Influence of Load Intensity on Postexercise Hypotension and Heart Rate Variability after a Strength Training Session

2015 ◽  
Vol 29 (10) ◽  
pp. 2941-2948 ◽  
Author(s):  
Tiago Figueiredo ◽  
Jeffrey M. Willardson ◽  
Humberto Miranda ◽  
Claudio M. Bentes ◽  
Victor M. Reis ◽  
...  
2015 ◽  
Vol 29 (6) ◽  
pp. 1556-1563 ◽  
Author(s):  
Tiago Figueiredo ◽  
Matthew R. Rhea ◽  
Mark Peterson ◽  
Humberto Miranda ◽  
Claudio M. Bentes ◽  
...  

2016 ◽  
Vol 30 (7) ◽  
pp. 1813-1824 ◽  
Author(s):  
Tiago Figueiredo ◽  
Jeffrey M. Willardson ◽  
Humberto Miranda ◽  
Claudio M. Bentes ◽  
Victor Machado Reis ◽  
...  

2020 ◽  
Vol 21 (3) ◽  
pp. 292-296 ◽  
Author(s):  
Jesús Álvarez-Herms ◽  
Sonia Julià-sánchez ◽  
Hannes Gatterer ◽  
Francisco Corbi ◽  
Gines Viscor ◽  
...  

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
R Fenici ◽  
M Picerni ◽  
D Brisinda

Abstract Background Quantitative assessment of individual body adaptability to physical training performed with the purposes of health maintenance is particularly necessary in the elderly age, to avoid the risk of overstrain induced by inappropriate exercises workload and physical stress. For that purpose, heart rate monitors and heart rate variability (HRV) analysis are nowadays commercially available. However, their reliability to guide individualized fitness training in elderly people needs to be tested, knowing that users might not have medical education. Objective To preliminary quantify autonomic nervous system (ANS) responses to graded physical effort and recovery in healthy elderly basing on the parasympathetic nervous system (PNSi), the sympathetic nervous system (SNSi) and the stress (STRi) indices, derived by short-term and time-varying HRV analysis. Methods ECG of a 75 healthy male subject was monitored, from April to November 2020, during three times/week training sessions with a professional bike–ergometer. Each session consisted of 10 minutes baseline rest, 5 minutes warm-up, 30 minutes work and 10 minutes recovery. According to age, the training workload was graded from low (65–75 watt/min), to moderate (75–85 watt/min), semi-intensive (85–95 watt/min) and intensive (95–110 watt/min). For this pilot study, ECG data of only 40 training sessions (10 sessions for each workload to evaluate reproducibility) were analyzed with Kubios Premium software (version 3.4.1), in the time (TD) and frequency (FD) domains, with nonlinear (NL) methods and with time-varying (TV) algorithms. Short-time HRV was calculated from 2-minutes intervals. The PNSi, SNSi and STRi induced by each workload were averaged and compared. Results Average values of PNSi, SNSi and STRi were significantly different (p<0.05) among training sessions carried out with different workloads (Table 1A) and among measurements obtained at rest, at every 5 minutes step of each 30 minutes training session, and at 1 and 5 minutes of recovery (Table 1B). Interestingly, the correlation between SNSi and STRi was strictly linear (R= 0,98), whereas that between PNSi and STRi was better fitted by a cubic function (R=0,82 with cubic vs 0.68 with linear function), when evaluated either as a function of the sessions' workloads (Figure 1A), or of four time-intervals of each training session (Figure 1B). PNSi and SNSi were inversely correlated, with cross-point at about 15 minutes of training and 75 watt/min workload. Conclusions The calculation of PNSi, SNSi and STRi from HRV analysis is an efficient method for quick and simplified quantitative assessment of dynamic ASN adaptation to effort-induced stress from HRV analysis. If confirmed, the method may be useful for safer and even remote monitoring of training/rehabilitation in elderly. However, more detailed evaluation of spectral and NL parameters may be necessary to interpret more complex patterns of abnormal cases. FUNDunding Acknowledgement Type of funding sources: None. Table 1 Figure 1


2019 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Jhennyfer A. L. Rodrigues ◽  
Bruna C. Santos ◽  
Leonardo H. Medeiros ◽  
Thiago C. P. Gonçalves ◽  
Carlos R. B. Júnior

Neurology ◽  
2019 ◽  
Vol 93 (14 Supplement 1) ◽  
pp. S2.2-S2
Author(s):  
Harrison Seltzer ◽  
Karim Elghawy ◽  
Robert Baker

ObjectiveUse biofeedback measures to manage a patient's long term recovery from concussion.BackgroundSports-related mild traumatic brain injury (MTBI) is estimated to affect 3.8 million people in the United States. Identifying quantitative measures of recovery has become a point of interest in treatment. Heart Rate Variability (HRV), the average fluctuation in the interval between heartbeats, shows promise as a noninvasive biomarker.Design/MethodsCase report following cardiovascular recovery of a 15 year old cross country runner 4 months post-injury. Average heart rate and maximum heart rate per training session were collected from the patient's smart device.ResultsA 15-year-old Caucasian male cross-country runner hit the back of his head during a soccer game suffering an MTBI. The patient rested from the activity for 1 week then returned to training. Two months after the injury the patient complained of persistent shortness of breath, fatigue, and increased heart rate while running. According to the patient, his average BPM while running prior to the injury was in the 160s. The patient's smart device post-concussion reports a spike into the 180s. 3 months post-concussion the patient was instructed to keep his heart rate below 170 during training. In the following month, the patient's condition improved gradually with a return to baseline activity.ConclusionsHRV is a promising point of investigation for the management of post-concussive symptoms. Further research is necessary to elucidate the long term effects of concussion on heart rate variability.


2019 ◽  
Vol 14 (4) ◽  
pp. 464-471 ◽  
Author(s):  
Mònica Solana-Tramunt ◽  
Jose Morales ◽  
Bernat Buscà ◽  
Marina Carbonell ◽  
Lara Rodríguez-Zamora

Purpose: To determine whether heart-rate variability (HRV) was correlated with other training-load and training-tolerance markers for monitoring the effect of a training session on elite synchronized swimmers. Methods: The authors recorded the resting HRV of 12 elite swimmers (mean age = 21.5 [3.5] y) 3 times over 1 wk with a cadence of 48 h prior to the 2015 World Swimming Championships. They continuously monitored heart rate and obtained salivary cortisol (SC) samples before and after the last training session of the week. The authors measured capillary blood lactate (La) 2, 4, and 8 min after the last training session and monitored recovery HRV. They assessed rating of perceived exertion (RPE) over the entire session and tested the association between the highest La concentration (Lapeak), SC, and RPE and relative changes (Δ%) in the natural logarithm of the root-mean-square successive difference of intervals (LnRMSSD). The authors also calculated the smallest worthwhile change of the averaged pre and post LnRMSSD measurements. Results: There were periods of pronounced bradycardia (60.5 [16.7] beats/min) during training exercises corresponding to apneic exercise. The magnitude-based inferences showed nonclinically meaningful changes of LnRMSSD. Lapeak (6.8 [2.7] mmol/L) correlated positively with Δ%LnRMSSD and Δ%SC (r = .89, P = .001 and r = .61, P = .04, respectively). Conclusions: There was no change in LnRMSSD and Lapeak, Δ%SC, and RPE indicated reduced sympathetic activation and positive adaptation to the stress imposed by the session. Isolated HRV assessment may reveal a controversial interpretation of autonomic nervous system status or the training tolerance in elite synchronized swimming athletes due to the influence of the diving response.


2020 ◽  
pp. 1-10
Author(s):  
F. ter Woort ◽  
G. Dubois ◽  
M. Didier ◽  
E. Van Erck-Westergren

The adoption of fitness tracker devices to monitor training in the equine market is in full expansion. However, the validity of most of these devices has not been assessed. The aim of this study was to examine the validity of heart rate (HR) and heart rate variability (HRV) measurements during high-intensity exercise by an integrated equine fitness tracker with an electrocardiogram (ECG) (Equimetre) in comparison to an ECG device (Televet). Twenty Thoroughbred racehorses were equipped with the two devices and completed a training session at the track. Data from 18 horses was readable to be analysed. Equimetre HR was compared to Televet HR derived from the corrected Televet ECG. HRV parameters were computed in a dedicated software (Kubios) on uncorrected and manually corrected ECG from both devices, and compared to the Televet corrected data. The HR was recorded on the entire training session and HRV parameters were calculated during the exercise and recovery periods. A strong correlation between the Equimetre HR and Televet HR on corrected data was found (Pearson correlation: r=0.992, P<0.001; root mean square error = 4.06 bpm). For HRV, the correlation was good for all parameters when comparing corrected Equimetre to corrected Televet data (Lin’s coefficient = 0.998). When comparing data obtained from uncorrected Equimetre data to the corrected Televet data, the correlation for HR was still good (Lin’s coefficient = 0.995) but the correlation for all HRV parameters was poor, except for the triangular index (Lin’s coefficient = 0.995). However, correlation between the uncorrected Televet HRV data and the corrected Televet data was equally poor (Lin’s coefficient <0.9). In conclusion, the integrated equine fitness tracker Equimetre satisfies validity criteria for HR monitoring in horses during high intensity exercise. When using corrected ECG data, it provides accurate HRV parameters as well.


2018 ◽  
Vol 39 (10) ◽  
pp. 773-781 ◽  
Author(s):  
Laurent Schmitt ◽  
Jacques Regnard ◽  
Nicolas Coulmy ◽  
Gregoire Millet

AbstractWe aimed to analyse the relationship between training load/intensity and different heart rate variability (HRV) fatigue patterns in 57 elite Nordic-skiers. 1063 HRV tests were performed during 5 years. R-R intervals were recorded in resting supine (SU) and standing (ST) positions. Heart rate, low (LF), high (HF) frequency powers of HRV were determined. Training volume, training load (TL, a.u.) according to ventilatory threshold 1 (VT1) and VT2 were measured in zones I≤VT1; VT1<II≤VT2; III>VT2, IV for strength. TL was performed at 81.6±3.5% in zone I, 0.9±0.9% in zone II, 5.0±3.6% in zone III, 11.6±6.3% in zone IV. 172 HRV tests matched a fatigue state and four HRV fatigue patterns (F) were statistically characterized as F(HF-LF-)SU_ST for 121 tests, F(LF+SULF-ST) for 18 tests, F(HF-SUHF+ST) for 26 tests and F(HF+SU) for 7 tests. The occurrence of fatigue states increased substantially with the part of altitude training time (r2=0.52, p<0.001). This study evidenced that there is no causal relationship between training load/intensity and HRV fatigue patterns. Four fatigue-shifted HRV patterns were sorted. Altitude training periods appeared critical as they are likely to increase the overreaching risks.


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