I. Gary Rosen

Development of a Real-Time Repeated-Measures Assessment Protocol to Capture Change over the Course of a Drinking Episode

AIMS We report on the development of a real-time assessment protocol that allows researchers to assess change in BrAC, alcohol responses, behaviors, and contexts over the course of a drinking event. METHOD We designed a web application that uses timed text messages (adjusted based on consumption pattern) containing links to our website to obtain real-time participant reports; camera and location features were also incorporated into the protocol. We used a transdermal alcohol sensor device along with software we designed to convert transdermal data into estimated BrAC. Thirty-two college students completed a laboratory session followed by a 2-week field trial. RESULTS Results for the web application indicated we were able to create an effective tool for obtaining repeated measures real-time drinking data. Participants were willing to monitor their drinking behavior with the web application, and this did not appear to strongly affect drinking behavior during, or 6 weeks following, the field trial. Results for the transdermal device highlighted the willingness of participants to wear the device despite some discomfort, but technical difficulties resulted in limited valid data. CONCLUSION The development of this protocol makes it possible to capture detailed assessment of change over the course of naturalistic drinking episodes.

Obtaining continuous BrAC/BAC estimates in the field: A hybrid system integrating transdermal alcohol biosensor, Intellidrink smartphone app, and BrAC Estimator software tools

Biosensors have been developed to measure transdermal alcohol concentration (TAC), but converting TAC into interpretable indices of blood/breath alcohol concentration (BAC/BrAC) is difficult because of variations that occur in TAC across individuals, drinking episodes, and devices. We have developed mathematical models and the BrAC Estimator software for calibrating and inverting TAC into quantifiable BrAC estimates (eBrAC). The calibration protocol to determine the individualized parameters for a specific individual wearing a specific device requires a drinking session in which BrAC and TAC measurements are obtained simultaneously. This calibration protocol was originally conducted in the laboratory with breath analyzers used to produce the BrAC data. Here we develop and test an alternative calibration protocol using drinking diary data collected in the field with the smartphone app Intellidrink to produce the BrAC calibration data. We compared BrAC Estimator software results for 11 drinking episodes collected by an expert user when using Intellidrink versus breath analyzer measurements as BrAC calibration data. Inversion phase results indicated the Intellidrink calibration protocol produced similar eBrAC curves and captured peak eBrAC to within 0.0003%, time of peak eBrAC to within 18min, and area under the eBrAC curve to within 0.025% alcohol-hours as the breath analyzer calibration protocol. This study provides evidence that drinking diary data can be used in place of breath analyzer data in the BrAC Estimator software calibration procedure, which can reduce participant and researcher burden and expand the potential software user pool beyond researchers studying participants who can drink in the laboratory.

A multimodal investigation of contextual effects on alcohol's emotional rewards

Regular alcohol consumption in unfamiliar social settings has been linked to problematic drinking. A large body of indirect evidence has accumulated to suggest that alcohol’s rewarding emotional effects—both negative-mood relieving and positive-mood enhancing—will be magnified when alcohol is consumed within unfamiliar versus familiar social contexts. But empirical research has never directly examined links between contextual familiarity and alcohol reward. In the current study, we mobilized novel ambulatory technology to examine the effect of social familiarity on alcohol reward in everyday drinking contexts while also examining how alcohol reward observed in these field contexts corresponds to reward observed in the laboratory. Heavy social drinking participants (N = 48, 50% male) engaged in an intensive week of ambulatory assessment. Participants wore transdermal alcohol sensors while they reported on their mood and took photographs of their social contexts in response to random prompts. Participants also attended 2 laboratory beverage-administration sessions, during which their emotional responses were assessed and transdermal sensors were calibrated to estimate breathalyzer readings (eBrACs). Results indicated a significant interaction between social familiarity and alcohol episode in everyday drinking settings, with alcohol enhancing mood to a greater extent in relatively unfamiliar versus familiar social contexts. Findings also indicated that drinking in relatively unfamiliar social settings was associated with higher eBrACs. Finally, results indicated a correspondence between some mood effects of alcohol experienced inside and outside the laboratory. This study presents a novel methodology for examining alcohol reward and indicates social familiarity as a promising direction for research seeking to explain problematic drinking.

Estimating the Quantity and Time Course of Alcohol Consumption from Transdermal Alcohol Sensor Data: A Combined Laboratory-Ambulatory Study

Transdermal alcohol sensors offer enormous promise for the continuous, objective assessment of alcohol use. Although these sensors have been employed as abstinence monitors for some time now, it is only recently that models have been developed aimed at allowing researchers to derive estimates of the precise amount and time course of drinking directly from transdermal data. Using data from a combined laboratory-ambulatory study, the current research aims to examine the validity of recently developed methods for estimating BrAC (breath alcohol concentration) directly from transdermal data. Forty-eight heavy social drinkers engaged in seven days of ambulatory assessment outside the laboratory, and also participated in a laboratory alcohol-administration session. Participants wore the SCRAM transdermal sensor throughout the study, and, during the seven days of ambulatory assessment, they provided daily self-reports of their drinking and also took randomly prompted photographs 6X/day which were then evaluated for evidence of alcohol consumption. Results indicated strong associations between daily self-reports of drinking quantity and estimates of BrAC derived from transdermal sensors at both the between and within-subject level. Data from randomly-prompted photos indicated that the time course of estimated BrAC also had validity. Results offer promise for novel methods of estimating BrAC from transdermal data, including those taking a nomothetic (population-based) approach to this estimation, thus potentially adding to our arsenal of techniques for understanding, diagnosing, and ultimately treating alcohol use disorder.

Applying a Novel Population-Based Model Approach to Estimating Breath Alcohol Concentration (BrAC) from Transdermal Alcohol Concentration (TAC) Biosensor Data

Alcohol biosensor devices have been developed to unobtrusively measure transdermal alcohol concentration (TAC), the amount of ethanol diffusing through the skin, in nearly continuous fashion in naturalistic settings. Because TAC data are affected by physiological and environmental factors that vary across individuals and drinking episodes, there is not an elementary formula to convert TAC into easily-interpretable metrics like blood and breath alcohol concentrations (BAC/BrAC). In our prior work, we addressed this conversion problem in a deterministic way by developing physics/physiological-based models to convert TAC to estimated BrAC (eBRAC), in which the model parameter values were individually determined for each person wearing a specific transdermal sensor using simultaneously collected TAC (via a biosensor) and BrAC (via a breath analyzer) during a calibration episode. We found these individualized parameter values produced relatively good eBrAC curves for subsequent drinking episodes, but our results also indicated the models were not fully capturing the dynamics of the system and variations across drinking episodes. Here, we report on a novel mathematical framework to improve our ability to model eBrAC from TAC data that uses aggregate population data instead of individualized calibration data to determine model parameter values via a random diffusion equation. We first provide the theoretical mathematical basis for our approach, and then test the efficacy of this method using datasets of contemporaneous BrAC/TAC measurements obtained by a) a single subject during multiple drinking episodes and b) multiple subjects during single drinking episodes. For each dataset, we used a set of drinking episodes to construct the population model, and then ran the model with another set of randomly-selected test episodes. We compared raw TAC data to model-simulated TAC curve, breath analyzer BrAC data to model eBrAC curves with 75% credible bands, episode summary scores of peak BrAC, times of peak BrAC, and area under the drinking curve also with 75% credible intervals, and report the percent of the raw BrAC captured within the eBrAC curve credible bands. We also display results when stratifying the data based on the relationship between the raw BrAC and TAC data. Results indicate the population-based model is promising, with better fit within a single participant when stratifying episodes. This study provides initial proof-of-concept for constructing, fitting, and using a population-based model to obtain estimates and error bands for BrAC from TAC. The advancements in this study, including new applications of math, the development of a population-based model with error bars, and the production of corresponding Matlab codes, represent a major step forward in our ability to produce quantitatively- and temporally-accurate estimates of BrAC from TAC biosensor data.