indicates baseline salivary cortisol; HRV Rest , baseline heart-rate variability; TT, technical routine; HR, heart rate; La, capillary blood lactate concentration; SC Post , salivary cortisol after the training session; Rec 20–25 , HRV during the recovery period 20 to 25 minutes; Rec 25–30 , HRV during the
Mònica Solana-Tramunt, Jose Morales, Bernat Buscà, Marina Carbonell and Lara Rodríguez-Zamora
Alejandro Javaloyes, Jose Manuel Sarabia, Robert Patrick Lamberts and Manuel Moya-Ramon
response highlights the importance of monitoring properly, because without the RPE data, functional overreaching might be interpreted as an improvement in training status. In addition to HRR, heart-rate variability (HRV), which focuses on the variability of successive R–R intervals, 13 also gained
Daniel J. Plews, Ben Scott, Marco Altini, Matt Wood, Andrew E. Kilding and Paul B. Laursen
striving for peak performances, the need to effectively monitor human movement and physiological state are important so that more objective decisions around training can be made. 2 The regular assessment of heart rate variability (HRV) has immerged as a measure of “physiological state” that has grown in
Gregory Severino, Marcos Sanchez-Gonzalez, Michelle Walters-Edwards, Michael Nordvall, Oksana Chernykh, Jason Adames and Alexei Wong
cardiovascular health and prognosis ( Schwartz, La Rovere, & Vanoli, 1992 ). Heart rate variability (HRV), measured by the variation in the beat-to-beat intervals, is a noninvasive tool for the evaluation of cardiac autonomic function and is shown to be negatively influenced by menopause ( Brockbank, Chatterjee
Joanne Perry, Ashley Hansen, Michael Ross, Taylor Montgomery and Jeremiah Weinstock
applications includes physiological feedback mechanisms (i.e., biofeedback, neurofeedback). Heart rate variability (HRV) is a biofeedback measurement that has received increasing attention, largely due to improvements in availability and portability of technology ( Bar-Eli, 2002 ; Beauchamp, Harvey
Al Haddad Hani, Paul B. Laursen, Ahmaidi Said and Buchheit Martin
To assess the effect of supramaximal intermittent exercise on long-term cardiac autonomic activity, inferred from heart rate variability (HRV).
Eleven healthy males performed a series of two consecutive intermittent 15-s runs at 95% VIFT (i.e., speed reached at the end of the 30-15 Intermittent Fitness Test) interspersed with 15 s of active recovery at 45% VIFT until exhaustion. Beat-to-beat intervals were recorded during two consecutive nights (habituation night and 1st night) before, 10 min before and immediately after exercise, as well as 12 h (2nd night) and 36 h (3rd night) after supramaximal intermittent exercise. The HRV indices were calculated from the last 5 min of resting and recovery periods, and the first 10 min of the first estimated slow wave sleep period.
Immediate post-supramaximal exercise vagal-related HRV indices were significantly lower than immediate pre-supramaximal exercise values (P < .001). Most vagal-related indices were lower during the 2nd night compared with the 1st night (eg, mean RR intervals, P = .03). Compared with the 2nd night, vagal-related HRV indices were significantly higher during the 3rd night. Variables were not different between the 1st and 3rd nights; however, we noted a tendency (adjusted effect size, aES) for an increased normalized high-frequency component (P = .06 and aES = 0.70) and a tendency toward a decreased low-frequency component (P = .06 and aES = 0.74).
Results confirm the strong influence of exercise intensity on short- and long-term post exercise heart rate variability recovery and might help explain the high efficiency of supramaximal training for enhancing indices of cardiorespiratory fitness.
Daniel J. Plews, Paul B. Laursen, Andrew E. Kilding and Martin Buchheit
Elite endurance athletes may train in a polarized fashion, such that their training-intensity distribution preserves autonomic balance. However, field data supporting this are limited.
The authors examined the relationship between heart-rate variability and training-intensity distribution in 9 elite rowers during the 26-wk build-up to the 2012 Olympic Games (2 won gold and 2 won bronze medals). Weekly averaged log-transformed square root of the mean sum of the squared differences between R-R intervals (Ln rMSSD) was examined, with respect to changes in total training time (TTT) and training time below the first lactate threshold (>LT1), above the second lactate threshold (LT2), and between LT1 and LT2 (LT1–LT2).
After substantial increases in training time in a particular training zone or load, standardized changes in Ln rMSSD were +0.13 (unclear) for TTT, +0.20 (51% chance increase) for time >LT1, –0.02 (trivial) for time LT1–LT2, and –0.20 (53% chance decrease) for time >LT2. Correlations (±90% confidence limits) for Ln rMSSD were small vs TTT (r = .37 ± .80), moderate vs time >LT1 (r = .43 ± .10), unclear vs LT1–LT2 (r = .01 ± .17), and small vs >LT2 (r = –.22 ± .50).
These data provide supportive rationale for the polarized model of training, showing that training phases with increased time spent at high intensity suppress parasympathetic activity, while low-intensity training preserves and increases it. As such, periodized low-intensity training may be beneficial for optimal training programming.
Adults’ cardiac autonomic regulation during exercise and in relation to peak oxygen uptake is well understood, however the situation in children is sparsely documented. Heart rate variability (HRV) analysis provides a non-invasive tool to research sympathovagal balance. A predominance of parasympathetic mediated modulation is characterized by a greater degree of HRV and vice versa. The available data indicate the child’s response to be similar to that observed in adults; heart rate increase arises through withdrawal of parasympathetic modulation with ensuing increase in sympathetic modulation; aerobic training increases HRV and a positive correlation between peak oxygen uptake and a parasympathetic preponderance.
Nicolas Olivier, Renaud Legrand, Jacques Rogez, FX Gamelin, Serge Berthoin and Thierry Weissland
To analyze the consequences on heart rate variability (HRV) of a hospitalization period due to surgery of the knee in sportsmen.
Ten soccer players who had undergone knee surgery took part in this study.
HRV was measured before and after hospitalization within a 7-day interval.
After the hospitalization phase, heart rate at rest increased significantly (3 beats/minute). A significant decrease of 7% in the cardiac inter beat interval (R-R interval), P < 0.05 and a 66% decrease in total power spectral density: −66%, P < 0.05 were observed. The disturbance of the autonomic nervous system could be due to a variation in cardiac vagal activity resulting in a 64% decrease in the high frequencies (P < 0.05). This variation was not associated with a modification in normalized markers (LFn.u., HFn.u.) and LF/HF ratio (P > 0.05).
In sportsmen, a hospitalization period led to an increase in resting heart rate and was associated with a disturbance of the autonomic nervous system.
Laurent Mourot, Nicolas Fabre, Aldo Savoldelli and Federico Schena
To determine the most accurate method based on spectral analysis of heart-rate variability (SA-HRV) during an incremental and continuous maximal test involving the upper body, the authors tested 4 different methods to obtain the heart rate (HR) at the second ventilatory threshold (VT2). Sixteen ski mountaineers (mean ± SD; age 25 ± 3 y, height 177 ± 8 cm, mass 69 ± 10 kg) performed a roller-ski test on a treadmill. Respiratory variables and HR were continuously recorded, and the 4 SA-HRV methods were compared with the gas-exchange method through Bland and Altman analyses. The best method was the one based on a time-varying spectral analysis with high frequency ranging from 0.15 Hz to a cutoff point relative to the individual’s respiratory sinus arrhythmia. The HR values were significantly correlated (r 2 = .903), with a mean HR difference with the respiratory method of 0.1 ± 3.0 beats/min and low limits of agreements (around –6/+6 beats/min). The 3 other methods led to larger errors and lower agreements (up to 5 beats/min and around –23/+20 beats/min). It is possible to accurately determine VT2 with an HR monitor during an incremental test involving the upper body if the appropriate HRV method is used.