scholarly journals HEART RATE BEHAVIOR DURING A BRAZILIAN JIU-JITSU TRAINING SESSION

2018 ◽  
Vol 88 (I) ◽  
pp. 141-144
Author(s):  
LUIZ HENRIQUE DA SILVA ◽  
LUIZ FERNANDO PAULINO RIBEIRO ◽  
ANTONIO CARLOS TAVARES JUNIOR ◽  
ALEXANDRE JANOTTA DRIGO
Keyword(s):  
Heart Rate ◽  
Training Session ◽  
Rate Behavior

scholarly journals Quantitative assessment of autonomic nervous system responses induced by graded training workloads in healthy elderly, through indices obtained from heart rate variability analysis

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
R Fenici ◽  
M Picerni ◽  
D Brisinda
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Nervous System ◽  
Autonomic Nervous System ◽  
Quantitative Assessment ◽  
Training Session ◽  
Time Varying ◽  
Autonomic Nervous ◽  
Healthy Elderly ◽  
Hrv Analysis

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

Determinants and interindividual variation of R-R interval dynamics in healthy middle-aged subjects

2001 ◽  
Vol 280 (3) ◽  
pp. H1400-H1406 ◽  
Author(s):  
Sirkku M. Pikkujämsä ◽  
Timo H. Mäkikallio ◽  
K. E. Juhani Airaksinen ◽  
Heikki V. Huikuri
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Supine Position ◽  
Population Sample ◽  
Interindividual Variation ◽  
Sitting Position ◽  
Middle Aged ◽  
Complexity Measures ◽  
Rate Behavior ◽  
Z Value

Determinants and intersubject variations of fractal and complexity measures of R-R interval variability were studied in a random population of 200 healthy middle-aged women (age 51 ± 6 yr) and 189 men (age 50 ± 6 yr) during controlled conditions in the supine and sitting positions. The short-term fractal exponent (α1) was lower in women than men in both the supine (1.18 ± 0.20 vs. 1.12 ± 0.17, P < 0.01) and sitting position ( P < 0.001). Approximate entropy (ApEn), a measure of complexity, was higher in women in the sitting position (1.16 ± 0.17 vs. 1.07 ± 0.19, P < 0.001), but no gender-related differences were observed in ApEn in the supine position. Fractal and complexity measures were not related to any other demographic, laboratory, or lifestyle factors. Intersubject variations in a fractal measure, α1 (e.g., 1.15 ± 0.20 in the supine position, z value 1.24, not significant), and in a complexity measure, ApEn (e.g., 1.14 ± 0.18 in the supine position, z value 1.44, not significant), were generally smaller and more normally distributed than the variations in the traditional measures of heart rate variability (e.g., standard deviation of R-R intervals 49 ± 21 ms in the supine position, z value 2.53, P < 0.001). These results in a large random population sample show that healthy subjects express relatively little interindividual variation in the fractal and complexity measures of heart rate behavior and, unlike the traditional measures of heart rate variability, they are not related to lifestyle, metabolic, or demographic variables. However, subtle gender-related differences are also present in fractal and complexity measures of heart rate behavior.

Effects of isolated or combined carbohydrate and caffeine supplementation between 2 daily training sessions on soccer performance

2015 ◽  
Vol 40 (5) ◽  
pp. 457-463 ◽  
Author(s):  
Victor Amorim Andrade-Souza ◽  
Romulo Bertuzzi ◽  
Gustavo Gomes de Araujo ◽  
David Bishop ◽  
Adriano Eduardo Lima-Silva
Keyword(s):  
Heart Rate ◽  
Perceived Exertion ◽  
Lactate Concentration ◽  
Training Session ◽  
Rating Of Perceived Exertion ◽  
Plasma Lactate ◽  
Repeated Sprint ◽  
Plasma Lactate Concentration ◽  
Main Effects ◽  
Previous Training

This study aimed to investigate whether isolated or combined carbohydrate (CHO) and caffeine (CAF) supplementation have beneficial effects on performance during soccer-related tests performed after a previous training session. Eleven male, amateur soccer players completed 4 trials in a randomized, double-blind, and crossover design. In the morning, participants performed the Loughborough Intermittent Shuttle Test (LIST). Then, participants ingested (i) 1.2 g·kg−1 body mass·h−1 CHO in a 20% CHO solution immediately after and 1, 2, and 3 h after the LIST; (ii) CAF (6 mg·kg−1 body mass) 3 h after the LIST; (iii) CHO combined with CAF (CHO+CAF); and (iv) placebo. All drinks were taste-matched and flavourless. After this 4-h recovery, participants performed a countermovement jump (CMJ) test, a Loughborough Soccer Passing Test (LSPT), and a repeated-sprint test. There were no main effects of supplementation for CMJ, LSPT total time, or best sprint and total sprint time from the repeated-sprint test (p > 0.05). There were also no main effects of supplementation for heart rate, plasma lactate concentration, rating of perceived exertion (RPE), pleasure–displeasure, and perceived activation (p > 0.05). However, there were significant time effects (p < 0.05), with heart rate, plasma lactate concentration, RPE, and perceived activation increasing with time, and pleasure–displeasure decreasing with time. In conclusion, isolated and/or combined CHO and CAF supplementation is not able to improve soccer-related performance tests when performed after a previous training session.

Is Heart Rate Variablity a Suitable method For Monitoring The Effect Of A Training Session In Synchronized Swimming?

2019 ◽  
Vol 51 (Supplement) ◽  
pp. 28
Author(s):  
Mònica Solana-Tramunt ◽  
Jordi Arboix-Alió ◽  
Joan Aguilera-Castells ◽  
Jose Morales ◽  
Bernat Buscà ◽  
...  
Keyword(s):  
Heart Rate ◽  
Training Session ◽  
Suitable Method ◽  
Synchronized Swimming

scholarly journals Heart Rate Variability and Mild Traumatic Brain Injury: Case Study and Review of Literature

Neurology ◽  
2019 ◽  
Vol 93 (14 Supplement 1) ◽  
pp. S2.2-S2
Author(s):  
Harrison Seltzer ◽  
Karim Elghawy ◽  
Robert Baker
Keyword(s):  
Traumatic Brain Injury ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Brain Injury ◽  
Mild Traumatic Brain Injury ◽  
Training Session ◽  
The United States ◽  
Smart Device ◽  
Cross Country

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.

Heart-Rate Recovery After Warm-up in Swimming: A Useful Predictor of Training Heart-Rate Response?

2017 ◽  
Vol 12 (6) ◽  
pp. 742-748 ◽  
Author(s):  
Sander P.M. Ganzevles ◽  
Arnold de Haan ◽  
Peter J. Beek ◽  
Hein A.M. Daanen ◽  
Martin J. Truijens
Keyword(s):  
Heart Rate ◽  
Heart Rate Recovery ◽  
National Level ◽  
Training Session ◽  
Training Load ◽  
Step Test ◽  
Swimming Velocity ◽  
Promising Alternative ◽  
Warm Up ◽  
Incremental Step

For training to be optimal, daily training load has to be adapted to the momentary status of the individual athlete, which is often difficult to establish. Therefore, the current study investigated the predictive value of heart-rate recovery (HRR) during a standardized warm-up for training load. Training load was quantified by the variation in heart rate during standardized training in competitive swimmers. Eight female and 5 male Dutch national-level swimmers participated in the study. They all performed 3 sessions consisting of a 300-m warm-up test and a 10 × 100-m training protocol. Both protocols were swum in front crawl at individually standardized velocities derived from an incremental step test. Velocity was related to 75% and 85% heart-rate reserve (% HRres) for the warm-up and training, respectively. Relative HRR during the first 60 s after the warm-up (HRRw-up) and differences between the actual and intended heart rate for the warm-up and the training (ΔHRw-up and ΔHRtr) were determined. No significant relationship between HRRw-up and ΔHRtr was found (F1,37 = 2.96, P = .09, R2 = .07, SEE = 4.65). There was considerable daily variation in ΔHRtr at a given swimming velocity (73–93% HRres). ΔHRw-up and ΔHRtr were clearly related (F1,37 = 74.31, P < .001, R2 = .67, SEE = 2.78). HRR after a standardized warm-up does not predict heart rate during a directly subsequent and standardized training session. Instead, heart rate during the warm-up protocol seems a promising alternative for coaches to make daily individual-specific adjustments to training programs.

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