Light exercise heart rate on-kinetics: a comparison of data fitted with sigmoidal and exponential functions and the impact of fitness and exercise intensity

Abstract

This study examined the suitability of sigmoidal (SIG) and exponential (EXP) functions for modeling HR kinetics at the onset of a 5‐min low‐intensity cycling ergometer exercise test (5MT). The effects of training status, absolute and relative workloads, and high versus low workloads on the accuracy and reliability of these functions were also examined. Untrained participants (UT abs; n = 13) performed 5MTs at 100W. One group of trained participants (n = 10) also performed 5MTs at 100W (ET abs). Another group of trained participants (n = 9) performed 5MTs at 45% and 60% V˙normalO2 max (ET 45 and ET 60, respectively). SIG and EXP functions were fitted to HR data from 5MTs. A 30‐s lead‐in time was included when fitting SIG functions. Functions were compared using the standard error of the regression (SER), and test‐retest reliability of curve parameters. SER for EXP functions was significantly lower than for SIG functions across all groups. When residuals from the 30‐s lead‐in time were omitted, EXP functions only outperformed SIG functions in ET 60 (EXP, 2.7 ± 1.2 beats·min−1; SIG, 3.1 ± 1.1 beats·min−1: P < 0.05). Goodness of fit and test–retest reliability of curve parameters were best in ET 60 and comparatively poor in UT abs. Overall, goodness of fit and test–retest reliability of curve parameters favored functions fitted to 5MTs performed by trained participants at a high and relative workload, while functions fitted to data from untrained participants exercising at a low and absolute workload were less accurate and reliable.

Cardiac autonomic responses during and after a single session of aerobic exercise with and without blood flow restriction

“…Slower and faster cardiovascular adjustments were demonstrated for low-load exercise with BFR when compared to low-load exercise without BFR and high-load exercise, respectively. This is in accordance with previous studies that investigated cardiovascular adjustments in different intensities of aerobic exercise 21,22 . From rest to exercise transition, the skeletal muscle contractions promote a parasympathetic withdrawal in the first few seconds via muscle mechanoreflex 23 and gradual sympathetic activation mediated principally via muscle metaboreflex 24 .…”

Evaluation of a rat model of exercise-induced fatigue using treadmill running with progressively increasing load

“…As such, heart rate is both a physiological and prognostic indicator of health (Oliva et al 2018). A simple and accurate marker of exercise status, such as heart rate, would be a valuable tool for indicating the level of recovery or adaptation of an athlete, recognizing exerciseinduced fatigue, and for facilitating adjustments in training load (Bellenger et al 2016, Trounson et al 2017. There is also a significant link between heart rate and behavior, as demonstrated by the rat model presented here.…”

Monitoring Effects of Sleep Extension and Restriction on Endurance Performance Using Heart Rate Indices

“…Data were transferred to Table Curve 2D software (SYSTAT Software Inc., San Jose, CA) and fitted to a monoexponential function:HR (beats⋅min−1)=a+A (1−e−(t−TD)τ)where a is baseline HR (beats·min −1 ), A is the amplitude of HR response (beats·min −1 ), t is time (s), TD is the time delay before HR increases (s), and τ is the curvature parameter (s). Curve fitting methods used to ensure an optimal fit were applied as previously described (35). The HR response at the onset of the test was assessed using the maximal rate of HR increase (rHRI, beats·min −1 ·s −1 ) derived from the first derivative maxima of the function according to the following equation:rHRI (beats⋅min−1⋅s−1)= A (e−1(t−TDτ))τ…”