基于心率变异的通气阈值估算--商用算法的验证

Timo Eronen, Jukka A. Lipponen, Vesa Hyrylä, Saana Kupari, Jaakko Mursu, Mika Venojärvi, Heikki O. Tikkanen, Mika P. Tarvainen
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摘要

通气阈值(VT1 和 VT2)在运动处方和运动训练中至关重要,是有氧代谢向无氧代谢过渡的标志。更具体地说,VT1 标志着乳酸累积的开始,而 VT2 则标志着代谢性酸中毒的开始。准确测定这些阈值对于优化训练强度至关重要。心率变异性(HRV)的分形相关特性,尤其是去趋势波动分析法(DFA-α1)的短期缩放指数α1,已证明具有实现这一目的的潜力。本研究验证了 Kubios 开发的商用通气阈值估计算法(VT 算法)的准确性。VT 算法采用了相对于心率储备和呼吸频率的瞬时心率(HR)以及 DFA-α1。64 名身体活跃的参与者接受了增量心肺运动测试 (CPET),并测量了心跳间歇 (RR)。DFA-α1 和 Kubios VT 算法用于评估换气阈值下的心率和摄氧量(VO2)。在真实 VT、DFA-α1 和 VT 算法得出的通气阈值下,平均 VO2 分别为 1.74、2.00 和 1.89 升/分钟(VT1)以及 2.40、2.41 和 2.40 升/分钟(VT2)。相应地,真实 VT、DFA-α1 和 VT 算法阈值下的平均心率分别为 141、151 和 142 bpm(VT1)以及 169、168 和 170 bpm(VT2)。与真实阈值相比,DFA-α1阈值的Bland-Altman误差统计(偏差±误差标准差)为-0.26±0.41升/分钟或-10±16 bpm(VT1)和0.00±0.34升/分钟或1±10 bpm(VT2),而VT-算法误差为-0.15±0.28升/分钟或-1±11 bpm(VT1)和0.01±0.20升/分钟或-1±7 bpm(VT2)。基于心率变异的 VT 测定算法可准确估算通气阈值,无需实验室设备即可深入了解训练区域、内部负荷和运动过程中的代谢转换。Kubios VT 算法结合了瞬时心率和射频以及 DFA-α1,对 VT1 和 VT2 的 VO2 和心率值提供了更高的准确性。
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Heart Rate Variability Based Ventilatory Threshold Estimation - Validation of a Commercially Available Algorithm
Ventilatory thresholds (VT1 and VT2) are critical in exercise prescription and athletic training, delineating the transitions from aerobic to anaerobic metabolism. More specifically, VT1 signifies the onset of lactate accumulation whilst VT2 signifies the onset of metabolic acidosis. Accurate determination of these thresholds is vital for optimizing training intensity. Fractal correlation properties of heart rate variability (HRV), particularly the short-term scaling exponent alpha 1 of Detrended Fluctuation Analysis (DFA-α1), have demonstrated potential for this purpose. This study validates the accuracy of commercial ventilatory threshold estimation algorithm (VT-algorithm) developed by Kubios. The VT-algorithm employs instantaneous heart rate (HR) relative to HR reserve and respiratory rate (RF), along with the DFA-α1. Sixty-four physically active participants underwent an incremental cardiopulmonary exercise test (CPET) with inter-beat interval (RR) measurements. DFA-α1 and the Kubios VT-algorithm were used to assess HR and oxygen uptake (VO2) at ventilatory thresholds. On average VO2 at true VT, DFA-α1, and VT-algorithm derived ventilatory thresholds were 1.74, 2.00 and 1.89 l/min (VT1) and 2.40, 2.41 and 2.40 l/min (VT2), respectively. Correspondingly, average HRs at the true VT, DFA-α1, and VT-algorithm thresholds were 141, 151 and 142 bpm (VT1) and 169, 168 and 170 bpm (VT2), respectively. When compared to the true thresholds, Bland-Altman error statistics (bias ± standard deviation of error) for the DFA-α1 thresholds were -0.26±0.41 l/min or -10±16 bpm at VT1 and 0.00±0.34 l/min or 1±10 bpm at VT2, whereas the VT-algorithm errors were -0.15±0.28 l/min or -1±11 bpm at VT1 and 0.01±0.20 l/min or -1±7 bpm at VT2. HRV based VT determination algorithms accurately estimate ventilatory thresholds, offering insights into training zones, internal loading, and metabolic transitions during exercise without the need of laboratory equipment. The Kubios VT-algorithm, which incorporates instantaneous HR and RF along with DFA-α1, provided higher accuracy for VO2 and HR values for both VT1 and VT2.
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