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

迈向可穿戴酒精生物传感器：使用大规模人体测试和机器学习算法检查新一代透皮技术的 BAC 估计值的准确性

• 批准号: 10298493
• 负责人: Catharine Fairbairn
• 金额: $ 46.01万
• 依托单位: UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-21 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10298493
• 关键词: Abstinence; Accelerometer; Address; Alcohol abuse; Alcohol consumption; Alcohol-Related Disorders; Alcoholic Intoxication; Alcoholic beverage heavy drinker; Alcohols; Algorithms; Ankle; Biosensor; Blood alcohol level measurement; Cellular Phone; Clinical; Cognitive; Complement; Complex; Computer Models; Consumption; Data; Data Analytics; Data Collection; Dependence; Development; Devices; Dose-Rate; Enrollment; Family; Generations; Grain; Harm Reduction; Health; Human; Individual; Influentials; Intervention; Laboratories; Laboratory Research; Laboratory Study; Lead; Left; Link; Machine Learning; Measures; Memory; Methods; Modeling; Monitor; Morbidity - disease rate; Neural Network Simulation; Outcome Assessment; Output; Participant; Patient Self-Report; Personal Satisfaction; Persons; Population; Prevention; Procedures; Race; Reading; Relapse; Research; Rest; Sampling; Science; Scientist; Skin; Source; Specificity; Surface; Techniques; Technology; Temperature; Testing; Time; Translating; Translations; Uncertainty; Work; addiction; alcohol abstinence; alcohol content; alcohol measurement; alcohol monitoring; alcohol prevention; alcohol research; analytical method; arm; contingency management; cost; deep neural network; design; drinking; fitbit; human subject; improved; interest; large datasets; machine learning algorithm; machine learning method; mortality; multimodality; novel; prevent; sensor; sex; wearable device

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.

可穿戴式酒精生物传感器可能代表着在帮助人们获得信息方面的巨大进步 关于饮酒的决定，并最终有助于遏制与酒精有关的发病率和死亡率。 透皮传感器，通过评估昏迷的人的酒精含量来测量酒精消耗量 汗液，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 饮酒可能对一系列人群具有吸引力。但酒精透皮透皮与 酒精浓度(TAC)和血液酒精浓度(BAC)是非常复杂的，不同的人和 并涉及一定程度的滞后时间。之前的研究，参与者极少 样本和检查了老一代的透皮装置，一直不适合对此进行建模 复杂性。因此，科学家们对如何转换透皮传感器产生的数据几乎一无所知 计入BAC的估计值。重要的是，在过去的十年里，我们看到了非凡的技术和分析 发展，为应对TAC-BAC翻译的挑战提供了潜力。特别是在最近， 多年来，已经开发出特别适合于高度建模的机器学习方法 较大数据集中的复杂且时间滞后的关系。也是在这段时间里，新一代 透皮设备正在开发中，其特点是时尚/紧凑的设计，智能手机集成， 以及对TAC进行采样的能力，其采样速度大约是老一代设备的90倍。这些 因此，传感器为机器学习模型提供了丰富的数据源，也是第一次 以实时产生透皮BAC估计值。建议的研究利用了机器学习，新颖 透皮技术，以及大规模多模式人体测试，将透皮传感器数据转换为 对BAC的估计。透皮传感器将在多模式研究的背景下进行检查，其特点是 在实验室内外检查了大而多样的参与者样本(N=240)。这个 拟议项目的非卧床部分旨在捕获 改变现实世界的饮酒环境，检查佩戴新一代透皮传感器的普通饮酒者 在日常环境中，同时提供提示的呼气测定仪读数。这项动态研究将是 辅以一个实验室研究部门，旨在检查个人之间的TAC-BAC关系 在有控制的环境中饮酒，同时系统地控制饮酒量和饮酒率。 机器学习算法，包括深度神经网络模型，将被用于创建BAC的估计 来自透皮感应器数据。这些估计将从它们的准确性、时间性 专一性，也依赖于上下文。因此，研究结果将对成瘾科学具有重要意义 翻译透皮感应器数据并阐明这些感应器在我们的技术库中的位置 评估、预防和治疗问题饮酒。