在COPD患者中,每日肺部状况与可穿戴语音和生理数据的关联
Sejal Bhalla1, Deshang Kong2, Salaar Liaqat2
1Computer Science, University of Toronto, Toronto, ON, Canada. sejal@cs.toronto.edu.
Scientific reports
|September 29, 2025
概括
智能手表语音分析揭示了慢性阻塞性肺病 (COPD) 恶化的关键指标. 声调特征可能表明更好的结果,而表皮特征则表明COPD患者的风险更高.
科学领域:
- 肺部医学 肺部医学
- 数字健康数字健康
- 语音科学 语言科学
背景情况:
- 慢性阻塞性肺病 (COPD) 构成了全球严重的健康负担,恶化导致住院治疗和死亡率.
- 目前的肺功能监测通常需要不方便的诊所访问,限制持续评估.
- 可穿戴技术为非侵入性,现实世界的生理监测提供了一个有希望的途径.
研究的目的:
- 调查智能手表数据语音分析对监测COPD的有用性.
- 识别与COPD恶化和不良结果相关的语言特征.
- 探索生理因素对基于语音的COPD标志物的影响.
主要方法:
- 分析了18名COPD患者在平均198.9天内使用智能手表的自由生活语音数据.
- 应用线性混合效应模型来将语音特征与COPD结果相关联.
- 通过心率变化和体力活动等生理共变量来检查适度效应.
主要成果:
- 在发音特征和不良COPD结果之间发现了负相关性.
- 观察到prosodic语音特征与COPD恶化风险增加之间的积极相关性.
- 通过心率变化和体力活动水平显著缓解这些关联.
结论:
- 语音分析,特别是语音和表情特征,显示了监测COPD的潜力.
- 在COPD中,呼吸功能,自主调节和发声之间的相互作用是复杂的.
- 将语音分析与可穿戴生理数据相结合,可能会产生针对肺功能受损的新型数字生物标志物.
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