Changes of heart rate variability during exercise

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
Abstract— We use recent developments in heart rate
dynamic estimation to detrend athlete’s heart rate measured
during effort tests and study how heart rate variability (HRV)
changes during exercise, in a sample of 18 young athletes. RR
interval standard deviation decays exponentially with the heart
rate, and the decay rate is linked with physical fitness.
I. INTRODUCTION
Heart rate variability (HRV) has been employed mainly at
rest as a marker of potential cardiac diseases [1] or as a
measure of training and physical fitness [2]. Although HRV
dynamics during exercise has been the subject of theoretical
developments [3] and may be a marker of physical condition
as well [4], few studies have focused on the experimental
evolution of HRV during exercise and its potential link with
training or physical condition. Decrease of the standard
deviation of RR intervals (the time between successive R
waves of the electrocardiogram) during exercise has been
reported [5], but a clear analysis of this evolution is still
lacking [6]. Using a simple first order differential equation to
estimate the mean heart rate (HR) dynamics during
incremental exercise as a function of the exercise load, we
study how the obtained stationary fluctuations of HR evolve
during the effort test, and estimate how fitness influence such
dynamics.
II. POPULATION
variable Mean value (sd)
Number of subjects 18
age (year) 15.22 (1.96)
Weight (kg) 64.76 (15.55)
Height (cm) 173.28 (10.45)
VO2 max (mL/kg) 39. 06 (7.51)
Maximum power (W) 235.00 (60.2 2)
Table 1: characteristic of t he studied group
18 young athletes (10 males and 8 females; 15.2  2 year-
old, Table 1) of the Regional Physical and Sports Education
Centre (CREPS) of French West Indies (Guadeloupe,
France), performed an incremental testing on a SRM Indoor
Trainer electronic cycloergometer. It consisted in a 3 minutes
*Research supported by the SNSF scientific exchange grant
IZSEZ0_183540 and the ICARUS SNSF fund 100019_166010
1 Quality of Care Unit, University Hospitals of Geneva, Geneva,
Switzerland
2 ACTES laboratory, UPRES-EA 35 96 UFR -STAPS, University o f the
French West Indies, Guadeloupe, France.
3 Departamento de Fisica Aplicada II, E.T.S.I. de Telecomunicacíon,
Universidad de Málaga, Málaga, Spain
rest phase, followed by a 3 min cycling period at 50 watts
and an incremental power testing of +15 Watts by minute
until exhaustion.
III. DETRENDING
Although the use of simple linear detrending is often used to
study HRV [7], we propose here to use recent developments
in dynamical analysis to model the non-stationary
component of HR. As already proposed for oxygen
consumption [8] and for heart rate [9], the main trend of
heart rate dynamic during an incremental effort test can be
modeled by a simple first order differential equation as
follow:





  (equation 1)
where 
 is the time derivative of the heart rate HR,  is
the characteristic exponential decay time of such first order
differential equation,  the resting heart rate,  the work
load (in Watt) during the step  of the incremental effort test,
 the associated gain, i.e. the ratio between the HR steady
state increase and the power increase of step  that caused it,
and  the number of power steps during the effort test. The
mean HR trend that such model can produce has the
advantage of relying only on experimental data (HR and
workload) and not on any user parameter.
Figure 1. Measured RR interval during an incremental maximal effort
test, its estimation by a first or der differential equation, and the resulting
detrended RR serie
To estimate the coefficients of equation 1, we used a two-
step procedure similar to the one developed by Boker and
co-authors [10], consisting in first estimating the HR time-
derivative by the means of a spline regression, to then
estimate equation 1 as a linear mixed effect regression. Each
set of estimated individual parameter and workload is used
to generate an estimated HR and RR curve, which is used to
detrend the data. An example of the final estimate of our
procedure is shown in Figure 1, together with the resulting
Changes of heart rate variability during exercise
D. Mongin
1
, C. Chabert
2
, M. Gomez Extremera
3
, O. Hue
2
, D. S. Courvoisier
1
, P. Carpena
3
,
P. A. Bernaola Galvan3
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detrended data. The dynamical model used produces an
individual estimation of RR with a median R2 of 0.93 over
the athletes [IQR: 0.87 – 0.95]. HRV
A. Mean trend
The analysis of HRV consisted in the computation of SDRR
for each power step of the effort test. It has been reported [6]
that the logarithm of SDRR (ln-SDRR) display a “somewhat
linear decrease” as a function of exercise intensity
(expressed as percentage of VO2 max, see figure 2).
Because HRV is a result of both parasympathetic and
sympathetic neuronal activity that both take part in the
regulation of the mean HR, we propose to display SDRR on
a logarithmic scale as a function of the corresponding mean
heart rate, normalized by its maximum value (see Figure 2).
A linear regression of ln-SDRR as a function of the
normalized HR leads to an R2 of 0.80 and an AIC of 294, to
be compared to AIC = 389 and R2= 0.71 obtained with the
regression between ln-SDRR and the exercise intensity.
Figure 2. mean SDRR (in log scale) for each power step, as a function of
Exercise intensity (left) and normalized HR (right).
The evolution of the SDRR is thus better explained by the
heart rate itself, following the model:
   !"
!"#$%&'& (equation 2)
B. Link with physical fitness
We are interested in studying how the parameter a
(Equation 2), i.e. the characteristic scale of HRV decreasing,
is linked with fitness. Performing a nonlinear regression of
equation 2 on the entire SDRR dataset yields a global value
of a =()*)+,  )*))-- (p < 10-6), meaning that an increase
of HR of 15% of its maximum value will decrease SDRR by
a bit more than one order of magnitude each effort test.
Doing the same for each individual effort test allow us to
determine the individual coefficients a of equation 2.
Strikingly, a is inversely correlated with VO2 max (.
()*//01  )*)23), maximum aerobic power reached during
the effort test (.  &()*+30 1  )*))42), and also with the
first and second ventilatory thresholds (.  &()*+)01 
)*))35 and . &()*/401  )*)43 respectively). These
results indicate that individuals with better physical
condition tend to have a faster decrease of their heart rate
variability with their heart rate increase during the effort test.
IV. CONCLUSION
Among young athletes, the main factor predicting the change
of beat to beat variability before, during and after an
incremental exercise is the normalized HR. The standard
deviation of the RR time observed decreases exponentially
with the heart rate increase, and this exponential decrease
of HRV with HR increase is faster for trained subjects.
Similarly to the well-known faster parasympathetic
reactivation observed for trained subject after effort [2], this
result may find its origin in a greater parasympathetic
deactivation during effort implying a quicker
sympathetic/parasympathetic equilibrium for trained person.
The novel approach presented should be further applied to
diverse population (e.g., different age, untrained persons)
and other types of effort, to explore the universality of HRV
change observed and its link with physical fitness.
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