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.

可穿戴的酒精生物传感器可能代表着帮助人们了解知情的巨大进步 关于他们饮酒的决定，并最终遏制与酒精有关的发病率和死亡率。 经透皮传感器，通过评估无敏感的酒精含量来测量酒精消耗 出汗，提供一种独特的非侵入性，被动和低成本的方法，用于连续评估 饮酒可能对一系列人群有吸引力。但是透皮酒精之间的关系 浓度（TAC）和血液酒精浓度（BAC）高度复杂，在个人之间有所不同，并且 环境和涉及一定程度的滞后时间。先前的研究，参与者非常小 样品并检查了旧代透皮设备，非常适合对此进行建模 复杂。因此，科学家对如何翻译透皮传感器产生的数据几乎没有任何感觉 估计BAC。重要的是，过去十年中，技术和分析性出色 发展，提供了应对TAC-BAC翻译挑战的潜力。特别是在最近 多年来，已经开发了机器学习方法，这些方法特别适合于高度建模 较大数据集中的复杂且时置的关系。同样在此期间，新一代 透皮设备正在开发中，具有时尚/紧凑的设计，智能手机集成， 以及对TAC进行采样的功能，大约是旧代设备速率的90倍。这些 因此，传感器为机器学习模型提供了丰富的数据来源，也是第一次的潜力 实时产生透皮BAC估计。拟议的研究利用机器学习，新颖 透皮技术和大规模的多模式人类测试，将透皮传感器数据转化为 BAC的估计值。将在多模式研究的背景下检查透皮传感器 大型和多样化的参与者样本（n = 240）在实验室内外检查了。这 拟议项目的卧床部门旨在捕获跨个人的TAC-BAC关系 不同的现实饮酒环境，检查了穿着新一代透皮传感器的普通饮酒者 在日常设置中，在提供促使呼吸分析器读数的同时。这项卧床研究将是 由实验室研究部门进行补充，旨在检查个体之间的TAC-BAC关系 在受控的环境中饮酒，而酒精剂量和消费率是系统地操纵的。 机器学习算法，包括深神经网络模型，将用于创建BAC的估计值 来自透皮传感器数据。这些估计将根据其准确性，时间进行检查 特异性以及上下文依赖性。因此，结果将对成瘾科学具有重要意义 翻译透皮传感器数据并阐明这些传感器的位置在我们的武器库中 评估，预防和治疗问题。