Zhang Yiqi, Wu Changxu, Wan Jingyan
Department of Industrial and System Engineering, University at Buffalo, the State University of New York, Buffalo, NY, USA.
Department of Industrial and System Engineering, University at Buffalo, the State University of New York, Buffalo, NY, USA.
Addict Behav. 2017 Aug;71:46-53. doi: 10.1016/j.addbeh.2017.02.022. Epub 2017 Feb 17.
To date, multiple models have been developed to estimate blood or breath alcohol concentration (BAC/BrAC). Several factors have been identified that affect the discrepancy between BACs/BrACs and retrospective estimation (eBAC) with existing equations. To the best of our knowledge, a model to quantify the effects of factors on the discrepancy between BAC/BrAC and eBAC is still nonexistent. The goal of this work was to develop a model to provide a more accurate retrospective estimation of breath alcohol concentration (eBAC).
A laboratory study with alcohol consumption and a driving task was conducted with 30 participants (17 male and 13 female) to explore the factors that may contribute to the discrepancy between BrAC and eBAC obtained with existing models. A new eBAC model was developed to improve the estimation of BrAC by modeling effects of gender, weight, and the delay of BrAC measurement on the discrepancy. The validity of the model was tested and established with the data from the experiment conducted in this study and two published research studies, and compared with existing eBAC models.
Results of the model validity examination indicated that the developed model had higher R squares and lower root-mean-squared errors (RMSE) in estimating BrAC in three experiments compared with the existing eBAC models, including the NHTSA equation, the Matthew equation, the Lewis equation, the Watson equation, and the Forrest equation.
The developed eBAC model had a better performance of BrAC estimation compared with existing eBAC models. The validation of the model with the data from three empirical studies indicated a high level of generalizability in estimating BrAC.
迄今为止,已开发出多种模型来估计血液或呼气酒精浓度(BAC/BrAC)。已确定了几个影响BAC/BrAC与使用现有方程进行回顾性估计(eBAC)之间差异的因素。据我们所知,量化这些因素对BAC/BrAC与eBAC之间差异影响的模型仍然不存在。这项工作的目标是开发一个模型,以提供更准确的呼气酒精浓度回顾性估计(eBAC)。
对30名参与者(17名男性和13名女性)进行了一项涉及饮酒和驾驶任务的实验室研究,以探索可能导致现有模型得出的BrAC与eBAC之间差异的因素。通过对性别、体重和BrAC测量延迟对差异的影响进行建模,开发了一种新的eBAC模型,以改进对BrAC的估计。使用本研究中进行的实验以及两项已发表的研究的数据对该模型的有效性进行了测试和验证,并与现有的eBAC模型进行了比较。
模型有效性检验结果表明,与现有的eBAC模型(包括美国国家公路交通安全管理局(NHTSA)方程、马修方程、刘易斯方程、沃森方程和福雷斯特方程)相比,所开发的模型在三个实验中估计BrAC时具有更高的决定系数(R平方)和更低的均方根误差(RMSE)。
与现有的eBAC模型相比,所开发的eBAC模型在估计BrAC方面具有更好的性能。用三项实证研究的数据对该模型进行验证表明,在估计BrAC方面具有较高的通用性。