# 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 · 2021 · $65,736

## Abstract

This Diversity Supplement award for predoctoral candidate Kyla-Rose Walden under the primary mentorship of
Professor Susan Luczak aims to both advance the objectives of the parent R01 AA026238 grant “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 (PIs Luczak & Rosen)” and
to support the research training and career advancement of Ms. Walden in her first 20 months in the University
of Southern California clinical sciences psychology doctoral program in the Department of Psychology. The goal
of the R01 parent study is to produce software to convert transdermal alcohol concentration (TAC) data into
estimates of breath and blood alcohol concentrations (eBrAC/eBAC). Devices are now available to reliably
measure 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 BrAC/BAC across individuals, environmental conditions, and devices. The anticipated result of this
R01 study is the development of 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. This Supplement
will focus on aspects of Aims 2 and 3 of the parent R01, specifically Aim 2b: to examine alternative options for
calibrating the models developed by the mathematics team, and Aim 3: to package the models into the BrAC
Estimator software program that can be used by non-mathematicians. During the time of this Supplement award,
we will collect data on 40 participants who will each participate in a field trial and two laboratory drinking sessions.
We also will have 20 researchers and clinicians run our software to process field trial data to determine the ease
of its use in research and clinical settings. Ms. Walden will assist with these data collection efforts. In addition,
she will receive broader training a) in alcohol theory and real-time (laboratory and field) alcohol research design
from mentor/PI Luczak, b) in the basics of mathematical modeling approaches, data integration/consolidation,
and MATLAB coding basics from MPI Rosen and Co-I Wang, and c) from Co-Is and research staff on the parent
R01 in their given areas of expertise, and d) in preparing first-author manuscripts and presenting her research
at conferences. She will use the data she has helped collect to develop her independent research in her first and
second years of graduate school. This research is not redundant with the originally proposed outcomes of this
R01 study, yet is within the study’s scope. We will extend this master’s level research into her dissertation in her
third and fourth years of graduate training as part of an F31 award application. This supplement is appropriate
for M...

## Key facts

- **NIH application ID:** 10402188
- **Project number:** 3R01AA026368-04S1
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** Susan E. Luczak
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $65,736
- **Award type:** 3
- **Project period:** 2018-04-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10402188

## Citation

> US National Institutes of Health, RePORTER application 10402188, 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 (3R01AA026368-04S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10402188. Licensed CC0.

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