Examining features of transdermal alcohol biosensor readings: A promising approach with implications for research and intervention

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).

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.

Use of the BACtrack Skyn alcohol biosensor: Practical applications for data collection and analysis

AIMS

Alcohol biosensors, including the BACtrack Skyn, provide an objective and passive method of continuously assessing alcohol consumption in the natural environment. Despite the many strengths of the Skyn, six key challenges in the collection and processing of data include (1) identifying consumed alcohol; (2) identifying environmental alcohol; (3) identifying and determining the source of missing or invalid data; (4) achieving high participant adherence; (5) integrating Skyn and self-report data; and (6) implications for statistical inference. In this report we outline these challenges, provide recommendations to address them and identify future needs.

DESIGN AND SETTINGS

Procedures from several laboratory and field-based pilot studies are presented to demonstrate practical recommendations for Skyn use. Data from a pilot study including a 7-day ecological momentary assessment period are also presented to evaluate effects of environmental alcohol on BACtrack Skyn readings.

CONCLUSIONS

To address challenges in the collection and processing of data from the BACtrack Skyn alcohol biosensor, researchers should identify goals in advance of data collection to anticipate the processing necessary to interpret Skyn data. The Transdermal Alcohol Sensor Data Macro (TASMAC) version 2.0 software can help to process data rapidly; identify drinking events, missing data and environmental alcohol; and integrate the sensor with self-report data. Thorough participant orientation and regular contact in field studies can reduce missing data and enhance adherence. Many recommended methods for Skyn use are applicable to other alcohol sensors and wearable devices.

Identifying Desired Features That Would Be Acceptable and Helpful in a Wrist-Worn Biosensor-Based Alcohol Intervention: Interview Study Among Adults Who Drink Heavily

BACKGROUND

Alcohol misuse is highly prevalent in the United States and results in a huge financial and public health burden. Current alcohol reduction treatments are underused, and there is a critical need for innovation in the field. Transdermal alcohol biosensors measure alcohol use passively and continuously and may be helpful tools in alcohol interventions. To date, however, alcohol biosensors have not been widely used to directly intervene on alcohol use. There is a new wrist-worn biosensor that could be used to help people reduce their drinking, although it is unclear how best to incorporate such a device into an alcohol intervention.

OBJECTIVE

We aimed to identify desired features that would be acceptable and helpful in a wrist-worn biosensor-based alcohol intervention for adults who drink heavily.

METHODS

Participants were recruited through an alcohol contingency management study, a contact registry, and participant referral. To qualify, participants had to be aged at least 40 years, report drinking at least twice per week, and indicate interest in reducing their drinking. We conducted a semistructured interview with each participant via Zoom (Zoom Video Communications, Inc). The interview guide addressed general thoughts on the wrist-worn biosensor, how participants thought a wrist-worn biosensor could be used to help people quit or reduce drinking, types of information that participants would want to receive from the biosensor, how they would want to receive this information, and how they thought this information could be used to change their behavior. Interviews were transcribed verbatim and analyzed using thematic analysis.

RESULTS

The sample comprised 20 adults (mean age 55.1, SD 6.1 years; 11/20, 55%, women; and 17/20, 85%, Black). Of the 20 participants, 9 (45%) had previous experience with the Secure Continuous Remote Alcohol Monitor continuous alcohol monitoring ankle biosensor from participating in an alcohol contingency management study. The desirable features could be grouped into 5 main themes: features that would influence willingness to use the biosensor (it should look attractive and be both comfortable to wear and accessible), personalized messaging (personalized biosensor-based prompts and feedback could be helpful), preference for time wearing the biosensor (for some, just wearing the biosensor could have an intervention effect), sharing data with others (this was appealing to many but not to all), and mental health support (many felt that mental health support could be incorporated into the biosensor).

CONCLUSIONS

Five main themes that would maximize interest in using a wrist-worn biosensor for alcohol intervention were identified. Taken together, the identified themes could inform the development of a just-in-time adaptive intervention that uses a wrist-worn biosensor to help adults who drink heavily reduce their alcohol use.

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.