Alcohol-related traffic crashes and deaths remain a major problem in the United States as 2014 data revealed that there were 32,675 traffic fatalities that year, with 31% of them being related to alcohol. The National Highway Traffic Safety Administration (NHTSA) and the Automotive Coalition for Traffic Safety (ACTS) began research in February 2008 aimed at identifying potential in-vehicle approaches to the problem of alcohol-impaired driving that are sensitive, reliable and less intrusive than ignition interlocks. The Driver Alcohol Detection System for Safety (DADSS) was created, and two passive technologies based on breath- and touch (tissue)- based systems for detecting alcohol were selected to be tested against a research grade hand-held breathalyzer device and venous blood.
Healthy male and female volunteers (age 21-40) signed an Institutional Review Board (IRB)-approved informed consent and participated in experiments in which they consumed 0.9 g/kg of alcohol (vodka) under a variety of drinking regimens and scenarios that mimicked real-life situations. The volunteers then provided passive breath and tissue (i.e., finger touch) samples and had their blood drawn for subsequent quantification of alcohol via gas chromatography. The lag time of appearance of alcohol in each sample as well as peak concentration, time to peak, and elimination rate were the primary dependent variables. The overall aim of the experiments was to test whether the alcohol levels measured by the two prototype devices correlate with venous blood under the following scenarios: lag time, eating a snack, eating a full meal, exercising, and “last call”.
The lag time experiment revealed that the order of alcohol appearance after drinking was (from quickest to slowest): breath, blood, and tissue, although the early breath samples were contaminated by mouth alcohol. However, the concentration-time curves for both prototype devices paralleled that of blood. Similar profiles were observed in the “last call” experiment with a “surge” of alcohol being observed after an extra drink was consumed during the distribution phase. The exercise scenario revealed similar profiles, although the touch-based device registered a slightly higher alcohol level. Finally, the two eating scenarios indicated that blood alcohol concentrations were lower after consuming a meal compared to a snack, and breath and touch samples reflected these patterns.
The sample size of 10 individual participants is small, but individuals served as their own controls by participating in more than one experiment. Furthermore, the study is ongoing and so the sampling limitation will be addressed. The data support the proof-of-concept that passive technologies can detect alcohol quickly and are not affected by many of the common scenarios that alter blood alcohol concentrations. Such devices, if proven to be reliable and reproducible with additional human testing, represent a significant technological breakthrough in strategies to reduce alcohol-impaired individuals from driving a vehicle and causing injuries and/or deaths.