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Fronchetti L, Nakamura FY, De-Oliveira FR, Lima-Silva AE, Lima JRP. Effects of high-intensity interval training on heart rate variability during exercise. JEPonline 2007; 10(4): 1-9. Heart rate variability (HRV), as indicated by SD1, decreases gradually during progressive incremental exercise, and presents a saturation point at ~3 ms (HRVthreshold). The objective of this study was to assess the effects of highintensity interval training on HRV threshold and HR-work rate curve during progressive incremental exercise. Twenty subjects were randomly assigned to two groups: training (T) and control (C). They underwent a progressive incremental test until exhaustion before and after experimental periods. The T group performed nine sessions of high intensity interval training on a cycle ergometer during 3-weeks (1-min at 130% of maximal aerobic work rate with 1-min rest intervals until volitional exhaustion). HRV was determined using the plot method of Poincaré. High intensity training induced an increase of HRV threshold in the T group (from 95.30 ± 21.9 to 130.0 ± 31.7 W, p ≤ 0.05), but had no effect in the C group. Submaximal HR decreased significantly in T group but did not decrease in the C group. We concluded that 3-weeks of high intensity training induced an increase of HRV threshold and a decrease of submaximal HR. These alterations may be due to the delay of parasympathetic withdrawal during incremental exercise.
Heart Rate Variability During Exercise
1
Journal of Exercise Physiology
online
(JEP
online
)
Volume 10 Number 4 August 2007
Managing Editor
Tommy Boone, Ph.D.
Editor-in-Chief
Jon Linderman, Ph.D.
Review Board
Todd Astorino, Ph.D.
Julien Baker, Ph.D.
Lance Dalleck, Ph.D.
Dan Drury, DPE.
Hermann Engels, Ph.D.
Eric Goulet, Ph.D.
Robert Gotshall, Ph.D.
Len Kravitz, Ph.D.
James Laskin, Ph.D.
Jon Linderman, Ph.D.
M. Knight-Maloney, Ph.D.
Derek Marks, Ph.D.
Cristine Mermier, Ph.D.
Daryl Parker, Ph.D.
Robert Robergs, Ph.D.
Brent Ruby, Ph.D.
Jason Siegler, Ph.D.
Greg Tardie, Ph.D.
Lesley White, Ph.D.
Chantal Vella, Ph.D.
Thomas Walker, Ph.D.
Ben Zhou, Ph.D.
Official Research Journal
of The American Society of
Exercise Physiologists
(ASEP)
ISSN 1097-9751
Fitness and Training
EFFECTS OF HIGH-INTENSITY INTERVAL TRAINING ON
HEART RATE VARIABILITY DURING EXERCISE
LENISE FRONCHETTI
1,2
, FÁBIO Y. NAKAMURA
2
, FERNANDO R. DE-
OLIVEIRA
1,3
,
ADRIANO E. LIMA-SILVA
1,5
, JORGE R. P. DE LIMA
4
.
1
Laboratory of Morphological and Functional Research University of Santa
Catarina State, Florianópolis, Brazil.
2
Group of Studies on Physiological Adaptations to the Training – Londrina
State University, Londrina, Brazil.
3
Nucleus of Studies of Human Movement – Federal University of Lavras,
Lavras, Brazil.
4
Laboratory of Motor Assessment, Federal University of Juiz de Fora, Juiz de
Fora, Brazil.
5
Laboratory of Multidisciplinar Measurement – Bom Jesus/IELUSC, Brazil.
ABSTRACT
Fronchetti L, Nakamura FY, De-Oliveira FR, Lima-Silva AE, Lima
JRP. Effects of high-intensity interval training on heart rate variability
during exercise. JEPonline 2007; 10(4): 1-9. Heart rate variability (HRV),
as indicated by SD1, decreases gradually during progressive
incremental exercise, and presents a saturation point at ~3 ms (HRV
threshold). The objective of this study was to assess the effects of high-
intensity interval training on HRV threshold and HR-work rate curve
during progressive incremental exercise. Twenty subjects were
randomly assigned to two groups: training (T) and control (C). They
underwent a progressive incremental test until exhaustion before and
after experimental periods. The T group performed nine sessions of high
intensity interval training on a cycle ergometer during 3-weeks (1-min at
130% of maximal aerobic work rate with 1-min rest intervals until
volitional exhaustion). HRV was determined using the plot method of
Poincaré. High intensity training induced an increase of HRV threshold
in the T group (from 95.30 ± 21.9 to 130.0 ± 31.7 W, p 0.05), but had
no effect in the C group. Submaximal HR decreased significantly in T
group but did not decrease in the C group. We concluded that 3-weeks
of high intensity training induced an increase of HRV threshold and a
decrease of submaximal HR. These alterations may be due to the delay
of parasympathetic withdrawal during incremental exercise.
Key Words: Cardiac Autonomic Modulation, Heart Rate Variability
Threshold, Training
Heart Rate Variability During Exercise
2
Table 1. Subjects` characteristics (mean ± S.D).
Group Age (yr) Height (cm) Weight (kg)
T (n = 13) 20.4 ± 1.2 173.8 ± 7.7 68.5 ± 10.1
C (n = 7) 22.7 ± 3.1 165.6 ± 10.8 63.5 ± 14.6
INTRODUCTION
It is widely recognized that exercise training induces acute and chronic adaptations in heart rate (HR),
but the exact mechanisms that mediate these changes are not clear (1,2,3,4). It is hypothesized that
training can affect autonomic regulation causing reduction in the sympathetic nerve activity and
increase in the parasympathetic outflow (5, 6). Previous studies have shown that the autonomic
modulation of HR can be studied by non-invasive methods utilizing heart rate variability (HRV) (7, 8,
9, 10, 11, 12). The HRV is associated with sympathovagal balance and it can be a practical and
accurate method to assess the effects of acute exercise and training on the autonomic modulation of
HR (6, 13). It is derived from analysis of consecutive beat-to-beat oscilations of sinus rhythm in time
or frequency domains, which are mainly mediated by the autonomic nervous system branches’
activities. However, other neural, humoral, and metabolic factors might also induce changes on HR
and on HRV parameters.
Tulppo et al. (14) reported that HRV decreases exponentially during progressive exercise and there is
almost complete removal of parasympathetic modulation at ~ 50-60% of VO
2max
. In our laboratory,
studies demonstrated that HRV-work rate curve presents a saturation point that occurs at ~ 3 ms. We
have named this point as “HRV threshold”. It was not significantly different from lactate threshold and
these indices were highly correlated (15, 16). It can be speculated that HRV threshold represents the
transition from parasympathetic to sympathetic domain of HR modulation during progressive
protocols.
Another study investigated the effects of aerobic training on HRV response during a progressive cycle
ergometer test (17). The training involved cycling during 30 min at 50% of the difference between
peak work rate during the progressive test and HRV threshold. The sessions were performed three
times per week throughout three weeks. The results showed that moderate-intensity training caused
an increase in work rate at HRV threshold while no significant changes were observed in the control
group. However, during progressive exercise test, the effects of high-intensity interval training on
HRV response have not been established yet.
Therefore, the purpose of this study was to investigate the effects of high-intensity interval training on
HRV threshold and on HRV-work rate curve during progressive exercise. We have hypothesized that
significant changes would occur in the autonomic cardiac control in response to this form of training
and consequently, HRV-work rate curve during progressive exercise would be shifted to the upward
and to the right directions, with concomitant reduction in heart rate in submaximal stages.
METHODS
Subjects
Twenty healthy sedentary subjects from both genders (males = 11 and females = 9) took part in the
investigation. All subjects signed an informed consent statement and they were not engaged in
training programs for the previous six months. The subjects were advised to avoid any alcohol or
caffeine ingestion and severe exercise 24 h before the tests. All procedures were reviewed and
approved by the local Ethics Committee.
Subsequently, subjects were randomly assigned into two groups: training (T) and control (C). The
physical characteristics of both groups are presented in table1.
Heart Rate Variability During Exercise
3
Table 2. Work rate applied during the 3 weeks of training regimen (values are means, standard deviation
,
minimum and maximum).
Week Mean ± SD Minimum Maximum
first 251.5 ± 52.3 189.5 335.2
second
189.5
335.2
third 263.1 ± 5165 189.5 349.8
Procedures
Progressive Test
Prior to the training program and two to five days after the last training session, both groups
performed a progressive test on a mechanical cycle ergometer (Monark
®
, Sweden). The subjects
remained seated for 3-min on the cycle ergometer to allow for resting HR and HRV measurements.
The test started with the subjects pedaling without resistance during 1-min, with increments of 90
kpm·min
-1
(~ 14.6 W) every minute until volitional exhaustion. The subjects were instructed to
maintain the pedal cadence at ~60 rpm, and all subjects were consistently encouraged throughout
the session.
High-Intensity Interval Training
The subjects of T group performed nine sessions of high-intensity interval training during a period of
three weeks. The sessions were performed three times per week, separated by at least one day of
rest. The work rate of the cycle ergometer for the exercise training was set at 130% of the individual
peak work rate obtained during a progressive test. The subjects cycled for 1-min interspersed with 1-
min rest periods until volitional exhaustion. The training was designed to cause exhaustion within 5
and 10 bouts of exercise. Work load adjustments throughout the three weeks of training were
necessary to maintain the target number of exercise bouts. All training sessions were performed on
the same ergometer and the pedal cadence was kept at ~ 60 rpm. During the training period of T
group, C group did not perform any systematic training and were asked to maintain their normal
habits.
Data Analyses
HR and HRV were measured during all tests using a heart rate monitor (Polar Electro Oy, S810i). The
data was downloaded to a computer and HRV of each stage was calculated by Poincaré plot analysis
(Polar Precision Performance software). The instantaneous beat-to-beat variability of the data was
derived from SD1 index. Details of SD1 analysis were described previously (14, 18). The SD1 index
was plotted against work rate and the first intensity at which the SD1 index reached values equal to or
lower than 3 ms was defined as the HRV threshold (15, 16). The mean HR of each stage was also
calculated and plotted against work rate to estimate the HR at HRV threshold. The maximal work rate
and maximal HR computed during the incremental tests were also compared in the pre- and post-
training.
Statistical Analyses
The following results are presented as means ± SD. Data between pre- and post-training and T and C
groups were compared using a two-way ANOVA followed by Scheffé post hoc test to identify the
differences. Statistical significance was set at 5%
.
RESULTS
The work rate utilized during training sessions was progressively increased from the first to the third
weeks in order to maintain the total initial relative workload, but the differences were not significant
(see methods). Values for average work rate performed during each week are presented in Table 2.
Heart Rate Variability During Exercise
4
The mean HRV threshold, peak work rate, and maximal heart rate in the progressive test for both pre-
and post-training are presented in Table 3. The work rate at post-training HRV threshold was
significantly greater than the pre-training only in the T group (95.3 ± 21.9 W vs. 130.1 ± 31.7 W, p
0.05), whereas no significant differences were observed in C group. The values of post-training in the
T group were significantly greater than the pre- and post-training values in the C group. Similar
tendency was observed when HRV threshold was expressed in percentage of peak work rate. In
contrast, the maximal work rate obtained in progressive test did not change in any groups. On the
other hand, the T group started the program with greater peak work rate than the C group, and the
difference persisted until the end of program (p 0.05).
Figure 1 shows that the point where SD1 index reached 3 ms (HRV threshold) was shifted to right
in the T group but was not altered in the C group. In addition, the HR at submaximal stages was
significantly reduced in the T group whereas no difference was found in the C group (figure 2).
However, exercise training had no effect on the HR at HRV threshold or maximal HR during the
incremental test (table 3).
Table 3. HRV threshold and peak variables during the incremental test for pre
-
and post
-
training in both
groups (values are means ± S.D).
Training group (n=13) Control group (n=7)
Pre Post Pre Post
HRV threshold (W) 95.3 ± 21.9 130.1 ± 31.7
a,b,c
85.4 ± 41.6 95.8 ± 43.6
HRV threshold (%) 47.7 ± 9.5 61.6 ± 13.4
a,b
46.1 ± 17.1 59.2 ± 22.2
HRV threshold (bpm) 134 ± 9 139 ± 14 130 ± 18 136 ± 14
peak work rate (W) 201.8 ± 41.6
c
210.8 ± 32.4
b,c
179.1 ± 33.4 158.2 ± 24.4
peak heart rate (bpm) 189 ± 7 182 ± 13 182 ± 13 179 ± 17
HRV threshold (%) is HRV threshold expressed in percentage of maximal work rate. ª Significantly different from T
pre-training (p 0.05);
b
Significantly different from C pre-training (p 0.05);
c
Significantly different from C post-
training (p 0.05).
Figure 1.
Mean h
eart rate variability (HRV) curve during incremental test in the pre
-
and post
-
training. The T
group data are presented in the left panel and C group data in the right panel. The point where SD1 index reached
3 ms (HRV threshold) is indicated by narrows.
Heart Rate Variability During Exercise
5
DISCUSSION
The present study has shown that the autonomic modulation during the incremental exercise was
altered by high-intensity interval training. In addition, the work load at HRV threshold, which can
indicate the transition from parasympathetic to sympathetic domain, was significantly greater after the
training period. These findings can not be attributed to test familiarization because no significant
changes were found in the control group.
The effects of exercise training on HR have been demonstrated in the literature. Several studies have
shown that aerobic training affects HR during rest and exercise, at least in part due to the changes in
sympathetic and parasympathetic modulation (6, 19, 20, 21). It can be postulated that HRV is
increased when HR is controlled predominantly by parasympathetic activity. On the other hand, when
HR is controlled by sympathetic activity, the HRV decreases (22, 23, 24, 25). It may be hypothesized
that parasympathetic withdrawal caused the progressive reduction observed in HRV until SD1 index
reached ~ 3 ms. Thus, HRV threshold may indicate the removal of parasympathetic modulation; from
this point the HR is mainly mediated by sympathetic activity. In the present study, the HRV values for
submaximal stages were greater in the post-training than in pre-training for T group, which suggests
that high-intensity interval training affects autonomic modulation and “delays” the sympathetic
activation.
A number of studies have also shown that aerobic training increases the work load of lactate and
ventilatory thresholds. For example, Lucía et al. (26) found in well-trained cyclists that work load
corresponding to lactate and ventilatory thresholds were significantly increased during prolonged
training periods. Laursen et al. (27) also found effects of 4-weeks of high-intensity interval training on
the ventilatory threshold. Based on the lactate and ventilatory threshold changes, it is reasonable to
assume that similar changes may also occur on HRV threshold. It is speculated that in exercise
performed above HRV threshold there is a disproportionate increase in plasma catecholamine
concentrations. It would be associated with increased muscle glycogen breakdown and blood lactate
production (28, 29).
Figure 2. Mean heart rate (HR) curve during incremental test in the pre
-
and post training. The T group data are
presented in the left panel and C group data in the right panel.
Heart Rate Variability During Exercise
6
The post-training HRV curve presented the HRV threshold shifted upward and to the right (T group:
95.3 ± 21.9 W vs. 130.1 ± 31.7 W, p 0.05), it is possible that high-intensity interval training can
delay the catecholamine release, thereby blood lactate accumulation is postponed. However, the
hypothesis must be tested in future.
It is suggested in the literature that training significantly increases the HRV in the submaximal stages
(5, 6, 11). For instance, Carter et al. (5) investigated the effects of 12-weeks of aerobic training on
autonomic regulation. They found that HRV increased during rest and submaximal exercise while
maximal HR was decreased. Hautala (11) showed that aerobic training at an intensity corresponding
to 70–80% of maximal HR during 8-weeks also caused a significant increase on parasympathetic
modulation during submaximal exercise. Indirect evidence of the effect of training status on the HRV
during submaximal exercise was obtained by Tulppo et al. (6) who found an impairment of
parasympathetic modulation (decrease on SD1 index) in individuals with poor aerobic fitness, (i.e.,
VO
2max
< 37 ml·kg
-1
·min
-1
), compared with groups with higher aerobic power. These results support
the notion that HRV in the submaximal stages potentially increases with training and can shift to the
right the point where SD1 index reach 3 ms. Therefore, this study showed that the cardiovascular
autonomic modulation presented positive adaptations in response to a short-term training period with
high intensity sessions.
The heart rate adaptations induced by the training was probably affected by neural and functional
changes (30, 31, 32, 33). It should be emphasized that maximal HR during the progressive test was
not altered by training. These results are in disagreement with that found by Tulppo et al. (6), who
demonstrated significant reduction of maximal HR following 8-weeks of training at 70-80% of maximal
HR. The authors attributed these modifications to the increase on high frequency component (vagal
activity) and decrease on low frequency component (sympathetic and parasympathetic tonus) of
spectral analysis indices. However, in the present study, submaximal HR response in post-training
were lower than in the pre-training and it can be linked to an increase of parasympathetic activity
and/or a decrease of sympathetic activity. It is possible that 3-weeks of training had not been
sufficient to decrease the maximal HR.
Although submaximal HR has been reduced due to high-intensity interval training, no effect was
observed on HR at HRV threshold. This result is supported by the findings of Lucía et al. (26) who
found no alteration of the HR at lactate and ventilatory thresholds throughout the training periods,
despite increased work load at threshold intensities. It is suggested, therefore, that HR at HRV
threshold can be considered a non-modifiable parameter of training.
It is not ruled out that morphological and functional changes could have also occurred and contributed
to the HR reduction. It is well established that training induces adaptations of plasma volume (30, 31),
stroke volume (32), and end-diastolic left ventricular diameter (33, 34). Yamamoto et al. (33)
observed a significant reduction on HR at rest until the 28
th
day of training. It was associated with a
significant increase of parasympathetic modulation whereas the changes of end-diastolic left
ventricular diameter were observed only between the 28
th
and 42
th
days of training. Laursen et al.
(27) also demonstrated no changes in plasma volume with 4-weeks of high intensity interval training.
These findings may suggest that high-intensity interval training provides a significant effect on
autonomic modulation of HR in the initial phases of training, while morphological and functional
changes entail a more marked effect into the latter phases.
The maximal work load during the progressive test was not different between any of the trials and
suggests that 3-weeks of high-intensity interval training have no effect on this variable. It is important
to emphasize that HRV threshold, in the present study, was significantly increased only in the T group
Heart Rate Variability During Exercise
7
when expressed in absolute and relative terms. These results suggest that high-intensity interval
training applied for 3-weeks may exert effects primarily on submaximal variables, i.e. HRV threshold.
Maximal work load change, therefore, would be probably detectable only during a more prolonged
time of training.
CONCLUSIONS
In summary, the present study shows that 3-weeks of high-intensity interval training induces a
significant increase on the work load at HRV threshold. Because HRV-work rate curve was shifted to
the right and upward directions, it is suggested that 3-weeks of high-intensity interval training results
in delay of parasympathetic withdrawal during progressive exercise. It has been suggested that
enhanced parasympathetic activity may have a cardioprotective effect (6, 35), and exercise above the
level of parasympathetic withdrawal may lead to an increased cardiac vulnerability (14). The present
study supports the notion that high-intensity interval training can be utilized for both increased
parasympathetic modulation of HR and can delay transition from the parasympathetic to the
sympathetic domain. Thus, the HRV threshold is likely a parameter that can be applied to evaluate
the aerobic capacity, specifically for training interventions.
ACKNOWLEDGEMENTS
The authors would like to thank the contributions of Cesar Adornato de Aguiar and Andreo Fernando
Aguiar in the development of this research, and also the assistance of Dr. Gleber Pereira.
Address for correspondence: Lenise Fronchetti, Laboratory of Morphological and Functional
Research University of Santa Catarina State. R. Pascoal Simone 358, Florianópolis, SC, 88080-
350. phone: (48) 3321 8641; Email: lefronchetti@yahoo.com.br
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... The HRVT was already identified in young individuals [2,5,14], type 2 diabetics [1], elderly [8,13], obese children and adolescents [4,6,7], overweight and/or obese adults [15] and athletes [11,12,16], being sensitive to the effects of physical training [4,6,17], using non-linear indexes, in the time domain [1][2][3][4]6,8,13,14,17], or frequency domain [11,12,18], and using different criteria for determination. However, limited studies have evaluated young obese at early adulthood, especially analyzing the use of different criteria and/or indexes of HRV to estimate the VT. ...
... The HRVT was already identified in young individuals [2,5,14], type 2 diabetics [1], elderly [8,13], obese children and adolescents [4,6,7], overweight and/or obese adults [15] and athletes [11,12,16], being sensitive to the effects of physical training [4,6,17], using non-linear indexes, in the time domain [1][2][3][4]6,8,13,14,17], or frequency domain [11,12,18], and using different criteria for determination. However, limited studies have evaluated young obese at early adulthood, especially analyzing the use of different criteria and/or indexes of HRV to estimate the VT. ...
... Firstly, the individual cycled for one min, without any additional workload, in order to get adapted to the cadence (60 rpm). Afterwards, an initial workload of 15 W was added and 15 W increments were applied every min until volitional exhaustion [17]. The rate of perceived exertion was monitored using the 15-points Borg's scale [19]. ...
... The HRVT was already identified in young individuals [2,5,14], type 2 diabetics [1], elderly [8,13], obese children and adolescents [4,6,7], overweight and/or obese adults [15] and athletes [11,12,16], being sensitive to the effects of physical training [4,6,17], using non-linear indexes, in the time domain [1][2][3][4]6,8,13,14,17], or frequency domain [11,12,18], and using different criteria for determination. However, limited studies have evaluated young obese at early adulthood, especially analyzing the use of different criteria and/or indexes of HRV to estimate the VT. ...
... The HRVT was already identified in young individuals [2,5,14], type 2 diabetics [1], elderly [8,13], obese children and adolescents [4,6,7], overweight and/or obese adults [15] and athletes [11,12,16], being sensitive to the effects of physical training [4,6,17], using non-linear indexes, in the time domain [1][2][3][4]6,8,13,14,17], or frequency domain [11,12,18], and using different criteria for determination. However, limited studies have evaluated young obese at early adulthood, especially analyzing the use of different criteria and/or indexes of HRV to estimate the VT. ...
... Firstly, the individual cycled for one min, without any additional workload, in order to get adapted to the cadence (60 rpm). Afterwards, an initial workload of 15 W was added and 15 W increments were applied every min until volitional exhaustion [17]. The rate of perceived exertion was monitored using the 15-points Borg's scale [19]. ...
Article
Objective To compare ventilatory threshold (VT) and heart rate variability threshold (HRVT) in normal weight (NW: 22.75 ± 1.66 kg/m²), overweight (Ov: 27.03 ± 1.48) and obese (O: 33.26 ± 2.39) young people (21.46 ± 2.06 years old). Methods The HRVT was determined at the first intensity with a SD1 index (HRVTSD1) and RMSSD (HRVTRMSSD) lower than 3 ms and with a visual deflection in the curve of intensity versus SD1 (HRVTSD1-v) and RMSSD (HRVTRMSSD-v) indexes. Results The HRVTSD1, HRVTSD1-V and HRVTRMSSD-V were identified in 100% of individuals and the HRVTRMSSD in 90.32%. The VT was identified at similar workloads (NW: 109.50 ± 36.09; Ov: 90.00 ± 16.20; O: 96.00 ± 25.69 W) and VO2 (NW: 20.52 ± 6.01; Ov; 18.01 ± 4.35; O: 15.14 ± 2.45 mL/kg/min), no differences (P > 0.05) among the HRVT and groups. Despite the correlations between VT and the diverse HRVT were from low to moderate for workload (r: 0.25 – 0.42) and VO2 (r: 0.38 – 0.62), there was a good agreement between them, mainly HRVTSD1. Conclusion The criteria and indexes of determination of VT by HRV, especially the HRVTSD1, can be used, in overweight and obese young people in the evaluation of aerobic fitness and in exercise prescription for this population.
... The anaerobic threshold can be identified using ventilatory markers [6], levels of endocrine hormones, such as catecholamines [7], metabolic markers-blood glucose and lactate [8], and recently by the autonomic nervous activity (heart rate variability-HRV), determined in several populations, such as athletes [9,10], normal weight young people [11][12][13], obese adolescents [14,15], type 2 diabetics [16], and elderly people [17], often demonstrating a high sensitivity to the effects of aerobic training [14,15,18,19]. HRV seems to be an interesting alternative method, once it is a non-invasive procedure and does not depend on the use of expensive and complex equipments, such as a gas analyzer or a ventilometer, nor on the expertise of specialized personal. ...
... Frequently, the researches that analyzed the HR during physical exercise verify the influence of age [26], gender [22], training state [18,26], aerobic fitness, and exercise modality [12], assessing only normal weight individuals or with pathologies [16,17,19]. Nonetheless, no studies investigated HRIP as an anaerobic threshold determining method in obese young people. ...
... HRVT 1 , heart rate variability threshold defined in the intensity of exercise, where the first SD1 value \3 ms; HRVT 2 , heart rate variability threshold defined by the difference between both consecutive intensities. where SD1 index\1 ms; HRIP, heart rate inflection point a n = 23 and n = 14, respectively, because the HRIP was not identified in one normal weight and two overweight volunteer [18,40], physically active young people [22,23,40], obese adolescents [15], and active middle-aged type 2 diabetics [16] for HRIP [22,23] and HRVT methods [16,18,40]. ...
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Anaerobic threshold (AT) can be determined by heart rate variability (HRVT) and by heart rate inflection point (HRIP), which is associated with lactate and ventilatory thresholds. Thus, this study aimed to compare the HRVT and HRIP in normal weight (NW: 22.18 ± 1.90 kg m−2), overweight (Ov: 27.12 ± 1.39), and obese (O: 32.85 ± 2.40) young people (21.62 ± 2.09 years old; n: 61). AT was determined by: (1) HRVT using two criteria: HRVT1: first workload with SD1 index <3 ms; HRVT2: first workload with a difference <1 ms in the SD1 index between two consecutive stages; and (2) HRIP: identified on the maximum distance between a polynomial adjustment and the linear values of HR. The HRIP was identified at similar workloads (NW: 89.35 ± 32.45; Ov: 83.57 ± 24.45; O: 105.71 ± 29.80 W) and heart rate (NW: 130.78 ± 15.32; Ov: 126.29 ± 11.71; 136.24 ± 13.43 bpm). No significant differences (p > 0.05) between criteria, methods or groups, and a significant (p < 0.05) correlation (r 0.28–0.63) were observed for all variables between HRVT1 and HVRT2; for workload between HRVT1 with HRIP (r 0.28); and to rate perceived exertion between HRVT1 (r 0.28) and HVRT2 (r 0.48) with HRIP, with a good agreement for HR between all the methods used for AT identification. In summary, obesity does not seem to interfere on the identification of AT, and HRIP can be used as a reliable method for untrained young people with a wide BMI range.
... was observed between heart rate at rest and the load corresponding to HRVT, suggesting that decreasing in heart rate at rest is associated with changes in aerobic fitness determined by HRVT. These results corroborate the literature showing that aerobic training reduces the heart rate at rest and improving sympathovagal balance, and during physical exercise parasympathetic is blunted resulting in higher loads of HRVT, lactate and ventilatory threshold (Fronchetti, Nakamura, De-Oliveira, Lima-Silva, & Lima, 2007;Laursen et al., 2005;Lucía et al., 2000;Tulppo et al., 2003(Yamamoto, Miyachi, Saitoh, Yoshioka & Onodera,2000. ...
... It is also suggested that the load increase in HRVT in maximum test after high intensity interval training of three weeks reflects the delay in parasympathetic withdrawal, due to improvements in cardiac autonomic modulation in sedentary individuals. Reinforcing the use of HRV analysis at rest and during exercise, in exercise prescription and monitoring of their responses (Fronchetti et al., 2007). ...
Article
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This study aimed to assess the blood pressure (BP), cardiac autonomic modulation at rest, in physical exercise and in the recovery in untrained eutrophic (E) and overweight (O) youth. The body mass index (BMI), waist circumference (WC), systolic BP-SBP (E: 109.80 ± 10.05; O: 121.85 ± 6.98 mmHg) and diastolic BP DBP (E: 65.90 ± 7.28; O: 73.14 ± 12.22 mmHg) were higher in overweight and the heart rate recovery (%HRR) was lower as compared with E volunteers. The BMI was associated with SBP (r= 0.54), DBP (r= 0.65), load on the heart rate variability threshold-HRVT (r=-0.46), %HRR²' (r=-0.48) and %HRR 5′ (r=-0.48), and WC was associated with SBP (r= 0.54), DBP (r= 0.64) and HRR²' (r=-0.49). The %HRR was associated to SBP, DBP and HRVT. In summary, the anthropometric variables, BP and cardiac autonomic modulation in the recovery are altered in overweight youth.
... It is also suggested that the load increase in HRVT in maximum test after high intensity interval training of three weeks reflects the delay in parasympathetic withdrawal, due to improvements in cardiac autonomic modulation in sedentary individuals. Reinforcing the use of HRV analysis at rest and during exercise, in exercise prescription and monitoring of their responses ( Fronchetti et al. , 2007 ). ...
... Silva, & Lima, 2007 ;Laursen et al. , 2005 ;Lucía et al. , 2000 ;Tulppo et al. , 2003 ( Yamamoto, Miyachi, Saitoh, Yoshioka & Onodera,2000. ...
... The reduction of HRmax may be related to a better adaptation of the cardiovascular system when it was subjected to a HIIT stimulus during the training program (19). According to the literature, training sensitizes the autonomic regulation mechanisms, causing reduced sympathetic nerve activity and increased parasympathetic flow (21). The metabolic adaptations promoted by HIIT have been important for the evolution and success of training programs in combat sports (18). ...
... Manual methods include visual inspection and manually removing signal parts with artifacts [15-18], selection of artifact-free segments of recordings [19][20][21], or segments with error ration below 5% [22]. Some authors do not mention correction methods at all [23,24]. A frequent habit is also application of automatic methods followed by manual inspection to verify the correctness of the algorithms [25]; visually identified artifacts are corrected using appropriate filters [25] or manual intervention is needed by the software for artifact removal [26]. ...
Article
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Heart rate variability (HRV) analysis can be a useful tool to detect underlying heart or even general health problems. Currently, such analysis is usually performed in controlled or semi-controlled conditions. Since many of the typical HRV measures are sensitive to data quality, manual artifact correction is common in literature, both as an exclusive method or in addition to various filters. With proliferation of Personal Monitoring Devices with continuous HRV analysis an opportunity opens for HRV analysis in a new setting. However, current artifact correction approaches have several limitations that hamper the analysis of real-life HRV data. To address this issue we propose an algorithm for automated artifact correction that has a minimal impact on HRV measures, but can handle more artifacts than existing solutions. We verify this algorithm based on two datasets. One collected during a recreational bicycle race and another one in a laboratory, both using a PMD in form of a GPS watch. Data include direct measurement of electrical myocardial signals using chest straps and direct measurements of power using a crank sensor (in case of race dataset), both paired with the watch. Early results suggest that the algorithm can correct more artifacts than existing solutions without a need for manual support or parameter tuning. At the same time, the error introduced to HRV measures for peak correction and shorter gaps is similar to the best existing solution (Kubios-inspired threshold-based cubic interpolation) and better than commonly used median filter. For longer gaps, cubic interpolation can in some cases result in lower error in HRV measures, but the shape of the curve it generates matches ground truth worse than our algorithm. It might suggest that further development of the proposed algorithm may also improve these results.
... For example, the use of high-intensity interval exercise performed at maximal, near-maximal, or supramaximal aerobic power may have a greater effect on HRV [7]. Another study found that high-intensity interval training at 130% of maximal oxygen uptake (VO 2max ) for three weeks increased HRV threshold [8]. On the other hand, training at moderate intensity levels for 24 sessions increased (VO 2max ) by 11% among sedentary middle-age men, but did not increase vagal modulation as measured using HRV [9]. ...
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The aim of this study was to investigate the effect of recreational aerobic physical activity (PA) type and volume on heart rate variability (HRV) in Arab men. This was a retrospective, cross-sectional study, and included men (n = 75, age = 37.6 ± 7.1 years, body mass index (BMI) = 26.7 ± 3.1 kg/m 2) who were members of a walking group, cycling group, or were inactive controls. Monthly distances from the past three months were obtained from walking and cycling groups, and the volume of PA was classified into three subgroups (high, moderate, low). HRV was measured using a computerized electrocardiographic data acquisition device. R-R interval recordings were performed while participants rested in a motionless supine position. RR intervals were recorded for 15 minutes, and a five-minute segment with minimal ectopic beats and artifacts was selected for HRV analysis. Time-domain parameters included the mean R-R interval, standard deviation of the mean R-R interval (SDNN), and root-mean-squared difference of successive RR intervals (RMSSD). The frequency-domain parameters included high-frequency power (HF), low-frequency power (LF), and LF to HF ratio (LF/HF). Results showed that there were no significant differences between walking, cycling, and control groups for all HRV parameters. Time-domain analyses based on PA volume showed that age-adjusted SDNN for the high-active group was greater than the low-active group (P = 0.03), and RMSSD for the moderate-active group was greater than the control group (P = 0.009). For the frequency domain, LF for the high-active group was greater than the low-active and control groups (P = 0.006), and HF for the moderate-active group was greater than the low-active group (P = 0.04). These data indicate that walking >150 km per month, or cycling >100 km per month at a speed >20 km/h may be necessary to derive cardiac autonomic benefits from PA among Arab men.
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Introduction: The present study investigates the effects of 10 weeks of High Intensity Interval Training (HIIT) and ginger consumption on structural and functional cardiac adaptations in overweight women. Materials and Methods: In this semi experimental randomized, placebo controlled study, 24 overweight women aged 20-30 years, randomly divided into 3 groups of Ginger (n=8), HIIT+ginger (n=8) and HIIT+placebo (n=8), were followed for 10 weeks of HIIT (40m-maximal Shuttle run) and ginger consumption (3 gr of ginger supplement or placebo pills daily). Results: Systolic Left Ventricular Dimensions (LVDs) increased in the HIIT+ginger (p=0.006) and HIIT+placebo (p=0.002) while stroke volume (SV) (p=0.019) and left atrium dimension (LA) (p=0.015) increased in the HIIT+ginger group. In addition, significant decreases of systolic blood pressure were seen in the HIIT+ginger (p=0.001) and the ginger (p=0.001) groups, and diastolic blood pressure attenuation in ginger (p=0.01) group only. However, no significant difference between groups in any variable was detected (p>0.05). Conclusion: Either HIIT per se or with ginger, leads to modest improvements of structural and functional cardiac adaptations in overweight women, while, consumption of just ginger, attenuated systolic and diastolic blood pressure. No significant difference was observed between the effects of High Intensity Interval Training and ginger consumption on cardiac structural and functional adaptations.
Article
Abstract: Background: Exercise plays an important role to improve cardiovascular performance. The purpose of this study was to determine the effect 8wk of aerobic cyclic training and a detraining period on left ventricular structure and function in non-athlete healthy men. Materials and Method: In this research, ten male non-athlete students were volunteered and participated in a 8- week running program (3days/week, at 70% of Maximum Heart Rate) and 4 weeks detraining afterwards. In each session, cyclic running was done in 5 nine-minute stages and there was a four-minute inactive rest among them. Results: Using echocardiography, there was a significant difference in interventricular septal thickness, percent of rapid shortening and percent of ejection fraction after 8 weeks training compared to before training. It was found no significant difference end-diastolic and end-systolic dimensions, posterior wall thickness, left atrium diameter, aortic root thickness, heart rate, systolic and diastolic blood pressures. There was a significant difference in end-systolic dimension, intervntricular septal thickness, left atrium diameter, percent of rapid shortening and ejection fraction following 4-week detraining after training compared to 8-week. Conclusion: Eight-week aerobic cyclic training and a detraining period can effect on left ventricle structure and function
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This study aimed to verify the degree of association between the resting heart rate (HRRest), different resting heart rate variability indexes and the exercise intensity related to the heart rate threshold variability. Twenty men (21,3 ± 2,6 years old) began the test protocol with aresting period sitting on a cycle ergometer and then were submitted to aprogressive test (14,6W/minute) until exhaustion. In the first load, the threshold variability was identified as lower than 3 ms in the decreasing variability HR curve. The HR and the variability were registered with a Polar® heart rate device. Spearman Rank (r) correlation was used to calculate the associations among these variables (p<0,05). The correlations between various variability indexes were statistically significant with r≥ 0,80. The HRRest showed a significant and a negative association with the variability indexes and with the threshold variability intensity (r ≥ -0,63). On the other hand, the threshold variability intensity showed a close relationship with the following indexes: SD1 (r = 0,51), SD2 (r = 0,46), RMSSD (r = 0,48), pNN50 (r = 0,55), HF (r = 0,50) e LF/HF (r = -0,56). These results showed that an elevate resting vagal activity can postpone the increase of the predominance of the sympathetic system during progressive exercises.
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Resumo: Em testes incrementais, há redução consistente da variabilidade da freqüência cardíaca (VFC) até aproximadamente o valor de 3 ms, quando calculada pela Plotagem de Poincaré (STDB), sendo observada estabilização desse indicador nas cargas seguintes. Este ponto, denominado limiar de VFC (LiVFC) por Lima e Kiss (1999), pode ser considerado um indicador da capacidade aeróbia, por sua alta relação com os limiares de lactato e ventilatório. O objetivo do estudo foi verificar os efeitos do treinamento aeróbio de três semanas sobre o LiVFC. A amostra de 25 indivíduos, não-atletas, foi dividida em Grupo Treinamento (TR, n = 18) e Grupo Controle, (C, n = 7), submetidos a teste incremental pré e pós-treinamento, com potência inicial de 0 W, e incrementos de 14,5 W por minuto, até a exaustão. Os intervalos R-R foram registrados pelo cardiofreqüencímetro Polar  , modelo S810i, e a VFC foi calculada pelo programa Polar Precision Performance. O TR realizou nove sessões de treinamento (3 x semana) de 30 min, com intensidade aproximada de 50% do intervalo entre a potência associada ao LiVFC (PLiVFC) e a potência de pico (P Pico). As comparações foram feitas por ANOVA two way para medidas repetidas, seguida do teste de Scheffé (p < 0,05). No teste pré–treinamento, não houve diferença significante no comportamento da VFC entre os grupos. No pós-treinamento, a VFC do TR foi maior até a potência de 145,75 W, o que provocou aumento da PLiVFC. O TR não experimentou melhora significante na P Pico e na FC máxima. A FC submáxima de TR no pós-treinamento foi menor até a potência de 189,5 W. Conclui-se que o LiVFC é sensível aos efeitos do treinamento aeróbio de curto prazo, sugerindo sua validade como indicador de capacidade aeróbia. Palavras-chave: Variabilidade da freqüência cardíaca, capacidade aeróbia e treinamento aeróbio. Abstract: During incremental tests, there is a consistent reduction in heart rate variability (VFC) until approximately 3 ms value, when calculated through the Poincaré Plotting (STDB). A stabilization of this index is observed in the subsequent intensities. This transition point, called VFC threshold (LiVFC) by Lima and Kiss (1999), can be considered as an indicator of aerobic capacity, because of its high correlation with the lactate and ventilatory thresholds. The aim of this study was to verify the effects of three weeks duration aerobic training program on LiVFC. A 25 non-athlete sample was divided in a Training Group (TR, n = 18) and a Control Group (C, n = 7), submitted to a pre-and post-training incremental test until exhaustion, with an initial power output of 0 W and increments of 14.5 W per minute. The R-R intervals were recorded using a Polar  equipment, S810i model, and the VFC was calculated by means of the Polar Precision Performance software. TR underwent nine training sessions (3 times a week) of 30 min duration, with the intensity of approximately 50% of the peak power output (P Pico) and that one associated to the LiVFC (PLiVFC) interval. The comparisons were made by a two way repeated measures ANOVA, followed by the Scheffé test (p < 0.05). During the pre-training test there was no significant difference in the VFC responses between the groups. In the post-training measure, the VFC of the TR was greater until the power output of 145.75 W, which caused the increase in PLiVFC. The TR experienced no significant improvement in the P Pico and in maximal FC. The submaximal FC of TR in the post-training was lower until the power output of 189.5 W. It was concluded that LiVFC presents sensibility to the short-term aerobic training effects, suggesting its validity as an indicator of aerobic capacity.
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Regular physical exercise is an important factor to reduce the indexes of cardiovascular and all causes morbimortality. However, there is, apparently, additional and independent benefits of the regular practice of physical exercise and the improvement of the level of aerobic condition. Heart rate (HR) is mediated primarily by the direct activity of the autonomic nervous system (ANS), specifically through the sympathetic and parasympathetic branches activities over the sinus node autorhythmicity, with predominance of the vagal activity (parasympathetic) at rest, that is progressively inhibited since the onset of the exercise. The HR behavior has been widely studied during different conditions and protocols associated to the exercise. A reduction of the cardiac vagal tone (parasympathetic function) and consequently a diminished HR variability in rest, independently of the protocol of measurement used, is related to an autonomic dysfunction, chronic-degenerative diseases and increased mortality risk. Individuals with high levels of aerobic condition have a lower resting HR, along with a larger parasympathetic activity or smaller sympathetic activity, but it is not necessarily a direct consequence of the exercise training, as long as other inherent adaptations to the aerobic conditioning can influence the resting HR. The HR response in the onset of the exercise represents the integrity of the vagus nerve, and the HR recovery on the post-exercise transient also denotes important prognostic information; by the way, individuals that have a slow HR recovery in the first minute post-exercise have increased mortality risk. In conclusion, the physiological mechanisms modulating HR during or after an exercise program are not totally clear, and further studies are needed.
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OBJETIVO: A variabilidade da freqüência cardíaca (VFC) tem sido estudada em repouso, como meio não-invasivo para avaliação da regulação autonômica cardíaca, sendo que sua diminuição está relacionada a maior risco cardiovascular. Entretanto, durante o exercício, quando ocorrem importantes alterações neurais, seu comportamento deve ser melhor documentado. Estudamos o comportamento da freqüência cardíaca (FC) e da sua variabilidade durante as diferentes fases metabólicas do exercício físico progressivo máximo, em jovens. MÉTODOS: Dezessete homens (28±6 anos) realizaram teste ergoespirométrico máximo em cicloergômetro (30W/3min), determinando-se a FC e a VFC (desvio-padrão) através da onda eletrocardiográfica, amplificada e gravada batimento a batimento em computador, numa freqüência da 125Hz (AT/Codas). RESULTADO: A FC aumentou concomitantemente ao aumento da intensidade do exercício. A VFC diminuiu progressivamente, atingindo níveis significantes em relação ao repouso a partir de 60% do consumo de oxigênio do pico do exercício, a partir de 45-60% da potência máxima e a partir da intensidade do limiar anaeróbio, estabilizando-se nos períodos subseqüentes. CONCLUSÃO: Nossos resultados sugerem que a VFC medida pelo desvio-padrão da FC diminui em fases do exercício nas quais o aumento da FC é determinado, principalmente, por retirada vagal.
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A consistent link appears to exist between predominance of vagal or sympathetic activity and predominance of HF or LF oscillations, respectively: RR variability contains both of these rhythms, and their relative powers appear to subserve a reciprocal relation like that commonly found in sympathovagal balance. In this respect, it is our opinion that rhythms and neural components always interact, just like flexor and extensor tones or excitatory and inhibitory cardiovascular reflexes, and that it is misleading to separately consider vagal and sympathetic modulations of heart rate. In humans and experimental animals, functional states likely to be accompanied by an increased sympathetic activity are characterized by a shift of the LF-HF balance in favor of the LF component; the opposite occurs during presumed increases in vagal activity. In addition, LF oscillation evaluated from SAP variability appears to be a convenient marker of the sympathetic modulation of vasomotor activity. Although based on indirect markers, the exploration in the frequency domain of cardiovascular neural regulation might disclose a unitary vision hard to reach through the assemblage of more specific but fragmented pieces of information.