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

A wearable alcohol biosensor: Exploring the accuracy of transdermal drinking detection

BACKGROUND

Trace amounts of consumed alcohol are detectable within sweat and insensible perspiration. However, the relationship between ingested and transdermally emitted alcohol is complex, varying across environmental conditions and involving a degree of lag. As such, the feasibility of real-time drinking detection across diverse environments has been unclear. In the current research we revisit sensor performance using new tools, exploring the accuracy of a new generation of rapid-sampling transdermal biosensor for contemporaneous drinking detection across diverse environments via machine learning.

METHODS

Regular drinkers (N = 100) attended three laboratory sessions involving the experimental manipulation of alcohol dose, rate of consumption, and environmental dosing conditions. Participants further supplied breath alcohol concentration (BAC) readings in the field over 14 days. Participants wore compact wrist sensors capable of rapid sampling (20sec intervals). Transdermal sensor data was translated into alcohol use estimates using machine learning, integrating only transdermal data collected prior to the point of BAC assessment.

RESULTS

A total of 5.39 million transdermal readings (28,615hours) and 12,699 BAC readings were collected for this research. Models indicated strong transdermal sensor accuracy for real-time drinking detection across both laboratory and field contexts (AUROC, 0.966, 95 % CI, 0.956-0.972; Sensitivity, 89.8 %; Specificity, 90.6 %). Models aimed at differentiating high-risk (≥0.08 %) drinking levels yielded intermediate (AUROC, 0.738; 95 % CI, 0.698-0.777; only drinking episodes) to strong (AUROC, 0.941, 95 % CI, 0.929-0.954; all data) accuracy levels.

CONCLUSIONS

Results indicate a range of useful future applications for transdermal alcohol sensors including long-term health tracking, medical monitoring, and just-in-time relapse prevention.

Development of an accelerometer-based wearable sensor approach for alcohol consumption detection

BACKGROUND

Alcohol is a commonly used substance associated with significant public health consequences. Treatment is often stigmatized and limited with regard to both access and affordability, demonstrating the need for innovations in alcohol treatment. Accelerometer sensors can detect drinking without user input and are widely incorporated into wearable devices, increasing accessibility and affordability.

METHODS

We compared a distributional and random forest classification approach to detect and evaluate sensor-based drinking data. Data were collected at a local state fair (n = 194), where participants drank water at specified intervals interspersed with confounding behaviors (e.g., touching nose, rubbing forehead, or yawning) while wearing an Android-based smartwatch for 10 min. Participants were randomized to receive one of three drinking container shapes: pint, martini, or wine.

RESULTS

The random forest model achieved an overall testing accuracy of 93% (sensitivity = 0.32; specificity = 0.99; positive predictive value = 0.74). The distributional algorithm achieved an overall accuracy of 95% (sensitivity = 0.76; specificity = 0.97; positive predictive value = 0.72). The distributional algorithm had a significantly greater accuracy (t(193) = 7.73, p < 0.001, d = 0.56) and sensitivity (t(193) = 24.5, p < 0.001, d = 1.76). Equivalency testing demonstrated significant equivalency to the ground truth for sip duration (tlower(193) = 16.92, p < 0.001; tupper(193) = -9.85, p < 0.001) and between-sip interval (tlower(193) = 1.72, p = 0.044; thigher(193) = -3.96, p < 0.001). However, the random forest did not have significant equivalency to the ground truth for between-sip interval (tlower(193) = 1.98, p = 0.025; thigher(193) = 0.160, p = 0.564).

CONCLUSIONS

Overall, the results indicated that consumer-grade smartwatches can be utilized to detect and measure alcohol use behavior using machine learning and distributional algorithms. This work provides the methodological foundation for future research to analyze the behavioral pharmacology of alcohol use and develop accessible just-in-time clinical interventions.

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.

A qualitative exploration of the experiences of transdermal alcohol sensor devices amongst people in receipt of treatment for alcohol use disorder

Introduction

Transdermal alcohol sensors (TAS) have the potential to be used as a clinical tool in alcohol treatment, but there is limited research with individuals with alcohol dependence using TAS. Our study is a qualitative evaluation of the views of people attending alcohol treatment and their experiences of wearing the BACtrack Skyn, within alcohol services in South London.

Methods

Participants with alcohol dependence wore a BACtrack Skyn TAS for one week and met with the researcher every two days, for a total of four meetings (for example: Monday, Wednesday, Friday, and Monday). In the final meeting, a post-wear survey (on their physical, social and comfort experience of the TAS) and semi-structured interview were completed. The Technology Acceptance Model (TAM) informed the topic guide and data analysis.

Results

Adults (N = 16) receiving alcohol treatment were recruited. Three core topics guided analysis: perceived usefulness, perceived ease of use and attitudes towards use. Participants found the TAS easy to wear and felt positive about its appearance and comfort. The only challenges reported were side effects, mostly skin irritation. The main two perceived uses were 1) TAS working as a drinking deterrent and 2) reducing daily breathalyser visits during detox.

Conclusion

Findings support the use of TAS amongst alcohol service users. Wearing the TAS for one week was acceptable and feasible for objective alcohol concentration measurement. Participants reported high perceived ease of use and usefulness of the Skyn in the context of alcohol treatment. These results are encouraging for the use of TAS in clinical settings.

Comparison of transdermal alcohol concentration and self-reported alcohol consumption in people with alcohol dependence attending community alcohol treatment services

AIM

We aimed to assess the accuracy and wearability of a transdermal alcohol sensor (TAS) (BACtrack Skyn) with people currently receiving treatment at alcohol services.

METHOD

A mixed methods observational study involving three NHS alcohol services in south London was conducted. All participants (7=male, 9=female) wore a TAS for 1 week and met with the researcher every other weekday to complete the TAS data download and a TimeLine Follow Back (TLFB). At the end of the week, a post-wear survey was completed. Transdermal Alcohol Concentration (TAC) from the TAS was compared to the TLFB. Post-wear survey responses, attendance voucher incentives and descriptive TAS data (removals, missing and skin temperature data) were analysed. We investigated different drinking event thresholds changing the criteria of TAC level and length of time TAC was increased and analysed each drinking threshold sensitivity, specificity, positive and negative predicative values, and percentage accuracy classification.

RESULTS

The TAS recorded the number of alcohol-drinking days with a high degree of accuracy compared to the TLFB as gold-standard. However, of the participation time of the 16 participants, 14.5% of the TAS data was missing in output and 16.4% of the recorded data suggests the TAS was not currently being worn. Of the data recorded, in line with the drinking event threshold of >15 ug/l TAC, >15minutes, we found that sensitivity = 93%, specificity = 84% and a Pearson correlation of r(16) =.926, p = <.001, BCa 95% CI [.855 -.981]. The threshold with the highest accuracy was TAC>15 ug/l, >60minutes which classified alcohol events with 90% accuracy, AUC =.910, sensitivity = 90%, specificity = 96%. The post-wear survey reported that most participants found it comfortable and that wearing it did not interfere with daily activities. Six participants reported side effects, including itching and a rash, but these would not deter them from wearing it again with all six reporting they would wear the TAS again and for longer than one week.

CONCLUSIONS

The TAS did not capture every drinking event that was self-reported but maintained a high correlation. There were instances of missing TAS data and TAS removals. Overall, our findings would support the acceptability and feasibility of TAS as a tool that could be used in clinical settings for objective alcohol monitoring with patients being responsible for the TAS.

College Students' Day-to-Day Maladaptive Drinking Responses to Stress Severity and Stressor-Related Guilt and Anger

BACKGROUND

Stress is a common part of college students' daily lives that may influence their physical activity (PA) and alcohol use. Understanding features of daily stress processes that predict health behaviors could help identify targets for just-in-time interventions.

PURPOSE

This study used intensive longitudinal data to examine whether prior day stress processes predict current day PA or alcohol use.

METHODS

Participants (N=58, Mage=20.5, 59% women, 70% White) were 18-to-25-year-old students who engaged in binge drinking at least twice monthly and used cannabis or tobacco in the past year. They wore activity (activPAL4) and alcohol (Secure Continuous Remote Alcohol Monitor) monitors for 11 days to assess daily PA (e.g., step counts) and alcohol use (e.g., drinking day), and completed daily surveys about yesterday's stress, including number of stressors (i.e., frequency), stressor intensity (i.e., severity), and frequency of affective states (e.g., guilt). Multilevel models examined prior day stress predicting current day PA or alcohol use.

RESULTS

Participants had higher odds of current day drinking (odds ratio=1.21) and greater area under the curve (B=0.08) when they experienced greater than usual stress severity the prior day. Participants had higher current day peak transdermal alcohol concentration (B=0.12) and area under the curve (B=0.11) when they more frequently experienced guilt due to stressors the prior day.

CONCLUSIONS

College students' unhealthy response of increasing alcohol use due to stress could adversely impact health outcomes. There is a critical need for interventions addressing students' ability to effectively manage and respond to the stress-inducing, daily demands of student life.

Accuracy of transdermal alcohol monitoring devices in a laboratory setting

The development of transdermal alcohol sensors (TASs) presents a new method to monitor alcohol consumption with the ability to objectively measure data 24/7. We aimed to evaluate the accuracy of two TASs (BACtrack Skyn and Smart Start BARE) in a laboratory setting. Thirty-two adults received a dose of ethanol 0.56 g/kg body weight as a 20% solution while wearing the two TASs and provided Breath Alcohol Concentration (BrAC) measurements for 3.5 h postalcohol consumption. Pearson's correlations and repeated measures analysis of variance tests were conducted on the peak, time-to-peak, and area under the curve data. Bland-Altman plots were derived. A time series analysis and cross-correlations were conducted to adjust for time lag. Both TASs were able to detect alcohol and increase within 20 min. BrAC peaked significantly quicker than Skyn and BARE. BrAC and Skyn peaks were negatively significantly correlated (r = -0.381, P = .035, n = 31), while Skyn and BARE peaks were positively significantly correlated (r = 0.380, P = .038, n = 30). Repeated measures analysis of variance found a significant difference between BrAC, Skyn, and BARE (F(1.946, 852.301) = 459.873, P < .001)). A time series analysis found when BrAC-Skyn and BrAC-BARE were adjusted for the delay to peak, and there was still a significant difference. Failure rates: 1.7% (Skyn) and 4.8% (BARE). Some evidence was obtained for TAS validity as both consistently detected alcohol. Failure rates and time lag show improvements in older device generations. However, neither TAS presented strong equivalence to the breathalyser even when the lag time was adjusted. With further testing and technology advancements, TAS could be a potential alcohol monitoring tool. Two of the newest TAS devices were worn in laboratory conditions for one afternoon to compare their accuracy of alcohol monitoring to a breathalyser. Findings suggest that the two TASs (BACtrack Skyn and SmartStart BARE) recorded significantly similar data postalcohol consumption, but not with the breathalyser.

Acceptability and anklet user experience with the SCRAM-CAM transdermal alcohol concentration sensor in regularly drinking young adults' natural environments

Recent developments in wearable technologies have allowed for device-based capture of alcohol concentration among participants in their natural environments. Currently, the Continuous Alcohol Monitor from SCRAM systems (SCRAM-CAM) is the most extensively studied and validated transdermal alcohol concentration (TAC) sensor. However, there has been relatively little work focusing on its acceptability from the participants' perspective. In the current study, we assess the user experience of the SCRAM-CAM anklet in a sample of 222 regularly heavy drinking young adults (mean age = 22.3) who wore the anklet in their natural environments for five 24-h periods spanning 6 consecutive days. Differences in the anklet user experience were measured along a number of dimensions, and differences were tested by sex at birth, white/non-white racial/ethnic group membership, and alcohol use disorder (AUD) risk (measured through Alcohol Use Disorder Identification Test [AUDIT] scores). Males and females differed significantly on six of the eight acceptability items, with males showing more positive responses toward the anklet than females. No differences were found by white/non-white racial/ethnic groups nor AUD risk. Results suggest positive levels of acceptability toward the device overall while in natural environments, further validating the usage of the device in studies that measure alcohol consumption among different groups, including those with high levels of alcohol consumption. Researchers should take into consideration the different levels of burden or discomfort in male versus female participants when using the device.

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.

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.

Skin-Interfaced Wearable Sweat Sensors for Precision Medicine

Wearable sensors hold great potential in empowering personalized health monitoring, predictive analytics, and timely intervention toward personalized healthcare. Advances in flexible electronics, materials science, and electrochemistry have spurred the development of wearable sweat sensors that enable the continuous and noninvasive screening of analytes indicative of health status. Existing major challenges in wearable sensors include: improving the sweat extraction and sweat sensing capabilities, improving the form factor of the wearable device for minimal discomfort and reliable measurements when worn, and understanding the clinical value of sweat analytes toward biomarker discovery. This review provides a comprehensive review of wearable sweat sensors and outlines state-of-the-art technologies and research that strive to bridge these gaps. The physiology of sweat, materials, biosensing mechanisms and advances, and approaches for sweat induction and sampling are introduced. Additionally, design considerations for the system-level development of wearable sweat sensing devices, spanning from strategies for prolonged sweat extraction to efficient powering of wearables, are discussed. Furthermore, the applications, data analytics, commercialization efforts, challenges, and prospects of wearable sweat sensors for precision medicine are discussed.

Measurement of Transdermal Alcohol Concentration using a Wrist Worn Enzymatic Transdermal Monitor

BACKGROUND

New-generation transdermal monitors such as the ION Research Alpha Prototypes (ION RAP) hold promise for real-time alcohol measurement, with improvements in design features such as sampling frequency, size and comfort. This paper aims to provide the first comparisons of the wrist worn enzyme-based ION RAP and the fuel-cell based SCRAM-CAM against breath alcohol concentration (BrAC) readings.

METHODS

Participants (N=23) completed a total of 69 laboratory alcohol administration sessions, while wearing both a prototype of the ION RAP wristband and a SCRAM-CAM ankle monitor; they also gave breath samples each 10 minutes. Analyses focused on latencies of transdermal alcohol concentration (TAC) after alcohol ingestion, correlations, and cross-correlations between BrAC and TAC measurements.

RESULTS

A high failure rate of the ION RAP was observed (61.5% of the sessions were excluded due to the sessions not containing enough valid data). On average, the SCRAM-CAM and ION RAP detected alcohol 43 (SD=21) and 50 (SD=27) minutes after the first drink, with peak values reached after 138 (SD=47) and 154 (SD=56) minutes, respectively. SCRAM-CAM TAC peak (r=0.185,p=0.375) and area under the curve (AUC; r=0.320, p=0.118) showed small- and medium-sized correlations to BrAC. ION RAP TAC peak (r=-0.082, p=0.698) and AUC (r=0.040, p=0.852) correlations to BrAC were close to zero.

CONCLUSIONS

In this study, the new-generation ION RAP and the traditionally used SCRAM-CAM show similar delays in detection and similar TAC curves over time, despite using either enzyme or fuel-cell based technologies, respectively. Due to high failure rates of the ION RAP prototypes and close to zero correlations to BrAC, further developments and improvements of these TAC wristbands are required for reliable and valid use in real-time alcohol measurement.

The Impact of Acetic Acid on Measuring Ethanol Concentrations in Water and Human Serum Using Short-Wave Infrared Spectroscopy

Ethanol intoxication, although an elemental part of life in many places around the world, still presents several issues associated with excessive consumption. These issues range from drunk driving, violence, and antisocial behavior to self-harm, all exerting an increased cost on the society. Monitoring of intoxication levels can help to limit the impact of these issues by preventing the use of automobiles or heavy machinery and personal monitoring. Previous works on noninvasive measurement of ethanol tissue concentration for estimation of blood alcohol concentration (BAC) performed worst during the first hour of intoxication. Gas chromatography research of intoxication shows that levels of acetic acid rise together at a similar rate as those of ethanol after initial imbibement. In this research, short-wave infrared (SWIR) spectroscopy was utilized with the aim of establishing the interaction between ethanol and acetic acid in water and serum mixtures. The most consistent and clear correlation between ethanol and acetic acid was recorded at 2262 and 2302 nm wavelengths. Partial least-squares (PLS) analysis indicates that the most effective region for consideration in measurement of ethanol is the therapeutic window four (IV) due to high variance in vibration of carbon bonds. The behavior of spectra at different concentration ranges was examined and described in detail in relation to the consequence of alcohol measurement. The investigation concluded that ethanol shows distinctive regions of absorbance at wavelengths of 2262 and 2302 nm, with variations arising from increasing concentrations of acetic acid, whilst also showing that therapeutic window four is amongst the most influential regions of the spectrum for SWIR.

To Drink or Not to Drink: Is That the Question? Examining Correspondence and Predictive Validity of Morning Drinking Intentions for Young Adults' Drinking Behaviors and Consequences

The Theory of Planned Behavior suggests that intentions are significant and proximal determinants of behavior. The purpose of this study was to test the predictive validity of drinking intentions for subsequent same-day drinking behaviors and negative consequences. Regularly drinking young adults (N = 222, 21-29 years, 84% undergraduates) completed an ecological momentary assessment (EMA) protocol and wore an alcohol monitor for five consecutive 24-h periods spanning 6 days (Wednesday-Monday). Each morning, participants reported their drinking intentions for the day and their previous day's alcohol consumption and the number of negative drinking consequences. Multilevel models showed that, at the within-person level, on days when people reported intending to drink, to get drunk, or to drink more than usual, they had higher odds of drinking, consumed more drinks, and had higher peak transdermal alcohol concentrations later that day. However, drinking occurred on 28% of days without drinking intentions, suggesting intentions were an imperfect signal for future drinking behavior. Morning drinking intentions also predicted experiencing more negative consequences, even after controlling for alcohol consumption. On average, young adults' morning-reported drinking-related intentions predicted increased odds of same-day drinking behavior and alcohol-related consequences. However, drinking frequently occurred on days participants did not intend to drink, suggesting that focusing only on drinking intention days will result in many missed prevention opportunities. Together, these results suggest the need for additional research to increase the predictive value of drinking intention assessments and for prevention interventions aimed at helping individuals follow through on their intentions not to drink.

The effect of forming implementation intentions on alcohol consumption: A systematic review and meta-analysis

ISSUES

Meta-analysis was used to estimate the effect of forming implementation intentions (i.e., if-then plans) on weekly alcohol consumption and heavy episodic drinking (HED). Sample type, mode of delivery, intervention format and timeframe were tested as moderator variables.

APPROACH

Cochrane, EThOS, Google Scholar, PsychArticles, PubMed and Web of Science were searched for relevant publications to 31 March 2021. Random-effects meta-analysis was used to estimate the effect size difference (d) between individuals forming versus not forming implementation intentions on weekly consumption and HED.

KEY FINDINGS

Sixteen studies were included in meta-analyses. The effect size difference for forming implementation intentions on weekly alcohol consumption was d+  = -0.14 confidence interval (CI) [-0.24; -0.03]. Moderator analyses highlighted stronger effects for: (i) community (d+  = -0.38, CI [-0.58; -0.18]) versus university (d+  = -0.04, CI [-0.13; 0.05]) samples; (ii) paper (d+  = -0.26, CI [-0.43; -0.09]) versus online (d+  = -0.04, CI [-0.14; 0.06]) mode of delivery; and (iii) volitional help sheet (d+  = -0.34, CI [-0.60; -0.07]) versus implementation intention format (d+  = -0.07, CI [-0.16; 0.02]). In addition, effects diminished over time (B = 0.02, SE = 0.01, CI [0.03; 0.01]). Forming implementation intentions had a null effect on HED, d+  = -0.01 CI [-0.10; 0.08].

IMPLICATIONS

Forming implementation intentions reduces weekly consumption but has no effect on HED.

CONCLUSION

This review identifies boundary conditions on the effectiveness of implementation intentions to reduce alcohol consumption. Future research should focus on increasing the effectiveness of online-delivered interventions and integrating implementation intention and motivational interventions.

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.

Wearable Sensor for Continuous Sweat Biomarker Monitoring

In recent years, wearable sensors have enabled the unique mode of real-time and noninvasive monitoring to develop rapidly in medical care, sports, and other fields. Sweat contains a wide range of biomarkers such as metabolites, electrolytes, and various hormones. Combined with wearable technology, sweat can reflect human fatigue, disease, mental stress, dehydration, and so on. This paper comprehensively describes the analysis of sweat components such as glucose, lactic acid, electrolytes, pH, cortisol, vitamins, ethanol, and drugs by wearable sensing technology, and the application of sweat wearable devices in glasses, patches, fabrics, tattoos, and paper. The development trend of sweat wearable devices is prospected. It is believed that if the sweat collection, air permeability, biocompatibility, sensing array construction, continuous monitoring, self-healing technology, power consumption, real-time data transmission, specific recognition, and other problems of the wearable sweat sensor are solved, we can provide the wearer with important information about their health level in the true sense.

Accuracy of Wearable Transdermal Alcohol Sensors: Systematic Review

BACKGROUND

There are a range of wearable transdermal alcohol sensors that are available and are being developed. These devices have the potential to monitor alcohol consumption continuously over extended periods in an objective manner, overcoming some of the limitations of other alcohol measurement methods (blood, breath, and urine).

OBJECTIVE

The objective of our systematic review was to assess wearable transdermal alcohol sensor accuracy.

METHODS

A systematic search of the CINAHL, Embase, Google Scholar, MEDLINE, PsycINFO, PubMed, and Scopus bibliographic databases was conducted in February 2021. In total, 2 team members (EB and SH) independently screened studies for inclusion, extracted data, and assessed the risk of bias. The methodological quality of each study was appraised using the Mixed Methods Appraisal Tool. The primary outcome was transdermal alcohol sensor accuracy. The data were presented as a narrative synthesis.

RESULTS

We identified and analyzed 32 studies. Study designs included laboratory, ambulatory, and mixed designs, as well as randomized controlled trials; the length of time for which the device was worn ranged from days to weeks; and the analyzed sample sizes ranged from 1 to 250. The results for transdermal alcohol concentration data from various transdermal alcohol sensors were generally found to positively correlate with breath alcohol concentration, blood alcohol concentration, and self-report (moderate to large correlations). However, there were some discrepancies between study reports; for example, WrisTAS sensitivity ranged from 24% to 85.6%, and specificity ranged from 67.5% to 92.94%. Higher malfunctions were reported with the BACtrack prototype (16%-38%) and WrisTAS (8%) than with SCRAM (2%); however, the former devices also reported a reduced time lag for peak transdermal alcohol concentration values when compared with SCRAM. It was also found that many companies were developing new models of wearable transdermal alcohol sensors.

CONCLUSIONS

As shown, there is a lack of consistency in the studies on wearable transdermal alcohol sensor accuracy regarding study procedures and analyses of findings, thus making it difficult to draw direct comparisons between them. This needs to be considered in future research, and there needs to be an increase in studies directly comparing different transdermal alcohol sensors. There is also a lack of research investigating the accuracy of transdermal alcohol sensors as a tool for monitoring alcohol consumption in clinical populations and use over extended periods. Although there is some preliminary evidence suggesting the accuracy of these devices, this needs to be further investigated in clinical populations.

TRIAL REGISTRATION

PROSPERO CRD42021231027; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=231027.

Using wearable technology to detect prescription opioid self-administration

ABSTRACT

Appropriate monitoring of opioid use in patients with pain conditions is paramount, yet it remains a very challenging task. The current work examined the use of a wearable sensor to detect self-administration of opioids after dental surgery using machine learning. Participants were recruited from an oral and maxillofacial surgery clinic. Participants were 46 adult patients (26 female) receiving opioids after dental surgery. Participants wore Empatica E4 sensors during the period they self-administered opioids. The E4 collected physiological parameters including accelerometer x-, y-, and z-axes, heart rate, and electrodermal activity. Four machine learning models provided validation accuracies greater than 80%, but the bagged-tree model provided the highest combination of validation accuracy (83.7%) and area under the receiver operating characteristic curve (0.92). The trained model had a validation sensitivity of 82%, a specificity of 85%, a positive predictive value of 85%, and a negative predictive value of 83%. A subsequent test of the trained model on withheld data had a sensitivity of 81%, a specificity of 88%, a positive predictive value of 87%, and a negative predictive value of 82%. Results from training and testing model of machine learning indicated that opioid self-administration could be identified with reasonable accuracy, leading to considerable possibilities of the use of wearable technology to advance prevention and treatment.

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.

Associations Between Patterns of Alcohol Use and Viral Load Suppression Amongst Women Living with HIV in South Africa

This study aimed to identify alcohol use patterns associated with viral non-suppression among women living with HIV (WLWH) and the extent to which adherence mediated these relationships. Baseline data on covariates, alcohol consumption, ART adherence, and viral load were collected from 608 WLWH on ART living in the Western Cape, South Africa. We defined three consumption patterns: no/light drinking (drinking ≤ 1/week and ≤ 4 drinks/occasion), occasional heavy episodic drinking (HED) (drinking > 1 and ≤ 2/week and ≥ 5 drinks/occasion) and frequent HED (drinking ≥ 3 times/week and ≥ 5 drinks/occasion). In multivariable analyses, occasional HED (OR 3.07, 95% CI 1.78–5.30) and frequent HED (OR 7.11, 95% CI 4.24–11.92) were associated with suboptimal adherence. Frequent HED was associated with viral non-suppression (OR 2.08, 95% CI 1.30–3.28). Suboptimal adherence partially mediated the relationship between frequent HED and viral non-suppression. Findings suggest a direct relationship between frequency of HED and viral suppression. Given the mediating effects of adherence on this relationship, alcohol interventions should be tailored to frequency of HED while also addressing adherence.

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.

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.

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.

Alcohol Use Disorder in the Age of Technology: A Review of Wearable Biosensors in Alcohol Use Disorder Treatment

Biosensors enable observation and understanding of latent physiological occurrences otherwise unknown or invasively detected. Wearable biosensors monitoring physiological constructs across a wide variety of mental and physical health conditions have become an important trend in innovative research methodologies. Within substance use research, explorations of biosensor technology commonly focus on identifying physiological indicators of intoxication to increase understanding of addiction etiology and to inform treatment recommendations. In this review, we examine the state of research in this area as it pertains to treatment of alcohol use disorders specifically highlighting the gaps in our current knowledge with recommendations for future research. Annually, alcohol use disorders affect approximately 15 million individuals. A primary focus of existing wearable technology-based research among people with alcohol use disorders is identifying alcohol intoxication. A large benefit of wearable biosensors for this purpose is they provide continuous readings in a passive manner compared with the gold standard measure of blood alcohol content (BAC) traditionally measured intermittently by breathalyzer or blood draw. There are two primary means of measuring intoxication with biosensors: gait and sweat. Gait changes have been measured via smart sensors placed on the wrist, in the shoe, and mobile device sensors in smart phones. Sweat measured by transdermal biosensors detects the presence of alcohol in the blood stream correlating to BAC. Transdermal biosensors have been designed in tattoos/skin patches, shirts, and most commonly, devices worn on the ankle or wrist. Transdermal devices were initially developed to help monitor court-ordered sobriety among offenders with alcohol use disorder. These devices now prove most useful in continuously tracking consumption throughout clinical trials for behavioral treatment modalities. More recent research has started exploring the uses for physical activity trackers and physiological arousal sensors to guide behavioral interventions for relapse prevention. While research has begun to demonstrate wearable devices' utility in reducing alcohol consumption among individuals aiming to cutdown on their drinking, monitoring sustained abstinence in studies exploring contingency management for alcohol use disorders, and facilitating engagement in activity-based treatment interventions, their full potential to further aid in understanding of, and treatment for, alcohol use disorders has yet to be explored.

Combining transdermal and breath alcohol assessments, real-time drink logs and retrospective self-reports to measure alcohol consumption and intoxication across a multi-day music festival

INTRODUCTION AND AIMS

Comprehensively investigating alcohol-related behaviours in the context of a dynamic multi-day alcohol-licensed event is important for understanding and minimising patron risk. We aimed to assess the measurement utility of implementing a multi-dimensional alcohol assessment battery using biometric data collection, real-time drink logs and retrospective self-report measures over the course of a 4-day music festival.

METHODS

Fourteen adults participated (n = 7 male, mean age 21.9 years). Breath and transdermal alcohol concentration (BrAC and TAC, respectively) were measured using breathalysers and transdermal alcohol bracelets. A real-time drink log was completed via smartphones on initiating each drink, and a retrospective questionnaire was administered up to twice daily throughout the event (6 timepoints total).

RESULTS

While almost all participants (92.9%) logged significantly fewer drinks in real-time than they retrospectively reported via the twice-daily questionnaires, logs provided important contextual information including the types of drinks consumed and drinking intensity. Compared to BrAC, TAC provided a better understanding of the time course of intoxication, indicating highest alcohol consumption outside of static BrAC assessment windows. However, BrAC provided a better assessment of present state: all participants were 0.00% BrAC at departure despite over two-fifths (42.9%) of the sample's last TAC reading exceeding 0.00%.

CONCLUSIONS

As standalone assessments, each method possessed limitations. As a combined battery, they were successfully administered simultaneously, resulting in a more comprehensive overview of alcohol consumption/intoxication over the prolonged drinking session. However, the marked burden of simultaneous administration should be considered, and measures should be chosen judiciously based on research needs.