Estimating BrAC/BAC from Transdermal Alcohol: Combining First-Principles Physiological Models with Machine-Learning to Create Software to Optimally Process and Quantitatively Interpret Biosensor Data

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Abstract Transdermal alcohol biosensors offer a promising method for unobtrusively collecting continuous alcohol levels in naturalistic settings over long periods of time. Devices are now available to reliably measure transdermal alcohol concentration (TAC), the amount of alcohol diffusing through the skin, but an often overlooked yet critical issue for making these biosensors valuable is that TAC does not consistently correlate with the easily interpretable measures of breath and blood alcohol concentrations (BrAC/BAC) across individuals, environmental conditions, and devices. The goal of this study is to produce software to convert TAC data into estimates of BrAC/BAC (eBrAC/eBAC). We will meet this goal by 1) developing mathematical models to produce quantitative eBrAC from TAC data, 2) examining alternative options for calibrating these models, 3) testing the model fits using varied types and amounts of very precise data, and 4) packaging the models into a comprehensive data assimilation software program. Specifically, we will enhance the fidelity of the models by integrating advanced physics/physiological-based models with statistical methods and data-driven machine- learning techniques. To reduce the burden currently required to calibrate the models for each individual, we will test a number of calibration procedures, including the replacement of the laboratory alcohol administration session with more varied drinking protocols as well as with population-based parameter estimates. We will test our models and protocols using detailed consumption data collected 1) on two of the investigators, 2) on 40 participants who will each participate in four controlled laboratory drinking sessions, and 3) on 40 participants who will each participate in a field trial and laboratory sessions. We will examine model fits across drinking patterns when using varying amounts of individualized alcohol data (e.g., breath analyzer, drink diary) to calibrate the models, and within and across individuals with differing characteristics (e.g., gender, weight) and under variable conditions (e.g., humidity, heart rate) that may affect model fit. We will create a data assimilation software system, the BrAC Estimator software, that incorporates all available data to produce the most accurate eBrAC measures. The software output will include the identification of drinking episodes, continuous eBrAC signal, and eBrAC summary scores (e.g., peak eBrAC, time of peak eBrAC, area under the drinking curve) with confidence bands. The software will be platform-portable to run alone or to be integrated into other mobile health technologies or precision medicine protocols. This proposal is innovative, technologically sophisticated, and feasible, and would result in the first tool to accomplish the TAC-eBrAC conversion, finally making it possible to obtain interpretable quantitative measurement of naturalistic alcohol consumption in the field. The anticipated result of this study is the expanded utility of TAC biosensors for researchers, clinicians, and individuals to monitor naturalistic alcohol consumption and easily understand the results.