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Rev Andal Med Deporte. 2023; 16(1):
Online
Revista Andaluza de
Medicina del Deporte
https://www.juntadeandalucia.es/deporte/ramd/
Original
Cardiac autonomic regulation in response to functional power threshold testing
in elite cyclists
M. Castillo-Aguilara,b , D. Mabe-Castroa,b, M. Mabe-Castrob,c , V. Oyarzob,d , K. Harris Kinga,b, P.
Valdés-Badillae,f, P. Delgado-Floodyg,h , J.González-Puelmab,c , C. Núñez-Espinosab,c *
a Departamento de Kinesiología, Universidad de Magallanes, Punta Arenas, Chile.
b Centro Asistencial de Docencia e Investigación (CADI-UMAG), Punta Arenas Chile.
c Escuela de Medicina, Universidad de Magallanes, Punta Arenas, Chile.
d Fonoaudiología, Universidad de Magallanes, Punta Arenas, Chile.
e Department of Physical Activity Sciences, Faculty of Education Sciences, Universidad Católica del Maule, Talca, Chile.
f Sports Coach Career, School of Education, Universidad Viña del Mar, Viña del Mar, Chile.
g Department of Physical Education, Sport and Recreation, Universidad de La Frontera, Temuco, Chile.
h Strength & Conditioning Laboratory, CTS-642 Research Group, Department Physical Education and Sports, Faculty of Sport Sciences, University of
Granada, Granada, Spain.
ARTICLE INFORMATION: Received 23 December 2022, accepted 28 March 2023, online 28 March 2023
ABSTRACT
Objective: To evaluate the impact of a functional power threshold test (FTP) on cardiac autonomic regulation indicators in high performance cyclists.
Methods: A total of 12 male elite cyclists (mean age 36.1 ± 11.2 years) were recruited. Body composition parameters were measured using
bioimpedancemetry and heart rate variability (HRV) before and after the application of the FTP assessment.
Results: We observed that a greater sympathetic nervous system (SNS) index and Stress index on baseline were correlated with a smaller decrease in the
parasympathetic nervous system (PNS) activity in response to the FTP test (ρ= 0.69, p = 0.013). Concerning morphological parameters, the skeletal
muscle index (SMI) was the only one that was inversely correlated with ∆PNS (ρ= -0.69, p = 0.02) whereas the muscle-bone index (MBI) displayed a
positive correlation with ∆SNS (ρ = 0.82, p = 0.001). In fully adjusted models we found that waist-to-hip ratio ( β= 7.90, CI95%[4.16, 11.63], t(8) = 4.88, p =
0.001) and SMI significantly influenced ∆PNS ( β = -1.38, CI95%[-1.84, -0.92], t(8) = -6.94, p < 0.001), whereas MBI (β= 10.26, CI95%[8.10, 12.42], t(8) =
10.96, p < 0.001) and the interaction between the latter and Power achieved during FTP influenced ∆SNS (β = -0.05, CI95%[-0.09, -4.99e-03], t(8) = -2.56, p
= 0.033).
Conclusion: Our findings indicate that the SMI had a negative effect on the ∆PNS, while the MBI was positively correlated with the ∆SNS in cyclists. These
findings suggest that a higher SMI and MBI could have a detrimental impact on the cardiac autonomic response to maximal aerobic exercise in high-
performance cyclists, such as FTP.
Keywords: Heart Rate; Physical performance; Athletes; Cardiovascular regulation.
Corresponding author.
E-mail-address: cristian.nunez@umag.cl (C. Núñez-Espinosa).
https://doi.org/10.33155/j.ramd.2023.03.002
e-ISSN: 2172-5063/ © 2023 Consejería de Turismo, Cultura y Deporte de la Junta de Andalucía. is is an open access article under the CC BY-NC-ND
license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
M. Castillo-Aguilar et al. / Rev Andal Med Deporte. 2023;16(1):
Online
Regulación autonómica cardiaca en respuesta a la prueba de umbral de potencia funcional en ciclistas de
élite
RESUMEN
Objetivo: Evaluar el impacto de una prueba de umbral de potencia funcional (FTP) sobre los indicadores de regulación autonómica cardiaca en ciclistas
de alto rendimiento.
Métodos: Se reclutó a un total de 12 ciclistas de élite masculinos (edad media 36.1 ± 11.2 años). Se midieron los parámetros de composición corporal
mediante bioimpedanciometría y la variabilidad de la frecuencia cardiaca (HRV) antes y después de la aplicación de la evaluación del FTP.
Resultados: Observamos que un mayor índice del sistema nervioso simpático (SNS) e índice de estrés basalmente se correlacionaron con una menor
disminución de la actividad del sistema nervioso parasimpático (PNS) en respuesta a la prueba FTP ( ρ= 0.69, p = 0.013). En cuanto a los parámetros
morfológicos, el índice músculo esquelético (SMI) fue el único que se correlacionó inversamente con el ∆PNS ( ρ= -0.69, p = 0.02) mientras que el índice
músculo-hueso (MBI) mostró una correlación positiva con ∆SNS (ρ = 0.82, p = 0.001). En los modelos totalmente ajustados encontramos que la relación
cintura-cadera (β= 7.90, CI95%[4.16, 11.63], t(8) = 4.88, p = 0.001) y el SMI influían significativamente en el ∆PNS (β= -1.38, CI95%[-1.84, -0.92], t(8) = -6.94,
p < 0.001), mientras que el MBI (β = 10.26, CI95%[8.10, 12.42], t(8) = 10.96, p < 0.001) y la interacción entre este último y la Potencia alcanzada durante el
FTP influían en el ∆SNS (β= -0.05, CI95%[-0.09, -4.99e-03], t(8) = -2.56, p = 0.033).
Conclusión: Nuestros hallazgos indican que el SMI tuvo un efecto negativo sobre el ∆PNS, mientras que el MBI se correlacionó positivamente con el ∆SNS
en ciclistas. Estos hallazgos sugieren que un mayor SMI y MBI podrían tener un impacto perjudicial en la respuesta autonómica cardíaca al ejercicio
aeróbico máximo en ciclistas de alto rendimiento, como el FTP.
Palabras clave: Frecuencia cardiaca; Rendimiento físico; Atletas; Regulación cardiovascular.
Regulação autonômica cardíaca em resposta ao teste de potência do limiar funcional em ciclistas de elite
RESUMO
Objetivo: Avaliar o impacto de um teste de potência de limiar funcional (FTP) nos indicadores de regulação autonômica cardíaca em ciclistas de alto
rendimento.
Métodos: Um total de 12 ciclistas de elite do sexo masculino (idade média de 36.1 ± 11.2 anos) foram recrutados. Parâmetros de composição corporal
foram medidos por bioimpedância e variabilidade da frequência cardíaca (VFC) antes e após a aplicação da avaliação FTP.
Resultados: Observamos que um índice basal mais alto do sistema nervoso simpático (SNS) e um índice de estresse correlacionaram-se com uma menor
diminuição da atividade do sistema nervoso parassimpático (SNP) em resposta ao teste de FTP ( ρ = 0.69, p = 0.013). Com relação aos parâmetros
morfológicos, o índice musculoesquelético (SMI) foi o único que se correlacionou inversamente com o ∆PNS ( ρ = -0.69, p = 0.02) enquanto o índice
músculo-ósseo (MBI) apresentou correlação positiva com o ∆ SNS ( ρ = 0,82, p = 0,001). Em modelos totalmente ajustados, descobrimos que a relação
cintura-quadril ( β = 7.90, IC95%[4.16, 11.63], t(8) = 4.88, p = 0.001) e SMI influenciaram significativamente o ∆PNS ( β = -1.38, IC95 %[-1.84 , -0.92], t(8)
= -6.94, p < 0.001), enquanto o MBI ( β = 10.26, IC95%[8.10, 12.42], t(8) = 10.96, p < 0.001) e a interação entre os últimos e a Potência alcançada durante
o FTP influenciou o ∆SNS ( β = -0.05, IC95%[-0.09, -4.99e-03], t(8) = -2.56, p = 0.033).
Conclusão: Nossos achados indicam que o SMI teve um efeito negativo no ∆PNS, enquanto o MBI se correlacionou positivamente com o ∆SNS em ciclistas.
Esses achados sugerem que o SMI e o MBI mais altos podem ter um impacto negativo na resposta autonômica cardíaca ao exercício aeróbico máximo em
ciclistas de alto desempenho, como o FTP.
Introduction
The regulation of non-voluntary physiological processes, such as
cardiovascular responses, is crucial for optimal athletic
performance. The autonomic nervous system plays a pivotal role
in this regulation, and heart rate variability (HRV) is considered a
viable marker to measure cardiac autonomic modulation.1,2
During high-energy-demand sports like cycling, the autonomic
system is essential for athletes’ response to the competition.3–5
The modulation of the autonomic nervous system is influenced by
various factors, including the volume, intensity, duration, and type
of exercise.6,7 Furthermore, athletes’ morphological variables, such
as body composition, can influence baseline HRV parameters and,
consequently, the athlete’s performance.8–10
In high-performance cyclists, body composition is relevant and
has been correlated with their physical performance during
competitions.11,12 A high muscle index and a low body fat
percentage are generally desired by physical trainers and athletes.
In this regard, research on amateur cyclists has shown a link
between anthropometric measures and training adaptations,
showing that training load is associated with a decrease in body
weight, body mass index, and body fat percentage.13 However, it is
not clear if this holds true for high-performance athletes when
accounting for other key physiological variables.
Although it is well established that physical exercise affects the
autonomic response and its immediate recovery,3–5,14 the impact of
morphological variables on this modulation is not fully
understood. Therefore, it is crucial to evaluate the immediate post-
exercise recovery, as it reflects the athlete’s aerobic capacity and
performance.3,15
In this context, the Functional Threshold Power (FTP) test has
been proposed as a reliable method to assess cyclist power for one
hour in a “near physiological steady state”.16 It is currently
proposed that FTP can be predicted by taking 95% of the power
output in a maximum of 20 min all-out effort test.17 In this way,
this test can predict the cyclist’s response to this maximum effort
in less time and also allows the evaluation of other physiological
parameters of interest to the athlete.18
Despite the relevance of the FTP test, no evidence has been
found on the relationship between body composition parameters,
autonomic regulation, and its immediate response after an
exercise protocol in high-performance cyclists. The present study
aims to fill this gap by evaluating the impact of an FTP test on
cardiac autonomic regulation indicators in high-performance
cyclists. This study’s findings may provide valuable insights into
the complex interplay between body composition and autonomic
regulation, ultimately informing training strategies to enhance
athletic performance.
Methods
Study design
A descriptive, correlational, and cross-sectional study was
conducted in two consecutive stages. In the first stage,
M. Castillo-Aguilar et al. / Rev Andal Med Deporte. 2023;16(1):
Online
morphological variables of body composition were measured, and
in the second stage, cardiovascular variables of HRV and the
physical test of FTP were evaluated. The participants were
selected through non-probabilistic sampling among professional
cyclists from the Magallanes region. The athletes were informed
about the assessments as well as the associated risks and benefits.
Participants
Before undergoing the protocol for this study, all the cyclists
signed an informed consent form. The inclusion criteria for the
study were as follows: (a) male cyclists between 20 and 40 years
of age until the year 2022; (b) permanent residence in the city of
Punta Arenas; (c) a minimum of 1 year of participation in
competitive cycling; (d) completion of the FTP test; (e) attendance
at the two assessment sessions. The exclusion criteria were: (a)
use of any supplement or medication that could affect HRV before
the physical test; (b) musculoskeletal injuries in the last three
months; (c) presence of pain during measurements; (d) cognitive
or motor disability. During the registration stage, 35 athletes
expressed interest in participating. After determining the
participants’ eligibility based on the inclusion and exclusion
criteria, 12 cyclists were recruited. The study involving human
participants was reviewed and approved by The Ethics Committee
of the University of Magallanes, Chile (Nº141CEC2018). All
participants provided written informed consent to participate in
the study.
Procedure
The measurements were conducted in the Movement Analysis
laboratory of the Center of Education, Healthcare, and Research
(CADI-UMAG) during the early afternoon for all cyclists. In the first
session, all morphological parameters, including body weight,
height, and anthropometry, were evaluated. In the second session,
cardiovascular parameters were assessed before, during, and after
the physical performance test.
Functional Threshold Power protocol
The participants wore sportswear appropriate for the test. All
participants were asked to: (a) get enough rest the night before,
sleeping 8 hours or more; (b) avoid stimulant drinks or drugs
before the measurements; (c) drink at least 2 liters of water the
day before; and (d) eat regularly without changing their diet. The
cyclists arrived 15 minutes before the test. The FTP protocol was
carried out in a laboratory designed for the experiment at 22 °C
and 30% relative humidity regulated by air conditioning.
Before starting the second stage of assessment, each cyclist
remained seated in absolute rest for 10 minutes while pretest HRV
was assessed. Five minutes of the recording were considered for
the analysis. Then, the cyclist got on the bike to begin the physical
test. Throughout the test, the athlete’s cardiac activity was
monitored, allowing them to monitor their cardiovascular health.
Moreover, the athletes could observe their heart rate on the screen
in front of them. After completing the test, the athlete recovered
for two minutes, and then 10 minutes of HRV assessment, while
sitting in absolute rest, were recorded again.
Morphological measures
The multi-frequency bioelectrical impedance analyzer, InBody
S10 (Biospace Co, Ltd, Korea/Model JMW140), was used in
accordance with the manufacturer’s instructions. This device
estimates body composition by measuring the differences in
conductivity of various tissues, which are determined by their
different biological characteristics.19 The body composition
parameters, including fat mass, fat-free mass, body cell mass,
appendicular skeletal muscle mass (ASM; kg/m2), whole-body
phase angle, and body water status, were measured according to
established scientific guidelines.20
Functional Threshold Power Test
A Tacx FLUX S Smart Direct Roller (Garmin®) was used for the
FTP assessment, as it can be adjusted to fit each athlete’s personal
bicycle. The roller program calculated the cadence of each athlete,
expressed in revolutions per minute (rpm).
The FTP test provides data on functional threshold heart rate
(FTHR) or FTP, which is essential for determining intensity levels
or zones to follow a training plan. There are two versions of this
test; one of long duration (1 hour), and the other of 45 minutes.
However, the 1-hour version can be overly demanding,
particularly for a group of users taking a high-demand test for the
first time. Therefore, we proposed carrying out the shorter-
duration test (45 min), which consists of the following phases: (i)
Warm-up, consisting of 5 minutes of free pedaling, 20 seconds of
resistance rhythm up to 130 W, 20 seconds of resistance rhythm at
165 W, 20 seconds of hard pedaling up to 195 W. After that, 3
minutes of easy pedaling up to 80W, 3 minutes of hard pedaling up
to 180 W, 2 minutes of hard pedaling up to 195 W, and 6 minutes
of easy pedaling up to 80 W. (ii) The main test involves the cyclist
pedaling for 20 minutes with maximum effort. (iii) The cooldown
phase is 5 minutes of easy pedaling.
Cardiovascular parameters
Cardiac autonomic modulation was determined by recording RR
intervals with a heart rate sensor strap (H10, Polar Electro Oy,
Kempele, Finland) using the Polar Team 2 system. The breathing
rate of the subjects was spontaneous, and artifacts and ectopic
heartbeats, which did not exceed 3% of the recorded data, were
excluded.2 The time-domain parameters analyzed were the square
root of the mean squared differences of successive RR intervals
(RMSSD, expressed in ms), which reflect parasympathetic
influence,21 and the standard deviation of the RR intervals (SDNN),
which reflect total variability, i.e., the sympathetic and
parasympathetic contribution of the autonomic nervous system to
the heart.22,23 The frequency domains considered in this study
were the high-frequency (HF) power band, which reflects
parasympathetic influence and respiratory sinus arrhythmia,24
and the low-frequency (LF) band, associated with baroreflex
activity.25 The very low-frequency (VLF) band is multifaceted and
strongly associated with emotional stress.26–28
Additionally, the Parasympathetic Nervous System (PNS) index,
Sympathetic Nervous System (SNS) index, and Stress Index (SI)
were considered. The PNS index reflects total vagal stimulation
and is calculated from the mean RR intervals, RMSSD, and
Poincaré plot index SD1 in normalized units (linked to RMSSD). It
reflects how many standard deviations above or below the normal
population averages the obtained values are. The SNS index
reflects total sympathetic stimulation and is calculated from the
mean RR intervals, Baevsky’s SI (a positively related value to
cardiovascular system stress and cardiac sympathetic activity),
and Poincaré plot index SD2 in normalized units (related to
SDNN). Its interpretation is similar to the PNS index.22,29 The SI
may be used as an indicator that represents the degree of load on
the Autonomic Nervous System control.30 It is normalized by using
the square root of Baevsky’s SI31 and calculated from the mode Mo
(taken as the median of R-R intervals), AMo (the amplitude of the
normalized RR interval histogram), and MxDMn (the distance
between the shortest and longest R-R intervals) by the following:
SI
=
AMO× 100 %
2× MO × MxDMn
All the data obtained were analyzed using the Kubios HRV
software.32
M. Castillo-Aguilar et al. / Rev Andal Med Deporte. 2023;16(1):
Online
Statistical analyses
Descriptive statistics were expressed as median and
interquartile range (IQR) for continuous variables, and absolute
and relative frequency (n [%]) for categorical outcomes.
To assess the relationship between autonomic indexes, we used
Spearman’s rank correlation since the data did not follow an
approximate Gaussian distribution, which was assessed through
graphical and analytical methods. To analyze the change in
autonomic parameters in response to FTP measurements, we
computed the mean difference with a 95% confidence interval (CI)
bias-corrected and accelerated, calculated through the bootstrap
resampling technique. Additionally, we reported the bias-
corrected standardized mean difference (Hedges’ g) with their
corresponding 95% CI.
To assess the influence of potential confounders on the
autonomic response to FTP, we fitted a robust version of linear
regression by iterated reweighted least squares (IRLS). This
approach assigns more weight to less extreme values and controls
for the influence of outliers when describing the estimated
parameters of the model. To this end, the predictors were centered
around their mean to interpret the intercept as the estimated
response while keeping the predictors constant and thus
controlling for their influence.
All analyses were performed using the R programming
language33 within Rstudio.34 We used complementary R packages
for analysis and plotting.35–39
Results
Sample characteristics and body composition parameters can be
observed in Table1.
Table 1. Body composition and sample characteristics. ECW,
Extracellular water; ICW, Intracellular water; TCW, Total cellular
water.
Domain Parameter Statistics (N = 12)
Median IQR (p25, p75)
Anthropometric Weight 72.9 (68.6, 75.8)
Height 170.2 (167.8, 179.1)
Body mass index 24.9 (22.4, 26.4)
Waist-Hip ratio 0.8 (0.8, 0.9)
Musculoskeletal Muscle bone index 2.7 (2.6, 2.8)
Skeletal muscle index 8.2 (7.9, 8.8)
Skeletal muscle mass 32.0 (31.0, 35.8)
Muscle mass 42.3 (41.2, 45.2)
Bone mass 15.3 (14.9, 16.5)
Residual mass 28.6 (27.6, 29.4)
Body composition Visceral fat 61.3 (35.5, 74.6)
Fat mass 12.3 (11.2, 14.8)
Water composition ECW/TCW 0.4 (0.4, 0.4)
ICW 26.4 (25.3, 29.4)
TCW 42.2 (40.4, 46.8)
ECW 16.1 (14.7, 17.4)
Autonomic activity and stress
When assessing the relationship between the associated
variables within the athletes, we observed that a greater SNS
activity and SI on baseline were associated with a smaller
decrease in the PNS activity in response to the FTP test (baseline
SI, ρ= 0.67, p = 0.017; baseline SNS, ρ = 0.69, p = 0.013).
In this sense, a greater baseline PNS activity was associated with
larger decreases on the PNS index in response to the FTP test ( ρ =
-0.61, p = 0.037), and this decrease in ∆ PNS was associated with
greater increases in the SNS activity and the SI in response to the
FTP test (∆SNS, ρ = -0.6, p = 0.039; ∆SI, ρ = -0.62, p = 0.033).
This is directly linked with PNS activity at post-SFT, whereas
greater levels were associated with lower increases in SI and SNS
activity levels in response to SFT test (∆SI, ρ= -0.69, p = 0.014;
∆SNS, ρ = -0.77, p = 0.003).
Unadjusted autonomic response
The mean observed difference in the PNS index was -2.03 points
(CI95%[-2.53, -1.62]), suggesting a decrease in PNS activity post-
SFT test (t(11) = -8.34, p < 0.001, Hedges g = 2.24, CI95%[1.17,
3.29]), while the SNS index experienced an increase of 6.28 points
(CI95%[4.47, 8.48], t(11) = 5.83, p < 0.001, Hedges’ g = -1.56, CI95%[-
2.38, -0.72]) relative to their baseline values. The SI and SNS index
tend to exhibit similar behavior between measurements of the
FTP (association between ∆SNS and ∆SI,
ρ
= 0.98, p < 0.001), so
the SI also experienced and increase from baseline relative to the
FTP measurements (mean difference = 23.1, CI95%[14.80, 33.27],
t(11) = 4.67, p < 0.001, Hedges’ g = -1.25, CI95%[-1.96, -0.50]). The
autonomic variations within subjects can be seen in F igure 1 .
Adjusted PNS response
Rank based correlation analyses suggest that from all body
composition parameters, SMI was the only one that was inversely
associated with ∆PNS, suggesting that lower levels of SMI were
related to a lower decrease in PNS activity in response to FTP test
( = -0.69, ρp = 0.02).
After fitting a simple linear model based on IRLS, we found that
the ∆PNS changed from -2.03 points (CI95%[-2.53, -1.62]) in the
first unadjusted comparison to -1.89 points (CI95%[-2.33, -1.45],
t(9) = -9.73, p < 0.001) when controlling for SMI ( = -0.71, CI95%[-
1.53, 0.10], t(9) = -1.99, p = 0.078).
Figure 1. Inter-individual variations of the autonomic regulation
indexes. Boxplots and errorbars with 95% CI based on bootstrap
resampling around the mean and the within subjects response to
the FTP test (represented by conected lines) are shown.
However, and after testing the influence of other predictors in
the model while still considering SMI as a predictor, we observed
that the inclusion of the waist-to-hip ratio (WHratio) yielded a
significant effect on ∆PNS ( = 7.90, CI95%[4.16, 11.63], t(8) = 4.88,
p = 0.001), as well as for the effect of SMI on the latter ( = -1.38,
CI95%[-1.84, -0.92], t(8) = -6.94, p < 0.001). Thus, after adjusting for
the effect of SMI and WHratio, the estimated response of ∆PNS to the
FTP test was -1.93 points (CI95%[-2.16, -1.70], t(8) = -19.15, p <
0.001). The final model explaining the PNS response to the FTP
test is best described by the following equation:
M. Castillo-Aguilar et al. / Rev Andal Med Deporte. 2023;16(1):
Online
Δ PNS
=
2.835
−
1.379× SMI
+
7.898 ×WH
ratio
Equation 1. Final model using SMI and WHratio to explain the ∆ of
PNS in response to the FTP test. The predictors in this equation
are not centered, so they can be used for prediction.
Adjusted SNS response
Spearman’s rank based correlation suggests a positive
association between the muscle-bone index (MBI) and the ∆SNS,
suggesting that greater values of MBI could be associated with
greater increases in SNS activity in response to the SFT test (ρ =
0.82, p = 0.001).
When fitting a robust linear regression with IRLS, we observed
that the response of SNS activity was maintained after adjusting
for MBI (Intercept = 6.20, CI95%[5.00, 7.40], t(10) = 11.53, p <
0.001), considering that for every 1 unit increase in MBI, we could
expect an increase in 11.72 points in the SNS activity in response
to the SFT test ( = 11.72, CI95%[7.39, 16.04], t(10) = 6.04, p <
0.001).
Despite of previous findings in simple models, and after trying
different combinations of predictors while keeping MBI in the final
model, we could identify an interaction effect between the mean
power achieved during the FTP test (PowerFTP) and MBI ( = -0.05,
CI95%[-0.09, -4.99e-03], t(8) = -2.56, p = 0.033), considering that
PowerFTP itself was not statistically influential on the outcome
response ( = -3.23e-03, CI95%[-0.02, 0.01], t(8) = -0.41, p = 0.692)
while MBI was still significant, even after including PowerFTP in the
equation ( = 10.26, CI95%[8.10, 12.42], t(8) = 10.96, p < 0.001). In
this sense, and after controlling for the effect of MBI and PowerFTP,
we observed that the estimated response of ∆SNS was 6.06 points
(CI95%[5.52, 6.59], t(8) = 26.01, p < 0.001). The linear relationship
between variables can be seen in F igure 2 . The final model that
best explains the variations in ∆SNS response was the following:
Δ SNS=−52.007+21.180 × MBI −0.134 × Power FTP−0.050 ×
(
MBI × Power FTP
)
Equation 2. Final model using MBI, PowerFTP and their interaction
to explain the ∆ of SNS in response to the FTP test. The predictors
in this equation are not centered, so they can be used for
prediction.
Figure 2. Linear response between autonomic parameters at
baseline, post-SFT and the variations between these time periods.
A, Interaction effect between PowerFTP and MBI; B, Linear
relationship between HRV measurements.
Discussion
In this study, we have identified important associations between
autonomic cardiac modulation parameters and body mass
composition parameters in response to aerobic maximal exercise,
as indicated by Spearman’s Rank correlation and IRLS.
Our findings suggest that cardiac autonomic response to the
FTP test, an aerobic maximal exercise, could be influenced by
muscle indices (SMI and MBI), with higher SMI and MBI negatively
affecting cardiac autonomic response, moving SNS and PNS out of
balance. Cyclists with lower SMI and MBI maintain a greater
cardiac autonomic balance between parasympathetic and
sympathetic activity when their response to this type of exercise is
observed.
Current evidence seems to support the hypothesis that
morphological variables may play a role influencing key
performance outcomes that are relevants to high-performance
cyclists.11,12 However, the autonomic effects of this type of variable
on cardiac regulation is still not clear. Some important precedents
indicate that cycling was the sport with the most sudden deaths
during its practice in Spain between 1995 and 2001, which
suggests that cycling is very demanding for human systems and
there is a need to develop monitoring strategies to assess the
neurophysiological regulation of the heart in athletes.40
Although the morphological composition of the cyclist partly
determines their performance in a competition, we have observed
in this study that it may also imply different characteristics of
cardiovascular recovery among athletes.4 1 Possibly the SMI would
reflect a greater autonomic wear and tear compared to a test as
demanding as the FTP. This wear could hinder autonomic
recovery, especially of the PNS, which we know exerts strong
regulation of the autonomic nervous system.4,42 Due to these
characteristics, the autonomic recovery of athletes with a higher
muscle index could be influenced by their morphology, which
should be considered for a better recovery of cyclists, both in
training and after a competition.
HRV, reflecting cardiac autonomic regulation, is known for being
a tool for identifying patients at risk of cardiovascular death and a
great predictor of prognosis in several neurological disorders. A
worse cardiac autonomic response to exercise, found in cyclists
with higher muscle indices, could lead to cardiovascular disorders
or decrease the effort threshold in longer competitive activities,
although these hypotheses have not been explored. Considering
this, professionals surrounding high-performance cyclists should
consider strategies for minimizing exercise-induced autonomic
dysregulation.
Possibly the SMI would reflect a greater autonomic wear and
tear compared to a test as demanding as the FTP.
One of the strengths of this study is that it sheds light on the
relationship between body composition and cardiac autonomic
regulation, which has not been previously investigated in this
population. Additionally, we used robust statistical methods such
as Spearman’s Rank correlation and IRLS to analyze the data,
which allowed for a more precise and accurate assessment of the
associations between variables.
Despite its strengths, our study has several limitations. First, our
sample size was relatively small, which may limit the
generalizability of our findings. Second, we only examined the
immediate response to the FTP test and did not investigate the
long-term effects of body mass composition on cardiac autonomic
regulation. Further studies with larger sample sizes and longer
follow-up periods are needed to better understand the effects of
body mass composition on cardiac autonomic regulation.
In addition, our study only focused on one type of exercise
(aerobic maximal exercise), and the effects of body mass
composition on cardiac autonomic regulation may differ in other
types of exercises or activities. Furthermore, we did not take into
account other potentially confounding factors such as age, sex, and
M. Castillo-Aguilar et al. / Rev Andal Med Deporte. 2023;16(1):
Online
medical history. Future studies should consider these factors in
their analysis.
In conclusion, our study provides important insights into the
effects of body mass composition on cardiac autonomic regulation
in high-performance cyclists. We found that muscle indices (SMI
and MBI) are associated with changes in cardiac autonomic
response to exercise, with higher SMI and MBI leading to an
imbalance between parasympathetic and sympathetic activity. Our
findings suggest that professionals surrounding high-performance
cyclists should consider strategies for minimizing exercise-
induced autonomic dysregulation in individuals with higher
muscle indices. However, considering our study limitations,
further research is needed to confirm and expand upon our
findings.
Conclusion
The results of this study highlight the crucial role of muscle
indices (SMI and MBI) in modulating the cardiac autonomic
response to FTP among elite cyclists. The negative effect of SMI on
∆PNS and the positive correlation of MBI with ∆SNS demonstrate
the potential impact of muscle composition on the body’s
physiological response to intense aerobic exercise. These findings
underscore the importance of optimizing muscle indices in high-
performance cyclists to improve cardiac autonomic regulation and
maximize athletic performance.
Authotship. All the authors have intellectually contributed to the development of the
study, assume responsibility for its content and also agree with the definitive version
of the article. Conflicts of interest. The authors have no conflicts of interest to
declare. Funding. This work was funded by resources from the National Fund for the
Promotion of Sports of Chile, code 2200120010 (Instituto Nacional de Deporte de
Chile, IND). Acknowledgements. We thank all study participants, and their coaches
for their contribution. Provenance and peer review. Not commissioned; externally
peer reviewed. Ethical Responsabilities. Protection of individuals and animals: The
authors declare that the conducted procedures met the ethical standards of the
responsible committee on human experimentation of the World Medical Association
and the Declaration of Helsinki. Confidentiality: The authors are responsible for
following the protocols established by their respective healthcare centers for
accessing data from medical records for performing this type of publication in order
to conduct research/dissemination for the community. Privacy: The authors declare
no patient data appear in this article.
References
1. Shaffer F, Ginsberg JP. An overview of heart rate variability
metrics and norms. Front Public Health. 2017; 258.
2. Stein PK, Bosner MS, Kleiger RE, Conger BM. Heart rate
variability: A measure of cardiac autonomic tone. Am Heart J.
1994;127(5): 1376–1381.
3. Stanley J, Peake JM, Buchheit M. Cardiac parasympathetic
reactivation following exercise: Implications for training
prescription. Sports Med. 2013;43(12): 1259–1277.
4. Fontolliet T, Pichot V, Bringard A, Fagoni N, Adami A, Tam E, et
al. Testing the vagal withdrawal hypothesis during light
exercise under autonomic blockade: A heart rate variability
study. J Appl Physiol. 2018;125(6): 1804–1811.
5. Freeman JV, Dewey FE, Hadley DM, Myers J, Froelicher VF.
Autonomic nervous system interaction with the cardiovascular
system during exercise. Prog Cardiovasc Dis. 2006;48(5): 342–
362.
6. Griesbach GS, Hovda D, Molteni R, Wu A, Gomez-Pinilla F.
Voluntary exercise following traumatic brain injury: Brain-
derived neurotrophic factor upregulation and recovery of
function. Neurosci. 2004;125(1): 129–139.
7. Mart nez-D az IC, Carrasco L. Neurophysiological stressı
ı
response and mood changes induced by high-intensity interval
training: A pilot study. Int J Environ Res Public Health.
2021;18(14): 7320.
8. Michael S, Graham KS, Davis GM. Cardiac autonomic responses
during exercise and post-exercise recovery using heart rate
variability and systolic time intervals—a review. Front Physiol.
2017;8: 301.
9. Mancia G, Grassi G. The autonomic nervous system and
hypertension. Circ Res. 2014;114(11): 1804–1814.
10. Lucini D, Spataro A, Giovanelli L, Malacarne M, Spada R, Parati
G, et al. Relationship between body composition and cardiac
autonomic regulation in a large population of italian olympic
athletes. J Pers Med. 2022;12(9): 1508.
11. Mujika I, R ønnestad BR, Martin DT. Effects of increased muscle
strength and muscle mass on endurance-cycling performance.
IJSPP. 2016;2015(0405): 3.
12. Cesanelli L, Ammar A, Arede J, Calleja-González J, Leite N.
Performance indicators and functional adaptive windows in
competitive cyclists: Effect of one-year strength and
conditioning training programme. Biol Sport. 2022;39(2):
329–340.
13. Alvero-Cruz JR, Garc a Romero JC, Ordonez FJ, Mongin D,ı
Correas-Gómez L, Nikolaidis PT, et al. Age and training-related
changes on body composition and fitness in male amateur
cyclists. Int J Environ Res Public Health. 2021;19(1): 93.
14. Holmes CJ, MacDonald HV, Esco MR, Fedewa MV, Wind SA,
Winchester LJ. Comparison of heart rate variability responses
to varying resistance exercise volume-loads. Res Q Exerc
Sport.2022;93(2): 391 –400.
15. Michael S, Jay O, Halaki M, Graham K, Davis GM. Submaximal
exercise intensity modulates acute post-exercise heart rate
variability. Eur J Appl Physiol. 2016;116(4): 697–706.
16. Allen H, Coggan A. Training and racing with a power meter.
Boulder, CO: VeloPress. 2012; 39–52.
17. Borszcz FK, Tramontin AF, Bossi AH, Carminatti LJ, Costa VP.
Functional threshold power in cyclists: Validity of the concept
and physiological responses. Int J Sports Med. 2018;39(10):
737–742.
18. Mackey J, Horner K. What is known about the FTP20 test
related to cycling? A scoping review. J Sports Sci. 2021;39(23):
2735–2745.
19. Buckinx F, Reginster JY, Dardenne N, Croisiser JL, Kaux JF,
Beaudart C, et al. Concordance between muscle mass assessed
by bioelectrical impedance analysis and by dual energy x-ray
absorptiometry: A cross-sectional study. BMC Musculoskelet
Disord. 2015;16(1): 1–7.
20. Park I, Lee JH, Jang DH, Kim J, Hwang BR, Kim S, et al.
Assessment of body water distribution in patients with sepsis
during fluid resuscitation using multi-frequency direct
segmental bioelectrical impedance analysis. Clin Nutr.
2020;39(6): 1826–1831.
21. Buchheit M, Chivot A, Parouty J, Mercier D, Al Haddad H,
Laursen P, et al. Monitoring endurance running performance
using cardiac parasympathetic function. Eur J Appl Physiol.
2010;108(6): 1153–1167.
22. Berntson GG, Thomas Bigger Jr J, Eckberg DL, Grossman P,
Kaufmann PG, Malik M, et al. Heart rate variability: Origins,
methods, and interpretive caveats. Psychophysiology.
1997;34(6): 623–648.
23. Buchheit M, Gindre C. Cardiac parasympathetic regulation:
Respective associations with cardiorespiratory fitness and
training load. Am J Physiol Heart Circ Physiol. 2006;291(1):
H451–H458.
M. Castillo-Aguilar et al. / Rev Andal Med Deporte. 2023;16(1):
Online
24. Akselrod S, Gordon D, Ubel FA, Shannon DC, Berger AC, Cohen
RJ. Power spectrum analysis of heart rate fluctuation: A
quantitative probe of beat-to-beat cardiovascular control.
Science. 1981;213(4504): 220–222.
25. Goldstein DS, Bentho O, Park MY, Sharabi Y. Low-frequency
power of heart rate variability is not a measure of cardiac
sympathetic tone but may be a measure of modulation of
cardiac autonomic outflows by baroreflexes. Exp Physiol.
2011;96(12): 1255–1261.
26. Malik M. Heart rate variability: Standards of measurement,
physiological interpretation, and clinical use: Task force of the
european society of cardiology and the north american society
for pacing and electrophysiology. Ann Noninvasive
Electrocardiol. 1996;1(2): 151–181.
27. Fisher A, Groves D, Eleuteri A, Mesum P, Patterson D, Taggart P.
Heart rate variability at limiting stationarity: Evidence of
neuro-cardiac control mechanisms operating at ultra-low
frequencies. Physiol Meas. 2014;35(2): 309.
28. McCraty R, Shaffer F. Heart rate variability: New perspectives
on physiological mechanisms, assessment of self-regulatory
capacity, and health risk. Glob Adv Health Med. 2015;4(1): 46 –
61.
29. Rajendra Acharya U, Paul Joseph K, Kannathal N, Lim CM, Suri
JS. Heart rate variability: A review. Med Biol Eng Comput
2006;44(12): 1031–1051.
30. Yoo HH, Yune SJ, Im SJ, Kam BS, Lee SY. Heart rate variability-
measured stress and academic achievement in medical
students. Med Princ Pract. 2021;30(2): 193–200.
31. Baevsky R, Berseneva A. Methodical recommendations use
kardivar system for determination of the stress level and
estimation of the body adaptability standards of
measurements and physiological interpretation. Moscow;
2008.
32. Tarvainen MP, Niskanen JP, Lipponen JA, Ranta-Aho PO,
Karjalainen PA. Kubios HRV–heart rate variability analysis
software. Comput Methods Programs Biomed. 2014;113(1):
210–220.
33. R Core Team. R: A language and environment for statistical
computing. Vienna, Austria: R Foundation for Statistical
Computing; 2021.
34. RStudio Team. RStudio: Integrated development environment
for r. Boston, MA: RStudio, PBC; 2022.
35. Venables WN, Ripley BD. Modern applied statistics with s.
Fourth. New York: Springer; 2002.
36. Ben-Shachar MS, Lüdecke D, Makowski D. effectsize:
Estimation of effect size indices and standardized parameters.
J Open Source Softw. 2020;5(56): 2815.
37. Makowski D, Ben-Shachar MS, Patil I, Lüdecke D. Estimation of
model-based predictions, contrasts and means. CRAN . 2020;
https://github.com/easystats/modelbased
38. Makowski D, Ben-Shachar MS, Patil I, Lüdecke D. Methods and
algorithms for correlation analysis in r. J Open Source Softw.
2020;5(51): 2306.
39. Wickham H. ggplot2: Elegant graphics for data analysis.
Springer-Verlag New York; 2016.
40. Suárez-Mier MP, Aguilera B. Causes of sudden death during
sports activities in spain. Rev Esp Cardiol. 2002;55(4): 347–
358.
41. Al-Khelaifi F, Donati F, Botrè F, Latiff A, Abraham D, Hingorani
A, et al. Metabolic profiling of elite athletes with different
cardiovascular demand. Scand J Med Sci Sports. 2019;29(7):
933–943.
42. Porges SW. Cardiac vagal tone: A physiological index of stress.
Neurosci Biobehav Rev . 1995;19(2): 225–233.
