Non-linear dynamics of heart rate variability during incremental cycling exercise

Abstract

Within the last years complex models of cardiovascular regulation and exercise fatigue have implemented heart rate variability (HRV) as a measure of autonomic nervous system. Using detrended fluctuation analysis (DFA) to assess heart rate correlation properties, the present study examines the influence of exercise intensity on total variability and complexity in non-linear dynamics of HRV. Sixteen cyclists performed a graded exercise test on a bicycle ergometer. HRV time domain measures and fractal correlation properties were analyzed using short-term scaling exponent alpha1 of DFA. Amplitude and complexity of HRV parameters decreased significantly. DFA-alpha1 increased from rest to low exercise intensity and showed an almost linear decrease from higher intensities until exhaustion. These findings support a qualitative change in self-organized heart rate regulation from a complex autonomic control at rest and low intensities towards a breakdown of the interaction in control mechanisms with non-autonomic heart rate control dominating at high intensities.

Fractal Correlation Properties of Heart Rate Variability: A New Biomarker for Intensity Distribution in Endurance Exercise and Training Prescription?

“…More precisely, from low to high intensity exercise, DFA-alpha1 indicates a biphasic course. Depending on the resting value (usually around 1.0 and 1.5), constant or moderately increasing values (up to 1.5) have been reported at very low to moderate intensities, and strongly, almost linearly decreasing values down to around 0.3 from moderate to high exercise intensity (Hautala et al, 2003;Casties et al, 2006;Karavirta et al, 2009;Blasco-Lafarga et al, 2017;Gronwald et al, 2019a). This behavior indicates an intensity-dependent change of HR dynamics from strongly correlated to uncorrelated/stochastic or anti-correlated behavior due to a notably strong vagal withdrawal and/or sympathetic activation Hottenrott and Hoos, 2017) as well as other factors such as possible intracardiac biochemical changes or coupling mechanisms of different physiological subsystems (Casties et al, 2006;Papaioannou et al, 2013).…”

“…According to the state of research DFA-alpha1 may provide valuable information for monitoring organismic internal load and individualizing endurance exercise and training prescription (Blasco-Lafarga et al, 2017). After a slight rise from rest, DFA-alpha1 falls rapidly with increasing work rates near the aerobic threshold demonstrating the potential usefulness of this parameter for low intensity zone demarcation (Casties et al, 2006;Gronwald et al, 2019a). The rate of change per workrate elevation seems highest near the aerobic threshold and since it is a complexity index of overall organismic demands (Gronwald et al, 2019a), no curve calibration or normalization is needed.…”

“…The rate of change per workrate elevation seems highest near the aerobic threshold and since it is a complexity index of overall organismic demands (Gronwald et al, 2019a), no curve calibration or normalization is needed. Studies by Casties et al (2006), ), Blasco-Lafarga et al (2017, and Gronwald et al (2019a) all indicate a clear decline in interbeat correlation properties with high work rates with values resembling white noise behavior at VO 2 levels historically in the realm between aerobic threshold (LT1, VT1) and anaerobic threshold (LT2, VT2). Within the context of different training models (e.g., polarized, pyramidal, or threshold training-intensity FIGURE 1 | Quantification and qualitative description of acute and chronic correlation properties (short-term scaling exponent alpha1 of DFA) within HR time series during resting conditions and during endurance-type exercise demands (Peng et al, 1995;Goldberger et al, 2002;Lipsitz, 2002;Tulppo et al, 2003;Gronwald et al, 2018).…”

“…Therefore, DFA and its short-term scaling exponent alpha1 were tested in several applied settings and seem also suitable for sport-specific applications including exercise from low to high intensities, non-stationary data, and relatively short recording times for different conditions (Tulppo et al, 2001 ; Hautala et al, 2003 , 2009 ). In such an application it is rather the qualitative information of organismic regulation during exercise that seems to be important in order to gain new insights from a dose-response perspective and DFA-alpha1 may be taken as an indicator for exercise prescription and intensity distribution monitoring in endurance training (Gronwald et al, 2019a ). Therefore, we would like to stress the notion that DFA-alpha1 can serve as a “global parameter” for the whole system and proxy for organismic demands and complex regulation at the intersection of the central and ANS.…”

New Directions in Exercise Prescription: Is There a Role for Brain-Derived Parameters Obtained by Functional Near-Infrared Spectroscopy?

“…HRV is considered to be a proxy of the actual health state [ 246 , 247 , 248 , 249 ] and stress level [ 247 , 250 ] of an individual. HRV also reflects, at least partly, the fitness level and daily readiness [ 243 , 244 , 251 , 252 , 253 , 254 , 255 ] of an individual, as well as the organismic demands [ 256 , 257 , 258 , 259 , 260 ]. There are numerous non-linear analysis approaches to appraise HRV, which reflect the systemic character of the organism [ 245 , 248 , 249 , 261 , 262 , 263 ].…”