Department of Physiology and Biochemistry, University School of Physical Education in Wrocław, 35 I.J. Paderewski Avenue, 51-612 Wrocław, Poland.
Int J Environ Res Public Health. 2021 Jul 18;18(14):7636. doi: 10.3390/ijerph18147636.
Individual changes in resting heart rate variability (HRV) parameters were assessed in seven Polish cyclists during a training process consisting of: a six-week period (P1) of predominantly low- and moderate-intensity training (L-MIT) and a six-week period (P2) where the proportion of high-intensity interval training (HIT) increased. Daily recorded HRV parameters included high-frequency spectral power (HF), square root of the mean squared difference between successive normal-to-normal RR intervals (RMSSD), and standard deviation of normal-to-normal RR intervals (SDNN). In each training microcycle, the average values of HF, RMSSD, and SDNN were calculated individually for each participant. In three cyclists, HF was higher in P2 compared to P1, whereas in one cyclist, HF was higher in P1 than in P2. Each of these four cyclists presented an individual correlation between the average daily duration HIT effort in training microcycles (HIT) and HF. Cyclists with low baseline values of HRV parameters showed increased activity of the parasympathetic nervous system, while in the cyclist with high baseline values of HRV parameters, an opposite change was observed. In conclusion, changes in resting HRV parameters between period P1 and P2 can be individualised. In the investigated group, it was possible to predict how HRV would change as a result of training intensification on the basis of HRV baseline values.
在一项包括六周低强度和中等强度训练（L-MIT）阶段（P1）和六周高强度间歇训练（HIT）比例增加阶段（P2）的训练过程中，评估了七名波兰自行车运动员静息心率变异性（HRV）参数的个体变化。每日记录的 HRV 参数包括高频谱功率（HF）、相邻正常 RR 间期均方根差的平方根（RMSSD）和正常 RR 间期标准差（SDNN）。在每个训练微周期中，为每个参与者单独计算 HF、RMSSD 和 SDNN 的平均值。在三名自行车运动员中，P2 时的 HF 高于 P1，而在一名自行车运动员中，P1 时的 HF 高于 P2。这四名自行车运动员中的每一位都表现出平均每日 HIT 训练微周期（HIT）努力与 HF 之间的个体相关性。HRV 参数基线值较低的自行车运动员表现出副交感神经系统活动增加，而 HRV 参数基线值较高的自行车运动员则观察到相反的变化。总之，P1 和 P2 之间静息 HRV 参数的变化可以个体化。在研究的群体中，根据 HRV 基线值，可以预测 HRV 如何因训练强度增加而发生变化。
