Fairbairn Catharine E, Han Jiaxu, Caumiant Eddie P, Benjamin Aaron S, Bosch Nigel
Department of Psychology, University of Illinois Urbana-Champaign, Champaign, IL 61820, USA.
Department of Psychology, University of Illinois Urbana-Champaign, Champaign, IL 61820, USA.
Drug Alcohol Depend. 2025 Jan 1;266:112519. doi: 10.1016/j.drugalcdep.2024.112519. Epub 2024 Nov 30.
Trace amounts of consumed alcohol are detectable within sweat and insensible perspiration. However, the relationship between ingested and transdermally emitted alcohol is complex, varying across environmental conditions and involving a degree of lag. As such, the feasibility of real-time drinking detection across diverse environments has been unclear. In the current research we revisit sensor performance using new tools, exploring the accuracy of a new generation of rapid-sampling transdermal biosensor for contemporaneous drinking detection across diverse environments via machine learning.
Regular drinkers (N = 100) attended three laboratory sessions involving the experimental manipulation of alcohol dose, rate of consumption, and environmental dosing conditions. Participants further supplied breath alcohol concentration (BAC) readings in the field over 14 days. Participants wore compact wrist sensors capable of rapid sampling (20sec intervals). Transdermal sensor data was translated into alcohol use estimates using machine learning, integrating only transdermal data collected prior to the point of BAC assessment.
A total of 5.39 million transdermal readings (28,615hours) and 12,699 BAC readings were collected for this research. Models indicated strong transdermal sensor accuracy for real-time drinking detection across both laboratory and field contexts (AUROC, 0.966, 95 % CI, 0.956-0.972; Sensitivity, 89.8 %; Specificity, 90.6 %). Models aimed at differentiating high-risk (≥0.08 %) drinking levels yielded intermediate (AUROC, 0.738; 95 % CI, 0.698-0.777; only drinking episodes) to strong (AUROC, 0.941, 95 % CI, 0.929-0.954; all data) accuracy levels.
Results indicate a range of useful future applications for transdermal alcohol sensors including long-term health tracking, medical monitoring, and just-in-time relapse prevention.
在汗液和不显性出汗中可检测到微量摄入的酒精。然而，摄入的酒精与经皮散发的酒精之间的关系很复杂，会因环境条件而异，且存在一定程度的滞后。因此，在不同环境中进行实时饮酒检测的可行性尚不清楚。在当前的研究中，我们使用新工具重新审视传感器性能，通过机器学习探索新一代快速采样经皮生物传感器在不同环境中进行同步饮酒检测的准确性。
经常饮酒者（N = 100）参加了三次实验室实验，涉及酒精剂量、饮酒速度和环境给药条件的实验操作。参与者还在14天内提供了现场呼气酒精浓度（BAC）读数。参与者佩戴能够快速采样（每隔20秒）的紧凑型腕部传感器。使用机器学习将经皮传感器数据转换为酒精使用估计值，仅整合在BAC评估点之前收集的经皮数据。
本研究共收集了539万个经皮读数（28615小时）和12699个BAC读数。模型表明，经皮传感器在实验室和现场环境中进行实时饮酒检测具有很高的准确性（受试者工作特征曲线下面积[AUC]，0.966；95%置信区间[CI]，0.956 - 0.972；敏感性，89.8%；特异性，90.6%）。旨在区分高风险（≥0.08%）饮酒水平的模型产生了中等（AUC，0.738；95%CI，0.698 - 0.777；仅饮酒发作情况）到较高（AUC，0.941；95%CI，0.929 - 0.954；所有数据）的准确性水平。
结果表明经皮酒精传感器在未来有一系列有用的应用，包括长期健康跟踪、医疗监测和即时预防复发。
