Accounting for sex-related differences in the estimation of breath alcohol concentrations using transdermal alcohol monitoring

Uncertainty Quantification in Estimating Blood Alcohol Concentration From Transdermal Alcohol Level With Physics-Informed Neural Networks

We develop an approach to estimate a blood alcohol signal from a transdermal alcohol signal using physics-informed neural networks (PINNs). Specifically, we use a generative adversarial network (GAN) with a residual-augmented loss function to estimate the distribution of unknown parameters in a diffusion equation model for transdermal transport of alcohol in the human body. We design another PINN for the deconvolution of the blood alcohol signal from the transdermal alcohol signal. Based on the distribution of the unknown parameters, this network is able to estimate the blood alcohol signal and quantify the uncertainty in the form of conservative error bands. Finally, we show how a posterior latent variable can be used to sharpen these conservative error bands. We apply the techniques to an extensive dataset of drinking episodes and demonstrate the advantages and shortcomings of this approach.

Flux-Type versus Concentration-Type Sensors in Transdermal Measurements

New transdermal biosensors measure analytes that diffuse from the bloodstream through the skin, making it important to reduce the system response time and understand measurement output. While highly customized models have been created for specific sensors, a generalized model for transdermal sensor systems is lacking. Here, a simple one-dimensional diffusion model was used to characterize the measurement system and classify biosensors as either flux types or concentration types. Results showed that flux-type sensors have significantly faster response times than concentration sensors. Furthermore, flux sensors do not measure concentration, but rather have an output measurement that is proportional to skin permeability. These findings should lead to an improved understanding of transdermal measurements and their relation to blood analyte concentration. In the realm of alcohol research, where the majority of commercially available sensors are flux types, our work advocates toward moving away from transdermal alcohol concentration as a metric, and instead suggests embracing transdermal alcohol flux as a more suitable alternative.

Correspondence between alcohol use measured by a wrist-worn alcohol biosensor and self-report via ecological momentary assessment over a 2-week period

BACKGROUND

Transdermal alcohol biosensors measure alcohol use continuously, passively, and non-invasively. There is little field research on the Skyn biosensor, a new-generation, wrist-worn transdermal alcohol biosensor, and little evaluation of its sensitivity and specificity and the day-level correspondence between transdermal alcohol concentration (TAC) and number of self-reported drinks.

METHODS

Participants (N = 36; 61% male, M age  = 34.3) wore the Skyn biosensor and completed ecological momentary assessment (EMA) surveys about their alcohol use over 2 weeks. A total of 497 days of biosensor and EMA data were collected. Skyn-measured drinking episodes were defined by TAC > 5 μg/L. Skyn data were compared to self-reported drinking to calculate sensitivity and specificity (for drinking day vs. nondrinking day). Generalized estimating equations models were used to evaluate the correspondence between TAC features (peak TAC and TAC-area under the curve (AUC)) and number of drinks. Individual-level factors (sex, age, race/ethnicity, body mass index, human immunodeficiency virus status, and hazardous drinking) were examined to explore associations with TAC controlling for number of drinks.

RESULTS

Using a minimum TAC threshold of 5 μg/L plus coder review, the biosensor had sensitivity of 54.7% and specificity of 94.6% for distinguishing drinking from nondrinking days. Without coder review, the sensitivity was 78.1% and the specificity was 55.2%. Peak TAC (β = 0.92, p < 0.0001) and TAC-AUC (β = 1.60, p < 0.0001) were significantly associated with number of drinks. Females had significantly higher TAC levels than males for the same number of drinks.

CONCLUSIONS

Skyn-derived TAC can be used to measure alcohol use under naturalistic drinking conditions, additional research is needed to accurately identify drinking episodes based on Skyn TAC readings.

Validating transdermal alcohol biosensors: a meta-analysis of associations between blood/breath-based measures and transdermal alcohol sensor output

BACKGROUND AND AIMS

Transdermal alcohol sensors carry immense promise for the continuous assessment of drinking but are inconsistent in detecting more fine-grained indicators of alcohol consumption. Prior studies examining associations between transdermal alcohol concentration (TAC) and blood/breath alcohol concentration (BAC) have yielded highly variable correlations and lag times. The current review aimed to synthesize transdermal validation studies, aggregating results from more than three decades of research to characterize the validity of transdermal sensors for assessing alcohol consumption.

METHODS

Databases were searched for studies listed prior to 1 March 2022 that examined associations between transdermal alcohol sensor output and blood and breath-based alcohol measures, resulting in 31 primarily laboratory-derived participant samples (27 precise effect sizes) including both healthy and clinical populations. Correlation coefficients and lag times were pooled using three-level random-effects meta-regression. Independent raters coded study characteristics, including the body position of transdermal sensors (ankle- versus arm/hand/wrist-worn device) and methodological bias (e.g. missing data).

RESULTS

Analyses revealed that, in this primarily laboratory-derived sample of studies, the average correlation between TAC and BAC was large in magnitude [r = 0.87, 95% confidence interval (CI) = 0.80, 0.93], and TAC lagged behind BAC by an average of 95.90 minutes (95% CI = 55.50, 136.29). Device body position significantly moderated both TAC-BAC correlation (b = 0.11, P = 0.009) and lag time (b = -69.41, P < 0.001). Lag times for ankle-worn devices were approximately double those for arm/hand/wrist-worn devices, and TAC-BAC correlations also tended to be stronger for arm/hand/wrist-worn sensors.

CONCLUSIONS

This meta-analysis indicates that transdermal alcohol sensors perform strongly in assessing blood/breath alcohol concentration under controlled conditions, with particular promise for the newer generation of wrist-worn devices.

Blood and Breath Alcohol Concentration from Transdermal Alcohol Biosensor Data: Estimation and Uncertainty Quantification via Forward and Inverse Filtering for a Covariate-Dependent, Physics-Informed, Hidden Markov Model

Transdermal alcohol biosensors that do not require active participation of the subject and yield near continuous measurements have the potential to significantly enhance the data collection abilities of alcohol researchers and clinicians who currently rely exclusively on breathalyzers and drinking diaries. Making these devices accessible and practical requires that transdermal alcohol concentration (TAC) be accurately and consistently transformable into the well-accepted measures of intoxication, blood/breath alcohol concentration (BAC/BrAC). A novel approach to estimating BrAC from TAC based on covariate-dependent physics-informed hidden Markov models with two emissions is developed. The hidden Markov chain serves as a forward full-body alcohol model with BrAC and TAC, the two emissions, assumed to be described by a bivariate normal which depends on the hidden Markovian states and person-level and session-level covariates via built-in regression models. An innovative extension of hidden Markov modeling is developed wherein the hidden Markov model framework is regularized by a first-principles PDE model to yield a hybrid that combines prior knowledge of the physics of transdermal ethanol transport with data-based learning. Training, or inverse filtering, is effected via the Baum-Welch algorithm and 256 sets of BrAC and TAC signals and covariate measurements collected in the laboratory. Forward filtering of TAC to obtain estimated BrAC is achieved via a new physics-informed regularized Viterbi algorithm which determines the most likely path through the hidden Markov chain using TAC alone. The Markovian states are decoded and used to yield estimates of BrAC and to quantify the uncertainty in the estimates. Numerical studies are presented and discussed. Overall good agreement between BrAC data and estimates was observed with a median relative peak error of 22% and a median relative area under the curve error of 25% on the test set. We also demonstrate that the physics-informed Viterbi algorithm eliminates non-physical artifacts in the BrAC estimates.

Transdermal sensor features correlate with ecological momentary assessment drinking reports and predict alcohol-related consequences in young adults' natural settings

BACKGROUND

Wearable transdermal alcohol concentration (TAC) sensors allow passive monitoring of alcohol concentration in natural settings and measurement of multiple features from drinking episodes, including peak intoxication level, speed of intoxication (absorption rate) and elimination, and duration. These passively collected features extend commonly used self-reported drink counts and may facilitate the prediction of alcohol-related consequences in natural settings, aiding risk stratification and prevention efforts.

METHOD

A total of 222 young adults aged 21-29 (M age = 22.3, 64% female, 79% non-Hispanic white, 84% undergraduates) who regularly drink heavily participated in a 5-day study that included the ecological momentary assessment (EMA) of alcohol consumption (daily morning reports and participant-initiated episodic EMA sequences) and the wearing of TAC sensors (SCRAM-CAM anklets). The analytic sample contained 218 participants and 1274 days (including 554 self-reported drinking days). Five features-area under the curve (AUC), peak TAC, rise rate (rate of absorption), fall rate (rate of elimination), and duration-were extracted from TAC-positive trajectories for each drinking day. Day- and person-level associations of TAC features with drink counts (morning and episodic EMA) and alcohol-related consequences were tested using multilevel modeling.

RESULTS

TAC features were strongly associated with morning drink reports (r = 0.6-0.7) but only moderately associated with episodic EMA drink counts (r = 0.3-0.5) at both day and person levels. Higher peaks, larger AUCs, faster rise rates, and faster fall rates were significantly predictive of day-level alcohol-related consequences after adjusting for both morning and episodic EMA drink counts in separate models. Person means of TAC features added little above daily scores to the prediction of alcohol-related consequences.

CONCLUSIONS

These results support the utility of TAC sensors in studies of alcohol misuse among young adults in natural settings and outline the specific TAC features that contribute to the day-level prediction of alcohol-related consequences. TAC sensors provide a passive option for obtaining valid and unique information predictive of drinking risk in natural settings.

A new generation of transdermal alcohol biosensing technology: practical applications, machine -learning analytics and questions for future research

The use of transdermal alcohol monitors has burgeoned in recent years, now encompassing hundreds of thousands of individuals globally. A new generation of sensors promises to expand the range of applications for transdermal technology exponentially, and advances in machine-learning modeling approaches offer new methods for translating the data produced by transdermal devices. This article provides (1) a review of transdermal sensor research conducted to date, including an analysis of methodological features of past studies potentially key in driving reported sensor performance; (2) updates on methodological developments likely to be transformative for the field of transdermal sensing, including the development of new-generation sensors featuring smartphone integration and rapid sampling capabilities as well as developments in machine-learning analytics suited to data produced by these novel sensors and; (3) an analysis of the expanded range of applications for this new generation of sensor, together with corresponding requirements for sensor accuracy and temporal specificity. We also note questions as yet unanswered and key directions for future research.

Deconvolving breath alcohol concentration from biosensor measured transdermal alcohol level under uncertainty: a Bayesian approach

The posterior distribution (PD) of random parameters in a distributed parameter-based population model for biosensor measured transdermal alcohol is estimated. The output of the model is transdermal alcohol concentration (TAC), which, via linear semigroup theory can be expressed as the convolution of blood or breath alcohol concentration (BAC or BrAC) with a filter that depends on the individual participant or subject, the biosensor hardware itself, and environmental conditions, all of which can be considered to be random under the presented framework. The distribution of the input to the model, the BAC or BrAC, is also sequentially estimated. A Bayesian approach is used to estimate the PD of the parameters conditioned on the population sample's measured BrAC and TAC. We then use the PD for the parameters together with a weak form of the forward random diffusion model to deconvolve an individual subject's BrAC conditioned on their measured TAC. Priors for the model are obtained from simultaneous temporal population observations of BrAC and TAC via deterministic or statistical methods. The requisite computations require finite dimensional approximation of the underlying state equation, which is achieved through standard finite element (i.e., Galerkin) techniques. The posteriors yield credible regions, which remove the need to calibrate the model to every individual, every sensor, and various environmental conditions. Consistency of the Bayesian estimators and convergence in distribution of the PDs computed based on the finite element model to those based on the underlying infinite dimensional model are established. Results of human subject data-based numerical studies demonstrating the efficacy of the approach are presented and discussed.

Sociodemographic and clinical factors associated with transdermal alcohol concentration from the SCRAM biosensor among persons living with and without HIV

BACKGROUND

Transdermal alcohol biosensors can objectively monitor alcohol use by measuring transdermal alcohol concentration (TAC). However, it is unclear how sociodemographic and clinical factors that influence alcohol metabolism are associated with TAC. The main aim of this study was to examine how sociodemographic factors (sex, age, race/ethnicity) and clinical factors (body mass index, liver enzymes: alanine aminotransferase [ALT] and aspartate transaminase [AST]), alcohol use disorder, and HIV status were associated with TAC while controlling for level of alcohol use.

METHODS

We analyzed data from a prospective study involving contingency management for alcohol cessation among persons living with and without human immunodeficiency virus (HIV) that used the Secure Continuous Remote Alcohol Monitoring (SCRAM) biosensor. Forty-three participants (M_age  = 56.6 years; 63% male; 58% people living with HIV) yielded 183 SCRAM-detected drinking days. Two indices derived from SCRAM: peak TAC (reflecting level of intoxication) and TAC area under the curve (TAC-AUC; reflecting alcohol volume)-were the main outcomes. Self-reported alcohol use (drinks/drinking day) measured by Timeline Followback was the main predictor. To examine whether factors of interest were associated with TAC, we used individual generalized estimating equations (GEE), followed by a multivariate GEE model to include all significant predictors to examine their associations with TAC beyond the effect of self-reported alcohol use.

RESULTS

Number of drinks per drinking day (B = 0.29, p < 0.01) and elevated AST (B = 0.50, p = 0.01) were significant predictors of peak TAC. Positive HIV status, female sex, elevated AST, and number of drinks per drinking day were positively associated with TAC-AUC at the bivariate level, whereas only self-reported alcohol use (B = 0.85, p < 0.0001) and female sex (B = 0.67, p < 0.05) were significant predictors of TAC-AUC at the multivariate level.

CONCLUSIONS

HIV status was not independently associated with TAC. Future studies should consider the sex and liver function of the participant when using alcohol biosensors to measure alcohol use.

A parallel test of the SCRAM-CAM transdermal monitors ensuring reliability

INTRODUCTION

Previous 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.

METHODS

Participants (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).

RESULTS

There 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 CONCLUSIONS

Results 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.

Pilot Study of an Integrated Smartphone and Breathalyzer Contingency Management Intervention for Alcohol Use

INTRODUCTION

Monitoring devices provide a platform for assessing alcohol use and implementing alcohol interventions. This pilot study focused on assessing the early-stage feasibility and usability of a smartphone-based application and breathalyzer used in a contingency management intervention for alcohol use.

METHODS

Six nontreatment-seeking participants completed a 9-week ABA within-subjects designed intervention targeting alcohol use. Participants submitted 2 to 8 alcohol breathalyzer samples per day and completed self-report drinking measures and usability assessments. During the A phases (weeks 1-3 and 8-9), participants received reinforces for submitting breathalyzer samples, regardless of their results. During the contingency management, B phase (weeks 4-7), and received reinforcers only when negative breathalyzer samples were submitted. Usability assessment of the application was also conducted during weeks 2 and 9.

RESULTS

Participants in the contingent B phase (49%) were more likely to submit alcohol-negative breathalyzer samples compared with the noncontingent A phases (27%; P < 0.001). Usability assessment of the application varied, and participants noted several technical concerns.

CONCLUSION

The use of smartphones and breathalyzers may be a practical solution to extend the reach of contingency management during and after treatment.

Effects of stomach content on the breath alcohol concentration-transdermal alcohol concentration relationship

INTRODUCTION

Wearable devices that obtain transdermal alcohol concentration (TAC) could become valuable research tools for monitoring alcohol consumption levels in naturalistic environments if the TAC they produce could be converted into quantitatively-meaningful estimates of breath alcohol concentration (eBrAC). Our team has developed mathematical models to produce eBrAC from TAC, but it is not yet clear how a variety of factors affect the accuracy of the models. Stomach content is one factor that is known to affect breath alcohol concentration (BrAC), but its effect on the BrAC-TAC relationship has not yet been studied.

METHODS

We examine the BrAC-TAC relationship by having two investigators participate in four laboratory drinking sessions with varied stomach content conditions: (i) no meal, (ii) half and (iii) full meal before drinking, and (iv) full meal after drinking. BrAC and TAC were obtained every 10 min over the BrAC curve.

RESULTS

Eating before drinking lowered BrAC and TAC levels, with greater variability in TAC across person-device pairings, but the BrAC-TAC relationship was not consistently altered by stomach content. The mathematical model calibration parameters, fit indices, and eBrAC curves and summary score outputs did not consistently vary based on stomach content, indicating that our models were able to produce eBrAC from TAC with similar accuracy despite variations in the shape and magnitude of the BrAC curves under different conditions.

DISCUSSION AND CONCLUSIONS

This study represents the first examination of how stomach content affects our ability to model estimates of BrAC from TAC and indicates it is not a major factor.

Ecological Momentary Assessment of Alcohol Consumption and Its Concordance with Transdermal Alcohol Detection and Timeline Follow-Back Self-report Among Adults Experiencing Homelessness

Background

Studies of alcohol use presume valid assessment measures. To evaluate this presumption, we examined the concordance of alcohol use as measured by ecological momentary assessment (EMA) self‐reports, transdermal alcohol concentration readings via the Secure Continuous Remote Alcohol Monitor (SCRAM), and retrospective self‐reports via the Timeline Follow‐Back (TLFB) among adults experiencing homelessness.

Methods

Forty‐nine adults who reported alcohol misuse (mean age = 47, SD = 9; 57% Black; 82% men) were recruited from a homeless shelter. For 4 weeks, alcohol use was assessed: (i) 5 times or more per day by EMA, (ii) every 30 minutes by a SCRAM device worn on the ankle, and (iii) by TLFB for the past month at the end of the study period. There were 1,389 days of observations of alcohol use and alcohol use intensity for 49 participants.

Results

EMA and SCRAM alcohol use data agreed on 73% of days, with an interrater agreement Kappa = 0.46. A multilevel analysis of concordance of 3 measures for alcohol use yielded statistically significant correlations of 0.40 (day level) and 0.63 (person level) between EMA and SCRAM. Alcohol use was detected on 49, 38, and 33% of days by EMA, SCRAM, and TLFB, respectively. For alcohol use intensity, EMA and SCRAM resulted in statistically significant correlations of 0.46 (day level) and 0.78 (person level). The concordance of TLFB with either EMA or SCRAM was weak, especially at the day level.

Conclusions

This is the first study to examine concordance of alcohol use estimates using EMA, SCRAM, and TLFB methods in adults experiencing homelessness. EMA is a valid approach to quantifying alcohol use, especially given its relatively low cost, low participant burden, and ease of use. Furthermore, any stigma associated with wearing the SCRAM or reporting alcohol use in person may be attenuated by using EMA, which may be appealing for use in studies of stigmatized and underserved populations.

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

OBJECTIVE

To 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.

METHODS

We 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.

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

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Wrist-worn alcohol biosensors: Strengths, limitations, and future directions

Wearable alcohol biosensors have emerged as a valuable tool for noninvasive, objective, and continuous monitoring of alcohol consumption. However, to date their research and clinical applications have been limited by several factors including large size, high cost, and social stigma. In contrast, recently developed wrist-worn alcohol biosensors are smaller, less expensive, and may be more acceptable for daily use. However, these devices are at the prototype phase and have just begun to be tested for research applications. In this paper, we describe our experiences with two prototypes of these new wrist-worn alcohol biosensors (i.e., Quantac Tally and BACtrack Skyn) and their associated smartphone applications in both a controlled laboratory setting and the real-world environment. Our preliminary experiences with these devices highlight their advantages including comfort, high participant acceptability, and good compliance. However, there are various limitations that should be addressed prior to future research applications of these biosensors, including large interpersonal variations in transdermal alcohol readings, lack of immediately applicable data analysis/interpretation software, and poor battery life after a few months. More research is also needed to further validate the new biosensors, and investigate individual (e.g., skin thickness, gender differences) and environmental factors (e.g., humidity, temperature) contributing to the variations in transdermal alcohol readings measured by wrist-worn alcohol biosensors.

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.

Temporal Dynamics of Transdermal Alcohol Concentration Measured via New-Generation Wrist-Worn Biosensor

BACKGROUND

The development of a transdermal alcohol biosensor could represent a tremendous advance toward curbing problematic drinking. But several factors limit the usefulness of extant transdermal technology, including relatively lengthy delays between blood alcohol concentration (BAC) and transdermal alcohol concentration (TAC), as well as the large/bulky designs of currently available transdermal sensors (e.g., ankle monitors). The current research examined the lag time between BAC and TAC using a prototype of BACtrack Skyn-a new-generation wrist-worn transdermal sensor featuring a compact design and smartphone integration.

METHODS

Participants (N = 30) received either a dose of alcohol (target BAC 0.08%) or a nonalcoholic beverage in the laboratory while wearing both the AMS SCRAM ankle monitor and a Skyn prototype. Participants were monitored in the laboratory until breath alcohol concentration (BrAC) dropped below 0.025%.

RESULTS

Device failure rates for Skyn prototypes were relatively high (18 to 38%) compared with nonprototype SCRAM devices (2%). Among participants with usable data, both Skyn- and SCRAM-measured TAC showed strong correlations with BrAC, and both Skyn and SCRAM devices detected alcohol within 30 minutes of first alcohol administration. Skyn-measured TAC peaked over 1 hour earlier than SCRAM-measured TAC (54 versus 120 minutes after peak BrAC, respectively), and time-series models suggested that, on average across all measured portions of the BrAC curve, Skyn TAC lagged behind BrAC by 24 minutes, whereas SCRAM TAC lagged behind BrAC by 69 minutes-all differences statistically significant at p < 0.001.

CONCLUSIONS

Results provide preliminary evidence for the validity of a new-generation wrist-worn transdermal sensor under controlled laboratory conditions and further suggest favorable properties of this sensor as they pertain to the latency of transdermal alcohol detection. The prototype version of Skyn employed here displayed a higher failure rate compared with SCRAM, and, in future, more reliable and robust Skyn prototypes will be required suitable to field testing across diverse environmental conditions.

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.

A preliminary randomized controlled trial of contingency management for alcohol use reduction using a transdermal alcohol sensor

AIMS

We tested the efficacy of daily contingent reinforcement for reducing alcohol use compared with (yoked) non-contingent reinforcement (NR) using a transdermal alcohol sensor to detect alcohol use.

DESIGN

Pilot randomized controlled design with 1 baseline week, 3 intervention weeks and 1-month follow-up.

SETTING

New England, USA.

PARTICIPANTS

Heavy drinking adults (46.7% female) not seeking treatment were randomized to (1) an escalating schedule of cash reinforcement (CR; n = 15) for days on which alcohol was neither reported nor detected or (2) yoked NR (n = 15).

INTERVENTION AND COMPARATOR

Reinforcement for CR participants started at $5 and increased $2 every subsequent day on which alcohol was not detected or reported, to a maximum of $17. Participants received no reinforcement for days on which alcohol use was detected or reported, and the reinforcer value was re-set to $5 the day after a drinking day. NR participants were yoked to the daily reinforcer value of an individual in the CR condition, in order of enrollment. Paired participants in CR and NR therefore received the same amount of money, but the amount for the NR participant was not behavior-related.

MEASUREMENTS

The primary outcome was percentage of days without sensor-detected drinking. Secondary outcomes were number of consecutive days with no detected drinking, peak transdermal alcohol concentration (TAC), self-reported drinks per week and drinking below NIH low-risk guidelines.

FINDINGS

Controlling for baseline, CR had a higher percentage of days with no drinking detected (54.3%) than NR (31.2%) during intervention weeks [P = 0.05, Cohen's d = 0.74; 95% confidence interval (CI) = 0.007-1.47]. The longest period of consecutive days with no drinking detected was 8.0 for CR versus 2.9 for NR (P = 0.03, d = 0.85; 95% CI = 0.08-1.61). Peak TAC during intervention showed a non-significant group difference (P = 0.20; d = 0.48; 95% CI = 0.00-1.18); a similar result was found for drinks per week (P = 0.12; d = 0.59; 95% CI = 0.00-1.30). Four times more participants in CR drank below NIH low-risk drinking guidelines during intervention than did participants in NR: 31.1 versus 7.1% (P = 0.07; d = 0.71; 95% CI = -0.04 to 1.46). At 1-month follow-up, the highest number of consecutive days without drinking (self-report) did not differ significantly between conditions (P = 0.26), but showed a medium effect size (d = 0.44; 95% CI = -0.32 to 1.18).

CONCLUSIONS

Cash incentives linked to a transdermal alcohol sensor can reduce heavy alcohol consumption while the incentives are in operation.

Detecting Drinking Episodes in Young Adults Using Smartphone-based Sensors

Alcohol use in young adults is common, with high rates of morbidity and mortality largely due to periodic, heavy drinking episodes (HDEs). Behavioral interventions delivered through electronic communication modalities (e.g., text messaging) can reduce the frequency of HDEs in young adults, but effects are small. One way to amplify these effects is to deliver support materials proximal to drinking occasions, but this requires knowledge of when they will occur. Mobile phones have built-in sensors that can potentially be useful in monitoring behavioral patterns associated with the initiation of drinking occasions. The objective of our work is to explore the detection of daily-life behavioral markers using mobile phone sensors and their utility in identifying drinking occasions. We utilized data from 30 young adults aged 21-28 with past hazardous drinking and collected mobile phone sensor data and daily Experience Sampling Method (ESM) of drinking for 28 consecutive days. We built a machine learning-based model that is 96.6% accurate at identifying non-drinking, drinking and heavy drinking episodes. We highlight the most important features for detecting drinking episodes and identify the amount of historical data needed for accurate detection. Our results suggest that mobile phone sensors can be used for automated, continuous monitoring of at-risk populations to detect drinking episodes and support the delivery of timely interventions.

Using drinking data and pharmacokinetic modeling to calibrate transport model and blind deconvolution based data analysis software for transdermal alcohol biosensors

Alcohol researchers/clinicians have two ways to collect subject /patient field data, standard-drink self-report and the breath analyzer, neither of which is passive or accurate because active subject participation is required. Transdermal alcohol sensors have been developed to measure transdermal alcohol concentration (TAC), but they are used primarily as abstinence monitors because converting TAC into more meaningful blood/breath alcohol concentration (BAC/BrAC) is difficult. In this paper, BAC/BrAC is estimated from TAC by first calibrating forward distributed parameter-based convolution models for ethanol transport from the blood through the skin using patient-collected drinking data for a single drinking episode and a nonlinear pharmacokinetic metabolic absorption/elimination model to estimate BAC. TAC and estimated BAC are then used to fit the forward convolution filter. Nonlinear least squares with adjoint-based gradient computation are used to fit both models. Calibration results are compared with those obtained using BAC/BrAC from alcohol challenges and from standard, linear, metabolic absorption, and zero order kinetics-based elimination models, by considering peak BAC, time of peak, and area under the BAC curve. Our models (with population parameters) could be included in a smart phone app that makes it convenient for the subject/patient to enter drinking data for a single episode in the field.

Time Delays in Transdermal Alcohol Concentrations Relative to Breath Alcohol Concentrations

AIMS

Monitors of transdermal alcohol concentration (TAC) provide an objective measurement of alcohol consumption that is less invasive than measurements in blood, breath or urine; however, there is a substantial time delay in the onset of TAC compared to blood or breath alcohol concentrations (BrACs). The current study examined the characteristics of the delay between peak TAC and peak BrAC.

METHODS

Data was aggregated from three experimental laboratory studies (N = 61; 32 men, 29 women) in which participants wore a TAC monitor and BrAC was monitored while drinking one, two, three, four and five beers in the laboratory. Analyses examined the sex- and dose-related differences in peak BrAC and TAC, the time-to-peak BrAC and TAC, and time lag between the peak BrAC and TAC values.

RESULTS

The times-to-peak were an increasing function of the number of beers consumed. At each level of beer consumption the peak TAC averaged lower than peak BrAC and times-to-peak TAC were longer than for BrAC. The time-to-peak BrAC and TAC was longer for women than men. The congruence between peak TAC and BrAC increased as a function of the beers consumed. No sex difference in the time lag between peak BrAC and TAC was detected.

CONCLUSIONS

The congruence between TAC and BrAC and time lags between TAC and BrAC are related to the number of beers consumed. Peak values of TAC and BrAC became more congruent with higher doses but the time lag increased as a function of the amount of alcohol consumed.

SHORT SUMMARY

The time delay (or lag) and congruence between transdermal vs. BrACs increases as the number of beers increases. Though sex differences are evident in peak transdermal and BrACs, no sex differences were evident in the time lag and the congruence between transdermal and breath alcohol concentrations.

Alternative sampling strategies for the assessment of alcohol intake of living persons

Monitoring of alcohol consumption by living persons takes place in various contexts, amongst which workplace drug testing, driving under the influence of alcohol, driving licence regranting programs, alcohol withdrawal treatment, diagnosis of acute intoxication or fetal alcohol ingestion. The matrices that are mostly used today include blood, breath and urine. The aim of this review is to present alternative sampling strategies that allow monitoring of the alcohol consumption in living subjects. Ethanol itself, indirect (carbohydrate deficient transferrin, CDT%) as well as direct biomarkers (ethyl glucuronide, EtG; ethyl sulphate, EtS; fatty acid ethyl esters, FAEEs and phosphatidylethanol species, PEths) of ethanol consumption will be considered. This review covers dried blood spots (CDT%, EtG/EtS, PEths), dried urine spots (EtG/EtS), sweat and skin surface lipids (ethanol, EtG, FAEEs), oral fluid (ethanol, EtG), exhaled breath (PEths), hair (EtG, FAEEs), nail (EtG), meconium (EtG/EtS, FAEEs), umbilical cord and placenta (EtG/EtS and PEth 16:0/18:1). Main results, issues and considerations specific to each matrix are reported. Details about sample preparation and analytical methods are not within the scope of this review.

Using contingency management procedures to reduce at-risk drinking in heavy drinkers

BACKGROUND

Treatments for alcohol use disorders typically have been abstinence based, but harm reduction approaches that encourage drinkers to alter their drinking behavior to reduce the probability of alcohol-related consequences, have gained in popularity. This study used a contingency management procedure to determine its effectiveness in reducing alcohol consumption among heavy drinkers.

METHODS

Eighty-two nontreatment-seeking heavy drinkers (ages 21 to 54, M = 30.20) who did not meet diagnostic criteria for alcohol dependence participated in the study. The study had 3 phases: (i) an Observation phase (4 weeks) where participants drank normally; (ii) a Contingency Management phase (12 weeks) where participants were paid $50 weekly for not exceeding low levels of alcohol consumption as measured by transdermal alcohol concentrations, <0.03 g/dl; and (iii) a Follow-up phase (12 weeks) where participants (n = 66) returned monthly for 3 months to self-report drinking after the contingencies were removed. Transdermal alcohol monitors were used to verify meeting contingency requirements; all other analyses were conducted on self-reported alcohol use.

RESULTS

On average 42.3% of participants met the contingency criteria and were paid an average of $222 during the Contingency Management phase, with an average $1,998 in total compensation throughout the study. Compared to the Observation phase, the percent of any self-reported drinking days significantly decreased from 59.9 to 40.0% in the Contingency Management and 32.0% in the Follow-up phases. The percent of self-reported heavy drinking days reported also significantly decreased from 42.4% in the Observation phase to 19.7% in the Contingency Management phase, which was accompanied by a significant increase in percent days of self-reported no (from 40.1 to 60.0%) and low-level drinking (from 9.9 to 15.4%). Self-reported reductions in drinking either persisted, or became more pronounced, during the Follow-up phase.

CONCLUSIONS

Contingency management was associated with a reduction in self-reported episodes of heavy drinking among nontreatment-seeking heavy drinkers. These effects persisted even after incentives were removed, indicating the potential utility of contingency management as a therapeutic intervention to reduce harmful patterns of drinking.

The Potential Clinical Utility of Transdermal Alcohol Monitoring Data to Estimate the Number of Alcoholic Drinks Consumed

OBJECTIVES

Transdermal alcohol monitoring is used extensively in forensic settings to identify whether individuals have violated court-ordered mandates to abstain from drinking. Despite widespread use in that setting, comparatively few studies have explored the clinical utility of transdermal alcohol monitoring. Furthermore, of the few studies conducted, most have relied on the forensically established conservative criteria to identify whether or not a drinking episode has occurred. Here, we explore how transdermal alcohol monitoring data can be used to estimate more clinically meaningful parameters relevant to clinical treatment programs.

METHODS

We developed a procedure to use transdermal data to objectively estimate the number of standardized drinks an individual has consumed. Participants included 46 men and women who consumed 1 to 5 beers within 2 hours in the laboratory on separate days while wearing devices to monitor transdermal alcohol concentrations (TAC).

RESULTS

A mathematical model was derived to estimate the number of standardized alcohol drinks consumed, which included a number of variables (time-to-peak TAC, area under the TAC curve, and sex). The model was then validated by applying it to data from a separate study. Our results indicate that transdermal alcohol devices can be used to estimate the number of standard drinks consumed.

CONCLUSIONS

Objective methods characterizing both the level of intoxication achieved and the number of drinks consumed, such as transdermal alcohol monitoring, could be useful in both research and treatment settings.

Using Transdermal Alcohol Monitoring to Detect Low-Level Drinking

BACKGROUND

Several studies demonstrate the utility of Alcohol Monitoring Systems' (AMS) transdermal alcohol concentration (TAC) monitor to objectively quantify drinking. AMS standard criteria (i.e., TAC >0.02 g/dl) used for drinking detection are deliberately conservative, but consequently only detect drinking equivalent to 5 or more standard drinks. Our study sought to characterize the sensitivity of TAC measurement to detect low-level drinking defined as the consumption of 1 to 3 beers.

METHODS

Data were pooled from 3 studies giving controlled doses of 1, 2, 3, 4, and 5 Corona© beers (12 oz = 0.92 standard units) to 32 male and 29 female research volunteers wearing TAC monitors under controlled conditions. Analyses describe the sensitivity to detect drinking at various peak TAC thresholds beginning with any positive reading >0 g/dl, and then using TAC thresholds of 0.02 and 0.03 g/dl.

RESULTS

Nearly 40% of participants drinking 1 beer did not have a positive TAC reading. However, positive TAC readings were observed in more than 95 and in 100% of participants drinking 2 and 3 or more beers, respectively. The probability of peak TAC detection was a positive function of the number of beers consumed and a negative function of the minimum TAC threshold for detection. Drinking was somewhat more likely to be detected in females than males drinking 2 to 5 beers, but not after 1 beer. Use of AMS standard criteria only reliably detected the consumption of 5 beers, and 45.9% of all occasions of drinking 1 to 3 beers were undetected using 0.02 g/dl as a threshold.

CONCLUSIONS

Peak TAC levels between 0 and 0.02 g/dl must be considered to detect the low-level drinking of 1 to 3 standard drinks, and such thresholds are necessary when researchers and clinicians want to detect low-level drinking.

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

BACKGROUND

Recently, 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.

METHODS

Drinkers (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.

RESULTS

Compared 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.

CONCLUSIONS

These 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.

Do variable rates of alcohol drinking alter the ability to use transdermal alcohol monitors to estimate peak breath alcohol and total number of drinks?

BACKGROUND

Transdermal alcohol monitoring is a noninvasive method that continuously gathers transdermal alcohol concentrations (TAC) in real time; thus, its use is becoming increasingly more common in alcohol research. In previous studies, we developed models that use TAC data to estimate peak breath alcohol concentration (BrAC) and standard units consumed when the rate of consumption was tightly controlled.

METHODS

Twenty-two healthy participants aged 21 to 52 who reported consuming alcohol on 1 to 4 days per week were recruited from the community. The final study sample included 11 men and 8 women. Both TAC and BrAC were monitored while each participant drank 1, 2, 3, 4, and 5 beers in the laboratory on 5 separate days. In contrast to previous studies, a self-paced alcohol administration procedure was used.

RESULTS

While there was considerable variation in the times it took to consume each beer, key TAC parameters were not affected by pace of drinking. TAC data were then used in combination with the previously derived equations and estimated peak BrAC and standard units of alcohol consumed.

CONCLUSIONS

Transdermal alcohol monitoring can be used to reliably estimate peak BrAC and standard number of units consumed regardless of the rate of consumption, further demonstrating its usefulness in clinical research.

Use of continuous transdermal alcohol monitoring during a contingency management procedure to reduce excessive alcohol use

BACKGROUND

Research on contingency management to treat excessive alcohol use is limited due to feasibility issues with monitoring adherence. This study examined the effectiveness of using transdermal alcohol monitoring as a continuous measure of alcohol use to implement financial contingencies to reduce heavy drinking.

METHODS

Twenty-six male and female drinkers (from 21 to 39 years old) were recruited from the community. Participants were randomly assigned to one of the two treatment sequences. Sequence 1 received 4 weeks of no financial contingency (i.e., $0) drinking followed by 4 weeks each of $25 and then $50 contingency management; Sequence 2 received 4 weeks of $25 contingency management followed by 4 weeks each of no contingency (i.e., $0) and then $50 contingency management. During the $25 and $50 contingency management conditions, participants were paid each week when the Secure Continuous Remote Alcohol Monitor (SCRAM-II™) identified no heavy drinking days.

RESULTS

Participants in both contingency management conditions had fewer drinking episodes and reduced frequencies of heavy drinking compared to the $0 condition. Participants randomized to Sequence 2 (receiving $25 contingency before the $0 condition) exhibited less frequent drinking and less heavy drinking in the $0 condition compared to participants from Sequence 1.

CONCLUSIONS

Transdermal alcohol monitoring can be used to implement contingency management programs to reduce excessive alcohol consumption.

Biomonitoring for Improving Alcohol Consumption Surveys: The New Gold Standard?

To assess alcohol consumption levels in large populations, researchers often rely on self-report measures. However, these approaches are associated with several limitations, particularly underreporting. Use of noninvasive biomonitoring approaches may help validate self-report alcohol consumption measurements and thus improve their accuracy. Two such devices currently are available, the WrisTASTM and SCRAMTM devices, both of which measure alcohol vapors emitted through the skin after alcohol consumption. Several studies assessing the utility of the WrisTASTM bracelet in determining alcohol consumption levels noted that it was associated with relatively high failure rates. The SCRAMTM is an ankle bracelet intended for court-ordered alcohol monitoring. In studies, its sensitivity exceeded that of the WrisTASTM and increased with increasing blood alcohol concentrations. Although early studies also identified some equipment concerns with the SCRAMTM, studies of its ability to detect moderate and heavy drinking recently have yielded good results. Biomonitoring devices already are valuable tools and with further improvements may become even more useful in both research and practical applications.