Transdermal Alcohol Concentration Data Research Articles

Profiles of alcohol intoxication and their associated risks in young adults' natural settings: A multilevel latent profile analysis applied to daily transdermal alcohol concentration data.

Transdermal alcohol concentration (TAC) sensors capture aspects of drinking events that self-reports cannot. The multidimensional nature of TAC data allows novel classification of drinking days and identification of associated behavioral and contextual risks. We used multilevel latent profile analysis (MLPA) to create day-level profiles of TAC features and test their associations with (a) daily behaviors and contexts and (b) risk for alcohol use disorders at baseline. Two hundred twenty-two regularly heavy-drinking young adults (Mage = 22.3) completed the Alcohol Use Disorders Identification Test (AUDIT) at baseline and then responded to mobile phone surveys and wore TAC sensors for six consecutive days. MLPA identified day-level profiles using four TAC features (peak, rise rate, fall rate, and duration). TAC profiles were tested as correlates of daily drinking behaviors, contexts, and baseline AUDIT. Four profiles emerged: (a) high-fast (8.5% of days), (b) moderate-fast (12.8%), (c) low-slow (20.4%), and (d) little-to-no drinking days (58.2%). Profiles differed in the odds of risky drinking behaviors and contexts. The highest risk occurred on high-fast days, followed by moderate-fast, low-slow, and little-to-no drinking days. Higher baseline AUDIT predicted higher odds of high-fast and moderate-fast days. Days with high and fast intoxication are reflective of high-risk drinking behaviors and were most frequent among those at risk for alcohol use disorders. TAC research using MLPA may offer novel and important insights to intervention efforts. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Supplemental Material for Profiles of Alcohol Intoxication and Their Associated Risks in Young Adults’ Natural Settings: A Multilevel Latent Profile Analysis Applied to Daily Transdermal Alcohol Concentration Data

Supplemental Material for Profiles of Alcohol Intoxication and Their Associated Risks in Young Adults’ Natural Settings: A Multilevel Latent Profile Analysis Applied to Daily Transdermal Alcohol Concentration Data

Examining new-generation transdermal alcohol biosensor performance across laboratory and field contexts.

Wrist-worn transdermal alcohol sensors have the potential to change how alcohol consumption is measured. However, hardware and data analytic challenges associated with transdermal sensor data have kept these devices from widespread use. Given recent technological and analytic advances, this study provides an updated account of the performance of a new-generation wrist-worn transdermal sensor in both laboratory and field settings. This work leverages machine learning models to convert transdermal alcohol concentration data into estimates of Breath Alcohol Concentration (BrAC) in a large-scale laboratory sample (N=256, study 1) and a pilot field sample (N=27, study 2). Specifically, in both studies, the accuracy of the translation is evaluated by comparing BAC estimates yielded by BACtrack Skyn to real-time breathalyzer measurements collected in the laboratory and in the field. The newest version of the Skyn device demonstrates a substantially lower error rate than older hand-assembled prototypes (0% to 7% vs. 29% to 53%, respectively). On average, real-time estimates of BrAC yielded by these transdermal sensors are within 0.007 of true BAC readings in the laboratory context and within 0.019 of true BrAC readings in the field. In both contexts, the distance between true and estimated BrAC was larger when only alcohol episodes were examined (laboratory = 0.017; field = 0.041). Finally, results of power-law-curve projections indicate that, given their accuracy, transdermal BrAC estimates in real-world contexts have the potential to improve markedly (>25%) with adequately sized datasets for model training. Findings from this study indicate that the latest version of the transdermal wrist sensor holds promise for the accurate assessment of alcohol consumption in field contexts. A great deal of additional work is needed to provide a full picture of the utility of these devices, including research with large participant samples in field contexts.

Accuracy of Wearable Transdermal Alcohol Sensors: Systematic Review.

BackgroundThere 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).ObjectiveThe objective of our systematic review was to assess wearable transdermal alcohol sensor accuracy.MethodsA 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.ResultsWe 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.ConclusionsAs 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.Trial RegistrationPROSPERO CRD42021231027; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=231027

(Invited) Transdermal Alcohol Monitoring with a Printed Amperometric Sensor

(Invited) Transdermal Alcohol Monitoring with a Printed Amperometric Sensor

Wrist-worn alcohol biosensors: Applications and usability in behavioral research.

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±10.2min 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.

Wearable Transdermal Alcohol Monitors: A Systematic Review of Detection Validity, and Relationship Between Transdermal and Breath Alcohol Concentration and Influencing Factors.

Research 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. The 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™. TAC 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). Transdermal 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.

Validation of transdermal alcohol concentration data collected using wearable alcohol monitors: A systematic review and meta-analysis

ObjectiveTo evaluate the diagnostic accuracy of transdermal alcohol content (TAC) data (i.e. index test) collected with wearable alcohol monitors for assessment of alcohol use or any other alcohol related outcome (e.g., excessive alcohol use) among adults 18 and older. MethodsWe will systematically search MEDLINE, EMBASE, PsycINFO, and the Social Sciences Citation Index (SSCI, Web of Science) for TAC validation studies. The reference standards for this systematic review study are alcohol use data collected through self-reports, breathalyzers, or blood samples. If enough studies are available to conduct a meta-analysis, we will use a hierarchical regression approach to pool the results and obtain summary point estimates.

Transdermal Alcohol Monitoring with a Printed Amperometric Gas Sensor

Introduction We report development of a prototype transdermal ethanol (EtOH) monitor, in wristwatch format, to provide a modern device for consumer, clinical and law enforcement applications. Our vision is an attractive, comfortable smart watch, which will measure transdermal alcohol concentration (TAC) and predict blood alcohol concentration (BAC) during drinking. Current commercially available alcohol monitors are bulky, ankle-fixed devices, predominantly used in court-ordered monitoring and some research studies [1]. There has been little advancement in wearability, comfort and analytical performance of these devices. Our long-term goal is to realize a new generation of wearable monitor that combines a sensitive ethanol measurement and smartwatch capabilities in a comfortable and attractive package. This device could greatly expand the possible applications employing this technology. Experimental Watch Fabrication. The sensor platform was fabricated based on a scaled down version of our dual gas sensor platform and printed amperometric gas sensor [2, 3]. The simple prototype device contains a single EtOH sensor, analog front end, 24 bit ADC, Bluetooth LE chip and T/P/RH sensor. The board is contained in a commercially available wristwatch enclosure (BodyCase B1606117) as in Fig. 1. Initial tests of linearity and S/N showed that the circuit was linear over at least 0-300 ppm EtOH at 25 C (R2 = 0.9996 – 0.9998 for 4 boards) with sensitivity 23 ± 2 nA/ppm (for 4 boards) and limit of detection (LOD) 0.26 ± 0.06 ppm.Laboratory Testing. A flow cell was designed to deliver 0 – 0.4 w/o EtOH (simulated BAC) through a Strat-M membrane, which served as a skin surrogate [4]. EtOH solutions in phosphate buffered saline pH 7.1 were used. The cell was configured such that EtOH permeated the Strat-M, with vapor entering the headspace above to which the watch was interfaced. Results and Conclusions Flow Cell Tests. EtOH steps between 0 and 0.4 %EtOH (simulated BAC) were applied with 30 min exposure at each step. A 6 hr stability test was included. High stability and S/N were demonstrated. The EtOH concentrations in simulated BAC solutions were standardized and calibrated to headspace vapor phase concentrations, giving a calibration curve for %EtOH in the PBS buffer (simulated %BAC) as a function of measured EtOH (ppm EtOH) in headspace above the membrane. For two devices, we obtained highly linear calibrations (R2 = 0.9983, 0.9992, respectively) with slope of 0.0013% and 0.0011% BAC/ppm EtOH, with near zero intercepts (-0.009 and -0.006 %BAC, respectively). From these two tests and the baseline noise we determined a limit of detection (LOD) of ±0.005% BAC. In later tests (e.g., Fig. 2) the LOD was considerably improved. The 6 hr stability test at 0.12% EtOH showed very stable output of 0.116 ±0.003 % EtOH.Human Tests. An example of real transdermal alcohol measurement using the watch prototype on a human subject is shown in Fig. 2. A male volunteer ingested six 6 oz. aliquots of 5 v/o alcoholic beverage over ca. 1 hr. Two different watches were used, one fixed to each wrist. The measured temperature at the devices was typically in the range 28 – 34 C during tests. The data in Fig. 2 are not temperature compensated. A commercial BACtrack Trace breathalyzer was used to collect contemporaneous breath alcohol data (BrAC).The TAC measurements taken on two wrists correlated quite well with each other. The TAC values tracked BrAC trend qualitatively, but with a shift to longer times, with the peak BrAC occurring ca. 1.5 hours before the peak TAC value. In other tests we have also observed lags on the order 30 min to 2 hr for TAC vs. BrAC measures. This lag is well-known for transdermal ethanol measurements [5] and presents a challenge for reliable instantaneous BAC prediction from TAC data. We note that the lag can depend on many factors including person-to-person variability of skin permeability, presence of sweat (humidity), food consumption and variable rates of alcohol consumption and metabolism, to name a few examples. Mechanical effects such as sudden movements and impacts can also affect the measurement.We demonstrated use of a printed amperometric sensor for TAC measurement. Controlled clinical trials are currently being performed in collaboration with Boston Medical Center. The ultimate goal is to reliably predict BAC from TAC data across a broad range of individuals and use cases.

Assessment of Alcohol Use in the Natural Environment.

The current article critically reviews 3 methodological options for assessing drinking episodes in the natural environment. Ecological momentary assessment (EMA) typically involves using mobile devices to collect self-report data from participants in daily life. This technique is now widely used in alcohol research, but investigators have implemented diverse assessment strategies. This article focuses on "high-resolution" EMA protocols that oversample experiences and behaviors within individual drinking episodes. A number of approaches have been used to accomplish this, including using signaled follow-ups tied to drinking initiation, asking participants to log entries before and after individual drinks or drinking episodes, and delivering frequent signaled assessments during periods of the day when alcohol use is most common. Transdermal alcohol sensors (TAS) are devices that are worn continuously and are capable of detecting alcohol eliminated through the skin. These methods are appealing because they do not rely upon drinkers' self-report. Studies using TAS have been appearing with greater frequency over the past several years. New methods are making the use of TAS more tractable by permitting back-translation of transdermal alcohol concentration data to more familiar estimates of blood alcohol concentration or breath alcohol concentration. However, the current generation of devices can have problems with missing data and tend to be relatively insensitive to low-level drinking. An emerging area of research investigates the possibility of using mobile device data and machine learning to passively detect the user's drinking, with promising early findings. EMA, TAS, and sensor-based approaches are all valid, and tend to produce convergent information when used in conjunction with one another. Each has a unique profile of advantages, disadvantages, and threats to validity. Therefore, the nature of the underlying research question must dictate the method(s) investigators select.

Applying a novel population-based model approach to estimating breath alcohol concentration (BrAC) from transdermal alcohol concentration (TAC) biosensor data.

Alcohol biosensor devices have been developed to unobtrusively measure transdermal alcohol concentration (TAC), the amount of ethanol diffusing through the skin, in nearly continuous fashion in naturalistic settings. Because TAC data are affected by physiological and environmental factors that vary across individuals and drinking episodes, there is not an elementary formula to convert TAC into easily interpretable metrics such as blood and breath alcohol concentrations (BAC/BrAC). In our prior work, we addressed this conversion problem in a deterministic way by developing physics/physiological-based models to convert TAC to estimated BrAC (eBrAC), in which the model parameter values were individually determined for each person wearing a specific transdermal sensor using simultaneously collected TAC (via a biosensor) and BrAC (via a breath analyzer) during a calibration episode. We found these individualized parameter values produced relatively good eBrAC curves for subsequent drinking episodes, but our results also indicated the models were not fully capturing the dynamics of the system and variations across drinking episodes. Here, we report on a novel mathematical framework to improve our ability to model eBrAC from TAC data that uses aggregate population data instead of individualized calibration data to determine model parameter values via a random diffusion equation. We first provide the theoretical mathematical basis for our approach, and then test the efficacy of this method using datasets of contemporaneous BrAC/TAC measurements obtained by a) a single subject during multiple drinking episodes and b) multiple subjects during single drinking episodes. For each dataset, we used a set of drinking episodes to construct the population model, and then ran the model with another set of randomly selected test episodes. We compared raw TAC data to model-simulated TAC curve, breath analyzer BrAC data to model eBrAC curve with 75% credible bands, episode summary scores of peak BrAC, times of peak BrAC, and area under the drinking curve also with 75% credible intervals, and report the percent of the raw BrAC captured within the eBrAC curve credible bands. We also display results when stratifying the data based on the relationship between the raw BrAC and TAC data. Results indicate the population-based model is promising, with better fit within a single participant when stratifying episodes. This study provides initial proof-of-concept for constructing, fitting, and using a population-based model to obtain estimates and error bands for BrAC from TAC. The advancements in this study, including new applications of math, the development of a population-based model with error bars, and the production of corresponding MATLAB codes, represent a major step forward in our ability to produce quantitatively- and temporally-accurate estimates of BrAC from TAC biosensor data.

Processing transdermal alcohol concentration (TAC) data to detect low-level drinking.

Several studies have objectively quantified drinking through the use of Alcohol Monitoring System's (AMS) transdermal alcohol concentration (TAC) device known as SCRAM CAM. Criteria that AMS uses to detect drinking are known to be conservative and only reliably detect heavy drinking equivalent to 5 or more standard drinks. Our group has developed Research Rules used to process TAC data in a manner that will detect low-level and moderate drinking even though it is below the AMS criteria for detection. Sixteen male and 14 female paid research volunteers wore TAC monitors for 28 days in their natural environments and responded daily to text message prompts to self-report the previous day's drinking. Current analyses describe the Research Rules that we developed and how use of those rules impacts the detection of self-reported drinking treated as the standard in sensitivity/specificity analysis. We observed 606 occurrences of positive TAC events over a total of 867 days and processed the TAC data to retain 345 as possible drinking events, even though AMS criteria confirmed drinking for only 163 of these events. The kinds of TAC events removed or retained by our rules are illustrated as cases of low and moderate drinking days that were detected by our rules but not by the conservative AMS criteria. AMS-confirmed TAC events have a high specificity (99.8%) to detect primarily heavy drinking, but have a poor sensitivity to detect lower-level drinking and a poor specificity as an indicator of alcohol abstinence. In contrast, our Research Rules detected 100% of TAC events detected by AMS but also detected 31% of the lower-level drinking events not detected by AMS, with 91% specificity. Reliance upon the AMS criteria for alcohol detection affords a high specificity for detection of heavy drinking but is a poor indicator of abstinence rates. In contrast, use of our Research Rules provides more sensitive means to quantify either any drinking or low-moderate levels of drinking while still maintaining good specificity.

Transdermal alcohol concentration data collected during a contingency management program to reduce at-risk drinking

BackgroundRecently, we demonstrated that transdermal alcohol monitors could be used in a contingency management procedure to reduce problematic drinking; the frequency of self-reported heavy/moderate drinking days decreased and days of no to low drinking increased. These effects persisted for three months after intervention. In the current report, we used the transdermal alcohol concentration (TAC) data collected prior to and during the contingency management procedure to provide a detailed characterization of objectively measured alcohol use. MethodsDrinkers (n=80) who frequently engaged in risky drinking behaviors were recruited and participated in three study phases: a 4-week Observation phase where participants drank as usual; a 12-week Contingency Management phase where participants received $50 each week when TAC did not exceed 0.03g/dl; and a 3-month Follow-up phase where self-reported alcohol consumption was monitored. Transdermal monitors were worn during the first two phases, where each week they recived $105 for visiting the clinic and wearing the monitor. Outcomes focused on using TAC data to objectively characterize drinking and were used to classify drinking levels as either no, low, moderate, or heavy drinking as a function of weeks and day of week. ResultsCompared to the Observation phase, TAC data indicated that episodes of heavy drinking days during the Contingency Management phase were reduced and episodes of no drinking and low to moderate drinking increased. ConclusionsThese results lend further support for linking transdermal alcohol monitoring with contingency management interventions. Collectively, studies to date indicate that interventions like these may be useful for both abstinence and moderation-based programs.

Estimating BrAC from transdermal alcohol concentration data using the BrAC estimator software program.

Transdermal alcohol sensor (TAS) devices have the potential to allow researchers and clinicians to unobtrusively collect naturalistic drinking data for weeks at a time, but the transdermal alcohol concentration (TAC) data these devices produce do not consistently correspond with breath alcohol concentration (BrAC) data. We present and test the BrAC Estimator software, a program designed to produce individualized estimates of BrAC from TAC data by fitting mathematical models to a specific person wearing a specific TAS device. Two TAS devices were worn simultaneously by 1 participant for 18days. The trial began with a laboratory alcohol session to calibrate the model and was followed by a field trial with 10 drinking episodes. Model parameter estimates and fit indices were compared across drinking episodes to examine the calibration phase of the software. Software-generated estimates of peak BrAC, time of peak BrAC, and area under the BrAC curve were compared with breath analyzer data to examine the estimation phase of the software. In this single-subject design with breath analyzer peak BrAC scores ranging from 0.013 to 0.057, the software created consistent models for the 2 TAS devices, despite differences in raw TAC data, and was able to compensate for the attenuation of peak BrAC and latency of the time of peak BrAC that are typically observed in TAC data. This software program represents an important initial step for making it possible for non mathematician researchers and clinicians to obtain estimates of BrAC from TAC data in naturalistic drinking environments. Future research with more participants and greater variation in alcohol consumption levels and patterns, as well as examination of gain scheduling calibration procedures and nonlinear models of diffusion, will help to determine how precise these software models can become.

Blind deconvolution for distributed parameter systems with unbounded input and output and determining blood alcohol concentration from transdermal biosensor data

We develop a blind deconvolution scheme for input–output systems described by distributed parameter systems with boundary input and output. An abstract functional analytic theory based on results for the linear quadratic control of infinite dimensional systems with unbounded input and output operators is presented. The blind deconvolution problem is then reformulated as a series of constrained linear and nonlinear optimization problems involving infinite dimensional dynamical systems. A finite dimensional approximation and convergence theory is developed. The theory is applied to the problem of estimating blood or breath alcohol concentration, (respectively, BAC or BrAC) from biosensor-measured transdermal alcohol concentration (TAC) in the field. A distributed parameter model with boundary input and output is proposed for the transdermal transport of ethanol from the blood through the skin to the sensor. The problem of estimating BAC or BrAC from the TAC data is formulated as a blind deconvolution problem. A scheme to identify distinct drinking episodes in TAC data based on a Hodrick Prescott filter is discussed. Numerical results involving actual patient data are presented.

Comparing the detection of transdermal and breath alcohol concentrations during periods of alcohol consumption ranging from moderate drinking to binge drinking.

Binge drinking is a public health concern due to its association with negative health outcomes as well as increased legal and social consequences. Previous studies have frequently used self-reported alcohol consumption to classify binge drinking episodes; however, these measures are often limited in both detail and accuracy. Some researchers have begun using additional measures such as blood (BAC) and breath (BrAC) alcohol concentrations to supplement self-report data. Transdermal alcohol testing, or the detection of alcohol expiration through the skin, offers advantages over BAC and BrAC measures by allowing for continuous and noninvasive monitoring of an individual's drinking behavior in real time. Despite these advantages, this technology has not been widely used or studied outside of forensic applications. The present research compares transdermal alcohol concentration (TAC) and BrAC readings during the consumption of alcohol ranging from moderate drinking to binge drinking in 22 adult regular drinkers in order to investigate the sensitivity and specificity of the TAC monitors. We observed that BrAC and TAC measures were broadly consistent. Additionally, we were able to develop an equation that could predict BrAC results using TAC data, indicating TAC data would be an appropriate substitute in research and clinical contexts where BrAC readings are typically used. Finally, we were able to determine a cutoff point for peak TAC data that could reliably predict whether a participant had engaged in moderate or more-than-moderate drinking, suggesting TAC monitors could be used in settings where moderate or reduced drinking is the goal.