Can a first‐order exponential decay model fit heart rate recovery after resistance exercise?

Abstract

The time-constant of postexercise heart rate recovery (HRRτ ) obtained by fitting heart rate decay curve by a first-order exponential fitting has being used to assess cardiac autonomic recovery after endurance exercise. The feasibility of this model was not tested after resistance exercise (RE). The aim of this study was to test the goodness of fit of the first-order exponential decay model to fit heart rate recovery (HRR) after RE. Ten healthy subjects participated in the study. The experimental sessions occurred in two separated days and consisted of performance of 1 set of 10 repetitions at 50% or 80% of the load achieved on the one-repetition maximum test [low-intensity (LI) and high-intensity (HI) sessions, respectively]. Heart rate (HR) was continuously registered before and during exercise and also for 10 min of recovery. A monoexponential equation was used to fit the HRR curve during the postexercise period using different time windows (i.e. 30, 60, 90, … 600 s). For each time window, (i) HRRτ was calculated and (ii) variation of HR explained by the model (R(2) goodness of fit index) was assessed. The HRRτ showed stabilization from 360 and 420 s on LI and HI, respectively. Acceptable R(2) values were observed from the 360 s on LI (R(2) > 0.65) and at all tested time windows on HI (R(2) > 0.75). In conclusion, this study showed that using a minimum length of monitoring (~420 s) HRR after RE can be adequately modelled by a first-order exponential fitting.

Associations Between Heart Rate Recovery Dynamics With Estradiol Levels in 20 to 60 Year-Old Sedentary Women

“…This simple Δ method is interesting because it can analyze the HRR within different segments of the recovery period which could offer more insights into the HR autonomic modulation after maximal physical exertion. On the other hand, the exponential analysis characterizes the entire HRR response by explicit data modeling techniques ( Bartels-Ferreira et al, 2014 ). Despite the fact that exponential modeling does not analyze the HRR in different segments, it uses the entire HRR dataset to build a model that describes the general overview of the HR modulation during the recovery period.…”

“…In addition, it is complicated to find the physiological meaning for “extra” parameters and the best fitting approach is a balance between fitting quality and physiological meaning ( Hughson and Morrissey, 1983 ; Hughson et al, 1988 ; Hughson, 2009 ). Therefore, this study applied a single exponential equation to fit HRR data following previous studies ( Bearden and Moffatt, 2001 ; Bartels-Ferreira et al, 2014 ).…”

“…The data fitting quality was evaluated by residual analysis, CI 95 ( Fawkner et al, 2002 ; Keir et al, 2016 ), Pearson correlation coefficient and the statistical significance level ( p ) of the estimated parameters. As the parameter “τ” is a time constant in a negative decreasing exponential function, lower values mean faster HRR dynamics ( Bearden and Moffatt, 2001 ; Bartels-Ferreira et al, 2014 ).…”

Heart rate recovery fast‐to‐slow phase transition: Influence of physical fitness and exercise intensity

“…Postexercise heart rate (HR) recovery is a noninvasive method for the assessment of cardiac autonomic function recovery after a stressful stimulus (Peçanha, Silva‐Junior, & Forjaz, ). HR recovery presents an exponential decay (Bartels‐Ferreira, R., de Sousa, E. D., Trevizani, G. A., Silva, L. P., Nakamura, F. Y., & Forjaz, C. L. ) with two distinct phases: a fast phase, characterized by parasympathetic‐mediated abrupt decay of HR (Imai, K., Sato, H., Hori, M., Kusuoka, H., Ozaki, H., & Yokoyama, H. ), and a slow phase, characterized by more gradual HR reduction, determined by both parasympathetic reactivation and sympathetic withdrawal (Imai et al., ; Perini, R., Orizio, C., Comande, A., Castellano, M., Beschi, M., & Veicsteinas, A. ).…”

“…Reduced HR recovery—caused by slow parasympathetic reactivation and/or sympathetic withdrawal (Peçanha, T., Silva‐Junior, N. D., & Forjaz, C. L. )—is a strong predictor of cardiovascular morbidity and mortality, even in asymptomatic subjects (Cole, Foody, Blackstone, & Lauer, ). For this reason, in the last years several methods for HR recovery assessment have been proposed and tested (Bartels‐Ferreira et al., ; Goldberger, Johnson, Subacius, Ng, & Greenland, ; Imai et al., ; Johnson & Goldberger, ; Perini et al., ; Pierpont, Stolpman, & Gornick, ). Most of these methods rely on simple arithmetic differences between peak HR and HR after 60 or 120 s in recovery (Cole, Blackstone, Pashkow, Snader, & Lauer, ; Cole et al., ; Mora et al., ; Vivekananthan, Blackstone, Pothier, & Lauer, ); other approaches include mathematical fitting of the initial or entire recovery curve (Bartels‐Ferreira et al., ; Imai et al., ; Perini & Veicsteinas, ; Pierpont et al., ).…”