Zhao Puyang, Yang James J, Buu Anne
Department of Biostatistics & Data Science, University of Texas Health Science Center, Houston, TX, USA.
Department of Health Promotion and Behavioral Sciences, University of Texas Health Science Center, Houston, TX, USA.
Am J Drug Alcohol Abuse. 2025 Mar 4;51(2):165-172. doi: 10.1080/00952990.2024.2441868. Epub 2025 Feb 10.
Wearable devices have been increasingly adopted to collect physiological data such as heart rate that may infer momentary risk of substance use. Yet, innovative methods capable for handling these complex time series data as presented in the statistics or data science literature may not be accessible to substance use researchers. This study introduces a series of statistical methods to analyze heart rate data and identify features that are associated with nicotine vaping. Nontechnical description of the methods coupled with the information about open-source software packages that implemented these methods was provided. The analytical procedure included 5 steps: (1) de-noising by the singular spectrum analysis (SSA); (2) sleep region identification by the Sum of Single Effects (SuSiE) model; (3) repeated heart rate pattern identification by the matrix profile; (4) dimension reduction by the linear regression; and (5) comparing repeated heart rate patterns across non-vaping and vaping regions by the linear mixed model. Secondary analysis was conducted on heart rate and ecological momentary assessment (EMA) data collected from 35 young adult e-cigarette users (66% female) for 7 days. Effectiveness of the methods was demonstrated by graphical presentations showing that the extracted features characterize sleep patterns and heart rate changes before and after vaping events quite well. Secondary analysis found that heart rate was higher and changed faster before vaping. Statistical methods can effectively extract useful features from heart rate data that may inform momentary vaping risk and optimal timings for delivering messages in mobile-phone based interventions.
可穿戴设备越来越多地被用于收集生理数据，如心率，这些数据可能用于推断物质使用的瞬时风险。然而，物质使用研究人员可能无法使用统计或数据科学文献中提出的能够处理这些复杂时间序列数据的创新方法。本研究介绍了一系列统计方法来分析心率数据，并识别与尼古丁 vaping 相关的特征。提供了这些方法的非技术描述以及实现这些方法的开源软件包的信息。分析过程包括 5 个步骤：(1) 通过奇异谱分析 (SSA) 去噪；(2) 通过单效应总和 (SuSiE) 模型识别睡眠区域；(3) 通过矩阵轮廓识别重复的心率模式；(4) 通过线性回归进行降维；(5) 通过线性混合模型比较非 vaping 和 vaping 区域的重复心率模式。对从 35 名年轻成年电子烟使用者（66% 为女性）收集的 7 天心率和生态瞬时评估 (EMA) 数据进行了二次分析。通过图形展示证明了这些方法的有效性，表明提取的特征很好地刻画了睡眠模式以及 vaping 事件前后的心率变化。二次分析发现，在 vaping 之前心率更高且变化更快。统计方法可以有效地从心率数据中提取有用特征，这些特征可能为瞬时 vaping 风险以及在基于手机的干预中传递信息的最佳时机提供依据。
