Altini Marco, Amft Oliver
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:2610-2613. doi: 10.1109/EMBC.2016.7591265.
We describe an approach to support athletes at various fitness levels in their training load analysis using heart rate (HR) and heart rate variability (HRV). A smartphone-based application (HRV4Training) was developed that captures heart activity over one to five minutes using photoplethysmography (PPG) and derives HR and HRV features. HRV4Training integrated a guide for an early morning spot measurement protocol and a questionnaire to capture self-reported training activity. The smartphone application was made publicly available for interested users to quantify training effect. Here we analyze data acquired over a period of 3 weeks to 5 months, including 797 users, breaking down results by gender and age group. Our results suggest a strong relation between HR, HRV and self-reported training load independent of gender and age group. HRV changes due to training were larger than those of HR. We conclude that smartphone-based training monitoring is feasible and a can be used as a practical tool to support large populations outside controlled laboratory environments.
我们描述了一种利用心率（HR）和心率变异性（HRV），在训练负荷分析中支持不同体能水平运动员的方法。开发了一款基于智能手机的应用程序（HRV4Training），该程序使用光电容积脉搏波描记法（PPG）在1至5分钟内捕捉心脏活动，并得出HR和HRV特征。HRV4Training集成了一份清晨现场测量方案指南和一份用于获取自我报告训练活动的问卷。该智能手机应用程序已向感兴趣的用户公开，以量化训练效果。在此，我们分析了3周至5个月期间收集的数据，包括797名用户，并按性别和年龄组对结果进行了分类。我们的结果表明，HR、HRV与自我报告的训练负荷之间存在紧密关系，且不受性别和年龄组的影响。训练引起的HRV变化大于HR的变化。我们得出结论，基于智能手机的训练监测是可行的，并且可以用作在受控实验室环境之外支持大量人群的实用工具。
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