Applying a novel population-based model approach to estimating breath alcohol concentration (BrAC) from transdermal alcohol concentration (TAC) biosensor data

Abstract

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 such as 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 curve 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.

Keywords: Alcohol biosensor; BrAC estimation; Ecological momentary assessment; Real-time assessment; Transdermal alcohol concentration.

Conflict of interest statement

The authors report no conflicts of interest.

Declaration of interests: none

Copyright © 2018 Elsevier Inc. All rights reserved.

Figures

Figure 1.

Dataset 1 contained 11 drinking episodes obtained over 18 days, with the first episode conducted in the laboratory and the 10 subsequent naturalistic drinking episodes obtained in the field. The raw BrAC are shown here over the entire 18 days.

Figure 2.

Dataset 2 contained 15 drinking laboratory drinking episodes by 15 Asian American college students (47% female). The raw BrAC are shown here overlaying all 15 episodes.

Figure 3.

Dataset 1 training phase estimation results, including the estimated BrAC (eBrAC) curve and 75% credible bands along with raw BrAC, raw TAC, and simulated TAC (sTAC), when all eight episodes are used as one sample in the training phase.

Figure 4.

Dataset 2 training phase estimation results, including the estimated BrAC (eBrAC) curve and 75% credible bands along with raw BrAC, raw TAC, and simulated TAC (sTAC), when all 12 episodes are used as one sample in the training phase.

Figure 5.

Dataset 1 testing phase estimation results, including the estimated BrAC (eBrAC) curve and 75% credible bands along with raw BrAC, raw TAC, and simulated TAC (sTAC), when using all eight episodes as one sample (top row) or when stratified in the training phase into two groups based on whether raw BrAC or raw TAC is higher (bottom row).

Figure 6.

Dataset 1 training phase estimation results, including the estimated BrAC (eBrAC) curve and 75% credible bands along with raw BrAC, raw TAC, and simulated TAC (sTAC), when the eight episodes are stratified in the training phase into two groups based on whether raw BrAC or raw TAC is higher.

Figure 7.

Dataset 2 testing phase estimation results, including the estimated BrAC (eBrAC) curve and 75% credible bands along with raw BrAC, raw TAC, and simulated TAC (sTAC), when using all 12 episodes as one sample (top row) or when stratified in the training phase into two groups based on whether raw BrAC or raw TAC is higher (bottom row).

Figure 8.

Dataset 2 training phase estimation results, including the estimated BrAC (eBrAC) curve and 75% credible bands along with raw BrAC, raw TAC, and simulated TAC (sTAC), when the 12 episodes are stratified in the training phase into two groups based on whether raw BrAC or raw TAC is higher.