Download Does the level of chronic physical activity alter heart rate variability

Clinical Science (2004) 107, 29–35 (Printed in Great Britain)
Does the level of chronic physical activity alter
heart rate variability in healthy older women?
Sylvia RELAND∗ , Nathalie S. VILLE†‡, Sara WONG§, Lotfi SENHADJϧ
and François CARRÉ∗ ‡
∗
Groupe de Recherche Cardiovasculaire, Université de Rennes 1, 35042 Rennes cedex, France, †Laboratoire de Physiologie et
Biomécanique de l’Exercice Musculaire, UFR STAPS Université de Rennes 2, 35044 Rennes, France, ‡Groupement d’Intérêt
Scientifique Sciences du Mouvement, Université de Rennes 2, 35044 Rennes, France, and §Laboratoire Traitement du Signal
et de l’Image, INSERM Université de Rennes 1, 35042 Rennes cedex, France
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In the present study, we investigated the effects of three levels of chronic physical activity on
HRV (heart rate variability) in healthy older women. ECG recordings were taken in three (low-,
moderate- and high-) activity groups in supine position with free and with controlled breathing, and
during orthostatic stress. Temporal and spectral HRV indices were obtained from the ECG signal
processing. The main results showed that, in supine position with free and controlled breathing,
the HF (high-frequency) spectral component (P < 0.01) and the rMSSD (square root of the mean
squared differences) between two adjacent RR intervals (P < 0.05 and P < 0.01 respectively) were
elevated in the high-activity group compared with the low-activity group. No significant difference
was observed between groups during the orthostatic test. Within groups, in the supine position,
the change from free to controlled breathing produced a decrease in the LF (low-frequency)
spectral component in all three groups (P < 0.01). The change from supine to standing position
produced a decrease in RR in all three groups (P < 0.05 in low- and moderate-activity groups,
and P < 0.01 in high-activity group); the rMSSD and the HF spectral component decreased only in
the high-activity group (P < 0.01). In conclusion, this study performed on older women showed
that parasympathetic indices of resting HRV were significantly elevated in a high physical activity
group compared with in a low physical activity group. Furthermore, parasympathetic indices of
HRV decreased during an orthostatic test only in the high-activity group. The influence of chronic
moderate physical activity on HRV in older women was small in the present study.
INTRODUCTION
HR (heart rate) is mainly controlled by autonomic nerve
activity to the sinoatrial node. Sympathetic and parasympathetic drive can be non-invasively investigated
using HRV (HR variability) analysis [1]. A low level of
HRV associated with low vagal parasympathetic activity
has been identified as a risk marker for all causes of
mortality [2].
HRV can be altered by physiological factors, such
as aging, gender and physical fitness. The aging process
decreases HRV towards a lower parasympathetic modulation [3,4]. Concerning gender, parasympathetic
modulation of HRV seems to be generally higher in
women than in men [5,6]; however, aging tends to
attenuate this difference [6], the change apparently
beginning at the menopause [7]. It is widely accepted that
a high cardiorespiratory fitness is associated with an
Key words: aerobic fitness level, aging, autonomic nervous system, female, orthostatic stress, parasympathetic modulation.
Abbreviations: HF, high-frequency; HR, heart rate; HRmax, maximum HR; HRV, HR variability; LF, low-frequency; MET,
metabolic equivalent task; rMSSD, square root of the mean squared differences; SDRR, S.D. of all RR intervals; TVAR, time-varying
auto-regressive; TP, total power; V̇o2 max, maximal oxygen uptake; Wmax, maximal exercise power.
Correspondence: Dr Sylvia Reland, present address: Unité de Biologie et Médecine du sport, Centre Hospitalier Universitaire
Pontchaillou, 35000 Rennes, France (e-mail sylvia [email protected]).
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elevated level of HRV and this has been shown in
endurance-trained young and older men [8–12]. Indeed, a
moderate level of physical activity is recommended in the
older population [13], which is known to progressively
decrease its level of physical activity [14]. In older women,
few studies have concerned the effects of physical activity
on HRV [15–17]. No alteration [17] or an increase [15,16]
of HRV with a high level of physical activity has been
reported. Thus these data highlight that, in older women,
the effects of physical training on HRV are still a matter
of debate and need to be clarified.
The cardiovascular adaptations induced by physical
training are known to be more detectable during physical
stress, such as orthostasis [18]; however, the relationship
between HRV and physical training has been studied
more in the older population at rest in the supine position
with free or controlled breathing [11,19–21]. In addition,
older people are known to have a restricted range of
sympathetic and parasympathetic responses to orthostasis [3,4], which may have important clinical implications given the high risk of falls observed in elderly
subjects during the postural change to a standing position
[22]. Thus it is of particular interest to establish the
effects of training on HRV during a postural change, as
recommended by some authors [23].
Thus the aim of the present study was to assess the
effects of three levels of chronic physical activity on HRV
in healthy older women at rest in the supine position
with both free or controlled breathing and during an
orthostatic test. We hypothesize that, in older women,
the level of physical training performed influences
HRV at rest and the change in HRV induced by the
physiological stress of standing, and that the parasympathetic modulation of HRV is particularly affected.
MATERIALS AND METHODS
Subjects
Forty-five healthy postmenopausal women (aged 60–
70 years) participated in the present investigation. The
nature, purpose and risks of the study were explained to
each subject and written informed consent was obtained.
This study was approved by the Ethics Committee of
the Faculty of Medicine of the Rennes University. The
participants were all non-smokers, normotensive, free
of cardiovascular disease by history and clinical examination, which included resting blood pressure measurement and ECG. None was taking cardioactive
medication. Self-reported usual physical activity levels
were assessed using a physical activity questionnaire [24].
A MET (metabolic equivalent task) value was assigned to
each activity (1 MET = 4.185 kJ · kg−1 · h−1 ). The number
of hours spent at each activity/week was multiplied by
the appropriate MET value and the subject’s body mass
to obtain a value of energy expenditure (kJ · week−1 ).
The sum of the activity values was an estimation of
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weekly exercise energy expenditure. Three groups were
then identified. Subjects who were not engaged in any
regular physical activity and reported less than 4185 kJ ·
week−1 (1000 kcal · week−1 ) constituted the low-activity
group (n = 15). This limit corresponded to the recommendations for minimal activity required to provide
substantial health benefits [25]. Subjects who reported
more than 8370 kJ · week−1 (2000 kcal · week−1 ) constitute the high-activity group. This corresponds to a
physical activity level above which there is a reduced
age-adjusted relative risk of myocardial infarction [26].
Subjects came from a cycle touring club and had cycled
for more than 4 years regularly in excess of 80 km/
week (n = 15). Subjects who reported physical activity
levels between 4185–8370 kJ · week−1 (1000–2000 kcal ·
week−1 ) constituted the moderate-activity group. This
group had regularly practiced voluntary gymnastics for
more than 4 years (n = 15). Aerobic fitness was assessed
from a V̇o2 max (maximal oxygen uptake) measurement,
as V̇o2 max represents the usual index of maximal cardiovascular function. V̇o2 max in the low-activity group
was lower than predicted [27]. In the moderateactivity group, V̇o2 max was between 100–120 % of that
predicted, whereas in the high-activity group it was
> 130 % of predicted uptake. Body fat percentage
was measured using the skin fold thickness method
[28]. The physical characteristics of the three groups are
described in Table 1.
Experimental procedure
All subjects performed a graded maximal exercise
test to measure V̇o2 max (ml · min−1 · kg−1 ), HRmax
(maximum HR; beats/min), Wmax (maximal exercise
power, measured in Watts) and to reveal medical exclusion
criteria for the study. The graded exercise test was
performed on an ergocycle (ERG 900, Marquette Hellige;
Milwaukee, WI, U.S.A.) with continuous recording 12lead ECG (Cardio System, Marquette Hellige) and
breath-by-breath gas exchanges analysis (Oxycon Delta;
Jaeger, Hoechberg, Germany).
The subjects sat quietly on the ergocycle for 2 min,
connected to the gas analyser. Following a 3 min warming-up period at 20 Watts, the work rate was increased by
10 Watts every minute until exhaustion. The mean V̇o2
and HR measured during the last 30 s of each stage were
taken into account. The exercise test was stopped when at
least three classic criteria of V̇o2 max were attained [29].
ECG recordings for HRV measurement were always
performed 1 week after the maximal exercise test. The
subjects performed two trials, one familiarization and
one experimental, at an interval of 1 week. Subjects were
instructed to refrain from any excessive physical activity
and from ingesting beverages containing caffeine or alcohol for at least 24 h before testing. All trials were held in
the morning, 3 h after a light meal, at the same time of the
day and in the same room. In all cases, room temperature
Fitness level and heart rate variability
Table 1 Physical characteristics of low-, moderate- and high-activity groups
∗∗
∗∗∗
Values are means +
− S.E.M. P < 0.01 and P < 0.001 compared with the high-activity group; ††P < 0.01 and †††P < 0.001 compared with the
low-activity group; ‡P < 0.05 and ‡‡P < 0.01 compared with the moderate-activity group.
Age (years)
Weight (kg)
Height (cm)
Body mass index (kg · m−2 )
Body fat (%)
Energy expenditure (kJ · week−1 )
V˙o2 max (ml · min−1 · kg−1 )
Percentage predicted V˙o2 max
HRmax (beats/min)
W max (Watts)
Low activity (n = 14)
Moderate activity (n = 13)
High activity (n = 14)
67.2 +
− 1.1
57.5 +
− 1.8
159.5 +
− 1.2
22.5 +
− 0.5
32.6 +
− 0.5
2963 +
− 400 (1464−4018)‡
19.3 +
− 0.7‡
92.8 +
− 2.3‡‡
151.6 +
− 2.7
77.8 +
− 3.7‡‡
65.4 +
− 3.0
57.7 +
− 2.2
157.4 +
− 1.6
23.2 +
− 0.7
32.9 +
− 0.7
∗∗∗
6260 +
− 381 (5800−8110)
∗∗∗
+
22.4 − 0.6
∗∗∗
112.4 +
− 1.8
+
149.1 − 3.4∗∗
∗∗∗
95.4 +
− 2.9
64.6 +
− 1.0
59.8 +
− 1.6
159.8 +
− 1.0
23.4 +
− 0.7
33.0 +
− 1.6
14 417 +
− 1714 (10 923−21 970)†††
30.1 +
− 1.1†††
146.5 +
− 4.3†††
165.2 +
− 3.3††
137.1 +
− 4.2†††
was between 20 and 22 ◦ C, room lighting and the number
of technicians present were kept constant. Noise level
was minimized during the trial. At the beginning
of each trial, subjects rested for 10 min in a supine
position. The subjects were not informed that ECG recording had begun. A three-lead ECG was recorded
under three different conditions defined as usual clinical tests to study HRV [20,30]: free breathing in the
supine position (Test 1), controlled frequency breathing (20 breaths/min) in the supine position (Test 2)
and orthostatic stress (Test 3). During a pilot study, we
tested various controlled breathing protocols at 12, 15
and 20 breaths/min. Since this latter rate seemed the most
practical to maintain in our subjects, it was retained for
this present study. To achieve 20 breaths/min, subjects
inspired (1.5 s) and expired (1.5 s) in synchronization
with an audible signal. No attempt was made to influence
tidal volume. After controlled breathing, the subjects
remained in an undisturbed supine resting position for
10 min. During Test 3, subjects were instructed to stand
up abruptly, as quickly as possible, and to remain standing
unsupported for 6 min. The ECG recording started from
the beginning of the postural change. In each test, ECG
and breathing frequency were recorded over a 6 min
period.
ECG data analysis
The ECG was sampled at 1000 Hz with the PowerLab®
acquisition system (ADInstruments Pty Ltd, Castle Hill,
Australia) installed on a Macintosh computer (Power
Mac). Thus the accuracy of the measurements was 1 ms.
The first minute of each ECG recording was disregarded
to allow for stabilization of the data prior to analysis.
The detection of the QRS complex was conducted
using the Gritzali’s algorithm [31]. RR interval sequence
was defined by the duration between two consecutive
R-peaks. These data were edited to eliminate any glitches,
due to premature cardiac contraction, using the procedure
reported by Bruggeman and Andersen [32]. Each RR
interval was visually validated by two experts before
temporal and spectral analysis. For each RR sequence,
three classical temporal parameters were then extracted
[33]: the mean RR, which represents mean HR; S.D. of
all RR intervals (SDRR), which reflects all the cyclic
components responsible for variability in the period of
recording, and rMSSD (square root of the mean squared
differences) between adjacent RR intervals, which is
considered as an index of parasympathetic modulation of
HR. Prior to power spectrum density estimation, the RR
sequence, which is intrinsically non-evenly spaced data,
was linearly interpolated in order to obtain a series of
uniformly sampled data. An interpretation of frequency
contents of HRV was therefore possible independently of
the mean RR value. The retained sampling rate was then
set to 2 Hz. Using a sliding window of 64 s duration,
time-varying auto-regressive (TVAR) modelling of the
interpolated RR sequence was performed to estimate
its power spectrum (ms2 ) in order to eliminate the
slight non-stationarities of the sequence. On the basis
of the well-known Akaike information criteria, the
order of the TVAR model was set to 12 [34]. The frequency component of HRV were therefore the LF (lowfrequency) component, which was defined between
0.04–0.15 Hz, and the HF (high-frequency) component,
which was defined as a narrow frequency band (0.06 Hz)
centred around the breathing frequency of each individual
[33,35]. TP (total power) was defined as being the area
under the curve of the whole power spectrum from 0.04–
1 Hz. Because of the short recording duration, the very
LF component (0.00–0.04 Hz) was not taken into account
as recommended [33]. The spectral power of the RR series
in these frequency bands was then calculated and averaged
over the last 5 min of each recording [36].
The HF component of HRV corresponds to respiratory sinus arrhythmia and represents vagally mediated
modulations in HR. The LF component is influenced
by both sympathetic and parasympathetic activity.
LF/HF is described as a classical marker of sympathovagal balance [33].
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Table 2 HRV indices in the low-, moderate- and high-activity groups during the three tests
Values are means +
− S.E.M. Test 1, supine position with free breathing; Test 2, supine position with controlled breathing frequency (20 breaths/min); and Test 3,
orthostatic stress. ∗ P 0.05 and ∗∗ P < 0.01 compared with Test 2; †P < 0.05 and ††P < 0.01 compared with Test 1; ‡P < 0.05 and ‡‡P < 0.01 compared
with high-activity group; §P < 0.05 compared with high-activity group.
Groups
Test
RR (ms)
SDRR (ms)
rMSSD (ms)
HF (ms2 )
LF (ms2 )
TP (ms2 )
LF/HF
Low
1
2
3
1
2
3
1
2
3
847 +
− 29
829 +
− 29
805 +
− 24†
903 +
− 37
892 +
− 33
871 +
− 33†
924 +
− 25
910 +
− 97
845 +
− 38††
25.0 +
− 2.6‡
23.7 +
− 2.4
24.0 +
− 1.8
25.5 +
− 2.0§
24.6 +
− 2.0
26.3 +
− 2.6
∗∗
34.1 +
− 3.3
+
25.0 − 2.0
29.0 +
− 3.1†
15.2 +
− 1.9‡‡
15.5 +
− 1.8‡
14.3 +
− 1.7
19.0 +
− 2.7
17.8 +
− 2.6
15.9 +
− 1.8
24.4 +
− 2.9
24.0 +
− 3.1
17.5 +
− 3.5††
25.9 +
− 6.4‡‡
32.7 +
− 7.6‡‡
26.6 +
− 10.4
54.4 +
− 13.0
52.2 +
− 12.8
39.6 +
− 10.4
127.7 +
− 50.0
96.8 +
− 24.8
49.7 +
− 18.2††
∗∗
238.8 +
− 72.0
126.63 +
− 38.3
141.8 +
− 21.1
∗∗
175.6 +
− 35.7
111.1 +
− 23.0
201.3 +
− 59.9
∗∗
296.7 +
− 55.2
+
129.1 − 20.2
226.1 +
− 58.9
∗
270.3 +
− 38.7 ‡‡
193.8 +
− 36.1‡‡
253.7 +
− 88.8
∗
345.9 +
− 122.1
228.2 +
− 47.2
299.4 +
− 69.4
∗
558.2 +
− 109.1
+
342.3 − 79.2
380.1 +
− 117.4†
10.9 +
− 2.3
6.0 +
− 1.6
12.4 +
− 2.7
7.7 +
− 2.6
4.6 +
− 1.6
13.2 +
− 6.2
∗
5.5 +
− 1.6
+
2.4 − 0.6
25.0 +
− 11.0†
Moderate
High
Statistical analysis
Data are reported as means +
− S.E.M. A one-way
ANOVA was performed to compare the physical
characteristics between the three groups. HRV indices
obtained during the experimental trial were tested for
normality. As the data were not normally distributed,
the Kruskall–Wallis test, followed by the Mann–Whitney
test, were performed to assess the difference between
groups. The difference within groups was tested using
a Wilcoxon test. The three tests for the trial were the
following: free breathing in supine position (Test 1),
controlled breathing in supine position (Test 2) and
orthostatic stress (Test 3). Because of the lack of controlled breathing during Test 3, Test 2 with Test 3 were
not compared. The relationship between V̇o2 max and
parasympathetic indices of HRV, rMSSD of the RR
interval and the HF component of HRV were assessed
in the pooled population using Spearman’s correlation
coefficient. A P value < 0.05 was considered as significant. Statistical analyses were performed using Statistica
software version 5. 97 (StatSoft Inc, Maisons-Alfort,
France).
RESULTS
Two subjects did not complete the study (after the
maximal exercise test) for personal reasons and two
subjects were excluded because of technical problems.
Thus 41 subjects finally completed the study (lowactivity group, n = 14; moderate-activity group, n = 13;
high-activity group, n = 14). A few subjects (three in
the low-, four in the moderate- and four in the highactivity groups) were receiving hormone-replacement
therapy, whose effect on HRV is controversial [37,38]. All
populations would therefore have been affected similarly
by potential influences of hormone therapy. Moreover,
individual data of the subjects receiving this therapy were
dispersed within each group.
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No significant difference was observed between the
three groups with regard to age, height, weight, body mass
index or body fat percentage. As expected, the score for
physical activity questionnaire, V̇o2 max, the percentage
of V̇o2 max predicted and Wmax were the highest in the
high-activity group (P < 0.001), and were higher in
the moderate- than the low- activity groups (P < 0.05
for physical activity questionnaire and Wmax, and
P < 0.01 for V̇o2 max and the percentage of V̇o2 max
predicted). HRmax was significantly elevated in the highactivity group compared with both other groups (P <
0.01).
Concerning HRV indices, significant differences were
observed between and within groups (Table 2). Betweengroups comparisons showed that, during Test 1, the
high-activity group had the highest SDRR (P < 0.05).
During Test 1 and Test 2, rMSSD (P < 0.01 for Test 1,
and P < 0.05 for Test 2), the HF component (P <
0.01) and TP (P < 0.01) were elevated in the high-activity
group compared with the low-activity group. No other
significant differences were observed.
Within groups, from Test 1 to Test 2, the mean
frequency peak of the HF component shifted to
the right (0.34 +
− 0.005 Hz), without any significant
change in its spectral power. LF component (P < 0.01)
and TP (P < 0.05) decreased in all three groups. The
LF/HF ratio decreased in the high-activity group
(P < 0.05) and tended to be lower in the low- and
moderate- activity groups (P = 0.05 and P = 0.09 respectively). Moreover SDRR decreased only in the highly
active subjects (P < 0.01).
From Test 1 to Test 3, RR decreased in the
low- (P < 0.05), moderate- (P < 0.05) and high-activity
groups (P < 0.01). However, SDRR (P < 0.05), rMSSD
(P < 0.01), HF component (P < 0.01), TP (P < 0.05) and
LF/HF ratio (P < 0.05) decreased only in the highactivity group. Figures 1 and 2 illustrate the tachogram
of RR values and HRV spectra respectively, in a highly
active subject during the three Tests.
Fitness level and heart rate variability
Figure 1 Interval tachogram of 100 RR values in a highly
active subject in Test 1 (top), Test 2 (middle) and Test 3
(bottom)
Figure 3 Relationship between rMSSD between adjacent RR
intervals and V̇o2 max in all subjects in the supine position
with controlled breathing
n = 41. Correlation, r = 0.40, P < 0.01.
Figure 2 HRV spectra in a highly active subject in Test 1
(top), Test 2 (middle) and Test 3 (bottom)
The highly active subject was the same as in Figure 1.
Positive relationships were observed in all the
subjects pooled between V̇o2 max and rMSSD in Test 1
(r = 0.45, P < 0.01) and Test 2 (r = 0.40, P < 0.01;
Figure 3) and between V̇o2 max and HF in Test 1 (r =
0.42, P < 0.01) and Test 2 (r = 0.45, P < 0.01, Figure 4).
Figure 4 Relationship between the HF component and
V̇o2 max in all subjects in the supine position with controlled
breathing
DISCUSSION
n = 41. Correlation, r = 0.45, P < 0.01.
The major findings of the present study performed in
older women are that (i) a high level of chronic physical
activity increases parasympathetic indices of HRV in the
supine position with free and controlled breathing, and
(ii) orthostatic stress induces a significant decrease in these
indices only in the highly trained women.
HRV analysis is used to non-invasively assess the
influences of autonomic activity on the sino-atrial node.
In the present study, HRV values obtained in the supine
position with free and controlled breathing in the lowactivity group are in agreement with previous data
obtained in a similar population [17]. They are lower than
those reported in young women of low physical activity
[16]. These results are consistent with evidence that HRV
decreases with age, as reported previously [3,4]. Despite
the loss of HRV with age, when the older women in the
present study were considered together, two indices of
parasympathetic modulation of HRV showed a strong
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correlation with V̇o2 max, as expected in a younger age
group [39].
SDRR, rMSSD and HF at rest in the supine position
with free breathing were increased in the high- relative
to the low-activity group, and SDRR was increased in
the high- relative to the moderate-activity group. These
findings apparently contrast with reports in the literature
concerning the effects of physical training on cardiac
autonomic modulation in older people. The duration and
the intensity of physical training may be implicated in this
discrepancy. Thus Perini et al. [15] showed that a short
period (8 weeks) of intense aerobic training increased
V̇o2 max in older people without any change in resting
HRV [17]. Furthermore, a longer period (12 months)
of low-intensity endurance training did not alter resting
HRV in middle-aged men [21]. However, in competitive long-distance postmenopausal runners of national standard, who had been exercising for more than
10 years, resting HRV was increased [15,16]. Our highly
active subjects regularly participated in relatively long
and intense aerobic exercise. Thus, considering these data
together, it seems reasonable to propose that a long period
of high-intensity physical training is needed to induce
significant changes in parasympathetic indices of HRV in
an older population when tested in the supine position
with free breathing.
As recommended, we also performed HRV analysis
when breathing frequency was controlled [33,40]. The
HF component of HRV represents respiratory sinus
arrhythmia and primarily reflects the response of the
sinus node to parasympathetic activity [40]. Brown et al.
[41] observed a significant decrease in the HF component
in young subjects at rest with controlled breathing at
20 cycles · min−1 compared with free breathing. When
breathing was controlled, we have shown that, in the
high-activity group, there was a decrease in SDRR, an
index of HRV that includes that due to parasympathetic
modulation. Moreover, there was an apparent trend for
the HF component to decrease in this high-activity group,
but not in the others. In view of the data of Brown et al.
[41], the lack of change observed in HRV in response to
controlled breathing in the low- and moderate-activity
groups is consistent with a decrease in parasympathetic
modulation of HRV with aging.
Elderly populations often present orthostatic intolerance with a high risk of falls [22]. Postural changes
greatly modify cardiovascular homoeostasis and induce
autonomic reflexes that help to maintain arterial pressure. It is therefore recommended that cardiovascular adaptations to orthostatic stress are tested when examining
elderly subjects medically [22]. In young male subjects,
orthostatic stress increases HR and induces a withdrawal
of the parasympathetic influence associated with an
increase in the sympathetic influence [42]. However, it
has been reported [18,43] that the chronotropic and HRV
alterations to orthostasis are decreased by the aging
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process. Indeed, in sedentary older men, it was found
that the increase in the LF component is blunted or
absent and the HF component remains unchanged during
orthostatic stress [42]. In our present study, HR increased
significantly in all the groups studied. In the high-activity
group, rMSSD and the HF component were decreased
significantly without any change in the LF component.
By contrast, no significant alteration in rMSSD, or in the
HF or the LF components of HRV were observed in
the low- and moderate-activity groups. Thus, in highly
active older women, the increase in HR during orthostatic stress can be explained by a decrease in the parasympathetic modulation of HR; sympathetic modulation
was apparently not altered. Thus it seems that the higher
activity group have a high parasympathetic tone and can,
therefore, show a larger increase in HR by withdrawing
this parasympathetic tone; this may prevent orthostatic
intolerance in this section of the elderly population.
Conclusion
Our present study suggests that a chronic high level of
physical activity partly counteracts some of the deleterious effects of aging on parasympathetic regulation of
HR in healthy women. Resting parasympathetic indices
of HRV were significantly higher in a high-physical
activity group than in a low-physical activity group.
Furthermore, during orthostasis, parasympathetic indices
of HRV were reduced only in the high-activity group.
These results emphasize the importance of performing
an orthostatic test in studies on the changes in HRV
associated with physical training.
ACKNOWLEDGMENTS
We gratefully thank the medical and technical staff of
the Centre Cardio-Pneumologique and of the Service de
Biologie et de Médecine du Sport, Rennes, the volunteers
for their generous co-operation with our project, J. Y.
Bansard for his statistical assistance, and D. James for
editorial assistance prior to submission.
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Received 8 December 2003/11 February 2004; accepted 23 February 2004
Published as Immediate Publication 23 February 2004, DOI 10.1042/CS20030405
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2004 The Biochemical Society
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