使用心率和步数数据估计心肺呼吸能力
Alexander Neshitov1, Konstantin Tyapochkin2, Marina Kovaleva2
1Welltory Inc., 541 Jefferson, Suite 100, Redwood City, CA, 94063, USA. alexander@welltory.com.
Scientific reports
|September 22, 2023
概括
这项研究引入了一种新的模型,利用可穿戴设备的心率和步数数据来预测心肺呼吸能力. 它提供了准确的预测,有助于运动跟踪和心血管风险评估.
科学领域:
- 运动生理学 运动生理学
- 生物医学工程 生物医学工程
- 心血管健康 心血管健康
背景情况:
- 心血管健康 (CRF) 是一个关键的健康指标.
- 准确的CRF评估对于运动监测和成人心血管风险分层至关重要.
- 现有的CRF估计方法通常需要专门的设备或复杂的协议.
研究的目的:
- 开发和验证最大氧吸收 (VO2max) 的预测模型,这是CRF的一个关键指标.
- 为了利用可穿戴设备的随时可用的数据,特别是心率和步数.
- 为CRF提供点估计和概率预测,包括不确定性.
主要方法:
- 使用人体测量,心率和步数数据开发了一个预测模型.
- 该模型在3115名健康受试者的不同队列上进行了训练,并在779名健康受试者上进行了测试.
- 使用可穿戴设备测量最大氧气摄入量 (VO2max),并与实验室跑步机测试进行验证.
主要成果:
- 该模型证明了最大氧气吸收 (VO2max) 的准确和可靠预测,平均测试设置误差为3.946ml/kg/min.
- 对实验室测量进行验证时,误差为4.982毫升/千克/分钟.
- 该模型独特地依赖于心率和步数,这些数据通常可在大多数健身追踪器上获得.
结论:
- 拟议的模型提供了一种非侵入性和可访问的方法来预测心肺健康状况.
- 这种方法可以提高可穿戴设备的实用性,用于个人健康监测和风险评估.
- 该模型提供概率预测和置信区间的能力为CRF估计增加了有价值的背景.
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