Use of the BACtrack Skyn alcohol biosensor: Practical applications for data collection and analysis

Author:

Gunn Rachel L.1ORCID,Merrill Jennifer E.1ORCID,Haines Anne M.1ORCID,Fernandez Mary Ellen1ORCID,Souza Timothy1,Berey Benjamin L.1ORCID,Leeman Robert F.23ORCID,Wang Yan4ORCID,Barnett Nancy P.1ORCID

Affiliation:

1. Brown University School of Public Health Providence RI USA

2. Department of Health Education and Behavior, College of Health and Human Performance University of Florida Gainesville FL USA

3. Department of Health Sciences, School of Community Health and Behavioral Sciences, Bouvé College of Health Sciences Northeastern University Boston MA USA

4. Department of Epidemiology University of Florida Gainesville FL USA

Abstract

AbstractAimsAlcohol biosensors, including the BACtrack Skyn, provide an objective and passive method of continuously assessing alcohol consumption in the natural environment. Despite the many strengths of the Skyn, six key challenges in the collection and processing of data include (1) identifying consumed alcohol; (2) identifying environmental alcohol; (3) identifying and determining the source of missing or invalid data; (4) achieving high participant adherence; (5) integrating Skyn and self‐report data; and (6) implications for statistical inference. In this report we outline these challenges, provide recommendations to address them and identify future needs.Design and settingsProcedures from several laboratory and field‐based pilot studies are presented to demonstrate practical recommendations for Skyn use. Data from a pilot study including a 7‐day ecological momentary assessment period are also presented to evaluate effects of environmental alcohol on BACtrack Skyn readings.ConclusionsTo address challenges in the collection and processing of data from the BACtrack Skyn alcohol biosensor, researchers should identify goals in advance of data collection to anticipate the processing necessary to interpret Skyn data. The Transdermal Alcohol Sensor Data Macro (TASMAC) version 2.0 software can help to process data rapidly; identify drinking events, missing data and environmental alcohol; and integrate the sensor with self‐report data. Thorough participant orientation and regular contact in field studies can reduce missing data and enhance adherence. Many recommended methods for Skyn use are applicable to other alcohol sensors and wearable devices.

Publisher

Wiley

Subject

Psychiatry and Mental health,Medicine (miscellaneous)

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