应用统计方法来识别与尼古丁vaping相关的心率特征
Puyang Zhao1, James J Yang1, Anne Buu2
1Department of Biostatistics & Data Science, University of Texas Health Science Center, Houston, TX, USA.
The American journal of drug and alcohol abuse
|February 10, 2025
概括
可穿戴设备可以跟踪心率变化,以预测尼古丁vaping风险. 先进的统计方法有助于识别这些模式,帮助及时提供物质使用干预措施.
科学领域:
- 身体生理学 身体生理学
- 数据科学数据科学数据科学
- 公共卫生 公共卫生
背景情况:
- 可穿戴设备收集生理数据,如心率,用于物质使用风险评估.
- 来自统计和数据科学的复杂时间序列分析方法往往无法获得物质使用研究人员.
研究的目的:
- 引入分析心率数据的统计方法.
- 识别与尼古丁vaping相关的心率特征.
- 为物质使用研究人员提供可访问的方法和开源软件.
主要方法:
- 单一光谱分析 (SSA) 用于消除噪音的心率数据.
- 单一效应总和 (SuSiE) 模型用于睡眠区域识别.
- 用于检测重复心率模式的矩阵配置文件.
- 线性回归用于缩小尺寸.
- 线性混合模型用于比较电子烟和非电子烟期间的心率模式.
- 使用年轻成年电子烟使用者的心率和生态瞬间评估 (EMA) 数据进行二次分析.
主要成果:
- 提取的特征有效地描述了在vaping事件周围的睡眠模式和心率变化.
- 观察到,在电子烟发作之前,心率更高,变化更快.
- 图形展示展示了这些方法在捕捉相关生理变化的有效性.
结论:
- 统计方法可以从心率数据中提取有意义的特征,用于电子烟风险预测.
- 识别的特征可以告知提供基于移动的干预信息的时间.
- 这种方法提高了可穿戴设备数据在物质使用研究和干预中的实用性.
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