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Research Article
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Underlying physiological and biomechanical mechanisms related to postural control of
Parkour practitioners: a pilot study
ANDRÉ F. V. VENEROSO
1
| PATRICK W. SEGUNDO
1
| DANIELA GODOI
1
1
Dinâmica - Motor Behavior Laboratory, Department of Physical Education, Federal University of São Carlos, São Carlos, SP, Brazil.
Correspondence to:!Daniela Godoi, Dinâmica - Motor Behavior Laboratory, Department of Physical Education, Federal University of São Carlos - Rod. Washington Luís,
km 235 - SP-310, CEP 13565-905, São Carlos, SP, Brazil.
email: danielagodoij@ufscar.br
https://doi.org/10.20338/bjmb.v15i2.207
HIGHLIGHTS
• Tracers show a lower amount of sway than
physically active subjects.
• Tracers show a lower amplitude of the torque
required for stabilization than physically active
subjects.
• Tracers show a higher degree of postural
stability than physically active subjects.
• The use of sensory inputs to control balance is
different in tracers.
• The underlying physiological and
biomechanical mechanisms related to postural
control are different in tracers.
ABBREVIATIONS
ANOVAs Analyses of variance
AP Anterior-posterior
CoP Center of pressure
MD Mean distance between
successive peaks
ML Medial-lateral
MP Mean value of the peaks
MT Mean time interval between
successive peaks
RMS Root mean square
SDC Sway Density Curve
PUBLICATION DATA
Received 19 10 2020
Accepted 05 12 2020
Published 01 06 2021
BACKGROUND: Parkour can be seen as a sport, an art, a philosophy, a state of mind, an art of living. Practitioners
(known as “tracers”) have to overcome obstacles in their path by adapting their movements to the given
environment to reach somewhere or something or to escape from someone or something. However, the
knowledge about the underlying mechanisms related to postural control in tracers is still lacking.
AIM: To examine the postural control in tracers using global, structural, and spectral stabilometric descriptors.
METHOD: Five tracers and five controls, all-male, stood upright for 30 seconds, under different conditions of vision
(open or closed eyes), surface (soft or rigid), and base of support (bipedal, semi-tandem, or Parkour stance).
RESULTS: In more challenging conditions, the tracers compared to controls, showed a lower amount of sway,
needed less postural commands, and used sensory information to control balance differently.
CONCLUSION: Tracers have better postural control than controls. Moreover, although current findings are based
on data from a small number of subjects, the results suggest that these differences between groups are related
to different underlying physiological and biomechanical mechanisms related to postural control.
KEYWORDS: Tracers | Postural Control | Sensory Information | Control Mechanisms
INTRODUCTION
Postural control involves not only balance but also the ability to assume and
maintain a desired orientation; so, every movement involves postural control.
1
Therefore,
the postural control system's accurate functioning allows us to interact with the environment
properly. However, for this to occur, it is necessary to get information about the environment,
which is possible from multiple sources of sensory inputs.
Sensory information comes from, mainly, the visual, vestibular, and somatosensory
sensory systems.
2
Nevertheless, sensory integration for postural control is not merely a
summation of inputs from different sensory systems, but a non-linear process named
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sensory reweighting.
3,4
In this way, whenever environmental or central nervous system
conditions change, sensory inputs must be dynamically reweighed to optimize the control of
postural stability.
3
This dynamic sensory reweighting process allows us to properly perceive the
environment and then appropriately act in that environment. Thus, the action is influenced
by the perceived environment, and that action may influence the perceived environment.
5
In
Gibson’s words, action leads to the detection of information, and information plays a vital
role in controlling the action.
6
So, it can be said that people perceive to move and move to
perceive
7
.This mutual dependency of action and perception is designated as the formation
of an action-perception pattern
5
or cycle
1
; that is, an action-perception coupling.
7
Interestingly, postural control functioning is not ready at the beginning of life; on the
contrary, it changes throughout life. As a result, the ability to select and use sensory
information for the appropriate and consistent functioning of the postural control system
according to the environmental demands depends on age,
4,8,9,
and practice.
10,11
For this reason, several studies have examined the sport training effects on postural
control
11,12,13,14
in experimental conditions that manipulated vision
11,12,14
and
somatosensory
14
information. And, among athletic training, Parkour emerge as an
interesting option.
Parkour is derived from the French word parcourt meaning “obstacle course,” and
was created in Paris's suburbs by David Belle and Sébastien Foucan.
15
It is defined as an
art allowing to pass any obstacle to go from one point of space to another with the
possibilities offered by the human body.
16
Thus, Parkour involves practitioners (called
“tracers”) training to overcome obstacles in their path by adapting their movements to the
given environment to reach somewhere or something or escaping from someone or
something.
17
There are several specific Parkour movements, and these movements vary
according to environmental conditions. In general, it can be mentioned vaults (movements
that involves overcoming an obstacle by climbing, jumping, or diving over an obstacle while
using feet, hands, or not touching it at all), precision jumps (jump from or jump to a specific
point from a stationary position), wall runs (horizontal or vertical runs, used to get over a wall
too high), and “cat leaps” or arm leap (jump used to land on a ledge, a wall, or a fence).
18
Due to the rapid growth of this activity worldwide
16,19
and the fact that this activity
had been recognized as a discipline by the International Gymnastics Federation, studies
have been conducted to understand tracer’s performance better. Most studies have been
interested in injuries caused by this activity,
15
sociocultural aspects,
16,19
strength and power
performance,
20
and biomechanics characteristics of landing.
17
However, few studies have been carried out to evaluate the effect of such practice
on postural control. To the best of our knowledge, there are only two studies that
investigated postural control of tracers. One pilot study
18
that investigated postural control
during the maintenance of a standing upright position, and another study
21
that investigated
postural control during Parkour landing. Nonetheless, in these studies, postural stability was
described by linear descriptors (global descriptors such as the area of the center of pressure).
Thus, it was possible to describe the amount of sway but not the dynamics that regulate
balance control.
To describe and understand these underlying physiological and biomechanical
mechanisms related to postural control, more robust analyses should have been employed,
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including spectral and structural stabilometric descriptors. In this sense, it can be mentioned
some analyses such as the Sway Density Curve (SDC) analysis of the center of pressure
(CoP),
22
which provide information related to the process of generating sequences of
postural commands,
23,24,25
or spectral analysis in different bands of frequency, which
assumes that different frequency bands are related to control based on different sources of
sensory inputs
12,13
and, therefore, provides information about the use of different sources of
sensory inputs by the postural control system.
Thereby, overall knowledge about the underlying physiological and biomechanical
mechanisms related to postural control in tracers is still lacking. Hence, there is a need to
gain insight into the dynamics that regulate these practitioners' balance control.
Therefore, the present pilot study aimed to examine the postural control in tracers
using global, structural, and spectral stabilometric descriptors. It was hypothesized that
tracers (a) would show a lower amount of sway (global descriptors), (b) would need less
postural commands to control balance (structural descriptors), and (c) would use sensory
inputs in a different way (spectral descriptors), when compared to control subjects with no
prior experience in Parkour.
METHODS
Participants
Ten male participants (tracers and controls) volunteered for this study. The Tracer
group (n=5; mean age: 21.40±2.70 years) had participated in Parkour training for at least
one year. They trained around two times per week, and each training session lasted between
70 and 80 minutes, divided into four parts: 1) 5-10 min of warm-up activities; 2) 25-30 min of
specific Parkour movements, such as vaulting, climbing, precision jump, quadrupedal
movement, wall run; 3) 15-20 min of path activities where the goal is to go from one point of
space to another by using specific Parkour moves to overcome obstacles in their path by
adapting their movements to the given environment; and 4) 15-20 min of challenging
activities which included tasks (either technical Parkour movements or path activities) more
difficult than those practiced earlier in that session training.
The Control group (n=5; mean age: 23.80±2.95) had been involved in other physical
activities for at least one year, and had no prior experience in Parkour training. All
participants gave their informed consent after the procedures were fully explained. The study
was conducted following the Declaration of Helsinki and approved by the Institutional Review
Board of the Federal University of São Carlos (3.021.618/2018).
Procedures
Participants were asked to maintain an upright position barefoot on a force platform
(Advance Mechanical Technology Inc.– AMTI – AccuGait) with their arms alongside their
body, look straight ahead, and maintain their position for 30 seconds. All participants
underwent different conditions of vision (open or closed eyes), surface (soft or rigid), and
base of support (bipedal stance, semi-tandem stance, or Parkour landing stance) (Figure 1).
These conditions were manipulated to generate different levels of challenge to the postural
control system, which is considered a classic strategy to unveil the postural control
functioning.
3
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Figure 1. Pictures of the experimental conditions showing the conditions of rigid surface (A-D), soft surface
(E-G), bipedal stance (A, B, E), semi-tandem stance (C, D, F), and Parkour stance (G-H).
Under the open eyes condition, participants fixated on a target (a filled circle in black
with 5 cm of diameter in white background) placed at eye level and 1.0 meter in front of
them.Under the soft surface condition, a 10-cm thick foam block (density=35.0 kg/m
3
; elastic
modulus=50,000 N/m
2
) was placed on force platform (Figure 1E-G). Under the bipedal
stance condition, participants stood with their feet parallel to each other and approximately
shoulder-width apart (Figure 1A-B, E). Under the semi-tandem stance, participants stood
with the foot’s hallux positioned behind, touching the medial edge of the calcaneus of the
other foot (Figure 1C-D, F). Under the Parkour stance condition, participants stood on the
forefoot, bending the knees without varus or valgus movement of the knees and using the
arms to keep balance (Figure 1G-H).
These conditions of vision, surface, and base of support resulted in twelve
experimental conditions: (1) Eyes open, rigid surface and bipedal stance; (2) Eyes open,
rigid surface and semi-tandem stance; (3) Eyes open, soft surface and bipedal stance; (4)
Eyes open, soft surface and semi-tandem stance; (5) Eyes closed, rigid surface and bipedal
stance; (6) Eyes closed, rigid surface and semi-tandem stance; (7) Eyes closed, soft surface
and bipedal stance; (8) Eyes closed, soft surface and semi-tandem stance; (9) Eyes open,
rigid surface and Parkour stance; (10) Eyes open, soft surface and Parkour stance; (11)
Eyes closed, rigid surface and Parkour stance; and (12) Eyes closed, soft surface and
Parkour stance.
Two trials were performed in each condition, totalizing 24 trials per participant, with
the order of conditions defined randomly. A resting period ranging from 10 to 20 seconds
was provided after each trial.
Data analysis
Signals from the force platform were recorded at a frequency of 200 Hz and filtered
using a 2
nd
order zero-lag low-pass Butterworth filter, with a cut-off frequency of 12.5 Hz. To
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compute features from the CoP displacements, global, spectral, and structural analyses
were employed in the anterior-posterior (AP) and medial-lateral (ML) directions, except in
the case of the structural analysis.
The global analyses were used to describe postural control performance. The
descriptors selected were: root mean square (RMS), mean velocity, and amplitude of the
CoP. RMS was calculated by subtracting the average position from the original signal (using
a de-trending operation) and then by calculating the standard deviation of the AP and ML
CoP trajectories (in centimeters). Mean velocity is the average velocity of the CoP
displacement in the AP and ML directions (in centimeters per second). Amplitude is the
absolute value of the difference between the smallest and largest values in the AP and ML
time series (in centimeters).
The structural analysis chosen was the SDC, which is defined as a time-dependent
curve that counts the number of consecutive CoP samples falling inside a circle with a 2.5-
mm radius for each instant of time.
22,23
The peaks of the resulting curve represent instants
of momentary postural stabilization, while the valleys are related to shifts between
stabilization events.
25
The SDC descriptors selected for analysis in the present study were:
MP, the mean value of the peaks (in seconds), which is an estimate of the degree of postural
stability; MD, the mean distance between successive peaks (in centimeters), which
corresponds to the amplitude of torque required for stabilization; MT, the mean time interval
between successive peaks (in seconds), which is related to the rate of torque production.
23,25
These descriptors describe the process of generating sequences of postural commands.
22
Therefore, this analysis may reveal more clearly physiological and biomechanical
mechanisms related to postural control.
The spectral analyses were used to infer the use of sensory information by the
postural control system. The descriptor selected was the mean power spectrum of the CoP.
The power spectrum of the CoP displacement obtained in each trial was estimated using
Welch’s method. The mean power spectrum was calculated in the intervals 0.0-0.3, 0.3-1.0,
and 1.0-3.0 Hz.
12,26
This analysis assumes that the low-frequency band is related to visual
control. The middle-frequency band is related to vestibular and somatosensory information,
and the high-frequency band is related to proprioceptive control and muscle activity
12,13,26
.
Thus, this analysis may provide relevant information about the physiological mechanisms
related to postural control.
All variables were calculated using a custom-designed Matlab code.
Statistical Analysis
The average of two trials in each experimental condition was calculated for each
participant. Data were normalized by each participants' body weight and height.
27
The
Normality and the homogeneity of the data were verified by the Shapiro-Wilk test and the
Levene test. Then, analyses of variance (ANOVAs) with group (control and Parkour) as
factor were conducted for each experimental condition. Dependent measures were RMS,
mean velocity, MD, MT, MP, mean power in the intervals 0.0-0.3, 0.3-1.0, and 1.0-3.0 Hz
(low-, middle-, and high-frequency bands). The effect size (ES) was calculated for all
dependent variables and classified as small (<0.2), moderate (0.2-0.79), and large (>0.8).
28
All the analyses were performed utilizing IBM SPSS software and α-level at 0.05.
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RESULTS
Results revealed significant differences between tracers and controls in global,
structural, and spectral descriptors under some experimental conditions.
Global Descriptors
ANOVAs revealed that under experimental condition 11, the tracers showed lower
values for RMS in ML direction [F(1,9)=8.776, p=0.018, ES: 0.52 (moderate)], mean velocity
in AP direction [F(1,9)=6.999, p=0.029, ES: 0.47 (moderate)], and amplitude in ML direction
[F(1,9)=5.910, p=0.041, ES: 0.42 (moderate)] than controls (Figure 2).
Figure 2. RMS in ML direction (A), Mean Velocity in AP direction (B), and Amplitude in ML direction (C) values
for both groups in the experimental condition 11 (Eyes closed, rigid surface and Parkour stance). *Significantly
different, p < 0.05. Values presented as mean and standard deviation.
Moreover, ANOVAs revealed that under experimental condition 12, the tracers
showed lower values for mean velocity in ML direction [F(1,9)=6.549, p=0.034, ES: 0.45
(moderate)], and amplitude in ML direction [F(1,9)=5.509, p=0.047, ES: 0.41 (moderate)]
than controls (Figure 3).
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Figure 3. Mean Velocity in ML direction (A), and Amplitude in ML direction (B) values for both groups in the
experimental condition 12 (Eyes closed, soft surface and Parkour stance). *Significantly different, p < 0.05.
Values presented as mean and standard deviation.
Structural Descriptors
ANOVAs revealed that the tracers showed lower values for MD than controls under
experimental conditions 2 [F(1,9)=6.234, p=0.037, ES: 0.44 (moderate)], 6 [F(1,9)=5.923,
p=0.041, ES: 0.43 (moderate)], and 12 [F(1,9)=6.309, p=0.036, ES: 0.44 (moderate)] (Figure
4).!
Figure 4. MD values for both groups in the experimental conditions 2 (Eyes open, rigid surface and semi-
tandem stance) (A), 6 (Eyes closed, rigid surface and semi-tandem stance) (B), and 12 (Eyes closed, soft
surface and Parkour stance) (C). *Significantly different, p < 0.05. Values presented as mean and standard
deviation.
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Furthermore, ANOVAs revealed that the tracers showed higher values for MP than
controls under experimental conditions 11 [F(1,30)=5.858, p=0.042, ES: 0.40 (moderate)],
and 12
[F(1,30)=5.393, p=0.049, ES: 0.43 (moderate)] (Figure 5).
Figure 5. MP values for both groups in the experimental conditions 11 (Eyes closed, rigid surface and Parkour
stance) (A), and 12 (Eyes closed, soft surface and Parkour stance) (B). *Significantly different, p < 0.05. Values
presented as mean and standard deviation.
Spectral Descriptors
ANOVAs revealed that under experimental condition 6, the tracers showed lower
values for mean power in ML direction at the middle-frequency band [F(1,9)=5.961, p=0.04,
ES: 0.43 (moderate)] than controls; under experimental condition 11, the tracers showed
lower values for mean power in AP direction at the high-frequency band [F(1,9)=8.607,
p=0.019, ES: 0.52 (moderate)], and for mean power in ML direction at the low-
[F(1,9)=14.884, p=0.003, ES: 0.69 (moderate)], middle- [F(1,9)=9.355, p=0.016, ES: 0.54
(moderate)], and high-frequency [F(1,9)=7.517, p=0.025, ES: 0.47 (moderate)] bands than
controls; under experimental condition 12, the tracers showed lower values for mean power
in AP direction at the high-frequency band [F(1,9)=6.382, p=0.035, ES: 0.44 (moderate)],
and for mean power in ML direction at the high-frequency band [F(1,9)=5.662, p=0.045, ES:
0.43 (moderate)] than controls (Figure 6).
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Figure 6. Mean power for both groups in the AP (A, C, E) and ML (B, D, F) direction at different frequency
bands under experimental conditions 6 (Eyes closed, rigid surface and semi-tandem stance) (A-B), 11 (Eyes
closed, rigid surface and Parkour stance) (C-D), and 12 (Eyes closed, soft surface and Parkour stance) (E-F).
*Significantly different, p < 0.05. Values presented as mean and standard deviation.
DISCUSSION
The present pilot study aimed to examine the postural control in tracers using global,
spectral, and structural stabilometric descriptors. The main results showed that tracers (a)
displayed lower variability, velocity, and amplitude of the CoP displacement than controls,
(b) needed less postural commands to control balance when compared to controls, and (c)
used sensory inputs differently when compared to controls. These differences between
tracers and controls were not observed in all experimental conditions, only in more
challenging conditions such as semi-tandem and Parkour stances, without vision information,
and under the soft surface. Thus, the hypotheses were partially confirmed. These results will
be discussed below.
Amount of Body Sway
Regarding the global descriptors, tracers showed lower variability, velocity and
amplitude of CoP displacement than controls. However, these differences were observed
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only during Parkour stance with eyes closed and rigid (Figure 2) and soft (Figure 3) surfaces.
Thus, the significant differences in global descriptors were found only in a specific posture
(Parkour stance) and in a more challenging condition (without vision information).
These results are similar to other studies that used global descriptors and found that
trained subjects
11,18
showed better postural control only in the trained postures. Asseman et
al.
11
found that expertise in gymnastics only affected postural stability when performing
postures similar to those that have been trained; on the contrary, in an unspecific posture
(i.e. bipedal stance), gymnasts had similar postural performance compared to untrained
subjects. Similarly, Jabnoun et al.
18
observed differences between tracers and recreationally
active subjects only in more challenging postural conditions.
Postural Commands and Use of Sensory Information
To the best of our knowledge this is the first study to use SDC and spectral
descriptors to investigate the postural control in tracers.
Concerning SDC descriptors, tracers showed lower MD values than controls when
standing on semi-tandem stance and rigid surface in both vision conditions, and when
standing on Parkour stance with closed eyes in the rigid surface. Moreover, tracers showed
higher MP values than controls when standing on Parkour stance with closed eyes in both
surface conditions.
The parameters extracted from the SDC analysis can be related to both anticipatory
and postural stability control.
26
Thus, the SDC attempts to identify the motor control actions
hidden in the CoP signals.
24
In this sense, the higher MP values observed for tracers (Figure
5) reflects the higher degree of postural stability.
23,25
In addition, considering that the SDC
peaks are related to the active torque component,
23,26
the lower MD values observed for the
tracers (Figure 4) indicates lower amplitudes of the torque required for stabilization.
23,25
Although there were no studies about Parkour with these descriptors, lower MP
values, and higher MD values have been associated with poor postural stability.
22,24
Thus,
our results indicate that tracers have better postural control than controls.
Regarding the spectral analysis, significant differences between tracers and controls
were found (Figure 6). Given that the information provided by each sensory modality is
unique, and each class of receptor operates optimally within a specific range of frequency
and amplitude of body motion,
3
the spectral analysis in different bands of frequency can
provide relevant information about distinct sources of sensory inputs used by subjects to
control balance.
This analysis revealed that under semi-tandem stance and rigid surface with closed
eyes condition, tracers showed lower values for mean power in ML direction at the middle-
frequency band than controls. Additionally, under Parkour stance and rigid surface with
closed eyes condition, tracers showed lower values for mean power in AP direction at the
high-frequency band, and for mean power in ML direction at all frequencies band than
controls. Finally, under Parkour stance and soft surface with closed eyes condition, tracers
showed lower values for mean power in AP and ML directions at the high-frequency band
than controls.
Assuming that the low-frequency band is related to visual control,
12,13,26
the middle-
frequency band is related to vestibular and somatosensory information,
12,26
and the high-
frequency band is related to proprioceptive control and muscle activity,
12,26
our findings
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indicate that tracers use these sensory inputs to control balance differently when compared
to controls.
Furthermore, higher values for mean power at the low-frequency band were related
to visual impairments,
26
higher values for mean power at the middle-frequency band were
related to cutaneous and vestibular feedback impairments,
26
and higher values for mean
power at the high-frequency band were related to proprioceptive and muscle activity
alterations
26
or increased muscle activity.
24
Therefore, these differences between tracers and controls observed in mean power
at all frequency bands may reveal some interesting aspects of their postural control's
underlying physiological mechanisms. Based on these higher mean power values displayed
by controls, it could be suggested that they exhibit less calibrated sensory reweighting than
tracers when exposed to similar environmental conditions. Thus, controls would not be able
to properly extract and use the most relevant sensory cues in order to control balance.
Otherwise, tracers would be able to do that. In this way, the lower values for mean power at
all frequency bands observed in tracers could mean that they exhibit more calibrated sensory
reweighting. Consequently, tracers also displayed lower amplitudes of the torque required
for stabilization, a higher degree of postural stability, and a lower amount of body sway.
All taken together, these results indicate that tracers have better postural control
than controls. This is probably due to Parkour training characteristics, which involve
numerous movements, and these movements have to be adapted according to the
environmental conditions. Considering that decision-making is essential for high-level
performance in an unpredictable environment,
29
the characteristics of Parkour seems to
demand a high decision-making performance. Also, to achieve success in decision-making
performance, athletes have to enhance action-perception coupling by improving the
detection and use of perceptual variables that inform which actions are possible or not to be
performed.
30
In this way, whenever environmental conditions change, sensory inputs must
be dynamically reweighed to optimize the control of postural stability
3,
which allows the
tracers to perceive the environment properly and then to act in that environment
appropriately (action-perception coupling).
In summary, the practice of Parkour provides sensorimotor experiences that
improve the underlying physiological and biomechanical mechanisms related to postural
control.
Limitations of this study are its exploratory nature and the small sample size. Even
though, these preliminary data provide interesting information about the underlying
physiological and biomechanical mechanisms related to postural control of tracers. However,
further research should be conducted to expand the knowledge of these mechanisms.
CONCLUSION
This pilot study revealed that tracers have better postural control than controls.
Specifically, tracers compared to controls show a lower amount of sway, need less postural
commands, and use sensory inputs to control balance differently. Therefore, although
current findings are based on data from a small number of subjects, the results suggest that
these differences are related to different underlying physiological and biomechanical
mechanisms related to postural control.
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ACKNOWLEDGEMENTS
The authors are grateful to all participants who volunteered to take part in this
study.
Citation: Veneroso AFV, Segundo PW, Godoi D. Underlying physiological and biomechanical mechanisms related to
postural control of parkour practitioners: a pilot study. BJMB. 2021. 15(2): 65-78.
Editors: Dr Fabio Augusto Barbieri - São Paulo State University (UNESP), Bauru, SP, Brazil; Dr José Angelo Barela -
São Paulo State University (UNESP), Rio Claro, SP, Brazil; Dr Natalia Madalena Rinaldi - Federal University of
Espírito Santo (UFES), Vitória, ES, Brazil.
Copyright: © 2021 Veneroso, Segundo and Godoi and BJMB. This is an open-access article distributed under the
terms of the Creative Commons Attribution-Non Commercial-No Derivatives 4.0 International License which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-
profit sectors.
Competing interests: The authors have declared that no competing interests exist.
DOI:!https://doi.org/10.20338/bjmb.v15i2.207