# 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

> **NIH NIH R01** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2022 · $493,911

## Abstract

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...

## Key facts

- **NIH application ID:** 10375443
- **Project number:** 5R01AA026368-05
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** Susan E. Luczak
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $493,911
- **Award type:** 5
- **Project period:** 2018-04-01 → 2024-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10375443

## Citation

> US National Institutes of Health, RePORTER application 10375443, 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 (5R01AA026368-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10375443. Licensed CC0.

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