Towards a Wearable Alcohol Biosensor: Examining the Accuracy of BAC Estimates from New-Generation Transdermal Technology using Large-Scale Human Testing and Machine Learning Algorithms

可穿戴的酒精生物传感器可以代表帮助人们了解信息的巨大进步 他们的饮酒决定，并最终控制与酒精有关的发病率和死亡率。 透皮传感器，通过评估无知觉的人的酒精含量来测量酒精消耗量。 出汗，提供了一种独特的非侵入性，被动和低成本的方法，用于持续评估 饮酒可能会吸引一系列人群。但透皮酒精之间的关系 血液酒精浓度（TAC）和血液酒精浓度（BAC）是非常复杂的，个体之间存在差异， 上下文，并涉及一定程度的滞后时间。先前的研究，其中有非常小的参与者 样品和检查的老一代透皮装置，一直不适合建模这一点 复杂性因此，科学家们对如何翻译透皮传感器产生的数据几乎没有任何意义 对BAC的估计。重要的是，在过去的十年里， 这是一个新的发展，提供了解决TAC-BAC翻译挑战的潜力。特别是近几 多年来，机器学习方法已经被开发出来，特别适合于高度建模。 在更大的数据集内的复杂和时间滞后的关系。在此期间，新一代的 透皮装置已经进入开发阶段，具有光滑/紧凑的设计，智能手机集成， 并且能够以大约90倍于老一代器件的速率对TAC进行采样。这些 因此，传感器为机器学习模型提供了丰富的数据来源，也是第一次， 以真实的时间产生经皮BAC估计。拟议的研究利用机器学习，新颖 透皮技术和大规模多模式人体测试，将透皮传感器数据转化为 BAC的估计。透皮传感器将在多模态研究的背景下进行检查， 在实验室内外检查了大量不同的参与者样本（N=240）。的 拟议项目的门诊部分旨在捕捉个体之间的TAC-BAC关系， 改变现实世界的饮酒环境，检查佩戴新一代透皮传感器的常规饮酒者， 在日常设置中，同时提供提示的呼吸测醉器读数。这项门诊研究将是 辅以实验室研究，旨在检查个体之间的TAC-BAC关系 在受控环境中饮酒，同时系统地操纵酒精剂量和消费率。 包括深度神经网络模型在内的机器学习算法将用于创建BAC的估计值 来自透皮传感器数据。这些估计将在其准确性，时间， 具体性，以及上下文依赖性。因此，研究结果将对成瘾科学具有重要意义， 翻译经皮传感器数据，并澄清这些传感器在我们的技术武器库中的位置， 评估、预防和治疗饮酒问题。

A wearable alcohol biosensor could represent a tremendous advance towards helping people make informed decisions about their drinking and, ultimately, towards curbing alcohol-related morbidity and mortality. Transdermal sensors, which measure alcohol consumption by assessing the alcohol content of insensible perspiration, offer a uniquely non-invasive, passive, and low-cost method for the continuous assessment of drinking likely to be attractive to a range of populations. But the relationship between transdermal alcohol concentration (TAC) and blood alcohol concentration (BAC) is highly complex, varying across individuals and contexts and involving some degree of lag time. Prior research, which has featured extremely small participant samples and examined old-generation transdermal devices, has been poorly suited to modeling this complexity. Thus, scientists are left with little sense for how to translate data produced by transdermal sensors into estimates of BAC. Importantly, the past decade has seen remarkable technological and analytic developments, offering the potential to tackle the challenge of TAC-BAC translation. In particular, in recent years, machine learning approaches have been developed that are particularly well suited to modeling highly complex and time-lagged relationships within larger datasets. Also during this time period, a new generation of transdermal device has come under development, featuring sleek/compact designs, smartphone integration, and capabilities for sampling TAC at approximately 90 times the rate of older-generation devices. These sensors thus provide a rich source of data for machine learning models and also, for the first time, the potential to produce transdermal BAC estimates in real time. The proposed research leverages machine learning, novel transdermal technology, and large-scale multimodal human testing to translate transdermal sensor data into estimates of BAC. Transdermal sensors will be examined in the context of multimodal research featuring a large and diverse participant sample (N=240) examined both inside and outside the laboratory. The ambulatory arm of the proposed project is aimed at capturing the TAC-BAC relationship across individuals in varying real-world drinking contexts, examining regular drinkers wearing new-generation transdermal sensors in everyday settings while providing prompted breathalyzer readings. This ambulatory research will be complemented by a laboratory study arm, aimed at examining the TAC-BAC relationship among individuals drinking in a controlled setting while alcohol dose and rate of consumption are systematically manipulated. Machine learning algorithms, including deep neural network models, will be used to create estimates of BAC from transdermal sensor data. These estimates will be examined in terms of their accuracy, temporal specificity, and also context-dependence. Thus, results will carry significance for addiction science by translating transdermal sensor data and clarifying the place of these sensors in our arsenal of techniques for assessing, preventing, and treating problem drinking.