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Influence of Heart Rate, Age, and Gender on Heart Rate Variability in Adolescents and Young Adults

  • The National Institute of Neurology and Neurosurgery, Havana, Cuba

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Key autonomic functions are in continuous development during adolescence which can be assessed using the heart rate variability (HRV). However, the influence of different demographic and physiological factors on HRV indices has not been fully explored in adolescents. In this study we aimed to assess the effect of age, gender, and heart rate on HRV indices in two age groups of healthy adolescents (age ranges, 13–16 and 17–20 years) and two groups of healthy young adults (21–24 and 25–30 years). We addressed the issue using 5-min ECG recordings performed in the sitting position in 255 male and female participants. Time, frequency, and informational domains of HRV were calculated. Changes in HRV indices were assessed using a multiple linear regression model to adjust for the effects of heart rate, age, and gender. We found that heart rate produced more significant effects on HRV indices than age or gender. There was a progressive reduction in HRV with increasing age. Sympathetic influence increased with age and parasympathetic influence progressively decreased with age. The influence of gender was manifest only in younger adolescents and young adults. In conclusion, age, gender, and particularly heart rate have a substantial influence on HRV indices, which ought to be considered to avoid biases in the study of the autonomic nervous system development. The lack of the gender-related effects on HRV indices in late adolescence could be related to non-completely achieved maturity of the autonomic mechanisms, which deserves further exploration.
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Chapter Title Influence of Heart Rate, Age, and Gender on Heart Rate Variability
in Adolescents and Young Adults
Copyright Year 2018
Copyright Holder Springer Nature Switzerland AG
Author Family Name Estévez-Báez
Given Name Mario
Organization Institute of Neurology and Neurosurgery, Ministry of Health
Address Havana, Cuba
Author Family Name Carricarte-Naranjo
Given Name Claudia
Division Faculty of Biology
Organization Havana University
Address Havana, Cuba
Author Family Name Jas-García
Given Name Javier Denis
Organization Center for Sports Research
Address Havana, Cuba
Author Family Name Rodríguez-Ríos
Given Name Evelyn
Organization Latin-American School of Medicine
Address Havana, Cuba
Author Family Name Machado
Given Name Calixto
Organization Institute of Neurology and Neurosurgery, Ministry of Health
Address Havana, Cuba
Author Family Name Montes-Brown
BookID __ChapID 292_Proof# 1 - 16/10/18
Given Name Julio
Division Department of Medicine & Health Science
Organization University of Sonora
Address Hermosillo, Mexico
Corresponding Author Family Name Leisman
Given Name Gerry
Division Faculty of Social Welfare and Health Sciences
Organization University of Haifa
Address Haifa, Israel
Organization National Institute for Brain and Rehabilitation Sciences
Address Nazareth, Israel
Organization University of the Medical Sciences, Faculty ‘Manuel
Address Havana, Cuba
Author Family Name Schiavi
Given Name Adam
Division Anesthesiology and Critical Care Medicine, Neurosciences
Critical Care Division
Organization Johns Hopkins Hospital
Address Baltimore, MD, USA
Author Family Name Machado-García
Given Name Andrés
Division Faculty of Biology
Organization Havana University
Address Havana, Cuba
Author Family Name Luaces
Given Name Claudia Sánchez
Division Faculty of Biology
BookID __ChapID 292_Proof# 1 - 16/10/18
Organization Havana University
Address Havana, Cuba
Author Family Name Pié
Given Name Eduardo Arrufat
Organization Institute of Basic and Preclinical Sciences “Victoria de
Address Havana, Cuba
Abstract Key autonomic functions are in continuous development during adolescence
which can be assessed using the heart rate variability (HRV). However, the
inuence of different demographic and physiological factors on HRV indices
has not been fully explored in adolescents. In this study we aimed to assess the
effect of age, gender, and heart rate on HRV indices in two age groups of
healthy adolescents (age ranges, 1316 and 1720 years) and two groups of
healthy young adults (2124 and 2530 years). We addressed the issue using
5-min ECG recordings performed in the sitting position in 255 male and
female participants. Time, frequency, and informational domains of HRV
were calculated. Changes in HRV indices were assessed using a multiple
linear regression model to adjust for the effects of heart rate, age, and
gender. We found that heart rate produced more signicant effects on HRV
indices than age or gender. There was a progressive reduction in HRV with
increasing age. Sympathetic inuence increased with age and parasympathetic
inuence progressively decreased with age. The inuence of gender was
manifest only in younger adolescents and young adults. In conclusion, age,
gender, and particularly heart rate have a substantial inuence on HRV
indices, which ought to be considered to avoid biases in the study of the
autonomic nervous system development. The lack of the gender-related
effects on HRV indices in late adolescence could be related to
non-completely achieved maturity of the autonomic mechanisms, which
deserves further exploration.
(separated by ‘-’)
Adolescents - Age - Autonomic nervous system - Gender - Heart rate
variability - Power spectral analysis
BookID __ChapID 292_Proof# 1 - 16/10/18
2Adv Exp Med Biol - Clinical and Experimental Biomedicine
4#Springer Nature Switzerland AG 2018
6Influence of Heart Rate, Age, and Gender
7on Heart Rate Variability in Adolescents
8and Young Adults
9Mario Estévez-Báez, Claudia Carricarte-Naranjo,
10 Javier Denis Jas-García, Evelyn Rodríguez-Ríos,
11 Calixto Machado, Julio Montes-Brown, Gerry Leisman,
12 Adam Schiavi, Andrés Machado-García,
13 Claudia Sánchez Luaces, and Eduardo Arrufat Pié
15 Abstract
16 Key autonomic functions are in continuous
17 development during adolescence which can
18 be assessed using the heart rate variability
19 (HRV). However, the inuence of different
20 demographic and physiological factors on
21 HRV indices has not been fully explored in
22 adolescents. In this study we aimed to assess
23 the effect of age, gender, and heart rate on
24 HRV indices in two age groups of healthy
25 adolescents (age ranges, 1316 and
26 1720 years) and two groups of healthy
27 young adults (2124 and 2530 years). We
28addressed the issue using 5-min ECG
29recordings performed in the sitting position in
30255 male and female participants. Time, fre-
31quency, and informational domains of HRV
32were calculated. Changes in HRV indices
33were assessed using a multiple linear regres-
34sion model to adjust for the effects of heart
35rate, age, and gender. We found that heart rate
36produced more signicant effects on HRV
37indices than age or gender. There was a pro-
38gressive reduction in HRV with increasing
39age. Sympathetic AU1inuence increased with
40age and parasympathetic inuence
M. Estévez-Báez and C. Machado
Institute of Neurology and Neurosurgery, Ministry
of Health, Havana, Cuba
C. Carricarte-Naranjo, A. Machado-García, and
C. S. Luaces
Faculty of Biology, Havana University, Havana, Cuba
J. D. Jas-García
Center for Sports Research, Havana, Cuba
E. Rodríguez-Ríos
Latin-American School of Medicine, Havana, Cuba
J. Montes-Brown
Department of Medicine & Health Science, University
of Sonora, Hermosillo, Mexico
G. Leisman (*)
Faculty of Social Welfare and Health Sciences, University
of Haifa, Haifa, Israel
National Institute for Brain and Rehabilitation Sciences,
Nazareth, Israel
University of the Medical Sciences, Faculty Manuel
Fajardo, Havana, Cuba
A. Schiavi
Anesthesiology and Critical Care Medicine,
Neurosciences Critical Care Division, Johns Hopkins
Hospital, Baltimore, MD, USA
E. A. Pié
Institute of Basic and Preclinical Sciences Victoria de
Girón, Havana, Cuba
41 progressively decreased with age. The inu-
42 ence of gender was manifest only in younger
43 adolescents and young adults. In conclusion,
44 age, gender, and particularly heart rate have a
45 substantial inuence on HRV indices, which
46 ought to be considered to avoid biases in the
47 study of the autonomic nervous system devel-
48 opment. The lack of the gender-related effects
49 on HRV indices in late adolescence could be
50 related to non-completely achieved maturity of
51 the autonomic mechanisms, which deserves
52 further exploration.
53 Keywords
54 Adolescents · Age · Autonomic nervous
55 system · Gender · Heart rate variability · Power
56 spectral analysis
157 Introduction
58 Sequential uctuations of heart inter-beat
59 intervals are the most ostensible evidence of the
60 chronotropic cardiovascular regulation exerted by
61 the autonomic nervous system. Heart rate
62 variability (HRV) has become one of the most
63 sensitive, noninvasive, and reliable assessment of
64 the integrity and functional status of the auto-
65 nomic nervous system (Mestanikova et al. 2016;
66 TonhajzerovaAU2 2016; Sassi et al. 2015; Task Force
67 of ESC and NASPE 1996; Kuusela 2013;
68 Sosnowski 2011). Among many factors related
69 to HRV, age and gender are the most recognized
70 and studied (Almeida-Santos et al. 2016;
71 Pothineni et al. 2016; Sharma et al. 2015;
72 Abhishekh et al. 2013; Michels et al. 2013;
73 Moodithaya and Avadhany 2012; Antelmi et al.
74 2004; Migliaro et al. 2001; Tsuji et al. 1994).
75 Heart rate is considered a modifying factor of
76 HRV, but its effect is not always considered in
77 HRV studies (Voss et al. 2013,2015; Kuo et al.
78 1999). Recently, different reports have
79 emphasized the importance of heart rate in the
80 HRV analysis (van Roon et al. 2016; Estévez-
81 Báez et al. 2015a,bAU3 ; Goldberger et al. 2014;
82 Monfredi et al. 2014; Sacha 2013; Nieminen
83 et al. 2007).
84The physiological mechanisms during adoles-
85cence are actively and progressively changing.
86HRV can be used to ascertain the evolution of
87the ontogenetic maturation (Evans et al. 2016;
88Moodithaya and Avadhany 2012; Dogru et al.
892010; Fontani et al. 2004). Although HRV has
90been extensively used to explore the function of
91the autonomic nervous system in many different
92age ranges, the early (1316 years) and late
93teenagers (1720 years) have not been fully
94explored. Therefore, this study seeks to dene
95the effects of the main factors known to inuence
96HRV, such as heart rate, age, and gender in
97healthy adolescents and young adults.
2.1 99Participants
100A cohort of 255 healthy subjects of both genders
101was studied. There were two age groups of healthy
102adolescents of 1316 and 1720 years of age and
103another two age groups of healthy young adults of
1042124 and 2530 years of age. To be included they
105had to willingly agree to participate in the study
106and to show a normal 12-lead electrocardiogram
107(ECG). Exclusion criteria were history of smoking,
108cardiorespiratory or neurological disorder, diabetes
109mellitus, and the use of medications with known
110autonomic nervous system effects. The subjects
111were categorized into four age groups: group A
112from 13 to 16, group B from 17 to 20, group C
113from 21 to 24, and group D from 25 to 30 years of
114age. The adolescents included were recruited from
115high schools and other educational centers near the
116National Institute of Neurology and Neurosurgery
117(INN) in Havana, Cuba. The young adults were
118recruited from physicians, medical students,
119nurses, and technicians from the INN and other
120nearby medical institutions.
2.2 121Experimental Sessions
122All subjects were studied from 08:00 a.m. to
12312:00 p.m. They were instructed to abstain from
124physical efforts the day before the study, avoid
M. Estévez-Báez et al.
125 caffeine, sleep for at least 7 h the night before,
126 have their usual breakfast, and to drink a glass of
127 fruit juice at least 1 h before the study. Body mass
128 (accuracy 0.1 kg) and body height (accuracy
129 0.1 cm) were measured with standard clinical
130 anthropometric instruments. Body mass index
131 (BMI) was calculated as body mass (kg) divided
132 by body height (m) squared. Female participants
133 were studied in the mid-follicular phase of their
134 menstrual cycle. All participants had to rest for
135 30 min sitting in a chair in a semi-reclining posi-
136 tion while the ECG electrodes were placed, the
137 equipment was set and calibrated, and during the
138 check of recording quality. The temperature in the
139 laboratory was maintained about 25 C.
2.3140 Electrocardiogram (ECG)
141 Recordings
142 ECG were recorded for 15 min with commercial
143 ampliers (monitor Hewlett Packard 78354A,
144 Palo Alto, CA) and digitized with a 12-bit ana-
145 log-digital (A/D) converter board (USB-6008
146 DAQ, National Instruments, Austin, TX). A/D
147 conversion was carried out with a sampling rate
148 of 1 kHz. To control the process of digitization
149 and storing of the ECG signal in the hard PC disk,
150 specic software was developed by one of the
151 authors (JDJG) and written in LabView v10.0
152 (National Instruments). Filters were set for a
153 band spectrum of 0.545 Hz. The ECG signal
154 was obtained from disposable electrodes placed
155 on the chest in positions CM2 and V5.
2.4156 Electrocardiogram Analysis,
157 Edition, and Preprocessing
158 ECG recordings were imported ofine to a soft-
159 ware tool developed in Delphi Embarcadero XP
160 by the authors MEB and AMG (MultiTools
161 v3.1.2, 20092016) for visual inspection and
162 detection of the duciary Rpeaks. Accurate
163 Rpeak automatic detections were visually
164 checked and properly corrected, as required.
165 Persons with isolated ventricular ectopic beats or
166 supraventricular events were not included in this
167study. Five-minute segments of artefact free R-R
168inter-beat intervals (RRi) were subject to the
169preprocessing procedures that included (a) RRi
170series resampling using an interpolation method
171applying cubic splines with a sampling frequency
172of 6.82 Hz, to transform the R-R sequences to
173evenly sampled time series; (b) R-R series
174demeaning, consisting of subtraction of the
175mean RR value from all RR items to signicantly
176reduce the DC component of RRi series; (c) linear
177detrending, computing a least squares tofa
178straight line sequence to the data and subtracting
179the resulting function from the RRi series; and
180(d) zero-phase-shift digital ltering of the RRi
181series using the order 6 Butterworth high-pass
182lter with a cutoff frequency of 0.02 Hz, to elimi-
183nate undesirable frequency components, without
184affecting the phase components.
2.5 185HRV Indices
186The following HRV indices were calculated in
187the time domain: the mean R-R inter-beat period
188duration (MRRi), the standard deviation of the
189normal RRi (SDNN), the root mean square of
190successive differences (RMSSD), and the varia-
191tion coefcient ((SDNN/MRRi)*100) expressed
192as percent (CVar).
193For the frequency domain indices, a total of
1942048 samples (5-min RRi series) were used in the
195computation of the Welch modied periodogram
196with a Hamming window, using segments of
197512 samples and overlapping periods of
198256 samples (50%). A more detailed description
199of this method can be found elsewhere (Estévez-
200Báez et al. 2015a,b; Machado et al. 2014;
201Machado-Ferrer et al. 2013). The limits for the
202spectral HRV very low-frequency (VLF) band
203were 0.020.04 Hz. The low-frequency band
204(LF) was considered 0.040.15 Hz and the high-
205frequency (HF) band was 0.150.40 Hz. The
206absolute power spectral density estimations were
207calculated as the integral of each one-sided qua-
208dratic spectrogram in the frequency ranges previ-
209ously dened and marked with the acronyms
210P_VLF, P_LF, P_HF, and P_Tot. The ratio of
211power spectra of LF-to- HF bands (LF/HF) also
Influence of Heart Rate, Age, and Gender on Heart Rate Variability in...
212 was calculated. The relative power content for the
213 three specied bands was normalized as the
214 corresponding percent of the total power spectral
215 density of RRi series (P_Tot) and the following
216 acronyms were used for the indices calculated:
217 nu_VLF, nu_LF, and nu_HF. The complexity of
218 the RRi series was calculated with the Shannon
219 entropy index, considered the informational
220 domain index (Bravi et al. 2011), using the
221 expression:
222 where p
is the probability of every possible value
223 of the RRi duration and Nis the total number of
224 samples.
225 Digital signal processing in this study was
226 carried out using the custom-tailored programs
227 developed by our staff with MATLAB
228 (MathWorks v9.1.0.441655 R2016b).
2.6229 Adjustment of Measured HRV
230 Indices
231 The inuence of age on HRV indices between the
232 groups was investigated as the effect of the covari-
233 ation of heart rate and gender, with the intent to
234 adjust for those correlations if present. Likewise,
235 the inuence of gender was investigated as the
236 effect of the covariation of heart rate and age.
237 The inuence of the heart rate on HRV indices
238 was investigated as the effect of the covariation of
239 the reciprocal values of heart rate, i.e., the mean
240 heart inter-beat period duration (MRRi). Gender
241 was coded as a binary variable with zero
242 representing female and one representing male. A
243 linear model was computed for the different HRV
244 indices and was used for adjustments of heart rate
245 and gender and for adjustments of heart rate and
246 age in the following form:
247 where y is the adjusted HRV index; β
248 and β3 are the regression coefcients of the
249factors included; and their interactions are
250represented by the product x
251The linear model used for the evaluation of the
252effect of heart rate, represented by its reciprocal
253value, i.e., MRRi, was in the form:
ðÞ ð3Þ
254The validity of the linear models was assessed
255using an F-test to prove the null hypothesis that
256the regression coefcients β
, and β3 were all
257equal zero or to say that the model was constant in
258the form:
259Then, a t-test for each individual regression coef-
260cient determined if the covariation of the factor
261associated with the HRV index was signicant. If
262the F-statistics yielded a nonsignicant result at
263p > 0.05, then the HRV index did not require
264adjustment. Otherwise, the residuals of the linear
265model were calculated as the differences between
266the observed values and the values predicted by
267the model. If the adjustment was considered
268meaningful, its distribution was evaluated with
269the Kolmogorov-Smirnov test. For normal distri-
270bution, the calculated residuals became the
271adjusted values for the HRV index and were
272submitted to statistical comparison between the
2.7 274Statistical Analysis
275The results are presented as means SD. For
276comparisons between age of male and female
277subjects in each of the four groups, a t-test was
278used. Possible differences in BMI between
279groups were tested using one-way ANOVA.
280Normality of data distribution was evaluated
281with the Shapiro-Wilk test. The non-normally
282distributed data were modied using natural or
283common logarithmic transformations. The
284parametric correlation measures of the Pearson
285product moment were used to test the strength of
286the relationships between heart rate and age with
287the HRV indices, before and after adjustments.
288The Spearman rank nonparametric correlation
M. Estévez-Báez et al.
289 measures were used to test the relationships
290 between gender and HRV indices.
291 For comparison ofAU4 HRV indices between the
292 four age groups without adjustments and after
293 adjusting for MRRi, and MRRi and gender,
294 one-way ANOVA was used. The statistical
295 power for each test was only considered valid
296 for values over 0.7, and the post-hoc comparisons
297 were conducted using the Scheffe test. To evalu-
298 ate differences related to gender, after adjusting
299 for heart rate and age, a factorial ANOVA was
300 used followed by the post-hoc Duncan test. Sig-
301 nicance was set at p < 0.05. All statistical
302 analyses were performed using a commercial sta-
303 tistical package of Statistica v10 (StatSoft Inc.,
304 Tulsa, OK).
306The age and gender characteristics of the
307participants included in this study are presented
308in Table 1. There were no signicant differences
309(F (3,251) ¼1.968; p¼0.12) between the BMI
310values in the four age groups (Group A,
31121.1 2.2 kg/m
; Group B, 21.1 2.7 kg/m
312Group C, 21.7 2.9 kg/m
; and Group D,
31321.6 2.8 kg/m
). A nonlinear relationship was
314shown between the values of the mean heart period
315against the corresponding mean heart rate values
316(reciprocal values) for the 255 participants of this
317study (Fig. 1).
t:1Table 1 Age and gender characteristics of the participants
Groups Age range (years) Males (n) Females (n) Whole group (n) p
A1316 (30) 14.4 0.7 (31) 14.6 0.8 (61) 14.4 0.8 0.237t:3
B1720 (27) 18.2 0.9 (38) 18.4 0.9 (65) 18.4 0.9 0.318t:4
C2124 (31) 22.8 1.2 (37) 22.6 1.1 (68) 22.6 1.2 0.413t:5
D2530 (28) 28.3 1.6 (33) 27.7 1.5 (61) 28.0 1.6 0.118t:6
Total 1330 (116) 21.3 5.2 (139) 21.1 4.7 (255) 21.2 5.0 0.809t:7
t:8Data are means SD; nnumber of subjects; p-value, t-test for independent samples for age comparison between males
and females
40 50 60 70
Heart Rate (bpm)
MRRI (ms)
90 100 110
Fig. 1 Scatterplot diagram
obtained by polynomial
tting of the values
observed in the R-R
tachograms of heart
frequency and its
corresponding heart period
reciprocal values, in the
group of 255 healthy
adolescents and young
adults. Points represent
observed values and the
continuous line depicts the
tting curve. Note the
evident differences in RR
interval ranges (vertical
arrows) corresponding to
increments of identical
range of the heart rate
values at lower
(5060 bpm) and higher
values (90100 bpm).
MRRi, mean heart inter-
beat period duration; bpm,
beats per min
Influence of Heart Rate, Age, and Gender on Heart Rate Variability in...
3.1318 Comparisons of Original Heart
319 Rate Variability (HRV) Indices
320 Without Corrections
321 The strength of the relationship between different
322 HRV indices and MRRi, age, and gender is
323 substantiated by signicant correlations of
324 MRRi with 10 out of the 11 HVR indices
325 (90.9%). There were signicant correlations of
326 the age factor with 6 HVR indices (54.5%),
327 while the gender factor correlated with 4 HVR
328 (36.4%) (Table 2).
329 The MRRi values showed signicant
330 differences between the younger adolescents
331 (Group A) and both groups of adults (C and D)
332 and between the older adolescents (Group B) and
333 older adults (Group D) as shown in Table 3. The
334 complexity Shannons entropy index showed sig-
335 nicant differences only between the adolescents
336 of group A and the values of the other groups.
337 The original values of the power spectral density,
338 expressed as the absolute terms, showed
339 differences only for the index P_HF between the
340 younger adolescents (Group A) and the older
341 adults (Group D) (Table 3). The indices of time
342 domain variability SDNN and RMSSD did not
343 show signicant differences between age groups.
344 The values of power spectral density in
345 normalized units showed signicant differences
346 between Group A and Group C for the nu_LF
347 index and for the LF/HF ratio.
3.2348 Adjustment Procedures
349 Calculations carried out for the adjustment of
350 some of the spectral HRV indices detailed in
351 Methods (Sect. 2.6) are exemplied in Table 4.
352 Data concern the adjustment for MRRi, age, and
353 the interaction of the two. The F-tests show
354 highly signicant values for the four HRV indices
355 and also indicate that the calculated residuals
356 were distributed normally, checked with the
357 Kolmogorov-Smirnov tests. According to the
358rules of the procedure, these residuals could then
359be considered as values statistically free of the
360effect of MRRi, age, and their interaction. In
361addition to this information, we can observe that
362not all the factors, or their interaction, signi-
363cantly contributed to the results of the whole
364adjustment procedure. All the t-statistic values
365were signicant for the beta estimates of the linear
366regression model for the P_LF index, but not
367entirely so for the other indices. This information
368may be used to better understand the strength of
369the interrelationships of heart rate, age, and gen-
370der included in the regression model.
3.3 371Values Adjusted for the Mean
372R-R Inter-Beat Period Duration
374The adjustment procedure shows that the nu_VLF
375index did not need to be adjusted, because the
376F-statistic was nonsignicant. After adjusting for
377the effect of MRRi, all HRV indices showed
378signicant correlations with age, but the
379correlations with gender remained unchanged
380(Table 2).
381There were signicant differences between the
382age groups for all the absolute HRV power spec-
383tral indices except for the values of the VLF band.
384The time domain variability indices (SDNN,
385RMSSD) showed signicant differences between
386age groups that were not detected before correc-
387tion. The Shannon entropy complexity index
388showed signicant differences between the values
389of the young adolescents (Group A) and the other
390three groups and between the older adolescents
391(Group B) and the young adults Groups C and D
392(Fig. 2). Signicant differences between the ado-
393lescent Groups A and B were found for the
394indices P_HF, SDNN, and RMSSD. The correc-
395tion for the LF/HF ratio showed signicant
396differences between the younger adolescent
397Group A and younger adult Group C and also
398between Group A and older adult Group D.
M. Estévez-Báez et al.
t:1Table 2 Correlations between HRV indices and the original values and after successive adjustments for MRRi, gender, and age
HRV index
Original values Adjustment for MRRi Adjustment for MRRi and gender Adjustment for MRRi and age
MRRi Age Gender Age Gender Age Gendert:3
ln P_VLF (ms
)0.38 (0.00..) 0.05 (0.45) 0.10 (0.12) 0.17 (0.01) 0.05 (0.42) 0.18 (0.00..) 0.03 (0.61)t:4
ln P_LF (ms
)0.37 (0.00..) 0.04 (0.58) 0.18 (0.01) 0.15 (0.01) 0.14 (0.03) 0.20 (0.00..) 0.11 (0.07)t:5
ln P_HF (ms
)0.41 (0.00..) 0.20 (0.01) 0.01 (0.89) 0.35 (0.00..) 0.05 (0.44) 0.36 (0.00..) 0.07 (0.26)t:6
ln P_Tot (ms
)0.46 (0.00) 0.11 (0.09) 0.11 (0.07) 0.27 (0.00..) 0.06 (0.32) 0.27 (0.00..) 0.04 (0.53)t:7
nu_VLF (%) 0.05 (0.44) 0.11 (0.07) 0.02 (0.80) 0.13 (0.04) 0.02 (0.72) 0.13 (0.04) 0.03 (0.67)t:8
nu_LF (%) 0.15 (0.02) 0.21 (0.00..) 0.16 (0.01) 0.26 (0.00..) 0.18 (0.01) 0.27 (0.00..) 0.18 (0.01)t:9
nu_HF (%) 0.15 (0.02) 0.23 (0.00..) 0.15 (0.02) 0.27 (0.00) 0.17 (0.01) 0.29 (0.00..) 0.17 (0.01)t:10
ln LF/HF ratio (nu) 0.15 (0.02) 0.22 (0.00..) 0.16 (0.01) 0.27 (0.00..) 0.19 (0.01) 0.28 (0.00..) 0.19 (0.01)t:11
SDNN (ms) 0.51 (0.00..) 0.11 (0.07) 0.11 (0.07) 0.31 (0.00..) 0.06 (0.37) 0.31 (0.00..) 0.04 (0.53)t:12
RMSSD (ms) 0.58 (0.00..) 0.18 (0.01) 0.02 (0.73) 0.43 (0.00..) 0.07 (0.29) 0.45 (0.00..) 0.09 (0.14)t:13
Shannon entropy (cu) 0.54 (0.00..) 0.41 (0.00..) 0.07 (0.30) 0.30 (0.00..) 0.00 (0.94) 0.29 (0.00..) 0.03 (0.64)t:14
t:15 Values are presented as Pearsons or Spearmans correlation coefcients and its associated probabilities shown in parenthesis. Abbreviations used for HRV indices are those
described in Methods. Highlighted values are signicant at p<0.05; nu non-dimensional units, cu conventional units, ln natural logarithm; log
common logarithm (base 10);
0.00.., highly signicant values for at least p< 0.001
Influence of Heart Rate, Age, and Gender on Heart Rate Variability in...
3.4399 Values Adjusted for the Effect
400 of the Mean R-R Inter-Beat
401 Period Duration (MRRi)
402 and Gender
403 The nu_VLF index did not require adjustment,
404 according to the F-statistic value. The adjustment
405 produced signicant values only for the correla-
406 tion indices between the HRV indices and the
407factor age (Table 2). The HRV indices that sig-
408nicantly differed between the age groups are
409shown in Fig. 3. The general feature of spectral
410indices, expressed as the absolute power, was a
411reduction of their values with increasing age. The
412LF/HF ratio increased with age. Low frequency,
413expressed in normalized units (nu_LF), showed
414an increment with age, while the opposite was
415observed for high frequency (nu_HF). Corrected
t:1Table 3 Statistical differences observed for the original heart rate variability (HRV) indices calculated in the four age
groups of healthy adolescents and young adults (one-way ANOVA)
HRV index
Group A Group B Group C Group D
F (3251) p
Observed power
(n ¼61) (n ¼65) (n ¼68) (n ¼61)t:3(α¼0.05)t:4
MRRi (ms) 781.43 814.75 838.46 873.47 8.16 0.00003 0.99t:5
92.70 107.80{107.20* 116.40*t:6
SDNN (ms) 65.40 61.50 60.51 58.55 ––t:7
22.70 23.30 20.30 23.80t:8
SDNN (ms) 1.79 1.76 1.76 1.74 1.19 0.31 0.31t:9
0.15 0.15 0.15 0.17t:10
RMSSD (ms) 61.43 53.53 47.63 50.33 ––t:11
32.70 29.20 24.01 36.10t:12
RMSSD (ms) 1.73 1.67 1.64 1.64 3.01 0.03 0.68t:13
0.22 0.22 0.19 0.22t:14
Shannon entropy (cu) 6.77 7.64 7.61 7.54 48.28 0.0000 1.00t:15
0.48 0.45** 0.45** 0.50**t:16
Ln P_VLF (ms
) 10.82 10.79 10.70 10.77 0.35 0.79 0.12t:17
0.72 0.71 0.75 0.75t:18
Ln P_LF (ms
) 12.65 12.61 12.67 12.53 0.44 0.73 0.14t:19
0.78 0.70 0.75 0.77t:20
Ln P_HF (ms
) 12.60 12.36 12.20 12.12 3.23 0.02 0.74t:21
0.85 1.00 0.96 0.93*t:22
Ln P_Tot (ms
) 13.45 13.34 13.31 13.22 1.01 0.39 0.27t:23
0.74 0.75 0.73 0.74t:24
nu_VLF (%) 8.12 8.89 8.71 9.93 1.51 0.21 0.40t:25
4.10 4.30 4.20 4.40t:26
nu_LF (%) 46.67 50.49 54.73 53.17 3.79 0.01 0.81t:27
12.80 14.60 13.70** 13.30t:28
nu_HF (%) 45.20 40.62 36.55 36.90 4.05 0.008 0.84t:29
14.60 15.90 15.70* 17.10**t:30
LF/HF ratio (nu) 1.26 1.63 2.06 2.05 ––t:31
0.76 1.09 1.53 1.71t:32
Ln LF/HF ratio (nu) 0.04 0.25 0.47 0.41 4.13 0.007 0.85t:33
0.63 0.72 0.73** 0.85t:34
t:35 Values are means SD; transformed values are presented when it was necessary to achieve normality distributions.
Abbreviations used for HRV indices are those described in Methods; ln natural logarithm; log
common logarithm (base
10); cu, conventional units, cu normalized units, nu non-dimensional units, Fvalues of Fishers statistics for (n, m)
degrees of freedom, passociated probability for observed F values, Observed Power power analysis of the F statistics for
an αerror of 0.05 (valid results only were accepted for values over 0.7). Results for signicant post hoc Scheffes tests are
indicated with symbols in the corresponding columns: *p< 0.05, **p< 0.01 Groups B, C, and D vs. Group A, {p< 0.05
Group B vs. Group D
M. Estévez-Báez et al.
t:1Table 4 Summary of calculations of the adjustment procedure applied to some indices of heart rate variability for
MRRi, age, and their interaction in the investigated adolescents and young adult subjects (n¼255)
HRV index
Estimated coefcients of the linear regression model
F(3251) pK
t:2βestimates SE t-statistic pt:3
P_VLF Intercept 6.527 1.416 4.61 0.00 18.92 0.00.. 0.055t:4
Age 0.085 0.064 1.33 0.184 p> 0.20t:5
MRRi 0.006 0.002 3.36 0.001t:6
Age*MRRi 0.0001 0.0001 1.74 0.082t:7
P_LF Intercept 7.081 1.445 4.90 0.00 18.66 0.00.. 0.042t:8
Age 0.147 0.065 2.26 0.024t:9
MRRi 0.007 0.002 4.16 0.00 p> 0.20t:10
Age*MRRi 0.0002 0.0001 2.64 0.009t:11
P_HF Intercept 8.307 1.722 4.82 0.00 32.99 0.00 0.041t:12
Age 0.016 0.077 0.21 0.837 p> 0.20t:13
MRRi 0.007 0.002 3.13 0.002t:14
Age*MRRi 0.001 0.00001 1.08 0.28t:15
P_Tot Intercept 8.431 1.344 6.28 0.00 33.17 0.00 0.038t:16
Age 0.088 0.060 1.46 0.146 p> 0.20t:17
MRRi 0.007 0.002 4.24 0.00t:18
Age*MRRi 0.0002 0.0001 2.12 0.034t:19
t:20 Abbreviations of HRV indices are those described in Methods. Fvalues of Fishers statistics for (n, m) degrees of
freedom, passociated probability to the corresponding F-Statistic values, Kvalue of the Kolmogorov-Smirnov test and
associated probability for rejecting the hypothesis of normality of the calculated residuals; values in bold font correspond
to signicant results; 0.00, highly signicant values for at least p< 0.00001
P_VLF (ms2)
F(3,251)=2.636; p=0.050
P_LF (ms2)
F(3,251)=2.810; p=0.030
P_HF (ms2)
F(3,251)=11.104; p<0.0001
P_Tot (ms2)
F(3,251)=6.676; p=0.0002
nu_VLF (%)
F(3,251)=1.341; p=0.140
nu_LF (%)
F(3,251)=5.542; p=0.001
0.4 A
Logn LF/HF(cu)
F(3,251)=6.013; p=0.0006
39.2 A
nu_HF (%)
F(3,251)=6.025; p=0.0006
Log10 RMSSD (ms)
F(3,251)=19.330; p=0.009
Log10 CVar(%)T
F(3,251)=6.226; p=0.00001
Shannon Entropy
F(3,252)=46.228; p<0.0001
Log10 SDNN (ms)
F(3,251)=0.008; p=0.00001
Fig. 2 Comparison of heart rate variability (HRV) indices
in the two age groups of adolescents (A and B) and two
age groups of adults (C and D) after adjusting for the mean
R-R inter-beat period duration (MRRi). The results
obtained from one-way ANOVA are specied in the
upper part of each diagram. Vertical bars denote SE.
Signicant results of post hoc Scheffe tests are represented
by double-arrow lines for p< 0.01. The symbol
(*) indicates p< 0.05
Influence of Heart Rate, Age, and Gender on Heart Rate Variability in...
416 MRRi values showed a progressive increment
417 with age, while the variability indices SDNN
418 and RMSSD evidently decreased with increasing
419 age. Values of the Shannon entropy index showed
420 signicant differences between all the groups,
421 with a sharp increase between the younger and
422 older adolescents (Group A vs. Group B) and
423 progressive signicant reductions between
424 Group B and both younger and older adults
425 (Groups C and D).
3.5426 Values Adjusted for the Effect
427 of MRRi and Age Interaction
428 The nu_VLF index did not require adjustment.
429 Gender inuence, after adjusting the original RRi
430 series by MRRi and age, was found only in 4 out
431 of the 11 HRV indices investigated (Fig. 4). For
432 the nu_LF index, signicant inter-gender
433 differences were detected only in the older adult
434 Group D, with higher values for males. For the
435 nu_HF index, signicant inter-gender differences
436were detected in both younger and older adult
437Groups C and D, with higher values for females.
438The LF/HF ratio was signicantly higher for the
439male, compared to female, participants of Group
440D. The corrected MRRi index for age was signi-
441cantly lower in female, compared to male,
442participants, only in the adolescents of Group A.
444The heart rate factor produced more signicant
445effects on HRV than age and gender, modifying
446the original values in that the detection of
447differences between the participants of the four
448age groups was strongly reduced. There was a
449progressive reduction of HRV with increasing
450age, shown by the absolute indices of spectral
451power density in all the HRV bands and also for
452the total power. Sympathetic inuence increased
453with age, which consisted of increases in the
454LF/HF ratio and in spectral power density
455expressed in normalized units for the
P_VLF (ms2)
F(3,251)=2.716; p=0.045
Log10 SDNN (ms)
F(3,251)=9.317; p=0.0001
Logn LF/HF (cu)
F(3,251)=6.906; p=0.0002
Log10 RMSSD (ms)
F(3,251)=20,796; p<0.00001
Log10 CVar (%)
F(3,251)=0.066; p=0.00001
MRRi (ms)
F(3,251)=8.431; p<0.0001
Shannon Entropy
F(3,251)=46.155; p<0.00001
P_Tot (ms2)
F(3,251)=6.990; p=0.0002
P_LFTot (ms2)
F(3,251)=4,752; p=0.003
F(3,251)=6.646; p=0.0002
nu_ HF (%)
nu_LFTot (%)
F(3,251)=6,906; p=0.0001
P_HF (ms2)
F(3,251)=11.730; p<0.0001
Fig. 3 Comparison of HRV indices showing signicant
differences between the two age groups of adolescents
(A and B) and the two age groups of adults (C and D)
after adjusting for the mean heart period and gender.
Vertical bars denote SE. Signicant results of post hoc
Scheffe tests are represented by double-arrow lines for
p< 0.01. The symbol (*) indicates p< 0.05
M. Estévez-Báez et al.
456 low-frequency spectral band (P_LF), and a reduc-
457 tion in the global variability of the time domain
458 index SDNN. Parasympathetic inuence, on the
459 other hand, progressively declined with age,
460 which consisted of decreases in P_HF, nu_HF,
461 and RMSSD. Nevertheless, the MRRi, an integral
462 index of the central control of the autonomic
463 nervous system on cardiac chronotropic activity,
464 increased progressively with age, leading to a
465 reduction in HR. The effect of gender on HRV
466 consisted of signicant differences in young
467 adults (Groups C and D), demonstrating
468 increments in the sympathetic HRV indices
469 P_LF and nu_LF and a concomitant reduction in
470 the parasympathetic nu_HF in males. The MRRi
471 index was signicantly lower, pointing to a
472 higher heart rate in females, but only in the youn-
473 ger adolescents (Group A).
474To the best of our knowledge, this is the rst
475study to explore the autonomic cardiovascular
476control in adolescents between the ages of
4771316 and 1720 years, compared with young
478adults of 2124 and 2530 years of age. Here,
479we assessed the inuence of age, gender, and
480heart rate on HRV indices in these age groups.
481The relevance of the two age ranges of
482adolescents stems from the fact that in many
483cultures individuals aged 1720 are chronologi-
484cally and legally considered as mature enough to
485assume a number of responsibilities such as
486driving a vehicle, serving in the armed forces,
487voting, or marrying (Arnett 2007; Christie and
488Viner 2005; Sisk and Foster 2004). We found
489considerable age-related differences in several
490HRV indices between both adolescent groups as
491well as between the older adolescent and both
Fig. 4 Inter-gender differences in the four age groups
after adjusting the HRV original values by age and mean
heart period. Solid lines represent females and dashed lines
represent males. Vertical bars denote SE. Signicant
results of post hoc Duncan tests are represented by
double-arrow lines for p< 0.01. The symbol (*) indicates
p< 0.05
Influence of Heart Rate, Age, and Gender on Heart Rate Variability in...
492 adult groups. The inuence of gender on HRV
493 akin to that observed in this study has been
494 reported in a range of age groups in other studies
495 (Pothineni et al. 2016; Voss et al. 2015;
496 Abhishekh et al. 2013; Moodithaya and
497 Avadhany 2012; Lutand Sukkar 2011; Reimann
498 et al. 2010; Kuo et al. 1999). In this study, how-
499 ever, we did not nd any gender-related
500 differences in the group of older adolescents
501 aged 1720. Thus, further exploration of the
502 effect of gender on the maturity of cardiovascular
503 control in this age group is required.
504 Our ndings for the inuence on HRV of age
505 factor agree with those reported for other age
506 groups in other studies (Almeida-Santos et al.
507 2016; Tonhajzerova et al. 2016; Abhishekh et al.
508 2013; Voss et al. 2013,2015; Boettger et al. 2010;
509 Zhang 2007; Choi et al. 2006; Migliaro et al.
510 2001; Kuo et al. 1999). Changes in HRV with
511 age have been related with dynamic changes of
512 serum levels of testosterone, cortisol, estradiol,
513 and adrenocorticotrophic hormones (Evans et al.
514 2016; Dogru et al. 2010; Fontani et al. 2004).
515 Dynamic changes observed in the different HRV
516 indices in the two groups of adolescents in the
517 present study show that the autonomic regulatory
518 inuences on the cardiovascular system are
519 actively varying during that period of life and
520 the trend is akin to that observed in the other
521 extreme age ranges such as fetal development
522 (Lange et al. 2005) and elderly humans (Antelmi
523 et al. 2004).
524 The inuence of age and gender has been more
525 extensively studied than the inuence of heart rate
526 on HRV. It has been long since accepted that
527 there is a reduction in HRV with increasing
528 heart rate and vice versa, stemming from a regu-
529 latory action of the autonomic nervous system.
530 However, as of the 1990s, a nonlinear relation-
531 ship between heart rate and its reciprocal value
532 has been recognized of the kind we found in this
533 study for the whole group of participants,
534 depicted in Fig. 1. It has been recommended to
535 use only the cardiac inter-beat duration for time
536 and frequency domain calculations of HRV to
537 avoid a bias (Tsuji et al. 1996; Bigger et al.
538 1992,1989). Coumel et al. (1994) have
539 emphasized that the role of heart rate in the
540assessment of HRV could not be longer ignored.
541Those authors remark that the observed strong
542correlations between heart rate and HRV do not
543support the simplied conclusion that looking at
544HRV is just a complex way to measure heart rate
545since the information is redundant. It has been
546conclusively shown that HRV indices are not a
547mere surrogate of heart rate (Stauss 2014). Heart
548rate, expressed in units of frequency or as its
549reciprocal (MRRi), is in fact an orthogonal factor
550to the time and frequency domains of calculated
551HRV indices before and even after applying a
552correction for the effect of heart rate on HRV
553indices (Estévez-Báez et al. 2015a,b).
554The Sacha group of researchers (Sacha 2014a,
555b; Sacha et al. 2013; Sacha and Pluta 2008) noted
556the nonlinear relationships of heart rate and HRV
557and proposed different mathematical correcting
558methods. The complexity of this problem has
559been highlighted and the correction between the
560two time domains of HRV indices, SDNN and
561RMSSD, and heart rate has been achieved using
562the mathematical expressions that best t their
563experimental results (Monfredi et al. 2014). In
564contradistinction, corrections proposed by Sacha
565et al. (2013) took into consideration only the
566variations in the length of cardiac cycle. The
567statistical approach to the correction of HRV
568due to the effect of heart rate has consisted of
569using the multiple linear regression models
570(Estévez-Báez et al. 2015a,b,2018; Abhishekh
571et al. 2013; Lange et al. 2005; Antelmi et al. 2004;
572Tsuji et al. 1994,1996). Recently, a simpler par-
573simonious mathematical correction has been pro-
574posed by van Roon et al. (2016).
575In the present study, the effect of the reciprocal
576value of the heart rate (MRRi) on HRV was
577clearly described for the four age groups of
578healthy participants including the adolescent
579groups. Therefore, we strongly recommend to
580adjust HRV results for heart rate changes in the
581future studies using HRV indices. With respect to
582HRV changes in the younger and older groups of
583adolescents, we conrm the presence of an appre-
584ciable inuence of the autonomic nervous system
585on the cardiovascular regulation, the inuence
586that becomes progressively akin to that observed
587in young adults.
M. Estévez-Báez et al.
5588 Conclusions
589 During adolescence, gender and particularly heart
590 rate have a substantial inuence on heart rate
591 variability. This inuence ought to be considered
592 to avoid biases in studies on the regulatory effect
593 of the autonomic nervous system. The observed
594 absence of gender-related differences in heart rate
595 variability in late adolescence of 1720 years of
596 age could result from the non-completely
597 achieved maturity of the autonomic mechanisms
598 involved with the cardiac chronotropic control,
599 which deserves further exploration with alterna-
600 tive study designs. It is strongly recommended to
601 correct for the heart rate effect on heart rate
602 variability while evaluating the indices of heart
603 rate variability.
604 Conicts of Interest The authors declare no conicts of
605 interest in relation to this article.
606 Ethical Approval All procedures performed in studies
607 involving human participants were in accordance with the
608 ethical standards of the institutional and/or national
609 research committee and with the 1964 Helsinki declaration
610 and its later amendments or comparable ethical standards.
611 The study was approved by the Ethics Committee of the
612 Institute of Neurology and Neurosurgery of the Ministry of
613 Health in Havana, Cuba.
614 Informed Consent Written informed consent was
615 obtained from all individual participants included in the
616 study.
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Influence of Heart Rate, Age, and Gender on Heart Rate Variability in...
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... Due to the intricate involvement of our heart with our emotions (not just metaphorically), this variation reflects our physiological state and the level of emotional arousal we are experiencing. [48][49][50][51][52][53][54][55] The emerging technology of CEEG in intensive care units gives practitioners the ability to identify changes in the ANS by recording an electrocardiogram (ECG) leads and calculating heart rate (HR) and heart rate variability (HRV). ...
... In the presence of cardiovascular disorders or one of their risk factors such as stress, the sympathetic nervous system predominates in the heart over the parasympathetic one, leading to increased heart rate (HR) and thus a decrease in the beat-to beat variability. 24,25,37,39,42,48,50,56 HRV is affected by various factors, including mental activity and changes in emotion and mood, such as practicing meditation and physical exercises. This represents some adaptation of the heart to external stimulators and their response. ...
... Vagal dominance occurs when the vagus nerve, a parasympathetic nerve in a stable state, is more active than the sympathetic nerves. 39,50,[62][63][64][65] In 1996, the Task Force of the European Society of Cardiology (ESC) and the North American Society of Pacing and Electrophysiology (NASPE) defined and established standards for the measurement, physiological interpretation, and clinical use of HRV. Time-domain and frequency-domain indices and geometric measures are standard clinical parameters. ...
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Introduction: We have recently recorded, processed, and published preliminary research (Part I) on brain activity by quantitative electroencephalography (EEG) tomography (QEEGt), comparing separate subjects remembering their near-death experiences (NDEs) and mystical experiences (SCE). Several reports have affirmed that NDE and SCE are related to important functional changes in the autonomic nervous system (ANS). The autonomic nervous system and the hypothalamus regulate pulse, blood pressure, breathing, and arousal in response to emotional cues. When activated, the sympathetic nervous system prepares the body for emergency actions by controlling the glands of the endocrine system. Heart rate variability (HRV) refers to how much an individual's heart rate (HR) varies, and it is a powerful method to assess the ANS. Several reports have affirmed that NDE and SCE are related to important functional changes in the ANS. We used the continuous EEG monitoring (CEEG) system to compare the memories of two groups with a NDE and a SCE. CEEG permits continuous electrocardiogram monitoring, allowing calculation of HR and HRV during SCE and NDE remembering. Hence, using HRV methodology, it is possible to assess the emotional effect of remembering NDE and SCE. Conclusion: We demonstrated the usefulness of using the CEEG methodology, which allows us to continuously assess the ANS through the HRV methodology, showing important significant functional changes in the autonomic nervous system (ANS), and comparing SCE and NDE.
... Very few studies have been done exploring the hereditary manifestations in the off-springs when their blood pressure is still optimal i.e<120/80 mmHg i.e at a young age when effect of environmental factors is still less. Different studies have found different components of frequency domain analysis of HRV to be associated with cardiovagal function [8,14]. By incorporating non-invasive tests in one study and comparing the results, how autonomic response is altered with genetic/ hereditary predisposition, at a young age when the individuals are still normotensive with BP < 120/80 mm of Hg, can be studied in a simpler manner. ...
... Mario Estévez-Báez et al. in their study on Influence of Heart Rate, Age, and Gender on Heart Rate Variability in Adolescents and Young Adults found that heart rate produced more significant effects on HRV indices than age or gender [14]. ...
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Background: Autonomic functions, sympathetic and vagal/ parasympathetic, are found to be deranged in hypertensive subjects. The derangement of autonomic functions in terms of sympatho-vagal imbalance has also been found in off-springs of hypertensive people. Method: Spectral analysis of heart rate variability (HRV) was recorded in 30 subjects of control group & 30 subjects of case /study group( offsprings of hypertensive parents) before and after Hand Grip Test(HGT). Body mass index (BMI), basal heart rate (BHR), blood pressure (BP) was recorded at the outset. Spectral indices of HRV included Standard deviation of NN intervals (SDNN), total power (TP), normalized low frequency power (LFnu), normalized high frequency power (HFnu), ratio of low frequency power to high frequency power (LF-HF ratio), mean heart rate (mean RR), square root of the mean squared differences of successive normal to normal intervals (RMSSD) and the number of interval differences of successive NN intervals greater than 50 ms (NN50). Result: In study group SDNN & LF/HF finding was significant which means there is a sympathetic dominant effect. Pre and Posttest HGT and HRV values were found to be significant when study and control groups were compared. This means changes occur in sympathetic variables before actual onset of hypertension. No correlation was seen as regards to HRV & BMI. Conclusion: It can be concluded that offsprings of Hypertensive parents tend to have some degree of autonomic imbalance before the actual onset of essential hypertension.
... Furthermore, HRV is known to change based on many variables, such as ageing, gender, or medication intake [45,46]. SQI f uzzy resulted in being too optimistic in the quality scores, misclassifying almost all the segments with unacceptable quality. ...
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Cardiovascular diseases (CVD) represent a serious health problem worldwide, of which atrial fibrillation (AF) is one of the most common conditions. Early and timely diagnosis of CVD is essential for successful treatment. When implemented in the healthcare system this can ease the existing socio-economic burden on health institutions and government. Therefore, developing technologies and tools to diagnose CVD in a timely way and detect AF is an important research topic. ECG monitoring patches allowing ambulatory patient monitoring over several days represent a novel technology, while we witness a significant proliferation of ECG monitoring patches on the market and in the research labs, their performance over a long period of time is not fully characterized. This paper analyzes the signal quality of ECG signals obtained using a single-lead ECG patch featuring self-adhesive dry electrode technology collected from six cardiac patients for 5 days. In particular, we provide insights into signal quality degradation over time, while changes in the average ECG quality per day were present, these changes were not statistically significant. It was observed that the quality was higher during the nights, confirming the link with motion artifacts. These results can improve CVD diagnosis and AF detection in real-world scenarios.
... Related measurements are suggested to be included in future studies. Finally, the present findings of HRV in response to the RES and SIE protocols were obtained from untrained NOB and OB young men, the applications of the current findings as guidelines in exercise prescription for the purpose of athletic training in athletes or non-pharmacological strategies for promoting cardiovascular and metabolic health in women or elderly individuals should be with cautions (Aubert et al. 2003;Estévez-Báez et al. 2019). ...
This study examined the alterations of heart rate variability (HRV) following iso-duration resistance (RES) and sprint-interval (SIE) exercises by comparing with that of non-exercise control (CON) in 14 non-obese (NOB) and 15 obese (OB) young men. Time and frequency domain measures as well as non-linear metrics of HRV were assessed before and immediately after exercise, and during every 20 min until 120 min post exercise. The variables during the first 4 hrs of actual sleep time at night, and the period of 12-14 hrs post exercise were also measured. All trials were scheduled at 20:00. It was found that RES and SIE attenuated the HRV in both NOB and OB (P <0.05), and the attenuated HRV restored progressively during subsequent recovery. Although the changes in HRV indices among various time points during the recovery period and its interaction across RES, SIE and CON were not different between NOB and OB, the restoration of the declined HRV indices to corresponding CON level in the two exercise trials in OB appeared to be sluggish in relative to NOB. Notwithstanding, post-exercise HRV that recorded during actual sleep at night and during 12-14 hrs apart from exercise were unvaried among the three trials in both groups (P>0.05). These findings suggest that obesity is likely to be a factor hindering the removal of exercise-induced cardiac autonomic disturbance in young men. Nonetheless, the declined HRV following both the RES and SIE protocols were well restored after a resting period of ~10 hrs regardless of obesity. The study was registered at ISRCTN as DOI:10.1186/ISRCTN88544091.
... is study is not without limitations. First of all, in order to exclude the influence of age, heart disease, hypertension, diabetes, and other factors on HRV [22][23][24][25][26], this study strictly set the inclusion criteria, in which the age of the population was set at 20-30 to reduce the possibility of suffering from basic diseases affecting the indicators. However, this caused the disadvantages of limited age and small number of cases in the study. ...
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Objective: To explore the autonomic nerve rhythm and the correlation between palpitations below the heart (PBTH) and autonomic nerve function in patients with PBTH based on heart rate variability (HRV). Methods: The outpatients or ward patients of Wenzhou Hospital of Traditional Chinese Medicine were collected and divided into two groups: the PBTH group and the normal group. The HRV of each group was detected. Single-factor statistical methods, Spearman correlation analysis, and logistic regression were used to describe and analyze the rhythm and characteristics of autonomic nerves in patients with PBTH and the correlation between PBTH and autonomic nerve function. Results: (1) In the comparison of HRV in different time periods in the same group, the SDNN, RMSSD, pNN50, TP, and HF in the PBTH group at night were significantly higher than those in the daytime (P < 0.01), while the LF/HF ratio was significantly lower than that in the daytime (P < 0.01). (2) In the comparison of HRV between the two groups in the same time period, the RMSSD and pNN50 of the PBTH group during the daytime period were significantly higher than those of the normal control group (P < 0.05), and the LF/HF was significantly lower than that of the normal group (P < 0.05). (3) In the Spearman correlation analysis, PBTH was significantly correlated with RMSSD, pNN50, and LF/HF ratio in the daytime period, with correlation coefficients of 0.424, 0.462, and -0.524, respectively (P < 0.05). (4) Logistic regression analysis showed that the decrease of LF/HF ratio during the daytime period was an independent risk factor for PBTH in TCM (OR = 0.474, 95% CI: 0.230-0.977, P < 0.05). Conclusions: The changes in parasympathetic nerve function in patients with PBTH have a circadian rhythm, which is characterized by increased activity during the nighttime. At the same time, the autonomic nerve activity of people with PBTH during the daytime is unbalanced, and the decrease of LF/HF ratio during the day is an independent high risk factor for PBTH.
... The present findings of HRV in response to the SIE protocols in untrained young men should not be generalized for either trained, elderly, or female individuals. 8,39 Further, the exercise-induced changes in the hormones or metabolites were not measured in this study, so the mechanisms elucidating our results are postulated. Besides, rebound of post-exercise cardiac parasympathetic activity above pre-exercise levels, partly attributed to the post-exercise hypervolemia, has been observed in the hours or days after exercise, and was considered as the optimal training period for attaining cardiorespiratory adaptations. ...
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Objectives This study examined the influences of the volume of all-out sprint-interval exercise (SIE) on acute post-exercise heart rate variability (HRV) recovery. Methods HRV recovery following a session of (i) 2 × 30-s SIE (SIE2), (ii) 4 × 30-s SIE (SIE4), and (iii) non-exercising control (CON) were compared in 15 untrained young males. Time domain [standard deviation of normal-to-normal intervals, root mean square of successive R-R differences] and frequency domain [low frequency (0.04–0.14 Hz), high frequency (0.15–0.40 Hz)] measures of HRV were assessed every 20 min for 140 min after the exercise, and every hour during the first 4 h of actual sleep time at immediate night. All trials were scheduled at 19:00. Results In comparison to CON, both SIE2 and SIE4 attenuated the HRV markedly (p < 0.05), while the declined HRV restored progressively during recovery. Although the sprint repetitions of SIE4 was twice as that of SIE2, the declined HRV indices at corresponding time points during recovery were not different between the two trials (p > 0.05). Nevertheless, the post-exercise HRV restoration in SIE2 appeared to be faster than that in SIE4. Regardless, nocturnal HRV measured within 10 h following the exercise was not different among the SIE and CON trials (p > 0.05). Conclusion Such findings suggest that the exercise volume of the SIE protocol may be a factor affecting the rate of removal of the cardiac autonomic disturbance following the exercise. In addition, rest for ∼10 h following either session of the SIE protocol appears to be appropriate for the cardiovascular system to recover.
Bei der Aufrechterhaltung der Körperhomöostase spielt das vegetative Nervensystem (VNS) eine zentrale Rolle. Kommt es zu Regulationsstörungen, sind (u.U. persistierende) körperliche Störungen und die Entstehung von Krankheiten vorprogrammiert. Zudem ist das VNS auch für einige Prozesse der Schmerzentstehung und -verarbeitung verantwortlich. Die Analyse der Herzratenvariabilität kann in dem nebulösen Bereich somatoformer Störungen über die Darstellung vegetativer Afferenzen und Efferenzen einen diagnostischen Ansatz bieten und somit die Behandlung erleichtern.
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Objectives Patients with psychosomatic disorders suffer from social isolation that might further lead to destabilization and exacerbation of bodily symptoms via autonomic pathways. We aimed to investigate the influence of controlled social stress (model of social ostracism) on the autonomic nerve system (ANS) in an inpatient cohort with psychosomatic disorders. Methods We examined heart rate variability (HRV), skin conductance (SC) and skin temperature (ST) as well as ECG-derived respiration rate (EDR) and subjective reports on stress during exposure to experimental social stress (cyberball game). Data were collected from 123 participants (f:m = 88:35, 42.01 ± 13.54 years) on admission and upon discharge from the university psychosomatic clinic. All data were recorded during baseline, inclusion and exclusion phases of the cyberball game as well as during the recovery phase. Results We found significant changes between admission and discharge with a decline in parasympathetic-related HRV parameters (SDRR −3.20 ± 1.30 ms, p = 0.026; RMSSD: −3.77 ± 1.28 ms, p = 0.007) as well as a decrease in SC (−0.04 ± 0.17 μS, p = 0.019) and EDR (−0.01 ± 0.01 Hz, p = 0.007), suggesting a drop in sympathetic tonus, with no changes in ST ( p = 0.089) and subjective stress levels ( p = 0.322). HRV parameters decreased during the cyberball game (SDRR p = 0.026; RMSSD p = 0.002; lnHF p < 0.001). In contrast, both SC ( p < 0.001) and EDR ( p < 0.001) increased during the game with SC being slightly lower during the exclusion phase. This can point toward a stimulation of sympathetic nervous system during game participation, which was concordant with the rise in subjective stress values ( p < 0.001). ST showed a continuous, unspecific rise over time ( p < 0.001). Conclusion Our data demonstrate the decrease of ANS parameters during experimental social stress when data upon discharge were compared to those upon admission. These results are partially contradictory to previous studies that showed a rise in HRV in a psychiatric cohort over the course of (outpatient) treatment. Further research is required to help attributing these differences to effects of treatment or acute states relating to admission to or discharge from a psychosomatic department.
Exaggerated cardiovascular (CV) reactivity to stress is associated with negative cardiovascular outcomes. This study aimed to investigate the effects of acute high-intensity interval exercise (HIIE) and moderate-intensity exercise (MIE) on CV reactivity in response to a stress challenge in untrained males. Thirteen, normotensive males (age: 22.8 ± 2 years, BMI: 21.9 ± 3.6 kg/m²) underwent three conditions in counterbalanced order: HIIE (bodyweight exercises; 80–90% HRR), MIE (treadmill-jog; 55–60% HRR) and seated rest (CON) separated by 7–10 days. Thirty minutes after performing HIIE, MIE or CON, subjects underwent a 2-min cold pressor task (CPT). Blood pressure (BP) and heart rate (HR) were measured before, during, and after CPT. CV reactivity, i.e., the change in BP and HR responses were compared across conditions. Systolic BP reactivity were attenuated following HIIE (−60%, p = 0.015) and MIE (−42%, p = 0.033) compared to CON, but no differences were observed between HIIE and MIE. HR reactivity was not different across all conditions. We conclude that performing HIIE or MIE 30 minutes prior to acute stress exposure lowers BP reactivity compared to rest in untrained males. These findings highlight the potential benefits of HIIE in lowering stress-induced elevations in blood pressure
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Reports in the literature about the very high frequency (VHF) range (0.4-0.9 Hz) of heart rate variability (HRV) are scanty. Its presence in cardiac transplanted patients and other conditions associated to reduced vagal influence to the heart encouraged us to explore this spectral band in healthy subjects and in patients with a definite diagnosis of cardiac autonomic neuropathy (CAN), and to assess the potential clinical value of some VHF indices. The study included 80 healthy controls, and 48 type-2 SCA2 patients with the diagnosis of CAN. Short duration (5 minutes) free of artifacts electrocardiographic recordings were submitted to three different spectral analysis methods, including the most generally accepted procedure, and two other methods that use the novel method of the Hilbert-Huang transform. In both groups we could show the presence of VHF activity. In the SCA2 patients the VHF power spectral density was significantly higher when expressed in relative normalized units (36.08±17.4% vs. 22.87±14.1%) and also was higher the instantaneous VHF spectral frequency (0.583±0.05Hz vs. 0.637±0.07Hz). These findings were related with the severity of CAN. Concluding, the VHF activity of HRV is not an artifactual phenomenon and is associated with the integrity of the cardiovascular autonomic control.
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The current study examined mean level and change in extraversion and neuroticism across adolescence in relation to physiological stress reactivity to social evaluation. Adolescents (n = 327) from the Dutch general population reported on personality measures at five annual assessments. At age 17 years, adolescents participated in a psychosocial stress procedure characterized by social evaluation during which cortisol, heart rate, pre-ejection period (PEP) and heart rate variability were assessed. Dual latent growth curve models were fitted in which the intercepts (mean level) and slopes (change) of personality across adolescence predicted the intercepts (baseline) and slopes (reactivity) of the physiological stress measures. Most comparisons revealed no relation between personality and stress reactivity. Adolescents with higher mean level scores on extraversion did show lower cortisol reactivity. Adolescents with higher mean level neuroticism scores showed higher PEP reactivity. Our findings lend partial support for a relation between personality and physiological stress reactivity.
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To develop a method to correct the nonlinear effect of the heart rate (HR) on different heart rate variability (HRV) indices of heart rate variability. The study included 265 healthy participants (17–69 years old), a group of 36 type 1 diabetes mellitus patients, including 15 patients with positive diagnosis of cardiovascular autonomic neuropathy (CAN), and a group of 24 CAN positive type-2 spinocerebellar ataxia patients. HR and HRV indices were calculated for 5-min resting ECG recordings. The proposed correction method (CM) included the joint application of multiple regression analysis and Z-transformations of HR and HRV indices. To assess the effect of the CM, correlation analysis, multivariate factor analysis, and the ANOVA test were applied to both groups before and after corrections. The CM was able to remove the effect of HR on HRV indices, and at the same time, were preserved the expected differences between HR and HRV indices between controls and patients. Sample size was not a factor. Our method may be considered a novel approach, and may represent an alternative to the use of currently developed procedures. Studies of HRV without an appropriately HR correction should not be considered in the future.
In their article, Monfredi et al1 addressed the relationship between mean heart rate (HR) and its variability (HRV). In their carefully conducted experiments and with clear reasoning, they demonstrated a universal exponential decay-like relationship between HRV and HR and concluded from this that HRV cannot be used in any simple way to assess autonomic nerve activity to the heart. Like Stauss,2 we tend to agree with this conclusion as it points out a highly relevant and important issue in the field of cardiovascular medicine. However, we note that the complex biophysical model presented is merely an example of a restriction-of-range phenomena, which has been acknowledged in psychophysiological research3,4. These insights were not picked-up and included in the guiding HRV Task Force paper5 and likely, therefore, largely missed. Thirty years ago, Akselrod et al6 elegantly explained that the nature of the interbeat interval (IBI) time series can explain the relationship between HRV and HR. In …
Major depressive disorder (MDD) is characterized by dysphoric mood, which may be accompanied by suicidal ideation. It is supposed that MDD is associated with dysfunction of the autonomic nervous system, but studies in pediatric patients are rare. Therefore, we aimed to study the relationship between MDD and autonomic regulation in adolescence using the electrodermal activity as an index of sympathetic cholinergic control. We examined 25 adolescents suffering from MDD without comorbidities and prior to pharmacotherapy (13 girls, mean age 14.6 ± 0.4 year) and 25 age/gender-matched healthy control subjects. The electrodermal activity was continuously recorded during 5 min of supine rest. The value of this activity in μS was averaged for each minute of the recording. We found that in depressed patients, electrodermal activity was significantly lower each minute of the recording compared to that in the control group. The study demonstrates electrodermal hypoactivity in adolescent patients with MDD, which points to dysfunctional regulation of the sympathetic part of the autonomic nervous system. This finding could represent a potential pathomechanism leading to higher risk of negative health outcomes in pediatric depressed patients. Further research is needed to elucidate the incompletely understood interaction between MDD and autonomic regulatory outputs at young age.
We aimed to study complex cardiovagal control using heart rate variability (HRV), linear and nonlinear analyses at rest and during negative emotional stress in healthy students with varying depressive symptoms. ECG recording in 20 students was performed at baseline, negative emotional stress, and recovery period. The HRV parameters evaluated were the following: RR interval, spectral power in high-frequency band (HF-HRV), and symbolic dynamics index 2LV%. The subjects were divided into two groups based on the score of the Beck Depression Inventory (BDI) – normal mood (BDI: 0.6 ± 0.2) and mild mood disturbance (BDI: 14.3 ± 1.4). We found significantly lower logHF-HRV during emotional stress in mild mood disturbance compared with normal mood (p = 0.047). No significant differences were found in the remaining parameters. We conclude that negative emotional stress attenuated the cardiovagal control during mood disturbance, which points to discrete abnormalities in the neurocardiac reflex system associated with depressive symptoms. Hampered cardiovagal control could represent a potential pathomechanism leading to depression-linked cardiovascular complications.
Background: The autonomic nervous system (ANS) is a key regulator of the cardiovascular system. The two arms of the ANS, sympathetic and parasympathetic (vagal) have co-regulatory effects on cardiac homeostasis. ANS modulation and dysfunction are also believed to affect various cardiac disease states. Over the past decade, there has been increasing evidence suggesting gender differences in ANS activity. Methods: In multiple previous studies, ANS activity was primarily assessed using heart rate variability, muscle sympathetic nerve activity, coronary blood flow velocity, and plasma biomarkers. Heart rate variability is a non-invasive measure, which can be analyzed in terms of low frequency and high frequency oscillations, which indicate the sympathetic and parasympathetic tone, respectively. These measures have been studied between women and men in states of rest and stress, and in cardiac disease. Conclusion: Studies support the concept of a significant gender difference in ANS activity. Further studies are indicated to elucidate specific differences and mechanisms, which could guide targeted therapy of various cardiovascular disease states.
Objectives: To evaluate the effects of aging, gender and body mass index on the heart rate variability (HRV), and to compare the patterns of global autonomic regulation (GAR) and parasympathetic outflow (PO) throughout the aging process. Design, setting and participants: cross-sectional: Large sample of community-based adults and elderly people. Individuals aged from 40 to 100 years, functionally independent and with satisfactory cognitive function defined as the self-capacity to interact with an interviewer (N = 1743). Material and methods: The study enrolled individuals of both genders, stratified into five age-groups. We did adjustments for hypertension, dyslipidemia and non-insulin-dependent diabetes, as well as the body mass index (BMI). All groups undertook long-term electrocardiograms and five time-domain HRV parameters were measured, three (SDNN, SDANN, SDNN-index) reflecting the GAR and two (rMSSD and pNN50) the PO. Results: SDNN, SDANN and SDNN-index decreased linearly with age and BMI, and women had lower values than men (p < 0.001). There was a U-shaped pattern of rMSSD and pNN50, with the nadir between 60 and 69 years for both genders, and women had higher values than men (p < 0.001). The lowest levels of all HRV variables were found in diabetics (p < 0.001). There was no influence of hypertension or dyslipidemia. Conclusions: The GAR decreased linearly with the age in both genders. It is comparatively lower in women, diabetics and overweight individuals. The PO presented the U-shape in both genders with the nadir at the 7th decade. It was also comparatively lower in men and diabetics. Hypertension and dyslipidemia imparted no significant influence.