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

Abstract

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 alcoholrelated 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.

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

“…Russell et al (2022) study integrating smartphone and wearable alcohol biosensor technology to measure real‐world drinking behavior and related consequences is a timely and important contribution to the alcohol research field. Most wearable alcohol biosensors detect alcohol use by measuring the small amount (~1%) of consumed alcohol that is present in the skin via sweat or diffusion (Swift, 1993).…”

“…To date, the complex nature of the TAC‐to‐estimated BrAC/BAC conversion process remains a barrier to wider application of transdermal alcohol biosensors. In their paper, Russell et al (2022) adopted a different approach: rather than attempting to estimate BrAC/BAC from TAC, they instead extracted five previously identified features of the TAC signal during real‐world drinking events and examined the associations between those features and alcohol use and drinking‐related consequences. Their work represents an intriguing and novel “blueprint” for future analyses of alcohol biosensor data and suggests that TAC data may have significant value to the alcohol research field beyond its utility as a proxy measure of BrAC/BAC.…”

“…To date, the complex nature of the TAC-to-estimated BrAC/BAC conversion process remains a barrier to wider application of transdermal alcohol biosensors. In their paper, Russell et al (2022) adopted a different approach: rather than attempting to estimate BrAC/BAC from TAC, they instead extracted five previously identified features of the TAC signal during real-world drinking events and examined the associations between those features and alcohol use and drinking-related consequences.…”

“…Choosing the right alcohol biosensor will depend upon the individual clinical or research question, with different types of devices having different strengths and weaknesses depending on their intended function. Furthermore, establishing consensus guidelines for clinical and nonclinical applications of biosensor technology will likely require collaboration over multiple research groups to perform large-scale analyses of continuous TAC data over drinking and non-drinking events, comparison of output from different device types (e.g., SCRAM-CAM anklet versus newer wrist-worn devices), examination of individual TAC features such as those described by Russell et al (2022), and integration of TAC with corresponding EMA and BAC/BrAC data. Such efforts could pay off greatly by facilitating the development of software to automatically clean TAC data, eliminate artifacts, and detect drinking events.…”

“…In sum, the paper by Russell et al (2022) presents an intriguing approach to analyzing TAC data beyond its utility as a proxy measure of BAC and intoxication. Their data indicate that examining individual TAC features may help researchers identify both behavioral and physiological risk factors for alcohol-related problems.…”