基于从可穿戴设备收集的节拍间隔 (BBI) 数据预测电子烟使用事件的统计方法
James J Yang1, Megan E Piper2, Premananda Indic3
1Department of Biostatistics and Data Science, University of Texas Health Science Center at Houston, Houston, Texas, USA.
Statistics in medicine
|May 31, 2024
概括
吸烟的年轻人面临着由于动机和支持不足而放弃的挑战. 可穿戴式传感器显示在蒸汽之前心率上升,这表明电子烟戒烟的潜在干预时间.
科学领域:
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
- 公共卫生 公共卫生
背景情况:
- 在美国的年轻成年人中,使用电子烟普遍 (14%).
- 许多用户打算戒烟,但缺乏动力和支持.
- 可穿戴式传感器为实时生理数据收集提供了时间干预的潜力.
研究的目的:
- 开发和评估用于消除噪音和总结智能手表生理数据的统计方法.
- 为了确定电子烟使用事件之前的生理模式.
- 将拟议的方法与传统的心率变化 (HRV) 分析进行比较.
主要方法:
- 收集了12名使用智能手表的年轻成年电子烟用户的7天节拍间隔 (BBI) 数据.
- 应用单一频谱分析 (SSA) 来消除BBI数据的噪音.
- 开发了一个二次多项式模型来总结无噪声数据,并将其与现有的HRV方法进行比较.
主要成果:
- 单一频谱分析 (SSA) 有效地消除了变量BBI数据的噪音.
- 拟议的二次多项式模型实现了0.76的最高曲线下面积 (AUC),超过了现有的HRV方法.
- 在vaping事件之前观察到心率上升的趋势.
结论:
- 该研究证明了SSA在消除BBI数据噪声方面的有效性,以及HRV分析的新多项式模型.
- 在vaping之前观察到的生理变化表明存在潜在的干预窗口.
- 调查结果可能有助于开发及时干预措施,帮助年轻人戒除电子烟.
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