基于分形相关特性的非线性心率变异性(HRV)指数,称为趋势波动分析的α1(DFA-α1),已被证明随耐力运动强度而变化。其独特的优点是,它提供了有关当前绝对运动强度的信息,而无需事先进行乳酸或气体交换测试。因此,使用可穿戴监测设备在野外条件期间对该度量的实时评估可以直接提供有价值的锻炼强度分布,而无需针对耐力运动中的不同应用领域设置的先前实验室测试。直到最近,没有基于移动的产品可以使用现成的消费产品实时显示DFA-α1。最近为iOS和Android设备设计的应用程序,HRV记录器,更新以实时评估DFA-α1。这份简短的研究报告说明了实时监测该指标的潜在优点,以进行有氧阈值(AT)估计以及前奥运会铁人三项运动员的低(区域1)和中等(区域2)之间的运动强度划分。在单一案例可行性研究中,在自行车运动中成功评估了三个实际相关的场景,(1)通过典型的循环阶段测试,使用KubiosHRV软件估计HRV阈值(HRVT)作为AT的适当代理,(2)使用循环6分钟阶段测试在实时监测期间估计HRVT,(3)模拟1小时训练,执行低强度边界和实时HRVT确认。这种单例现场评估说明了使用HRV的分形相关特性对有氧阈值和运动强度分布进行易于使用且低成本的实时估计的潜力。此外,这种方法可能会促进科学转化为耐力运动实践,以适应未来的现实环境。
A non-linear heart rate variability (HRV) index based on fractal correlation properties called alpha1 of Detrended Fluctuation Analysis (DFA-alpha1), has been shown to change with endurance exercise intensity. Its unique advantage is that it provides information about current absolute exercise intensity without prior lactate or gas exchange testing. Therefore, real-time assessment of this metric during field conditions using a wearable monitoring device could directly provide a valuable exercise intensity distribution without prior laboratory testing for different applied field settings in endurance sports. Until of late no mobile based product could display DFA-alpha1 in real-time using off the shelf consumer products. Recently an app designed for iOS and Android devices, HRV Logger, was updated to assess DFA-alpha1 in real-time. This brief research report illustrates the potential merits of real-time monitoring of this metric for the purposes of aerobic threshold (AT) estimation and exercise intensity demarcation between low (zone 1) and moderate (zone 2) in a former Olympic triathlete. In a single-
case feasibility study, three practically relevant scenarios were successfully evaluated in cycling, (1) estimation of a HRV threshold (HRVT) as an adequate proxy for AT using Kubios HRV software via a typical cycling stage test, (2) estimation of the HRVT during real-time monitoring using a cycling 6 min stage test, (3) a simulated 1 h training ride with enforcement of low intensity boundaries and real-time HRVT confirmation. This single-
case field evaluation illustrates the potential of an easy-to-use and low cost real-time estimation of the aerobic threshold and exercise intensity distribution using fractal correlation properties of HRV. Furthermore, this approach may enhance the translation of science into endurance sports practice for future real-world settings.