A Multi-Component Data Processing Program for Transdermal Alcohol Sensors

描述(由申请人提供)：目前已有测量人皮肤汗液中酒精含量的传感器，法院和NIAAA资助的研究和治疗疗效研究的研究人员都使用这种传感器来监测酒后驾车违法者。这两种可用的设备(SCRAM和WrisTAS)在研究和应用方面的潜力很大，但对用户来说存在严重的挑战。传感器内部或传感器之间没有共同的程序来识别酒精使用情况，经皮酒精浓度(TAC)值在数量上不等于血液或呼吸酒精含量(BAC/BRAC)，TAC曲线相对于BAC/BRAC曲线延迟。此外，传感器数据文件很大，而且由于数据的性质，计算汇总变量可能非常耗时。这些挑战损害了传感器数据研究的有效性和可靠性，并限制了酒精生物传感器技术在研究和治疗中的采用。这项建议的主要目标是利用成瘾研究、流行病学、数学和生物统计学方面的现有数据和已知专家来推进酒精经皮传感器的研究，并解决传感器使用者面临的关键挑战。其具体目的是：(1)利用两个可用的经皮传感器建立检测酒精使用事件的最佳方法，以及(2)开发用于两个可用的传感器的从TAC中解卷积BAC估计的方法，并通过这样做消除TAC中观察到的时间滞后。在解决每个目标的同时，我们将生产专门的分析工具，将研究结果纳入其中。对于目标1，我们将修改当前的原型，透皮酒精传感器宏(Tasmac)，它包含了读取传感器数据输入文件的每一行的Visual Basic代码；根据用户定义的参数，该工具将识别酒精消费事件，创建汇总变量，并格式化适合用于统计的数据输出文件 软件。对于目标2，BAC Estimator软件将由Gary Rosen博士开发，使用不需要酒精挑战的方法来校准估计模型。这两个工具都有图形用户界面，便于使用，而且这两个工具都可以在Microsoft Excel中运行。我们将与使用其中一个传感器的八名研究人员一起使用协作、跨学科的方法；合作者将提供来自受控实验室管理研究、实地研究和干预试验的数据，以评估研究目标和开发工具功能，并将使用他们的数据测试工具作为评估的一部分。研究成果和 由此产生的工具将简化复杂的数据处理和计算，从而消除利用透皮酒精传感器进行基础、流行病学和应用临床研究的障碍。

DESCRIPTION (provided by applicant): Sensors that measure alcohol in the perspiration on a person's skin are currently available and are used by courts to monitor DUI offenders and by researchers in NIAAA-funded research and treatment efficacy studies. The potential of the two available devices (SCRAM and WrisTAS) for research and applied use is considerable, but there are serious challenges for users. There are no shared procedures within or across sensors for identifying alcohol use episodes, transdermal alcohol concentration (TAC) values are not quantitatively equivalent to blood or breath alcohol content (BAC/BrAC), and TAC curves are delayed relative to BAC/BrAC curves. Furthermore, sensor data files are large and, due to the nature of the data, calculating summary variables can be very time consuming. These challenges compromise the validity and reliability of research with sensor data, and limit the adoption of the alcohol biosensor technology in research and treatment. The primary objective of this proposal is to use existing data and known experts in addiction research, epidemiology, mathematics, and biostatistics to advance research with alcohol transdermal sensors and to address the key challenges for users of the sensors. The specific aims are to: (1) establish the best methods for detecting alcohol use episodes with both available transdermal sensors, and (2) develop methods to deconvolve BAC estimates from TAC for both available sensors, and in doing so eliminate the observed time lag in TAC. Simultaneous with addressing each aim, we will produce specialized analytic tools that will incorporate the research findings. For Aim 1, we will modify a current prototype, the Transdermal Alcohol Sensor Macro (TASMAC) that contains Visual Basic code to read each line of a sensor data input file; according to user-defined parameters the tool will identify alcohol consumption episodes, create summary variables, and format a data output file suitable for use with statistical software. For Aim 2, the BAC Estimator software will be developed by Dr. Gary Rosen using methods that do not require an alcohol challenge to calibrate the estimation model. Both tools will have graphical user interfaces for ease of use, and both tools will work within Microsoft EXCEL. We will use a collaborative, interdisciplinary approach with eight researchers who have used one of the sensors; collaborators will provide data from controlled laboratory administration studies, field research, and intervention trials to evaluate research aims and develop the tool functions, and will test the tools with their data as part of the evaluation. Research findings and the resulting tools will simplify complex data processing and calculations, thereby removing a barrier to conducting basic, epidemiological, and applied clinical research with transdermal alcohol sensors.