Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, Indiana, USA.
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Addiction. 2023 Oct;118(10):2014-2025. doi: 10.1111/add.16228. Epub 2023 May 30.
Transdermal alcohol content (TAC) data collected by wearable alcohol monitors could potentially contribute to alcohol research, but raw data from the devices are challenging to interpret. We aimed to develop and validate a model using TAC data to detect alcohol drinking.
We used a model development and validation study design.
Indiana, USA PARTICIPANTS: In March to April 2021, we enrolled 84 college students who reported drinking at least once a week (median age = 20 years, 73% white, 70% female). We observed participants' alcohol drinking behavior for 1 week.
Participants wore BACtrack Skyn monitors (TAC data), provided self-reported drinking start times in real time (smartphone app) and completed daily surveys about their prior day of drinking. We developed a model using signal filtering, peak detection algorithm, regression and hyperparameter optimization. The input was TAC and outputs were alcohol drinking frequency, start time and magnitude. We validated the model using daily surveys (internal validation) and data collected from college students in 2019 (external validation).
Participants (N = 84) self-reported 213 drinking events. Monitors collected 10 915 hours of TAC. In internal validation, the model had a sensitivity of 70.9% (95% CI = 64.1%-77.0%) and a specificity of 73.9% (68.9%-78.5%) in detecting drinking events. The median absolute time difference between self-reported and model-detected drinking start times was 59 min. Mean absolute error (MAE) for the reported and detected number of drinks was 2.8 drinks. In an exploratory external validation among five participants, number of drinking events, sensitivity, specificity, median time difference and MAE were 15%, 67%, 100%, 45 minutes and 0.9 drinks, respectively. Our model's output was correlated with breath alcohol concentration data (Spearman's correlation [95% CI] = 0.88 [0.77, 0.94]).
This study, the largest of its kind to date, developed and validated a model for detecting alcohol drinking using transdermal alcohol content data collected with a new generation of alcohol monitors. The model and its source code are available as Supporting Information (https://osf.io/xngbk).
可穿戴酒精监测器收集的经皮酒精含量（TAC）数据可能有助于酒精研究，但设备的原始数据难以解释。我们旨在开发和验证一个使用 TAC 数据检测饮酒的模型。
我们使用模型开发和验证研究设计。
美国印第安纳州
2021 年 3 月至 4 月，我们招募了 84 名报告每周至少饮酒一次的大学生（中位数年龄为 20 岁，73%为白人，70%为女性）。我们观察了参与者一周的饮酒行为。
参与者佩戴 BACtrack Skyn 监测器（TAC 数据），实时（智能手机应用程序）提供自我报告的饮酒开始时间，并完成前一天饮酒的每日调查。我们使用信号过滤、峰值检测算法、回归和超参数优化开发了一个模型。输入是 TAC，输出是饮酒频率、开始时间和幅度。我们使用每日调查（内部验证）和 2019 年收集的大学生数据（外部验证）验证了该模型。
参与者（N=84）自我报告了 213 次饮酒事件。监测器收集了 10915 小时的 TAC。在内部验证中，该模型检测饮酒事件的敏感性为 70.9%（95%CI=64.1%-77.0%），特异性为 73.9%（68.9%-78.5%）。自我报告和模型检测到的饮酒开始时间之间的中位绝对时间差为 59 分钟。报告和检测到的饮酒量的平均绝对误差（MAE）为 2.8 杯。在五名参与者的探索性外部验证中，饮酒事件、敏感性、特异性、中位时间差和 MAE 分别为 15%、67%、100%、45 分钟和 0.9 杯。我们的模型输出与呼气酒精浓度数据相关（Spearman 相关系数[95%CI]为 0.88[0.77, 0.94]）。
这项迄今为止规模最大的研究开发并验证了一个使用新一代酒精监测器收集的经皮酒精含量数据检测饮酒的模型。该模型及其源代码可作为支持信息提供（https://osf.io/xngbk）。
