Predicting Heart Rate Slow Component Dynamics: A Model Across Exercise Intensities, Age, and Sex - Massimo Teso; Alessandro L. Colosio; Maura Loi; Jan Boone; Silvia Pogliaghi (Sports)
The heart rate slow component (scHR) is an intensity-dependent HR increment that emerges during constant exercises, partially dissociated from metabolism (<semantics><mover accent="true">V˙</mover></semantics>O2). The scHR has been observed during constant-workload exercise in young and older adults. Unless this scHR is accounted for, exercise prescription using HR targets lead to an undesired reduction in metabolic intensity over time. Purpose: The purpose of this study is to characterize scHR across intensities, sex, and age to develop and validate a predictive equation able to maintain the desired metabolic stimulus over time in a constant aerobic exercise session. Methods: In our study, 66 individuals (35 females; 35 ± 13 yrs) performed the following: (i) a ramp-test for respiratory exercise threshold (GET and RCP) and maximal oxygen uptake (<semantics><mover accent="true">V˙</mover></semantics>O2max) detection, and (ii) 6 × 9-minute constant exercises at different intensities. The scHR was calculated by linear fitting from the fifth minute of exercise (bpm⋅min−1). A multiple-linear equation was developed to predict the scHR based on individual and exercise variables. The validity of the equation was tested on an independent sample by a Pearson correlation and Bland–Altman analysis between the measured and estimated HR during constant exercises. Results: The scHR increases with intensity and is larger in males (p < 0.05). A multiple-linear equation predicts the scHR based on the relative exercise intensity to RCP, age, and sex (r2 = 0.54, SEE = 0.61 bpm⋅min−1). scHR (bpm⋅min−1) = −0.0514 + (0.0240 × relative exercise intensity to RCP) − (0.0172 × age) − (0.347 × Sex (males = 0 and females score = 1)). In the independent sample, we found an excellent correlation between the measured and estimated HR (r2 = 0.98, p < 0.001) with no bias (−0.01 b·min−1, z-score= −0.04) and a fair precision (±4.09 b·min−1). Conclusions: The dynamic of the scHR can be predicted in a heterogeneous sample accounting for the combined effects of relative intensity, sex, and age. The above equation provides the means to dynamically adapt HR targets over time, avoiding an undesired reduction in the absolute and relative training load. This strategy would allow the maintenance of the desired metabolic stimulus (<semantics><mover accent="true">V˙</mover></semantics>O2) throughout an exercise session in a heterogeneous population.