Effects of stomach content on the breath alcohol concentration-transdermal alcohol concentration relationship

Abstract

Introduction: Wearable devices that obtain transdermal alcohol concentration (TAC) could become valuable research tools for monitoring alcohol consumption levels in naturalistic environments if the TAC they produce could be converted into quantitatively-meaningful estimates of breath alcohol concentration (eBrAC). Our team has developed mathematical models to produce eBrAC from TAC, but it is not yet clear how a variety of factors affect the accuracy of the models. Stomach content is one factor that is known to affect breath alcohol concentration (BrAC), but its effect on the BrAC-TAC relationship has not yet been studied.

Methods: We examine the BrAC-TAC relationship by having two investigators participate in four laboratory drinking sessions with varied stomach content conditions: (i) no meal, (ii) half and (iii) full meal before drinking, and (iv) full meal after drinking. BrAC and TAC were obtained every 10 min over the BrAC curve.

Results: Eating before drinking lowered BrAC and TAC levels, with greater variability in TAC across person-device pairings, but the BrAC-TAC relationship was not consistently altered by stomach content. The mathematical model calibration parameters, fit indices, and eBrAC curves and summary score outputs did not consistently vary based on stomach content, indicating that our models were able to produce eBrAC from TAC with similar accuracy despite variations in the shape and magnitude of the BrAC curves under different conditions.

Discussion and conclusions: This study represents the first examination of how stomach content affects our ability to model estimates of BrAC from TAC and indicates it is not a major factor.

Keywords: alcohol biosensor; breath alcohol concentration estimation; real-time assessment; stomach content; transdermal alcohol concentration.

Figure 1.

Raw BrAC and raw TAC curves for all eight drinking session.

Figure 2.

Estimated BrAC curve with 75% credible band and simulated TAC curve from the BrAC Estimator software compared with raw BrAC and raw TAC measurements in all sessions for a) the female participant and b) the male participant, and c) in the three eating conditions for both the female and male left arm TAC device when calibrated using the No Food condition parameter values for that person-device pair.

Figure 2.

Estimated BrAC curve with 75% credible band and simulated TAC curve from the BrAC Estimator software compared with raw BrAC and raw TAC measurements in all sessions for a) the female participant and b) the male participant, and c) in the three eating conditions for both the female and male left arm TAC device when calibrated using the No Food condition parameter values for that person-device pair.

Figure 2.

Estimated BrAC curve with 75% credible band and simulated TAC curve from the BrAC Estimator software compared with raw BrAC and raw TAC measurements in all sessions for a) the female participant and b) the male participant, and c) in the three eating conditions for both the female and male left arm TAC device when calibrated using the No Food condition parameter values for that person-device pair.

Figure 3.

Raw BrAC and raw TAC curves showing an increase in TAC from 10 minutes of exercise half-way through descending limb of BrAC while infrared light (IR) indicates devices stayed properly adhered to the skin and ambient temperature (Temp) lowered from going outdoors.