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

Endpoints for Pharmacotherapy Trials for Alcohol Use Disorder

Alcohol use disorder (AUD) is a debilitating disorder, yet currently approved pharmacotherapies to treat AUD are under-utilized. The three medications approved by the US Food and Drug Administration (FDA) for the indication of AUD are disulfiram, acamprosate, and naltrexone. The current landscape of pharmacotherapies for AUD suggests opportunities for improvement. Clinical trials investigating novel pharmacotherapies for AUD traditionally use abstinence-based drinking outcomes or no heavy drinking days as trial endpoints to determine the efficacy of pharmacotherapies. These outcomes are typically measured through patient self-report endorsements of their drinking. Apart from these traditional outcomes, there have been recent developments in novel endpoints for AUD pharmacotherapies. These novel endpoints include utilizing the World Health Organization (WHO) risk drinking level reductions to promote a harm-reduction endpoint rather than an abstinence-based endpoint. Additionally, in contrast to patient self-report measurements, biological markers of alcohol use may serve as objective endpoints in AUD pharmacotherapy trials. Lastly, the National Institute on Alcohol Abuse and Alcoholism (NIAAA) definition of recovery from AUD and patient-oriented outcomes offer new frameworks to consider endpoints associated with more than alcohol consumption itself, such as the provider-patient experiences with novel pharmacotherapies. These recent developments in new endpoints for AUD pharmacotherapies offer promising future opportunities for pharmacotherapy development, so long as validity and reliability measures are demonstrated for the endpoints. A greater breadth of endpoint utilization may better capture the complexity of AUD symptomatology.

Baseline protective behavioral strategy use predicts more moderate transdermal alcohol concentration dynamics and fewer negative consequences of drinking in young adults' natural settings

OBJECTIVE

Test whether frequent protective behavioral strategies (PBS) users report (a) fewer alcohol-related consequences and (b) less risky alcohol intoxication dynamics (measured via transdermal alcohol concentration [TAC] sensor "features") in daily life.

METHOD

Two hundred twenty-two frequently heavy-drinking young adults (Mage = 22.3 years) wore TAC sensors for 6 consecutive days. TAC features peak (maximum TAC), rise rate (speed of TAC increase), and area under the curve (AUC) were derived for each day. Negative alcohol-related consequences were measured in the morning after each self-reported drinking day. Past-year PBS use was measured at baseline.

RESULTS

Young adults reporting more frequent baseline PBS use showed (a) fewer alcohol-related consequences and (b) lower intoxication dynamics on average (less AUC, lower peaks, and slower rise rates). Limiting/stopping and manner of drinking PBS showed the same pattern of findings as the total score. Serious harm reduction PBS predicted fewer negative alcohol-related consequences, but not TAC features. Multilevel path models showed that TAC features peak and rise rate partially explained associations between PBS (total, limiting/stopping, and manner of drinking) and consequences. Independent contributions of PBS subscales were small and nonsignificant, suggesting that total PBS use was a more important predictor of risk/protection than the specific types of PBS used.

CONCLUSIONS

Young adults using more total PBS may experience fewer alcohol-related consequences during real-world drinking episodes in part through less risky intoxication dynamics (TAC features). Future research measuring PBS at the daily level is needed to formally test TAC features as day-level mechanisms of protection from acute alcohol-related consequences. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Signal processing and machine learning with transdermal alcohol concentration to predict natural environment alcohol consumption

Wrist-worn alcohol biosensors continuously and discreetly record transdermal alcohol concentration (TAC) and may allow alcohol researchers to monitor alcohol consumption in participants' natural environments. However, the field lacks established methods for signal processing and detecting alcohol events using these devices. We developed software that streamlines analysis of raw data (TAC, temperature, and motion) from a wrist-worn alcohol biosensor (BACtrack Skyn) through a signal processing and machine learning pipeline: biologically implausible skin surface temperature readings (< 28°C) were screened for potential device removal and TAC artifacts were corrected, features that describe TAC (e.g., rise duration) were calculated and used to train models (random forest and logistic regression) that predict self-reported alcohol consumption, and model performances were measured and summarized in autogenerated reports. The software was tested using 60 Skyn data sets recorded during 30 alcohol drinking episodes and 30 nonalcohol drinking episodes. Participants (N = 36; 13 with alcohol use disorder) wore the Skyn during one alcohol drinking episode and one nonalcohol drinking episode in their natural environment. In terms of distinguishing alcohol from nonalcohol drinking, correcting artifacts in the data resulted in 10% improvement in model accuracy relative to using raw data. Random forest and logistic regression models were both accurate, correctly predicting 97% (58/60; AUC-ROCs = 0.98, 0.96) of episodes. Area under TAC curve, rise duration of TAC curve, and peak TAC were the most important features for predictive accuracy. With promising model performance, this protocol will enhance the efficiency and reliability of TAC sensors for future alcohol monitoring research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Development and validation of a model for measuring alcohol consumption from transdermal alcohol content data among college students

BACKGROUND AND AIMS

Transdermal alcohol content (TAC) data collected by wearable alcohol monitors could potentially contribute to alcohol research, but raw data from the devices are challenging to interpret. We aimed to develop and validate a model using TAC data to detect alcohol drinking.

DESIGN

We used a model development and validation study design.

SETTING

Indiana, USA PARTICIPANTS: In March to April 2021, we enrolled 84 college students who reported drinking at least once a week (median age = 20 years, 73% white, 70% female). We observed participants' alcohol drinking behavior for 1 week.

MEASUREMENTS

Participants wore BACtrack Skyn monitors (TAC data), provided self-reported drinking start times in real time (smartphone app) and completed daily surveys about their prior day of drinking. We developed a model using signal filtering, peak detection algorithm, regression and hyperparameter optimization. The input was TAC and outputs were alcohol drinking frequency, start time and magnitude. We validated the model using daily surveys (internal validation) and data collected from college students in 2019 (external validation).

FINDINGS

Participants (N = 84) self-reported 213 drinking events. Monitors collected 10 915 hours of TAC. In internal validation, the model had a sensitivity of 70.9% (95% CI = 64.1%-77.0%) and a specificity of 73.9% (68.9%-78.5%) in detecting drinking events. The median absolute time difference between self-reported and model-detected drinking start times was 59 min. Mean absolute error (MAE) for the reported and detected number of drinks was 2.8 drinks. In an exploratory external validation among five participants, number of drinking events, sensitivity, specificity, median time difference and MAE were 15%, 67%, 100%, 45 minutes and 0.9 drinks, respectively. Our model's output was correlated with breath alcohol concentration data (Spearman's correlation [95% CI] = 0.88 [0.77, 0.94]).

CONCLUSION

This study, the largest of its kind to date, developed and validated a model for detecting alcohol drinking using transdermal alcohol content data collected with a new generation of alcohol monitors. The model and its source code are available as Supporting Information (https://osf.io/xngbk).

Wearable alcohol monitors for alcohol use data collection among college students: Feasibility and acceptability

OBJECTIVE

We assessed the feasibility and acceptability of using BACtrack Skyn wearable alcohol monitors for alcohol research in a college student population.

METHODS

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.

RESULTS

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).

CONCLUSIONS

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.

Acceptability and validity of using the BACtrack skyn wrist-worn transdermal alcohol concentration sensor to capture alcohol use across 28 days under naturalistic conditions - A pilot study

Wrist-worn transdermal alcohol concentration (TAC) sensors have the potential to provide detailed information about day-level features of alcohol use but have rarely been used in field-based research or in early adulthood (i.e., 26-40 years) alcohol users. This pilot study assessed the acceptability, user burden, and validity of using the BACtrack Skyn across 28 days in individuals' natural settings. Adults aged 26-37 (N = 11, Mage = 31.2, 55% female, 73% non-Hispanic white) participated in a study including retrospective surveys, a 28-day field protocol wearing Skyn and SCRAM sensors and completing ecological momentary assessments (EMA) of alcohol use and duration (daily morning reports and participant-initiated start/stop drinking EMAs), and follow-up interviews. Day-level features of alcohol use extracted from self-reports and/or sensors included drinks consumed, estimated Blood Alcohol Concentration (eBAC), drinking duration, peak TAC, area under the curve (AUC), rise rate, and fall rate. Repeated-measures correlations (rrm) tested within-person associations between day-level features of alcohol use from the Skyn versus self-report or the SCRAM. Participants preferred wearing the Skyn over the SCRAM [t (10) = -6.79, p < .001, d = 2.74]. Skyn data were available for 5614 (74.2%) out of 7566 h, with 20.7% of data lost due to syncing/charging issues and 5.1% lost due to device removal. Skyn agreement for detecting drinking days was 55.5% and 70.3% when compared to self-report and the SCRAM, respectively. Correlations for drinking intensity between self-report and the Skyn were 0.35 for peak TAC, 0.52 for AUC, and 0.30 for eBAC, which were smaller than correlations between self-report and SCRAM, at 0.78 for peak TAC, 0.79 for AUC, and 0.61 for eBAC. Correlations for drinking duration were larger when comparing self-report to the Skyn (rrm = 0.36) versus comparing self-report to the SCRAM (rrm = 0.31). The Skyn showed moderate-to-large, significant correlations with the SCRAM for peak TAC (rrm = 0.54), AUC (rrm = 0.80), and drinking duration (rrm = 0.63). Our findings support the acceptability and validity of using the Skyn for assessing alcohol use across an extended time frame (i.e., 28 days) in individuals' natural settings, and for providing useful information about day-level features of alcohol use.

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.

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

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Hazardous drinking and alcohol use disorders

Alcohol is one of the most widely consumed psychoactive drugs globally. Hazardous drinking, defined by quantity and frequency of consumption, is associated with acute and chronic morbidity. Alcohol use disorders (AUDs) are psychiatric syndromes characterized by impaired control over drinking and other symptoms. Contemporary aetiological perspectives on AUDs apply a biopsychosocial framework that emphasizes the interplay of genetics, neurobiology, psychology, and an individual's social and societal context. There is strong evidence that AUDs are genetically influenced, but with a complex polygenic architecture. Likewise, there is robust evidence for environmental influences, such as adverse childhood exposures and maladaptive developmental trajectories. Well-established biological and psychological determinants of AUDs include neuroadaptive changes following persistent use, differences in brain structure and function, and motivational determinants including overvaluation of alcohol reinforcement, acute effects of environmental triggers and stress, elevations in multiple facets of impulsivity, and lack of alternative reinforcers. Social factors include bidirectional roles of social networks and sociocultural influences, such as public health control strategies and social determinants of health. An array of evidence-based approaches for reducing alcohol harms are available, including screening, pharmacotherapies, psychological interventions and policy strategies, but are substantially underused. Priorities for the field include translating advances in basic biobehavioural research into novel clinical applications and, in turn, promoting widespread implementation of evidence-based clinical approaches in practice and health-care systems.

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.

Review of Ethanol Intoxication Sensing Technologies and Techniques

The field of alcohol intoxication sensing is over 100 years old, spanning the fields of medicine, chemistry, and computer science, aiming to produce the most effective and accurate methods of quantifying intoxication levels. This review presents the development and the current state of alcohol intoxication quantifying devices and techniques, separated into six major categories: estimates, breath alcohol devices, bodily fluid testing, transdermal sensors, mathematical algorithms, and optical techniques. Each of these categories was researched by analyzing their respective performances and drawbacks. We found that the major developments in monitoring ethanol intoxication levels aim at noninvasive transdermal/optical methods for personal monitoring. Many of the "categories" of ethanol intoxication systems overlap with each other with to a varying extent, hence the division of categories is based only on the principal operation of the techniques described in this review. In summary, the gold-standard method for measuring blood ethanol levels is through gas chromatography. Early estimation methods based on mathematical equations are largely popular in forensic fields. Breath alcohol devices are the most common type of alcohol sensors on the market and are generally implemented in law enforcement. Transdermal sensors vary largely in their sensing methodologies, but they mostly follow the principle of electrical sensing or enzymatic reaction rate. Optical devices and methodologies perform well, with some cases outperforming breath alcohol devices in terms of the precision of measurement. Other estimation algorithms consider multimodal approaches and should not be considered alcohol sensing devices, but rather as prospective measurement of the intoxication influence. This review found 38 unique technologies and techniques for measuring alcohol intoxication, which is testament to the acute interest in the innovation of noninvasive technologies for assessing intoxication.

Sensitivity, specificity, and tolerability of the BACTrack Skyn compared to other alcohol monitoring approaches among young adults in a field-based setting

BACKGROUND

There is a need for novel alcohol biosensors that are accurate, able to detect alcohol concentration close in time to consumption, and feasible and acceptable for many clinical and research applications. We evaluated the field accuracy and tolerability of novel (BACTrack Skyn) and established (Alcohol Monitoring Systems SCRAM CAM) alcohol biosensors.

METHODS

The sensor and diary data were collected in a larger study of a biofeedback intervention and compared observationally in the present sub-study. Participants (high-risk drinkers, 40% female; median age 21) wore both Skyn and SCRAM CAM sensors for 1-6 days and were instructed to drink as usual. Data from the first cohort of participants (N = 27; 101 person-days) were used to find threshold values of transdermal alcohol that classified each day as meeting or not meeting defined levels of drinking (heavy, above-moderate, any). These values were used to develop scoring metrics that were subsequently tested using the second cohort (N = 20; 57 person-days). Data from both biosensors were compared to mobile diary self-report to evaluate sensitivity and specificity in relation to a priori standards established in the literature.

RESULTS

Skyn classification rules for Cohort #1 within 3 months of device shipment showed excellent sensitivity for heavy drinking (94%) and exceeded expectations for above-moderate and any drinking (78% and 69%, respectively), while specificity met expectations (91%). However, classification worsened when Cohort #1 devices ≥3 months from shipment were tested (area under curve for receiver operator characteristic 0.87 vs. 0.79) and the derived classification threshold when applied to Cohort #2 was inadequately specific (70%). Skyn tolerability metrics were excellent and exceeded the SCRAM CAM (p ≤ 0.001).

CONCLUSIONS

Skyn tolerability was favorable and accuracy rules were internally derivable but did not yield useful scoring metrics going forward across device lots and months of usage.