Abstract
BackgroundNon‐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.MethodTherefore, 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.ResultsThe initial search identified 442 articles (351 in PubMed, 91 in Scopus), of which 11 met all inclusion criteria.ConclusionsThe 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.
Highlights
In recent years, analytics conducted with non‐linear dynamics and the chaos theory have been adapted to gain further insights into the complex cardiovascular regulation during acute exercise bouts (Hottenrott & Hoos, 2017; Michael, Graham, & Davis, 2017)
Evaluations of absolute heart rate variability (HRV) values of the time (e.g., Standard deviation of all normal RR‐intervals (SDNN), root mean square of successive differences (RMSSD)) and frequency domain (e.g., Low‐ frequency band (LF), High‐frequency band (HF), LF/HF) show that exercise may diminish variability even at low to moderate exercise intensities to such an extent, that these measures may not be able to discriminate between a further increase in exercise intensity due to low signal‐to‐noise ratio (e.g., Casadei, Cochrane, Johnston, Conway, & Sleight, 1995; Hautala, Makikallio, Seppanen, Huikuri, & Tulppo, 2003; Sandercock & Brodie, 2006; Tulppo, Makikallio, Takala, Seppänen, & Huikuri, 1996)
detrended fluctuation analysis (DFA)‐alpha1 seems to be suitable to distinguish between different organismic demands and may prove helpful to monitor responses to different exercise intensities until voluntary exhaustion (Gronwald et al, 2019; Hautala et al, 2003)
Summary
Analytics conducted with non‐linear dynamics and the chaos theory have been adapted to gain further insights into the complex cardiovascular regulation during acute exercise bouts (Hottenrott & Hoos, 2017; Michael, Graham, & Davis, 2017). Present research suggests that cardiac dynamics are controlled by complex interaction effects between the sympathetic and parasympathetic branches of the autonomic nervous system on the sinus node, and non‐neural factors (Persson, 1996) These two branches act competitively, resulting in clear sympathetic activation and parasympathetic withdrawal during exercise (Sandercock & Brodie, 2006). Non‐linear measures of heart rate variability (HRV) may provide new opportunities to monitor cardiac autonomic regulation during exercise. Method: the purpose of this systematic review was to analyze studies that investigated the effects of acute dynamic endurance exercise on DFA‐alpha as a proxy of correlation properties in the HR time series. Non‐linear DFA of HRV is a suitable analytical approach, providing a differentiated and qualitative view of exercise physiology
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