Department of Performance, Neuroscience, Therapy and Health, MSH Medical School Hamburg, Hamburg, Germany.
Center for Sports and Physical Education, Julius Maximilians University of Wuerzburg, Wuerzburg, Germany.
Ann Noninvasive Electrocardiol. 2020 Jan;25(1):e12697. doi: 10.1111/anec.12697. Epub 2019 Sep 9.
Non-linear measures of heart rate variability (HRV) may provide new opportunities to monitor cardiac autonomic regulation during exercise. In healthy individuals, the HRV signal is mainly composed of quasi-periodic oscillations, but it also possesses random fluctuations and so-called fractal structures. One widely applied approach to investigate fractal correlation properties of heart rate (HR) time series is the detrended fluctuation analysis (DFA). DFA is a non-linear method to quantify the fractal scale and the degree of correlation of a time series. Regarding the HRV analysis, it should be noted that the short-term scaling exponent alpha1 of DFA has been used not only to assess cardiovascular risk but also to assess prognosis and predict mortality in clinical settings. It has also been proven to be useful for application in exercise settings including higher exercise intensities, non-stationary data segments, and relatively short recording times.
Therefore, the purpose of this systematic review was to analyze studies that investigated the effects of acute dynamic endurance exercise on DFA-alpha1 as a proxy of correlation properties in the HR time series.
The initial search identified 442 articles (351 in PubMed, 91 in Scopus), of which 11 met all inclusion criteria.
The included studies show that DFA-alpha1 of HRV is suitable for distinguishing between different organismic demands during endurance exercise and may prove helpful to monitor responses to different exercise intensities, movement frequencies, and exercise durations. Additionally, non-linear DFA of HRV is a suitable analytical approach, providing a differentiated and qualitative view of exercise physiology.
心率变异性(HRV)的非线性测量可能为监测运动期间心脏自主调节提供新的机会。在健康个体中,HRV 信号主要由准周期性振荡组成,但它也具有随机波动和所谓的分形结构。一种广泛应用于研究心率(HR)时间序列分形相关特性的方法是去趋势波动分析(DFA)。DFA 是一种量化时间序列分形尺度和相关程度的非线性方法。关于 HRV 分析,应该注意的是,DFA 的短期标度指数 alpha1 不仅被用于评估心血管风险,还被用于评估临床环境中的预后和预测死亡率。它也被证明在包括较高运动强度、非平稳数据段和相对较短记录时间的运动环境中的应用是有用的。
因此,本系统综述的目的是分析研究急性动态耐力运动对 DFA-alpha1 的影响,作为 HR 时间序列相关特性的替代指标。
最初的搜索确定了 442 篇文章(351 篇在 PubMed 中,91 篇在 Scopus 中),其中 11 篇符合所有纳入标准。
纳入的研究表明,HRV 的 DFA-alpha1 适合区分耐力运动期间不同的机体需求,并且可能有助于监测对不同运动强度、运动频率和运动持续时间的反应。此外,HRV 的非线性 DFA 是一种合适的分析方法,为运动生理学提供了一种差异化和定性的观点。