Estimating BrAC from Transdermal Alcohol Concentration Data Using the BrAC Estimator Software Program
Estimating BrAC from Transdermal Alcohol Concentration Data Using the BrAC Estimator Software Program
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DOI：
10.1111/acer.12478
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发表时间：
2014-08-01
影响因子：
3.2
通讯作者：
Rosen, I. Gary
中科院分区：
文献类型：
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作者：
Luczak, Susan E.;Rosen, I. Gary
背景：经皮酒精传感器(TAS)设备有可能允许研究人员和临床医生一次收集数周的自然饮酒数据，但这些设备产生的经皮酒精浓度(TAC)数据与呼吸酒精浓度(BRAC)数据并不一致。我们介绍并测试了BRAC Estimator软件，该程序旨在通过将数学模型与佩戴特定TAS设备的特定人进行拟合，从TAC数据生成个性化的BRAC估计。试验以实验室饮酒开始，以校准模型，随后是现场试验，共10次饮酒。模型参数估计和拟合指数在饮酒期间进行了比较，以检查软件的校准阶段。结果：在这一单受试者设计中，呼吸分析仪峰值BRAC得分在0.013到0.057之间，软件为两个TAS设备创建了一致的模型，尽管原始TAC数据不同，并且能够补偿在TAC数据中通常观察到的峰值brc的衰减和峰值brc时间的潜伏期。结论：该软件程序是使非数学家研究人员和临床医生能够在自然饮酒环境中从TAC数据中获得brc估计值的重要的第一步。未来的研究将有更多的参与者，酒精消费水平和模式的差异更大，以及对增益计划校准程序和非线性扩散模型的检查，这将有助于确定这些软件模型可以变得多精确。
Background: Transdermal alcohol sensor (TAS) devices have the potential to allow researchers and clinicians to unobtrusively collect naturalistic drinking data for weeks at a time, but the transdermal alcohol concentration (TAC) data these devices produce do not consistently correspond with breath alcohol concentration (BrAC) data. We present and test the BrAC Estimator software, a program designed to produce individualized estimates of BrAC from TAC data by fitting mathematical models to a specific person wearing a specific TAS device.Methods: Two TAS devices were worn simultaneously by 1 participant for 18 days. The trial began with a laboratory alcohol session to calibrate the model and was followed by a field trial with 10 drinking episodes. Model parameter estimates and fit indices were compared across drinking episodes to examine the calibration phase of the software. Software-generated estimates of peak BrAC, time of peak BrAC, and area under the BrAC curve were compared with breath analyzer data to examine the estimation phase of the software.Results: In this single-subject design with breath analyzer peak BrAC scores ranging from 0.013 to 0.057, the software created consistent models for the 2 TAS devices, despite differences in raw TAC data, and was able to compensate for the attenuation of peak BrAC and latency of the time of peak BrAC that are typically observed in TAC data.Conclusions: This software program represents an important initial step for making it possible for non mathematician researchers and clinicians to obtain estimates of BrAC from TAC data in naturalistic drinking environments. Future research with more participants and greater variation in alcohol consumption levels and patterns, as well as examination of gain scheduling calibration procedures and nonlinear models of diffusion, will help to determine how precise these software models can become.