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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
Particle
Given Name Mario
Suffix
Organization Institute of Neurology and Neurosurgery, Ministry of Health
Address Havana, Cuba
Author Family Name Carricarte-Naranjo
Particle
Given Name Claudia
Suffix
Division Faculty of Biology
Organization Havana University
Address Havana, Cuba
Author Family Name Jas-García
Particle
Given Name Javier Denis
Suffix
Organization Center for Sports Research
Address Havana, Cuba
Author Family Name Rodríguez-Ríos
Particle
Given Name Evelyn
Suffix
Organization Latin-American School of Medicine
Address Havana, Cuba
Author Family Name Machado
Particle
Given Name Calixto
Suffix
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
Particle
Given Name Julio
Suffix
Division Department of Medicine & Health Science
Organization University of Sonora
Address Hermosillo, Mexico
Corresponding Author Family Name Leisman
Particle
Given Name Gerry
Suffix
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
Fajardo’
Address Havana, Cuba
Email g.leisman@alumni.manchester.ac.uk
Author Family Name Schiavi
Particle
Given Name Adam
Suffix
Division Anesthesiology and Critical Care Medicine, Neurosciences
Critical Care Division
Organization Johns Hopkins Hospital
Address Baltimore, MD, USA
Author Family Name Machado-García
Particle
Given Name Andrés
Suffix
Division Faculty of Biology
Organization Havana University
Address Havana, Cuba
Author Family Name Luaces
Particle
Given Name Claudia Sánchez
Suffix
Division Faculty of Biology
BookID __ChapID 292_Proof# 1 - 16/10/18
Organization Havana University
Address Havana, Cuba
Author Family Name Pié
Particle
Given Name Eduardo Arrufat
Suffix
Organization Institute of Basic and Preclinical Sciences “Victoria de
Giro
´n”
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
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.
Keywords
(separated by ‘-’)
Adolescents - Age - Autonomic nervous system - Gender - Heart rate
variability - Power spectral analysis
BookID __ChapID 292_Proof# 1 - 16/10/18
1
2Adv Exp Med Biol - Clinical and Experimental Biomedicine
3https://doi.org/10.1007/5584_2018_292
4#Springer Nature Switzerland AG 2018
5
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
14
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 influence 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, 13–16 and
26 17–20 years) and two groups of healthy
27 young adults (21–24 and 25–30 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 significant effects on HRV
37indices than age or gender. There was a pro-
38gressive reduction in HRV with increasing
39age. Sympathetic AU1influence increased with
40age and parasympathetic influence
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
e-mail: g.leisman@alumni.manchester.ac.uk
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 influ-
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 influence 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 fluctuations 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 (13–16 years) and late
93teenagers (17–20 years) have not been fully
94explored. Therefore, this study seeks to define
95the effects of the main factors known to influence
96HRV, such as heart rate, age, and gender in
97healthy adolescents and young adults.
298Methods
2.1 99Participants
100A cohort of 255 healthy subjects of both genders
101was studied. There were two age groups of healthy
102adolescents of 13–16 and 17–20 years of age and
103another two age groups of healthy young adults of
10421–24 and 25–30 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 amplifiers (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 specific 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 0.5–45 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 offline to a soft-
159 ware tool developed in Delphi Embarcadero XP
160 by the authors MEB and AMG (MultiTools
161 v3.1.2, 2009–2016) for visual inspection and
162 detection of the fiduciary “R”peaks. Accurate
163 “R”peak 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 significantly
176reduce the DC component of RRi series; (c) linear
177detrending, computing a least squares fitofa
178straight line sequence to the data and subtracting
179the resulting function from the RRi series; and
180(d) zero-phase-shift digital filtering of the RRi
181series using the order 6 Butterworth high-pass
182filter 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 coefficient ((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 modified 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 0.02–0.04 Hz. The low-frequency band
204(LF) was considered 0.04–0.15 Hz and the high-
205frequency (HF) band was 0.15–0.40 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 defined 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 specified 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:
H¼X
N
i¼1
pilog2pið1Þ
222 where p
i
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 influence 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 influence of gender was investigated as the
236 effect of the covariation of heart rate and age.
237 The influence 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:
y¼β0þβ1x1þβ2x2þβ3x1∗x2ð2Þ
247 where y is the adjusted HRV index; β
0
,β
1
,β
2
,
248 and β3 are the regression coefficients of the
249factors included; and their interactions are
250represented by the product x
1
*x
2.
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:
y¼β0þβ1x1
ðÞ ð3Þ
254The validity of the linear models was assessed
255using an F-test to prove the null hypothesis that
256the regression coefficients β
1
,β
2
, and β3 were all
257equal zero or to say that the model was constant in
258the form:
y¼β0ð4Þ
259Then, a t-test for each individual regression coef-
260ficient determined if the covariation of the factor
261associated with the HRV index was significant. If
262the F-statistics yielded a nonsignificant 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
273groups.
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 modified 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 nificance 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).
3305Results
306The age and gender characteristics of the
307participants included in this study are presented
308in Table 1. There were no significant 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
2
; Group B, 21.1 2.7 kg/m
2
;
312Group C, 21.7 2.9 kg/m
2
; and Group D,
31321.6 2.8 kg/m
2
). 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
t:2
A13–16 (30) 14.4 0.7 (31) 14.6 0.8 (61) 14.4 0.8 0.237t:3
B17–20 (27) 18.2 0.9 (38) 18.4 0.9 (65) 18.4 0.9 0.318t:4
C21–24 (31) 22.8 1.2 (37) 22.6 1.1 (68) 22.6 1.2 0.413t:5
D25–30 (28) 28.3 1.6 (33) 27.7 1.5 (61) 28.0 1.6 0.118t:6
Total 13–30 (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
1400
1300
1200
1100
1000
900
800
700
600
500
40 50 60 70
Heart Rate (bpm)
MRRI (ms)
80
MRRI=29.41-33.87*x+0.12∗x2
90 100 110
Fig. 1 Scatterplot diagram
obtained by polynomial
fitting 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
fitting curve. Note the
evident differences in RR
interval ranges (vertical
arrows) corresponding to
increments of identical
range of the heart rate
values at lower
(50–60 bpm) and higher
values (90–100 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 significant correlations of
324 MRRi with 10 out of the 11 HVR indices
325 (90.9%). There were significant 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 significant
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 Shannon’s entropy index showed sig-
335 nificant 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 significant differences between age groups.
344 The values of power spectral density in
345 normalized units showed significant 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 exemplified in Table 4.
352 Data concern the adjustment for MRRi, age, and
353 the interaction of the two. The F-tests show
354 highly significant 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, signifi-
363cantly contributed to the results of the whole
364adjustment procedure. All the t-statistic values
365were significant 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
373(MRRi)
374The adjustment procedure shows that the nu_VLF
375index did not need to be adjusted, because the
376F-statistic was nonsignificant. After adjusting for
377the effect of MRRi, all HRV indices showed
378significant correlations with age, but the
379correlations with gender remained unchanged
380(Table 2).
381There were significant 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 significant differences between
386age groups that were not detected before correc-
387tion. The Shannon entropy complexity index
388showed significant 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). Significant 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 significant
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
t:2
MRRi Age Gender Age Gender Age Gendert:3
ln P_VLF (ms
2
)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
2
)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
2
)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
2
)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
log
10
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
log
10
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 Pearson’s or Spearman’s correlation coefficients and its associated probabilities shown in parenthesis. Abbreviations used for HRV indices are those
described in Methods. Highlighted values are significant at p<0.05; nu non-dimensional units, cu conventional units, ln natural logarithm; log
10
common logarithm (base 10);
0.00.., highly significant 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 significant values only for the correla-
406 tion indices between the HRV indices and the
407factor age (Table 2). The HRV indices that sig-
408nificantly 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
t:2
(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
Log
10
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
Log
10
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
2
) 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
2
) 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
2
) 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
2
) 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
10
common logarithm (base
10); cu, conventional units, cu normalized units, nu non-dimensional units, Fvalues of Fisher’s 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 significant post hoc Scheffe’s 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 coefficients 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 Fisher’s 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 significant results; 0.00, highly significant values for at least p< 0.00001
AB C D
11.2
11.0
10.8
10.6
10.4
P_VLF (ms2)
F(3,251)=2.636; p=0.050
AB C D
13.2
12.8
12.4
12.0
11.6
P_LF (ms2)
F(3,251)=2.810; p=0.030
AB C D
13.2
12.8
12.4
12.0
11.6
P_HF (ms2)
F(3,251)=11.104; p<0.0001
AB C D
∗
13.8
13.5
13.2
12.9
12.6
P_Tot (ms2)
F(3,251)=6.676; p=0.0002
9.4
9.2
9.0
8.8
8.6
A
nu_VLF (%)
B
F(3,251)=1.341; p=0.140
CD
51.8
51.6
51.4
51.2
51.0
50.8
A
nu_LF (%)
B
F(3,251)=5.542; p=0.001
CD
0.8
0.6
0.4
0.2
0.0
−0.2
−0.4 A
Logn LF/HF(cu)
B
F(3,251)=6.013; p=0.0006
CD
40.4
40.2
40.0
39.8
39.4
39.6
39.2 A
nu_HF (%)
B
F(3,251)=6.025; p=0.0006
CD
2.4
2.1
1.8
1.5
1.2
0.9
A
Log10 RMSSD (ms)
B
F(3,251)=19.330; p=0.009
CD
1.5
1.2
0.9
0.6
0.3
0.0
A
Log10 CVar(%)T
B
F(3,251)=6.226; p=0.00001
CD
8.4
8.0
7.6
7.2
6.4
6.8
6.0
A
Shannon Entropy
B
F(3,252)=46.228; p<0.0001
CD
2.6
2.4
2.2
2.0
1.6
1.8
1.4
1.2
1.0
A
Log10 SDNN (ms)
B
F(3,251)=0.008; p=0.00001
CD
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 specified in the
upper part of each diagram. Vertical bars denote SE.
Significant 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 significant 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 significant 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 influence, 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, significant 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, significant 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 significantly higher for the
439male, compared to female, participants of Group
440D. The corrected MRRi index for age was signifi-
441cantly lower in female, compared to male,
442participants, only in the adolescents of Group A.
4443Discussion
444The heart rate factor produced more significant
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 influence increased
453with age, which consisted of increases in the
454LF/HF ratio and in spectral power density
455expressed in normalized units for the
ABCD
11.2
11.0
10.8
10.6
10.4
P_VLF (ms2)
F(3,251)=2.716; p=0.045
ABCD
2.4
2.1
1.8
1.5
1.2
Log10 SDNN (ms)
F(3,251)=9.317; p=0.0001
ABCD
0.8
0.6
0.4
0.2
0.0
−0.2
−0.4
Logn LF/HF (cu)
F(3,251)=6.906; p=0.0002
ABCD ABC D
2.7
2.4
2.1
1.8
1.5
1.2
0.9
Log10 RMSSD (ms)
F(3,251)=20,796; p<0.00001
ABCD
1.5
1.2
0.9
0.6
0.3
0.0
Log10 CVar (%)
F(3,251)=0.066; p=0.00001
828.0
827.8
827.6
827.4
827.0
827.2
826.8
826.6
826.4
MRRi (ms)
F(3,251)=8.431; p<0.0001
ABCD ABC D
Shannon Entropy
8.4
8.0
7.6
7.2
6.8
6.4
6.0
F(3,251)=46.155; p<0.00001
ABCDAB C D
13.8
13.5
13.2
12.9
12.6
P_Tot (ms2)
F(3,251)=6.990; p=0.0002
AB C D
12.2
12.0
11.8
11.6
11.4
11.2
P_LFTot (ms2)
F(3,251)=4,752; p=0.003
∗
AB C D
40.4
40.2
40.0
39.8
39.6
39.4
39.2
F(3,251)=6.646; p=0.0002
nu_ HF (%)
51.8
51.6
51.4
51.2
51.0
50.8
nu_LFTot (%)
F(3,251)=6,906; p=0.0001
12.9
13.2
12.6
12.3
12.0
11.7
P_HF (ms2)
F(3,251)=11.730; p<0.0001
∗
Fig. 3 Comparison of HRV indices showing significant
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. Significant 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 influence, 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 significant 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 significantly 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 first
475study to explore the autonomic cardiovascular
476control in adolescents between the ages of
47713–16 and 17–20 years, compared with young
478adults of 21–24 and 25–30 years of age. Here,
479we assessed the influence 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 17–20 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. Significant
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 influence 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; Lutfiand Sukkar 2011; Reimann
498 et al. 2010; Kuo et al. 1999). In this study, how-
499 ever, we did not find any gender-related
500 differences in the group of older adolescents
501 aged 17–20. Thus, further exploration of the
502 effect of gender on the maturity of cardiovascular
503 control in this age group is required.
504 Our findings for the influence 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 influences 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 influence of age and gender has been more
525 extensively studied than the influence 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 simplified 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 fit 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 confirm the presence of an appre-
584ciable influence of the autonomic nervous system
585on the cardiovascular regulation, the influence
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 influence on heart rate
591 variability. This influence 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 17–20 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 Conflicts of Interest The authors declare no conflicts 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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Better as: A one way ANOVA was used
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