Download Vagal modulation of heart rate during exercise: effects of age and

Vagal modulation of heart rate during exercise:
effects of age and physical fitness
MIKKO P. TULPPO,1,2 TIMO H. MÄKIKALLIO,1,2 TAPIO SEPPÄNEN,1
RAIJA T. LAUKKANEN,2 AND HEIKKI V. HUIKURI1
1Department of Medicine, Division of Cardiology, University of Oulu, 90220 Oulu;
and 2Merikoski Rehabilitation and Research Center, 90100 Oulu, Finland
cardiovascular regulation; Poincaré plot
MEASUREMENT OF HEART RATE (HR) variability has become a widely used tool for assessing the autonomic
input to the heart under various physiological conditions (4–7, 15, 18). Previous research has suggested
that autonomic modulation of HR is influenced by
certain physiological factors such as aging and physical
fitness. Studies of HR variability have demonstrated a
decline in the vagal modulation of HR at rest at an
advanced age (15, 18, 25), but there is little evidence
regarding the extent to which these age-associated
changes depend on the simultaneous decline in aerobic
capacity. Although HR variability has been commonly
observed to be related to physical fitness (5–7, 13),
there is also some controversy regarding this issue (4,
14). In previous studies HR variability has been measured in standardized supine or upright positions or
during routine daily activities, but because the cardiovascular system at rest or in different postures functions at only a fraction of its capacity, results obtained
H424
at rest may not completely characterize this system or
its regulatory mechanisms. Relatively little information is available on the effects of physiological factors on
cardiovascular autonomic function during exercise.
Experimental studies (1, 19, 23) have shown that
vagal control of HR plays an important cardioprotective
role during exercise. Therefore, it would be important
to know whether certain physiological factors have
influence on cardiac vagal function during the course of
physical exercise. The present cross-sectional study
was designed to examine the association of aerobic
fitness and aging on vagal modulation of HR during
exercise in a random population of healthy males.
METHODS
Subjects. The subjects were recruited by placing a notice in
a newspaper, which attracted 210 replies. The subjects were
interviewed with a standardized scheme to ascertain their
medical histories and levels of physical activity. All smokers,
subjects with high body mass index (.27), and those with
diabetes mellitus or cardiovascular disorders were excluded.
We invited 130 subjects to our laboratory for a more specific
assessment of physical status and excluded 13 subjects on
account of various relative contraindications for the maximal
exercise test. We tested 117 subjects and excluded 7 from the
final analysis because of the number of artifacts or ectopic
beats in data acquisition. Finally, 110 male subjects (age 41 6
11 yr) were included in the analysis. The population was
divided into tertiles according to age and aerobic fitness. The
different age tertiles were matched for fitness, and the
different aerobic fitness tertiles were matched for age (Tables
1 and 2). The protocol was approved by the ethics committee
of the Merikoski Rehabilitation and Research Center, and all
subjects gave informed consent.
Protocol. Measurements of HR variability and oxygen
consumption were made on all the subjects at rest and during
incremental exercise on a bicycle ergometer. The subjects
were not allowed to eat or drink coffee for 4 h before the test,
and vigorous exercise and alcohol were forbidden for 48 h
before the day of testing. All the tests were performed
between 8:00 AM and 2:00 PM. The subjects stayed in a
supine position in a quiet room for 30 min, after which R-R
intervals (30 min) and blood pressure were measured in a
supine position. The R-R intervals for the last 2 min of each
load were analyzed during the exercise test. Twelve of the
subjects repeated the test protocol four times (with four days
of recovery in between) to ascertain the reproducibility of the
measurement of HR variability at rest and during exercise.
Measurement of peak O2 consumption. The subjects performed a graded maximal exercise test on a bicycle ergometer
(Ergomedic 818 E, Monark Exercise, Varberg, Sweden), starting at 25 W and following a ramp protocol with the work rate
increasing at a rate of 25 W every 3 min until voluntary
exhaustion. Ventilation (VE), gas exchange (M909 ergospirometer, Medikro, Kuopio, Finland), and HR responses (Polar
0363-6135/98 $5.00 Copyright r 1998 the American Physiological Society
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Tulppo, Mikko P., Timo H. Mäkikallio, Tapio Seppänen, Raija T. Laukkanen, and Heikki V. Huikuri.
Vagal modulation of heart rate during exercise: effects of age
and physical fitness. Am. J. Physiol. 274 (Heart Circ. Physiol.
43): H424–H429, 1998.—This study was designed to assess
the effects of age and physical fitness on vagal modulation of
heart rate (HR) during exercise by analyzing the instantaneous R-R interval variability from Poincaré plots (SD1) at
rest and at different phases of a bicycle exercise test in a
population of healthy males. SD1 normalized for the average
R-R interval (SD1n ), a measure of vagal activity, was compared at rest and during exercise among subjects of ages
24–34 (young, n 5 25), 35–46 (middle-aged, n 5 30), and
47–64 yr (old, n 5 25) matched for peak O2 consumption
(V̇O2 peak) and among subjects with V̇O2 peak of 28–37 (poor, n 5
25), 38–45 (average, n 5 36), and 46–60 ml · kg21 · min21
(good, n 5 25) matched for age. SD1n was higher at rest in the
young subjects than in the middle-aged or old subjects (39 6
14, 27 6 16, and 21 6 8, respectively; P , 0.001), but the
age-related differences in SD1n were smaller during exercise
[e.g., 11 6 5, 9 6 5, and 8 6 4 at the level of 100 W; P 5 not
significant (NS)]. The age-matched subjects with good, average, and poor V̇O2 peak showed no difference in SD1n at rest
(32 6 17, 28 6 13, and 26 6 11, respectively; P 5 NS), but
SD1n differed significantly among the groups from a low to a
moderate exercise intensity level (e.g., 13 6 6, 10 6 5, and 6 6
3 for good, average, and poor fitness groups, respectively; P ,
0.001, 100 W). These data show that poor physical fitness is
associated with an impairment of cardiac vagal function
during exercise, whereas aging itself results in more evident
impairment of vagal function at rest.
HEART RATE VARIABILITY AND FITNESS
Table 1. Characteristics of subjects at different
fitness-matched age tertiles
Young
Old
25
29 6 3
24–34
180 6 6
82 6 7
16 6 4
30
40 6 3a
35–46
178 6 5
81 6 7
18 6 3
25
53 6 4b,c
47–64
177 6 6
79 6 9
15 6 4
123 6 11
80 6 8
3.5 6 0.5
122 6 11
78 6 8
3.3 6 0.4
122 6 8
82 6 7
3.2 6 0.5
42 6 5
41 6 4
41 6 6
32–51
34–50
32–54
191 6 8
182 6 9a
170 6 13b,c
8.0 6 0.9
8.0 6 1.5
7.2 6 1.4
Values are means 6 SD except for ranges. V̇O2peak , peak O2
consumption. a P , 0.001, young vs. middle-aged; b P , 0.001, young
vs. old; and c P , 0.001, middle-aged vs. old, according to analysis of
variance followed by post hoc analysis (unpaired t-tests) between
groups.
a frequency of 1,000 Hz (17) and saved in a computer for
further analysis of HR variability with Heart Signal software
(Kempele, Finland). The R-R interval series was passed
through a filter that eliminates undesirable premature beats
and noise. An R-R interval was interpreted as a premature
beat if it deviated from the previous qualified interval by
.30%. All the R-R intervals were edited automatically, and
then by visual inspection, to exclude all the undesirable
beats, which accounted for ,1% in every subject. The details
of this analysis and the filtering technique have been described previously (9, 10). An autoregressive model was used
to estimate the power spectrum densities of R-R interval
variability (9, 10). The high-frequency (HF; 0.15–0.40 Hz)
component of power spectral analysis (HF power) was calculated as the square root of HF power and then divided by mean
R-R interval [coefficient of component variance (CCV%)] (8).
The Poincaré plot is a diagram (scattergram) in which each
R-R interval of a tachogram is plotted as a function of the
previous R-R interval. The Poincaré plots were analyzed
quantitatively. This quantitative method of analysis is based
on the notion of different temporal effects of changes in the
vagal and sympathetic modulation of the HR on the subsequent R-R intervals without a requirement for a stationary
quality of the data (22). The computerized analysis entails
fitting an ellipse to the plot, with its center coinciding with
Sport Tester, Polar Electro, Kempele, Finland) were monitored continuously during the ramp protocol. VE and gas
exchange were calculated on a breath-by-breath basis but
were reported as mean values for 30 s. A fingertip venous
blood sample was taken 2 min after the exercise to analyze
venous blood lactate (YSI 1500 Sport, YSI, Yellow Springs,
OH). The highest value of oxygen consumption measured
during the test was used as peak oxygen consumption (V̇O2peak)
(16).
Measurement and analysis of HR variability. The R-R
intervals were recorded (Polar R-R Recorder, Polar Electro) at
Table 2. Characteristics of subjects at different
age-matched fitness tertiles
Poor
n
Age, yr
Age range, yr
Height, cm
Weight, kg
Fat, %
Blood pressure,
mmHg
Systolic
Diastolic
V̇O2peak , l/min
V̇O2peak ,
ml · kg21 · min21
V̇O2peak range,
ml · kg21 · min21
Maximal heart rate,
beats/min
Maximal lactate,
mmol/l
Average
Good
25
43 6 10
27–56
178 6 7
83 6 8
18 6 4
36
40 6 9
27–54
179 6 5
81 6 7
17 6 3
25
40 6 9
29–56
178 6 4
78 6 7
14 6 4d,f
126 6 12
84 6 9
2.8 6 0.3
123 6 10
79 6 9a
3.3 6 0.3b
122 6 8
79 6 6c
4.0 6 0.3e,g
34 6 3
41 6 2b
51 6 4e,g
28–37
38–45
46–60
180 6 15
183 6 12
181 6 10
7.7 6 1.6
8.0 6 1.3
7.9 6 1.5
Values are means 6 SD except for ranges.
, 0.05 and b P ,
0.001: poor vs. average; c P , 0.05, d P , 0.01, and e P , 0.001: poor vs.
good; f P , 0.05 and g P , 0.001: average vs. good, according to
analysis of variance followed by post hoc analysis (unpaired t-tests)
between groups.
aP
Fig. 1. Two-dimensional (2-D) vector analyses of Poincaré plots for 2
subjects. A: subject with good aerobic fitness (age 5 35 yr, V̇O2 peak 5
45 ml · kg21 · min21 ) at rest and during exercise (100 W). B: subject
with poor aerobic fitness (age 5 29 yr, V̇O2 peak 5 34 ml · kg21 · min21 )
at rest and during exercise. A: example of 2-D vector analysis of a
Poincaré plot in subject at rest. Standard deviation of instantaneous
beat-to-beat R-R interval variability (SD1) is short diameter of ellipse
(axis 1), and standard deviation of continuous R-R interval variability (SD2) is long diameter (axis 2). SD1 is almost identical in these 2
subjects at rest (left) but is significantly higher in subject with good
aerobic fitness during exercise (right). HR, average heart rate. Each
R-R interval (R-Rn 1 1 ) is plotted as a function of previous R-R interval
(R-Rn ).
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n
Age, yr
Age range, yr
Height, cm
Weight, kg
Fat, %
Blood pressure,
mmHg
Systolic
Diastolic
V̇O2peak , l/min
V̇O2peak ,
ml · kg21 · min21
V̇O2peak range,
ml · kg21 · min21
Maximal heart rate,
beats/min
Maximal lactate,
mmol/l
Middle Aged
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H426
HEART RATE VARIABILITY AND FITNESS
RESULTS
Age and vagal modulation of HR. The mean HR and
mean values for SD1n and HF power in the young,
middle-aged, and old age groups during exercise are
shown in Fig. 2. The individual values for SD1n at rest
and at the level of 100 W are shown in Fig. 3A. HR did
not differ among the three age groups at rest (59 6 8,
58 6 7, and 56 6 8 beats/min for young, middle-aged,
and old subject groups, respectively) but was lower in
the old than in the younger subjects at the submaximal
and maximal exercise levels (Fig. 2A). Two-dimensional vector analysis of the Poincaré plots showed the
mean values for SD1n (Fig. 3A) and SD2n (116 6 29 vs.
80 6 21; P , 0.001) to be significantly higher in the
younger than in the older subjects at rest as well as
during exercise up to an exercise intensity of 75 W (Fig.
2B). The mean value of the HF power (32 6 12 vs. 17 6
7 CCV%; P , 0.001) was significantly higher in the
younger than in the older subjects at rest as well as
during exercise up to an exercise intensity of 75 W (Fig.
2C). SD1 decreased progressively to 1.79 6 0.32, 1.74 6
0.32, and 1.70 6 0.35 l/min in the young, middle-aged,
and old subject groups, respectively, during the graded
exercise (P 5 NS).
Fitness and vagal modulation of HR. The mean HR
and mean values for SD1n and HF power in the good,
Fig. 2. HR (A), 2-D vector analysis of Poincaré plots as indicated by
SD1 normalized for average R-R interval (SD1n; B), and highfrequency (HF) power of spectral analysis (HF power) normalized for
average R-R interval (CCV%; C) in 3 age groups (fitness-matched)
during exercise. Values are means 6 SD. Kruskal-Wallis H-tests
were used at each exercise intensity level (among all 3 groups)
followed by post hoc analysis (Mann-Whitney U-test) between young
group and old group. xx P , 0.01 and xxx P , 0.001 for young group
compared with old group. ns, Not significant.
average, and poor fitness groups during exercise are
shown in Fig. 4. The individual values for SD1n at rest
and at a level of 100 W are shown in Fig. 3B. The mean
HR was significantly lower in the good than in the poor
fitness group at rest (54 6 5 vs. 62 6 7 beats/min; P ,
0.001) and during submaximal exercise but not at the
end of the exercise (Fig. 4A). SD1n or HF power (27 6
14, 23 6 10, and 22 6 11 CCV% for good, average, and
poor fitness groups, respectively) did not differ significantly at rest among the different fitness groups (Fig.
3B). SD1n and HF power during exercise were significantly higher in the good than in the poor fitness group
up to the level of 150 W (Figs. 4B and 4C). SD2n did not
differ among the three fitness groups at rest, but SD2n
during exercise was significantly higher in the good
than in the poor fitness group at the levels of 100 and
125 W (P , 0.01). SD1 decreased progressively to
Downloaded from ajpheart.physiology.org on October 20, 2009
the center point of the markings (Fig. 1A, left). Axis 2 shows
the slope of the longitudinal axis, whereas axis 1 defines the
transverse slope, which is perpendicular to axis 2. In the
computerized analysis, the Poincaré plot is first turned 45°
clockwise, and the standard deviation of the plot data is then
computed around the horizontal axis (axis 2), which passes
through the data center (SD1). SD1 shows the instantaneous
beat-to-beat variability of the data. The standard deviation of
continuous long-term R-R intervals is quantified by turning
the plot 45° counterclockwise (SD2) and by computing the
data points around the horizontal axis (axis 1), which passes
through the center of the data. SD1 and SD2 were calculated
as absolute values and in normalized units (SD1n and SD2n,
respectively), obtained by dividing the absolute value by the
average R-R interval and then multiplying by 1,000.
Statistical methods. Normal Gaussian distribution of the
data was verified by the Kolmogorov-Smirnov goodness-of-fit
test. Whenever the data were not normally distributed (Z
value .1.0), the Kruskal-Wallis H test was used, followed by
post hoc analysis (Mann-Whitney U-test). Otherwise, analysis of variance was used and the differences between the
mean values were tested for significance using the unpaired
t-test. Reproducibility was estimated according to the recommendations of Bland and Altman (2). This method consists of
calculating the means of the repeated measures and the
standard deviation of the differences and plotting the difference between the measurements as a function of the mean
value for each subject. The smaller the confidence interval of
the difference, the better the reproducibility. The Friedman’s
randomized-block analysis of variance was used to compare
the paired data. The exercise intensity level at which the
instantaneous beat-to-beat R-R interval variability (SD1)
reached its minimum during the exercise was defined as the
first exercise intensity level of two consecutive points at
which SD1 decreased by ,1 ms between two exercise intensity levels.
HEART RATE VARIABILITY AND FITNESS
H427
DISCUSSION
There are two new findings in this study. First,
middle-aged subjects with good aerobic fitness have
enhanced cardiac vagal function during exercise compared with subjects with poor physical fitness. Second,
aging itself does not result in significant impairment in
vagal function during exercise despite reduction in
vagal modulation of HR at rest. Together, these findings may provide insight into the cardioprotective role
of physical fitness in middle-aged subjects.
Age and vagal modulation of HR. The present results
confirm previous observations that aging results in a
reduction in respiratory vagal modulation of HR at rest
and show that this reduction seems to be independent
of any impairment of physical fitness. Less profound
age-related differences were observed in instantaneous
HR variability in the course of exercise, showing that
aging itself entails a less marked impairment of cardiac
Downloaded from ajpheart.physiology.org on October 20, 2009
Fig. 3. Individual (s) and mean 6 SD (r) values for SD1n at rest and
during exercise (100 W). A: subjects categorized according to age and
matched for peak O2 consumption (V̇O2 peak). B: subjects categorized
according to aerobic fitness and matched for age. Kruskal-Wallis
H-tests were used, followed by post hoc analysis (Mann-Whitney
U-test) among groups. xx P , 0.01 and xxx P , 0.001 between groups
indicated.
1.90 6 0.28, 1.74 6 0.28, and 1.56 6 0.38 l/min in the
good, average, and poor fitness groups, respectively,
during the graded exercise, and that point was at a
significantly lower O2 consumption level in the poor
than in the good fitness group (P , 0.01). Examples of
Poincaré plots for subjects with good and poor aerobic
fitness are shown in Fig. 1.
Reproducibility of Poincaré plot analysis. The differences among 4 measurements of SD1 in the case of 12
subjects are plotted as a function of each subject’s mean
value at rest and during exercise in Fig. 5. The confidence interval (95%) of the difference for SD1n was 6
5.3 at rest and decreased during exercise, i.e., 63.3 and
62.2 for 50 and 75 W, respectively. The confidence
interval (95%) of the difference for HF power was 6 4.4
at rest and also decreased during exercise, i.e., 6 3.8
and 6 2.2 CCV% for 50 and 75 W, respectively. No
significant differences among the days of measurement
were found in HF power or in the two-dimensional
vector analysis parameters at rest or during exercise.
Fig. 4. HR (A), 2-D vector analysis of Poincaré plots (SD1n; B), and
HF power (C) in 3 fitness groups (age-matched) during exercise.
Values are means 6 SD. Kruskal-Wallis H-tests were used at each
exercise intensity level (among all 3 groups) followed by post hoc
analysis (Mann-Whitney U-test) between good fitness group and poor
fitness group. x P , 0.05, xx P , 0.01, and xxx P , 0.001 for good fitness
group compared with poor fitness group. ns, Not significant.
H428
HEART RATE VARIABILITY AND FITNESS
vagal modulation during physical exercise. The results
also confirm previous observations (22, 24) that vagal
modulation disappears at the level of 50–60% of maximal O2 consumption, whereafter the increase in HR is
mainly mediated by sympathetic activation. The exercise level at which instantaneous R-R interval variability disappeared was not related to age, suggesting that
advanced age may not result in adverse sympathovagal
balance during physical exercise unless concomitant
impairment of aerobic capacity occurs with aging.
Physical fitness and vagal modulation of HR. It is
widely assumed that good physical fitness and regular
exercise training induce adaptation of the autonomic
nervous system, which is most commonly observed in
the form of a decrease in basal HR at rest. This is
thought to be mediated in part by an increase in cardiac
vagal tone. Although it is commonly assumed that
cardiac vagal tone is increased in well-trained individuals, this is a somewhat controversial matter. Studies
(5–7, 13) comparing athletes and age-matched sedentary subjects have mostly observed higher HR variability in trained individuals than in controls, but Lazoglu
The authors appreciate the technical and financial support received from Polar Electro (Kempele, Finland) and the generous help
Downloaded from ajpheart.physiology.org on October 20, 2009
Fig. 5. Differences among 4 measurements performed on each of 12
subjects as a function of their mean values for SD1 at rest (A), during
exercise at an intensity level of 50 W (B), and during exercise at an
intensity level of 75 W (C). Horizontal lines indicate mean (solid
lines) and 95% confidence intervals (dashed lines) of differences
among 4 measurements.
et al. (14) did not find significant differences in 24-h HR
variability between cyclists and sedentary controls.
Controversial results may be due to differences in the
baseline characteristics among the study populations
and in the methodology of analysis of HR variability.
Previous studies may be partly influenced also by
differences in life-style habits other than physical fitness itself between well-trained athletes and sedentary
control subjects, because a population-based crosssectional study (4) did not observe any significant
relationship between maximal O2 consumption and HR
variability measured in subjects at supine, seated, or
standing postures. The present results in a random
population of healthy males also demonstrate that
there are no significant differences in HR variability at
rest between the subjects with poor and good physical
fitness despite significant differences in basal HR.
However, subjects with better exercise capacity have
significantly higher vagal modulation of HR during
exercise than those with poor physical fitness, and the
exercise intensity level at which their vagal modulation
disappears is also significantly higher, supporting the
concept that cardiac vagal function is related to aerobic
fitness in middle-aged subjects.
Reproducibility of Poincaré plot analysis. HR variability generally has rather poor reproducibility when
measured from short-term electrocardiogram recordings at rest (3), because it can potentially be influenced
by factors such as the time of day, food intake, mental
state, etc., when measurements are repeated on successive days. Exercise causes a marked alteration in
autonomic function, with a gradual vagal withdrawal
followed by sympathetic activation. The repeated tests
performed in the present study showed that the measurement of vagal modulation of HR is more reproducible during exercise than at rest, possibly because the
profound effects of the exercise itself on autonomic
function minimize the influence of other confounding
factors.
In conclusion, the main finding of this study, i.e., that
physical fitness is related to vagal modulation of HR
during exercise independent of aging, provides further
evidence that good aerobic fitness has beneficial effects
on cardiovascular autonomic function. Experimental
data have shown that vagal activity prevents ventricular fibrillation during exercise (1, 19, 23) and that
exercise training confers anticipatory protection from
sudden death by enhancing cardiovascular autonomic
function (12). There is also experimental and clinical
evidence that augmented sympathetic outflow is related to arrhythmogenesis and cardiovascular morbidity and mortality (20, 21). Rapid loss of vagal activity
followed by sympathetic activation during mild-tomoderate exercise intensity may be one potential mechanism for adverse clinical events, and the present data
support the concept that good aerobic fitness may exert
cardioprotective effects by enhancing the cardiac vagal
function during exercise.
HEART RATE VARIABILITY AND FITNESS
from Heart Signal (Kempele, Finland). The authors also thank Timo
Karppinen for help with the data processing.
This research was partly funded by grants from the Finnish
Foundation for Cardiovascular Research (Helsinki, Finland) and the
Medical Council of the Academy of Finland (Helsinki, Finland).
Address for reprint requests: H. V. Huikuri, Dept. of Medicine, Div.
of Cardiology, Univ. of Oulu, Kajaanintie 50, 90220 Oulu, Finland.
Received 18 February 1997; accepted in final form 24 September
1997.
REFERENCES
12. Hull, S. J., Jr., E. Vanoli, P. B. Adamsson, R. L. Verrier, R. D.
Foreman, and P. J. Schwartz. Exercise training confers
anticipatory protection from sudden death during acute myocardial ischemia. Circulation 89: 548–552, 1994.
13. Katona, P., M. McLean, D. Dighton, and A. Guz. Sympathetic
and parasympathetic cardiac control in athletes and nonathletes
at rest. J. Appl. Physiol. 82: 1652–1657, 1982.
14. Lazoglu, A. H., B. Glace, G. W. Gleim, and N. L. Coplan.
Exercise and heart rate variability. Am. Heart J. 131: 825–827,
1996.
15. Lipsitz, L. A., J. Mietus, G. B. Moody, and A. L. Goldberger.
Spectral characteristics of heart rate variability before and
during postural tilt: relations to aging and risk of syncope.
Circulation 81: 1803–1810, 1990.
16. McArdle, W. D., F. I. Katch, and V. L. Katch. Exercise
Physiology: Energy, Nutrition, and Human Performance (3rd
ed.). Philadelphia, PA: Lea and Febiger, 1991, p. 211–213.
17. Ruha, A., S. Sallinen, and S. Nissilä. A real-time microprocessor QRS detector system with a 1 ms timing accuracy for the
measurement of ambulatory HRV. IEEE Trans. Biomed. Eng. 44:
159–167, 1997.
18. Shannon, D. C., D. W. Carley, and H. Benson. Aging of
modulation of heart rate. Am. J. Physiol. 253 (Heart Circ.
Physiol. 22): H874–H877, 1987.
19. Schwartz, P. J., G. E. Billman, and H. L. Stone. Autonomic
mechanisms in ventricular fibrillation induced by myocardial
ischemia during exercise in dogs with healed myocardial infarction. An experimental preparation for sudden cardiac death.
Circulation 69: 780–785, 1984.
20. Schwartz, P. J., and S. G. Priori. Sympathetic nervous system
and cardiac arrhythmias. In: Cardiac Electrophysiology: From
Cell to Bedside, edited by D. P. Zipes and J. Jalife. Philadelphia,
PA: Saunders, 1990, p. 330–343.
21. Tsuji, H., M. G. Larson, F. J. Venditti, Jr., E. S. Manders,
J. C. Evans, C. L. Feldman, and D. Levy. Impact of reduced
heart rate variability on risk for cardiac events: the Framingham
heart study. Circulation 94: 2850–2855, 1996.
22. Tulppo, M. P., T. H. Mäkikallio, T. E. S. Takala, T. Seppänen,
and H. V. Huikuri. Quantitative beat-to-beat analysis of heart
rate dynamics during exercise. Am. J. Physiol. 271 (Heart Circ.
Physiol. 40): H244–H252, 1996.
23. Vanoli, E., G. M. De Ferrari, M. Stramba-Badiale, S. S. Hull,
R. D. Foreman, and P. J. Schwartz. Vagal stimulation and
prevention of sudden death in conscious dogs with a healed
myocardial infarction. Circ. Res. 68: 1471–1481, 1991.
24. Yamamoto, Y., R. L. Hughson, and J. C. Peterson. Autonomic
control of heart rate during exercise studied by heart rate
variability spectral analysis. J. Appl. Physiol. 71: 1136–1142,
1991.
25. Yoshimitsu, Y., M. Kodama, M. Matsuhisa, M. Kishimoto, H.
Ozaki, A. Tani, N. Ueda, Y. Ishida, and T. Kamada. Diurnal
heart rate variability in healthy subjects: effects of aging and sex
difference. Am. J. Physiol. 271 (Heart Circ. Physiol. 40): H303–
H310, 1996.
Downloaded from ajpheart.physiology.org on October 20, 2009
1. Billman, G. E., and R. S. Hoskins. Time-series analysis of
heart rate variability during submaximal exercise. Evidence for
reduced cardiac vagal tone in animals susceptible to ventricular
fibrillation. Circulation 80: 146–157, 1989.
2. Bland, J. M., and D. G. Altman. Statistical methods for
assessing agreement between two methods of clinical measurement. Lancet 1: 307–310, 1986.
3. Breuer, H.-W. M., A. Skyschally, M. Wehr, R. Schulz, and G.
Heusch. Poor reproducibility of parameters of heart rate variations. Z. Kardiol. 81: 475–481, 1992.
4. Byrne, E. A., J. L. Fleg, P. V. Vaitkevicius, J. Wright, and
S. W. Porges. Role of aerobic capacity and body mass index in
the age-associated decline in heart rate variability. J. Appl.
Physiol. 81: 743–750, 1996.
5. Davy, K. P., N. L. Miniclier, J. A. Taylor, E. T. Stevenson,
and D. R. Seals. Elevated heart rate variability in physically
active postmenopausal women: a cardioprotective effect? Am. J.
Physiol. 271 (Heart Circ. Physiol. 40): H455–H460, 1996.
6. De Meersman, R. E. Heart rate variability and aerobic fitness.
Am. Heart J. 125: 726–731, 1993.
7. Goldsmith, R. I., J. T. Bigger, R. C. Steinman, and J. L.
Fleiss. Comparison of 24-hour parasympathetic activity in endurance-trained and untrained young men. J. Am. Coll. Cardiol. 20:
552–558, 1992.
8. Hayano, J., Y. Sakakibara, A. Yamada, M. Yamada, S.
Mukai, T. Fujinami, K. Yokoyama, Y. Watanabe, and K.
Takata. Accuracy of assessment of cardiac vagal tone by heart
rate variability in normal subjects. Am. J. Cardiol. 67: 199–204,
1991.
9. Huikuri, H. V., M. K. Linnaluoto, T. Seppänen, K. E. J.
Airaksinen, K. M. Kessler, J. T. Takkunen, and R. J.
Myerburg. Circadian rhythm of heart rate variability in survivors of cardiac arrest. Am. J. Cardiol. 70: 610–615, 1992.
10. Huikuri, H. V., T. Seppänen, M. J. Koistinen, K. E. J.
Airaksinen, M. J. Ikäheimo, A. Castellanos, and R. J.
Myerburg. Abnormalities in beat-to-beat dynamics of heart rate
before the spontaneous onset of life-threatening ventricular
tachyarrhythmias in patients with prior myocardial infarction.
Circulation 93: 1836–1844, 1996.
11. Huikuri, H. V., J. O. Valkama, K. E. J. Airaksinen, T.
Seppänen, K. M. Kessler, J. T. Takkunen, and R. J. Myerburg. Frequency domain measures of heart rate variability
before the onset of nonsustained and sustained ventricular
tachycardia in patients with coronary artery disease. Circulation
87: 1220–1228, 1993.
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