Estimating BrAC from transdermal alcohol concentration data using the BrAC estimator software program

Abstract

Background: Transdermal alcohol sensor (TAS) devices have the potential to allow researchers and clinicians to unobtrusively collect naturalistic drinking data for weeks at a time, but the transdermal alcohol concentration (TAC) data these devices produce do not consistently correspond with breath alcohol concentration (BrAC) data. We present and test the BrAC Estimator software, a program designed to produce individualized estimates of BrAC from TAC data by fitting mathematical models to a specific person wearing a specific TAS device.

Methods: Two TAS devices were worn simultaneously by 1 participant for 18 days. The trial began with a laboratory alcohol session to calibrate the model and was followed by a field trial with 10 drinking episodes. Model parameter estimates and fit indices were compared across drinking episodes to examine the calibration phase of the software. Software-generated estimates of peak BrAC, time of peak BrAC, and area under the BrAC curve were compared with breath analyzer data to examine the estimation phase of the software.

Results: In this single-subject design with breath analyzer peak BrAC scores ranging from 0.013 to 0.057, the software created consistent models for the 2 TAS devices, despite differences in raw TAC data, and was able to compensate for the attenuation of peak BrAC and latency of the time of peak BrAC that are typically observed in TAC data.

Conclusions: This software program represents an important initial step for making it possible for non mathematician researchers and clinicians to obtain estimates of BrAC from TAC data in naturalistic drinking environments. Future research with more participants and greater variation in alcohol consumption levels and patterns, as well as examination of gain scheduling calibration procedures and nonlinear models of diffusion, will help to determine how precise these software models can become.

Keywords: BrAC Estimation; Ecological Momentary Assessment; Real-Time Assessment; Transdermal Alcohol Concentration; Transdermal Alcohol Sensor.

Copyright © 2014 by the Research Society on Alcoholism.

Figures

Figure 1

TAC (crosses) and BrAC (dots) data for a) Dataset 1 (top panel) and b) Dataset 2 (bottom panel). These two datasets were collected by the same participant over the same 18 days using two TAS devices and a breath analyzer.

Figure 1

TAC (crosses) and BrAC (dots) data for a) Dataset 1 (top panel) and b) Dataset 2 (bottom panel). These two datasets were collected by the same participant over the same 18 days using two TAS devices and a breath analyzer.

Figure 2

TAC and BrAC calibration results for Drinking Episode 1 for a) Dataset 1 (top panels) and b) Dataset 2 (bottom panels). The upper panel shows the Raw TAC (crosses) along with the Est TAC (solid line). The lower panel shows the Raw BrAC (dots) along with the Est BrAC (solid line) obtained by using the optimal values of the model (q1*, q2*) and regularization (r1*, r2*) parameters to deconvolve the TAC data shown in the upper panel.

Figure 2

TAC and BrAC calibration results for Drinking Episode 1 for a) Dataset 1 (top panels) and b) Dataset 2 (bottom panels). The upper panel shows the Raw TAC (crosses) along with the Est TAC (solid line). The lower panel shows the Raw BrAC (dots) along with the Est BrAC (solid line) obtained by using the optimal values of the model (q1*, q2*) and regularization (r1*, r2*) parameters to deconvolve the TAC data shown in the upper panel.

Figure 3

Calibrated impulse response functions for all 11 drinking episodes for a) Dataset 1 (top panel) and b) Dataset 2 (bottom panel). Each line labeled Event No. 1–11 represents the impulse response functions determined by drinking episodes 1–11, respectively. These functions indicate the BrAC Estimator software calibration results were consistent across the two datasets, but that there is a substantial variance among the models created using the 11 different drinking episodes.

Figure 4

Drinking episode results: a) Episode 7 in Dataset 2 on which the method performed well (upper panel), and b) Episode 5 in Dataset 1 on which the method performed less well (lower panel). These panels show results of calculating estimated BrAC with the software (Est BrAC; dashed line) compared with a breath analyzer (Raw BrAC, crosses) and a TAS device (Raw TAC; dots).

Figure 4

Drinking episode results: a) Episode 7 in Dataset 2 on which the method performed well (upper panel), and b) Episode 5 in Dataset 1 on which the method performed less well (lower panel). These panels show results of calculating estimated BrAC with the software (Est BrAC; dashed line) compared with a breath analyzer (Raw BrAC, crosses) and a TAS device (Raw TAC; dots).

Figure 5

Model results for Episode 8 in Dataset 2 when calibrated by Episode 1 (top panel) and Episode 3 (bottom panel). These graphs indicate improvement in model fit when calibrating the model with a drinking episode that more closely matches the episode being inverted.

Figure 5

Model results for Episode 8 in Dataset 2 when calibrated by Episode 1 (top panel) and Episode 3 (bottom panel). These graphs indicate improvement in model fit when calibrating the model with a drinking episode that more closely matches the episode being inverted.

Figure 6

Model results for Episode 3 in Dataset 2 when calibrated by Episode 1 (top panel) and Episode 8 (bottom panel). These graphs indicate improvement in model fit when calibrating the model with a drinking episode that more closely matched the episode being inverted.

Figure 6

Model results for Episode 3 in Dataset 2 when calibrated by Episode 1 (top panel) and Episode 8 (bottom panel). These graphs indicate improvement in model fit when calibrating the model with a drinking episode that more closely matched the episode being inverted.