1. The Transdermal Alcohol Sensor Macro (TASMAC): A Rapid Data Processing Tool for Use with the SCRAMx Alcohol Sensor Nancy P. Barnett, Timothy Souza, Tiffany R. Glynn, Susan E. Luczak, Robert Swift, and Gary Rosen Brown University School of Public Health, Center for Alcohol & Addiction Studies, Providence, RI University of Southern California, Los Angeles, CA
Background and Objectives
Process and Results
TASMAC INPUT
TASMAC RESULTS
Alcohol sensors are being used to validate self-report in survey research, in field research on real-time measurement of contextual factors, and in clinical interventions with at-risk and dependent drinkers.
The promise of using transdermal alcohol sensors is limited by the lack of an empirically derived and shared system for interpreting sensor data and for equating transdermal alcohol concentration (TAC) to breath alcohol concentration (BrAC).
The many stored readings per person per day comprise data sets that can be difficult to process quickly.
Objectives
Incorporate established criteria for detecting alcohol consumption using the Secure Continuous Remote Alcohol Monitoring (SCRAM) bracelet with other features into a tool that automates the complex functions & calculations needed for working with transdermal sensor data.
Integrate and evaluate the BrAC Estimator, which estimates breath alcohol concentration from detected transdermal alcohol episodes.
Using default criteria based on empirical research or user-defined criteria, TASMAC identifies alcohol consumption episodes.
Flexible settings and navigation tabs allow for user exploration and review of data, including day-level or episode-level viewing.
Interactive graphing and exportable plots allows user to graph and summarize data over a dynamic range of days.
Alerts provide warnings about data anomalies.
Provides downloads of the TASMAC reports and datasets in various formats for analysis.
TASMAC OUTPUT FEATURES
Calculates episode and time-based (i.e., day, week, etc.) summary statistics, including average and peak transdermal alcohol concentration (TAC), episode length, absorption and elimination rates, and area under the curve.
Incorporates information from Alcohol Monitoring Systems reports (confirmable alcohol episodes and tamper alerts).
Provides meaningful values for clinical research, including longest number of days with no alcohol detected.
Methods
TASMAC FINDINGS
Study 1: There were 97 days of self-reported drinking; TASMAC found 70 days (72%), with an average peak TAC of .099 (SD = .078). On 6.3% of days, TASMAC detected drinking that was not self-reported.
Study 2: 29 episodes of alcohol consumption were verified by measured BrAC > .02. TASMAC identified 72% of these episodes.
We used an iterative collaborative approach using scientific consultants and data from several sites to develop, test, and refine the TASMAC and BrAC Estimator.
BrAC ESTIMATOR FINDINGS
Study 1: Peak TAC was highly correlated with the peak estimated BrAC produced by the BrAC Estimator (r = .98), as was area under the curve from the TAC and eBrAC curves (r = .99), providing an initial indication that the BrAC Estimator model captures what we would expect in the relation between BrAC and TAC.
Study 2: In the lab setting where alcohol consumption was observed and breath tests were consistently taken, peak measured BrAC correlated .84 with peak estimated BrAC. However, in field settings, peak measured BrAC was not correlated with peak estimated BrAC, likely because breath observations were not frequent enough and/or mouth alcohol interfered with breath tests.
Study 1: 16 participants (43.8% women) wore the SCRAM for 14 days and provided daily self-report of alcohol use.
Study 2: 8 participants (50% women) consumed alcohol in a laboratory setting. Breath tests and SCRAM were collected in the laboratory and for one field week.
Data Processing: Data files are downloaded from SCRAMnet and uploaded to TASMAC.
The TASMAC runs as a macro in Microsoft EXCEL so requires no specialized software. The BrAC Estimator functions like an EXCEL “add-in” to the TASMAC.
The BrAC Estimator was developed using validated alcohol consumption episodes, first principles physics, and physiologically-based mathematical models.
Using TAC data produced by the TASMAC, the BrAC Estimator estimates breath alcohol concentration.
Conclusions
The TASMAC integrated with the BrAC Estimator incorporates established methods for researchers new to sensor data, while also providing the flexibility for a more seasoned user to adjust parameters to explore data. It addresses current limitations of the sensor data, and produces numerous values useful for analysis. It therefore has the potential to advance epidemiological and applied research with alcohol biosensors and lower the threshold for adoption of sensor technology by new users.
This project was supported by grant R21AA (Barnett) from NIAAA. The development of the BrAC Estimator was also supported by grant R21AA17711 (Luczak) from NIAAA.
Some of the data used for developing the BrAC Estimator were provided by the Indiana Alcohol Research Center - CAIS projects, with assistance from Martin Plawecki and Sean O’Connor.
We appreciate the guidance SCRAM SystemsTM and Alcohol Monitoring Systems has provided for our use of SCRAM bracelets in our research. AMS was not involved in the development of this software. Special thanks to project consultants Jason Bond, Donald Dougherty, Tom Greenfield, Thad Leffingwell, and Nancy Petry
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