果手表的心率变化数据可以量化压力吗?
Pedro Elkind Velmovitsky1, Matheus Lotto1,2, Paulo Alencar3
1School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada.
Frontiers in public health
|July 21, 2023
概括
果手表ECG应用程序无法使用当前的心率变化 (HRV) 分析可靠量化压力. 使用先进方法进行进一步的研究可能会提高其应激监测潜力.
科学领域:
- 生物医学工程 生物医学工程
- 心脏病学 心脏病学
- 数字健康数字健康
背景情况:
- 慢性压力带来重大健康风险,包括心血管疾病,高血压和糖尿病.
- 传统的压力监测依赖于主观的自我报告,强调客观,现实世界的数据收集的需要.
- 可穿戴的智能技术提供了一种新的方法,用于非侵入性地收集连续的健康数据.
研究的目的:
- 调查Apple Watch ECG应用程序作为监测个人压力水平的工具的潜力.
- 为了分析心率变化 (HRV) 功能,从Apple Watch的心电图数据中得出的数据与自我报告的压力相比.
- 评估使用可穿戴式心电图技术用于客观压力量化的可行性.
主要方法:
- 收集了36名健康参与者在日常活动中使用Apple Watch的心电图数据.
- 从心电图数据中提取了HRV特征.
- 通过重复测量ANOVA和斯皮尔曼相关性,使用DASS-21和LIKERT尺度对自我报告的压力分析HRV特征.
主要成果:
- 使用重复测量ANOVA,没有发现HRV特征和自我报告的压力之间的统计学意义.
- 斯皮尔曼相关性揭示了几个HRV特征和压力调查问卷之间非常弱的相关性.
- 果手表ECG应用程序,使用当前的分析方法,不适合量化压力.
结论:
- 果手表ECG应用程序无法使用传统的统计方法可靠量化压力.
- 未来的研究包含额外的参数和机器学习可能会提高压力量化能力.
- 可穿戴式心电图技术对压力监测具有前景,但需要进一步开发和验证.
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