1 g/kg. In the week after drinking, blood and urine samples were taken daily; in the second week, samples were taken every other day. PEth 16:0/18:1 and 16:0/18:2 were analyzed in blood by online-SPE-LC-MS/MS. Ethyl glucuronide and ethyl sulfate were determined in urine for abstinence monitoring. Prior to start of drinking, PEth 16:0/18:1 exceeded 30 ng/mL in blood samples of five volunteers despite the requested abstinence period. Positive PEth values resulted from drinking events prior to this abstinence period. After the start of drinking, maximum BACs were reached after 2 h with a mean of 0.80 ± 0.13 g/kg (range: 0.61–1.11 g/kg). PEth 16:0/18:1 increased within 8 h to maximum concentrations (mean: 88.8 ± 47.0 ng/mL, range: 37.2–208 ng/mL). After this event, PEth was detectable for 3 to 12 days with a mean half-life time of approximately 3 days. PEth has a potential in abstinence monitoring, since PEth could be detected for up to 12 days after a single drinking event. Further investigations are necessary, to establish cut-off levels for PEth as diagnostic marker for the determination of drinking habits like abstinence, social drinking, or risky alcohol consumption.

Wrist-worn alcohol biosensor technology has developed rapidly in recent years. These devices are light, easy to wear, relatively inexpensive, and resemble commercial fitness trackers. As a result, they may be more suitable for a wide range of clinical and research applications. In this paper, we describe three pilot projects examining the associations between reported drinking behavior and transdermal alcohol concentration (TAC) derived from a new, wrist-worn alcohol biosensor (BACtrack Skyn) in diverse participant groups and settings. Study 1 (N = 3) compared Skyn-derived TAC with that from an ankle-worn alcohol sensor (SCRAM CAM) and breath alcohol concentration (BrAC) in a laboratory setting. Study 2 (N = 10) compared Skyn TAC with BrAC during a naturalistic drinking episode in the field. Study 3 (N = 12) used the Skyn to monitor alcohol use in the field for 2 weeks. Studies 2 and 3 also collected usability and acceptability data from participants. The results of Study 1 showed that the Skyn produced a TAC curve that closely resembled that of the validated SCRAM CAM anklet. In Study 2, Skyn detected drinking for all 10 participants (peak BrAC range: 0.02–0.21) with an average delay of 35.6 ± 10.2 min after the start of self-reported drinking. In Study 3, Skyn reliably recorded continuous TAC data showing multiple drinking episodes over the monitoring period. Participants in Studies 2 and 3 both reported Skyn as highly acceptable. Collectively, the results of these pilot studies show that the Skyn was able to reliably detect drinking events in the laboratory and natural environments. We offer suggestions for further refinements of alcohol biosensors and accompanying analytic software that may facilitate adoption of these devices as cost-effective, user-friendly, and reliable tools to passively and accurately assess alcohol use in the field. •Wrist-worn alcohol biosensors may be a useful tool for real-life alcohol monitoring.•These biosensors can detect alcohol use in both laboratory and field settings.•The delay of detection from self-reported drinking initiation is about half an hour.•These biosensors have high usability and acceptability rated by participants.