Key words respiratory - mechanics - cycling
Introduction
Cycling is a demanding endurance sport that requires high physical fitness and
stamina. Professional cyclists exhibit specific physiological responses such as
considerable reliance on fat metabolism, even at high power outputs, and several
neuromuscular adaptations [1 ]
[2 ]
[3 ]. Respiratory
efficiency is also critical for cyclists [4 ]
[5 ]
[6 ]. Through
training, they can improve oxygen uptake, which helps to improve endurance and delay
the onset of fatigue during prolonged efforts [6 ]
[7 ]
[8 ].
In particular, they develop distinctive breathing patterns at high workloads that
are distinguished by a lack of tachypnoeic shift, as they maintain increased
pulmonary ventilation by increasing tidal volume rather than respiratory frequency
of breathing [5 ]
[9 ].
It is suggested that this breathing adaptation can enhance metabolic efficiency,
partly accounting for the VO2 kinetics [1 ].
While all such physiological adaptations are well documented, there is insufficient
information regarding respiratory mechanics, described as thoracoabdominal (THA)
motion patterns, i. e. displacement and coordination of the rib cage and abdomen
[10 ]. Generally speaking, THA motion patterns
modify according to the physical activity intensity, being dependent also on other
factors such as gender, posture, and motion task [11 ]
[12 ]
[13 ]
[14 ]. Conversely, long-term sports
training can permanently modify the THA motion patterns (evidenced by measurements
taken during rest breathing maneuvers), to the advantage of better respiratory
performance [15 ]
[16 ]
[17 ].
Especially, optimized lung and chest wall compliance, required to progressively
suppress dyspnea and minimize the work of breathing, elicits respiratory mechanics
adaptation [18 ]
[19 ].
At rest and during exercise, there are distinct patterns of muscular recruitment.
During rest, the rib cage muscles (as external intercostals, scalene,
sternocleidomastoid) and the diaphragm act as pressure generators, and the abdominal
muscle is not active. Instead, during exercise, the diaphragm behaves essentially
as
a flow generator because its shortening ability is greater than the inspiratory rib
cage muscles, and the rib cage and abdominal muscles (such as transversus abdominis
and internal and external oblique) are pressure generations [20 ]
[21 ].
Among different non-invasive technologies to measure chest wall displacements,
optoelectronic systems have been largely adopted, which record the 3D position of
physical markers distributed homogeneously on the trunk surface [15 ]
[16 ]
[22 ]
[23 ]
[24 ]. Traditionally, the shape of the chest wall is
partitioned into three functional compartments independent of each other [25 ]. This compartmental division provides information
on respiratory muscles’ action and assesses breathing movement patterns [18 ].
During pedaling, breathing muscles act as a crucial postural function, where optimal
relation between the diaphragm, abdominal wall, parasternal, and pelvic floor
provides production, transfer, and control of the active force [26 ]. In particular, the bike sitting posture may
induce physical constraints to THA expansion and could affect respiratory mechanics.
These differences in thoracoabdominal patterns become particularly prominent during
moderate-intensity efforts, where there is an expected gradual increase in
ventilation aligned with alveolar ventilation [27 ]
and respiratory function [28 ]
[29 ]. This phenomenon can be observed, for instance,
during a time trial race that involves cyclists completing a predetermined distance
as swiftly as possible. Such races have been utilized in laboratory settings to
simulate endurance events and predict aerobic performance indicators like
VO2 and lactate threshold [30 ].
Since 2008, our group has been investigating factors affecting THA motion patterns,
such as physical activities, exercise, long-term training, and more in general
anamnestic data [15 ]
[16 ]
[22 ]
[31 ]. Experienced swim training was shown to increase coordination of the THA
volumes and the ribs motion [22 ], and the
contribution of the abdominal compartment [15 ].
Experienced ballet dancers, both men and women, showed the predominant respiratory
contribution of the upper thorax and abdomen [16 ].
Interestingly, age was found to be a weak predictive factor for changes in breathing
motion patterns, contrary to what is commonly believed in the field of respiratory
physiology [31 ].
Capitalizing on such previous studies, this work aimed to investigate the THA
breathing motion pattern in experienced cyclists compared to non-cyclists. To
provide further clarity, a secondary objective was to examine potential disparities
in the THA motion pattern of experienced cyclists during periods of rest and
exercise, with the intention of evaluating the consistency and stability of such
patterns. To achieve these objectives, our study involved subjecting the cyclists
to
a targeted simulated 20-km time trial.
Material and Methods
Participants and experimental protocol
Twenty healthy and non-smoker participants were enrolled in this study. They were
divided into two sets, respectively control and cyclist groups. The control
group comprised ten males physically active in different recreational sports,
none of whom had ever been involved in intensive cycling practice. Every
participant in the control group engaged in physical exercise for more than
3 hours per week. The cyclist group was composed of ten trained cyclist males
with at least five years of competitive experience on speed bikes, who regularly
participate in regional cycling events. The acquisition protocol involved a
pulmonary function test performed by both groups. Specifically, three maneuvers
of forced vital capacity were monitored first throughout spirometry. Then
breathing maneuvers at rest, namely quiet breathing, and vital capacity trials,
were monitored by optoelectronic plethysmography ([Fig. 1a ]). The cyclist group performed a simulated 20-km time-trial
test ([Fig. 1b ]). The study was approved by the
University Ethics Committee (Number 59773616.0.0000.5153) in compliance with the
Declaration of Helsinki, and all participants provided written informed
consent.
Fig. 1 General protocol and tests performed.
Pulmonary function test
To compare the pulmonary function, a spirometer (Micro-medical, Rochester, Kent,
England) was used. The control and cyclist groups performed three maneuvers of
the forced vital capacity according to a standard protocol in a sitting posture
[32 ]. Measurements were obtained for forced
vital capacity (FVC), which is the greatest total amount of air expired, and for
the forced expiratory volume in one second (FEV
1 ), which is
the volume delivered in the first second of a forced vital capacity maneuver
according to the guidelines established by the American Thoracic
Society/European Respiratory Society [33 ]. These
data were expressed as mean and percentages of predicted values for the
Brazilian population, according to [34 ].
THA – movement patterns by three-dimensional kinematics of the trunk
compartments
Thirty-two retro-reflective markers were placed on the trunk of the participants
[25 ]. The markers were positioned as a grid,
using anatomical references allowing trunk division into three compartments
namely superior thorax (ST), from 2
nd
rib to the xiphoid
process, inferior thorax (IT), from this line to the 10
th
rib,
and abdomen (AB), from this line to transverse of the abdomen [16 ]. The 3D coordinates of the markers were
acquired with eleven OptiTrack Prime 17 W cameras (NaturalPoint, Corvallis, OR,
USA, 360 Hz), positioned around the participants. A low pass, cutoff frequency
of 10 Hz, was used to filter the marker trajectories. From the filtered marker
locations, the compartmental volumes were calculated frame by frame using a
volumetric convex hull method in the software Visual 3D (C-Motion, USA) [23 ]. The variation of the compartmental volume was
expressed as a function of the time, divided into breathing cycles, defined as
the beginning of inspiration to the end of expiration. Considering n
breathing cycles, the percentage of contribution of each compartment
(%ST , %IT , %AB ) was computed as:
where V
tot
is the total trunk volume in the
i
th
breathing cycle. We also calculated the
relative ratio between percentage of contribution of superior thorax and
abdomen, and inferior thorax and abdomen. In order to assess the coordination
among the THA compartments, the correlation coefficient was computed by means of
the cross-correlation between the pairs of the compartmental volume during the
inspiration phase. Positive and negative correlations indicated coordinated and
paradoxical movements, respectively.
Breathing maneuvers at rest
Participants (control group and cyclist group) were seated on a chair without
back support, with shoulder abduction, forearms leaned on a rigid support, 90º
of knee flexion, and feet on the ground ([Fig.
1a ]). Following an adaptation period (at least 2 min) to the
experimental setup, they performed two quiet breathing trials (30 s, each).
Then, they performed two trials of five cycles of vital capacity, characterized
by five periods of maximum inspiration followed by maximum forced expiration.
The participants were directed to perform this maneuver using a verbal cue.
Simulation of the 20-km time trial
The cyclists performed a self-paced time trial in laboratory settings ([Fig. 1b ]) using their bicycle coupled to a cycle
trainer (Computrainer ProLab 3D, Racermate, Seattle, WA, USA). The rear wheel of
the personal bike was changed to Powermeter equipment (PowerTap, Saris, Madison,
USA). The 20-km time trial protocol was configured in the Computrainer software
as a flat course and consisted of a 10-min warm-up, and a 20-km time trial
simulated test performed in the shortest time possible. The participants
received verbal encouragement stimulus when they were nearly to achieve a
distance of 5, 10, 15 km, and at the end of the test (20 km). They were
instructed to maintain their hands at brake hoods enabling trunk markers’
tracking. The THA movement pattern was evaluated one minute earlier each step in
5, 10, 15, and 20 kilometers. Heart rate (HR) and ratings of perceived exertion
(RPE) were acquired at the following distances, namely 5, 10, 15, and 20 km. HR
was measured using a Polar Fitness tracker chest strap and the data was acquired
by smartphone app (Elite HRV). The RPE was measured using Borg’s CR-10 scale (1
to 10) [35 ], where 1 corresponds to ‘no exertion’
and 10 corresponds to ‘extreme/maximal exertion’. The participants received
standardized instructions for each measure using this scale before the exercise
session.
Statistical analysis
The normality of the data was evaluated using the Shapiro-Wilk test. The
percentage of contribution and correlation coefficient were not normally
distributed and the arcsine transformation and Fisher’s z-transformation were
applied, respectively. Quiet breathing and vital capacity maneuvers were
analyzed separately. To analyze breathing maneuvers at rest, the Mann-Whitney
test was adopted to compare the descriptive participants’ characteristics
between groups. Repeated measure ANOVAs were then considered to evaluate the
percentage of contribution and correlation coefficient both with one between
factors (groups: control group and cyclist group) and one within factor
(contribution of compartments: superior thorax, inferior thorax and abdomen or
pairs compartments: superior thorax×inferior thorax, superior thorax×abdomen and
inferior thorax×abdomen, respectively). The repeated measure ANOVA with one
within factor (time) was applied to evaluate the power output and HR, and for
the RPE the Friedman test was used. To analyze the breathing motion pattern
during the 20-km time trial, the repeated measure ANOVA with two within factors
(contribution of compartments: superior thorax, inferior thorax and abdomen or
pairs compartments: superior thorax×inferior thorax, superior thorax×abdomen and
inferior thorax×abdomen and time: quiet breathing, vital capacity, 5, 10, 15,
20 km) were employed, respectively. Mauchly’s test of sphericity was performed
and the Huynh-Feldt correction was exploited to correct the variability in
experimental error. If significant F-ratios were detected, a Bonferroni post-hoc
comparison was applied to determine where the differences occurred. Cohen’s
d standardized effect sizes and confidence intervals (set at 95%)
were reported when appropriate. Statistical significance was set at
(α= 5%) for all analyses and was performed using SPSS version 18.0.
Results
The two groups did not differ significantly in terms of age, height, and weight
([Table 1 ]). All the participants showed
pulmonary function results equal to or higher than predicted, and no significant
differences between groups were found.
Table 1 Description of the study sample (mean± SD) for the
subjects’ characteristics, pulmonary function, and training.
Cyclists
Control
Subject characteristics
Age (years)
42.7± 6.3
41.9± 8.7
Height (m)
1.75± 0.07
1.7± 0.08
Weight (kg)
75.9± 7.4
81.4± 10.2
Pulmonary function
FEV
1 (l)
3.9± 0.6
3.6± 0.5
FEV
1 (% predict)
109.7± 19.3
105.2± 15.8
FVC (l)
4.3± 0.8
4.4± 0.7
FVC (% predict)
100.3± 20.6
104.8± 14.8
Training description
Time per week (h)
11.2± 2.1
5.9± 2.7
Experience (years)
19.2± 10.9
22.6± 14*
Regarding the relative ratio between inferior thorax and abdomen, the cyclist group
exhibited values of 0.81 for quiet breathing and 0.89 for vital capacity, whereas
the control group showed values of 0.56 for quiet breathing and 0.60 for vital
capacity. The ANOVA analyses of the percentage of contribution in quiet breathing
([Fig. 2a ]) showed that the effect of the factor
group was not significant (p= 0. 458). The effect of the factor
compartment was significant (p< 0. 001) and inferior thorax and
superior thorax had a similar contribution, while abdomen had a significantly higher
contribution than both inferior thorax (AB: 41.6± 7.9%, 95%CI [37.8, 45.3];
IT: 28.3± 7.7%, 95%CI [24.7, 31.9]; p= 0. 002.
d= 1. 68) and superior thorax (30.1± 5.1%, 95%CI [27.6, 32.4];
p= 0. 001, d= 1. 72). Additionally, there was no
significant interaction effect between the groups.
Fig. 2 Boxplot of the percentage of contribution of superior thorax
(ST), inferior thorax(IT), and abdomen (AB) during quiet Breathing(QB)
(a ) and vital Capacity (VC) (b ) for the control and
cyclist group; + : outlier data.
Again, in the vital capacity maneuver ([Fig. 2b ]),
the ANOVA analyses showed that the effect of the factor group was not significant
(p= 0. 336), and the factor compartment was significant
(p< 0. 001), the abdomen contributing significantly more than
inferior thorax (AB: 42.0± 8.4%, 95%CI [38.1, 45.9]; IT: 30.8± 6.8%,
95%CI [27.6, 34.0], p= 0. 003, d= 1. 46) and superior thorax
(27.2± 5.9%, 95%CI [24.4, 29.9], p< 0. 001,
d= 2. 03). In the cyclist group, the superior thorax had a lower
contribution than the inferior thorax (ST: 25.7± 6.0%, 95%CI [21.4, 30.0]; IT:
34.9± 5.1%, 95%CI [22.5, 30.9], p= 0.002, d= 1.65) and abdomen
(39.3± 4.8, 95%CI [35.9, 42.8], p= 0.004, d =2.49). In the
control group, the abdomen had a higher contribution than the inferior (AB:
44.6± 10.5%, 95%CI [37.1, 52.1]; IT: 26.7± 5.8%, 95%CI [31.3, 38.5],
p=0.001, d =2.11) and superior thorax (28.7± 5.6%, 95%CI [24.6, 32.7],
p<0.001, d= 1.88). Additionally, there was a significant interaction effect
between the factors and the inferior thorax contribution of the cyclist group, which
was significantly higher than the control group (p= 0.004,
d= 1. 46).
Both groups presented strong positive correlation coefficient values for quiet
breathing and vital capacity (0.9 to 0.97). The cyclist group had correlation
coefficient values significantly higher than the control group in quiet breathing
(p= 0. 01) and in vital capacity (p= 0. 034). The
inferior thorax×abdomen compartmental pair had significantly higher correlation
coefficient values than superior thorax×inferior thorax and superior thorax×abdomen
and superior thorax×inferior thorax was significantly higher than superior
thorax×abdomen. In both maneuvers, Cohen’s d values ranged from 0. 22
to 0. 99 (p< 0. 001). Both groups presented this coordination
pattern in quiet breathing. However, during vital capacity, this pattern changed.
While the control group had similar coordination between abdomen×inferior thorax and
superior thorax×inferior thorax, the abdomen×inferior thorax of the cyclist group
was higher than the superior thorax×inferior thorax. Summarized statistical results
are available in the supplementary material.
Due to missing data of some retro-reflective markers placed on the trunk of two
cycling athletes during time trial, the results were reported only for eight
participants ([Table 2 ]). The RPE was different from
each step of the time trial (χ
2 =22. 41,
p< 0. 001; 5> 10> 15> 20 km). In the
last step of the time trial (20 km), HR was significantly higher than 5 km
(p= 0. 001), 10 km (p= 0. 011), and 15 km
(p= 0. 017). Also, significant differences in power output were
found (p< 0. 001) and the 20 km was higher compared to 15 km
(p= 0. 021).
Table 2 Mean (± SD) values of the descriptive characteristics
and thoracoabdominal movement pattern during TT20.
5 km
10 km
15 km
20 km
RPE
3.6±0.5
5.1± 0. 67
*
6.87±0.76
*⃟
9.25± 0.31
*⃟†
HR (BPM)
171.5±3.45
173.4± 3.34
176.4±2.69
184± 3.18
*⃟†
Power Output (W)
245.76±14.93
239.78± 14.16
237.41±15.69
278.87± 20.81
†
Percentage of contribution
%ST
23.59± 3.06
23.60± 3.66
24.51±3.23
23.59± 4.60
%IT
32.88± 2.33
32.94± 3.54
32.47±2.84
33.01± 2.91
%AB
43.51± 3.28
43.45± 3.78
43.01±4.20
43.38± 5.22
ST:AB
0.54
0.53
0.59
0.54
IT:AB
0.76
0.76
0.76
0.76
Correlation coefficient
ST× IT
0.93± 0.03
0.92± 0.04
0.88± 0.07
0.89± 0.03
ST× AB
0.88± 0.06
0.87± 0.06
0.83± 0.09
0.84± 0.05
IT× AB
0.94± 0.04
0.94± 0.04
0.93± 0.03
0.92± 0.03
* significantly higher than 5 km;
⃟
significantly
higher than 10 km;
†
significantly higher than 15 km.
Significance level p< 0. 05.
Related to the percentage of contribution, compartments were different from each
other (p< 0. 001, [Table 2 ]), the
abdomen was significantly higher than inferior thorax (AB: 43.34± 3.98, 95%CI
[41.9, 44.78]; IF: 32.82± 2.8, 95%CI [31.81, 33.83], p= 0. 004,
d= 3. 08) and superior thorax (23.82± 3.52, 95%CI [22.55,
25.10]; p< 0. 001, d= 5. 31), and inferior thorax was
significantly higher compared to the superior thorax (p= 0. 004,
d= 2. 87). The percentage of contribution of each compartment was
the same in all the steps of the 20-km time trial (5, 10, 15, 20 km,
p= 0. 879). While the ratio superior thorax and abdomen oscillated
between the steps, inferior thorax and abdomen was stable during all of the 20-km
time trial ([Table 2 ]). All compartment pairs
presented strong correlation coefficient values. The superior thorax×abdomen was
significantly lower than superior thorax×inferior thorax and inferior thorax×abdomen
(p= 0. 004, p= 0. 001, respectively). In addition, the
correlation coefficient was statistically different between steps of the 20-km time
trial (p< 0. 001). Step 5 km was higher than 20 km
(p= 0. 039, d= 0. 72), and 10 km presented higher values
compared to 15 km (p= 0. 012, d= 0. 38) and 20 km
(p= 0. 036, d= 0. 55).
Since no significant differences were observed among the distances of 5 km, 10 km,
15 km, and 20 km, we employed the mean value for comparison with the rest maneuvers.
No statistical difference was found when the percentage of contribution of cyclists
at rest were compared with the 20-km time trial (p= 0. 19, [Fig. 3a ]). The relative ratios between superior
thorax and abdomen, likewise inferior thorax and abdomen, were higher in the quiet
breathing and vital capacity than in the last step of the test. The 20-km time trial
correlation coefficients of all compartmental pairs, superior thorax×inferior
thorax, superior thorax×abdomen, and inferior thorax×abdomen, were significantly
lower than quiet breathing and vital capacity (p< 0. 001, [Fig. 3b ]), and the Cohen’s d values ranged
from 2. 32 to 2. 78. Concise summary of the statistical outcomes was
reported ([Table 3 ]).
Fig. 3 Boxplot of the percentage of contribution of each compartment
superior thorax (ST), inferior thorax(IT), and abdomen (AB) (a ).
Boxplot of the correlation coefficients of superior thorax×Inferior
thorax(ST×IT), superior thorax×abdomen (ST×AB), and inferior thorax×abdomen
(IT×AB) (b ) during the quiet breathing (QB) and vital capacity (VC)
and during the 20-km time trial (20-TT).
Table 3 Summarized statistical results of quiet breathing, vital
capacity and 20-km time trial for both groups and variables.
Summarized Statistical Results – Rest Maneuvers
Control Group
Cyclist Group
Percentage of Contribution
Quiet Breathing
AB> ST>IT
AB> IT=ST
Vital Capacity
AB> ST=IT
AB=IT> ST
20-km Time Trial
n. a.
AB>IT>ST
Correlation Coefficient
Quiet Breathing
AB× IT= ST× IT= ST× AB
AB× IT>ST× IT= ST× AB
Vital Capacity
AB× IT=ST× IT>ST× AB
AB× IT=ST× IT>ST× AB
20-km Time Trial
n. a.
AB× IT=ST× IT>ST× AB
Legend: AB: abdomen; IT: inferior thorax; ST: superior thorax; n. a.: not
applicable.
Discussion
Globally, as verified in rest breathing maneuvers, the abdomen contribution was
greater than superior thorax and inferior thorax contributions individually in both
groups. Specifically, cyclists demonstrated a specific THA motion pattern
characterized by an increased role of the inferior thorax compartment with respect
to the superior thorax ([Fig. 1 ]), in opposition to
the control group. In addition, inferior thorax contribution showed higher
coordination with abdomen. The 20-km time trial test results highlighted the same
THA motion pattern, which was kept stable throughout the trial, thus confirming the
development of a permanent modification of the breathing mechanics in the cyclist
group ([Table 2 ]). Since no significant differences
in anthropometric and pulmonary function values ([Table
1 ]) were found between control group and cyclist group groups, this
suggests that the differences in THA motion pattern may be attributed to cycling
training.
While the statistical analysis supported the conclusions, the small cohort reduced
the generalization of the results. We did not calculate post hoc power analyses
given current statistical recommendations, but we do report confidence intervals for
our primary results, which may be a superior means of interpreting the null and
positive effects. In addition, the trained cyclists involved were not professionals;
however, differences in the THA motion pattern with non-cyclists were detected.
Early studies showed that intense engagement in sports may easily elicit long-lasting
THA patterns, with the greatest contribution to ventilation provided by the abdomen
compartment [18 ]
[36 ]
[37 ], and our results agree with these
findings. It is well known that physical activity recruits the abdominal muscles,
featuring a higher magnitude than rib cage muscles, leading to a rise in
diaphragmatic oxidative capacity [38 ]. Nonetheless,
it was clearly documented that exercise training improves diaphragm antioxidant
capacity and endurance [39 ]. More recent studies have
shown that sport specificity is responsible for developing diverse changes in THA
patterns [15 ]
[16 ]
[17 ], triggering specific improvements in breathing
mechanics efficiency [40 ]. Nonetheless, it was argued
that postural constraints may further contribute to the development of custom THA
patterns [1 ]. In Silvatti and coauthors [15 ], the authors argued that long-term swimming
training contributed to the development of very specific THA motion patterns.
Likewise, professional ballet dancers were able to keep unchanged breathing
mechanics in quiet breathing and vital capacity maneuvers [16 ]. The effect of training in Pilates practitioners was documented to
modify the thoracoabdominal motion pattern by increasing the contribution of the
abdomen [17 ]. A raise in the force, in both
appositional and insertional of the diaphragm, related to the inferior thorax
compartment, was already found in cyclists [12 ]
[41 ].
The 20-km time trial simulation allowed us to use an ecological methodology to
understand the THA pattern of cyclists, with a major contribution of inferior thorax
than superior thorax, along with different coordination with abdomen. As a matter
of
fact, all the cyclists showed a U power-output strategy, with higher values at the
beginning and end of the test. Nonetheless, as shown, the THA pattern was not
different during the steps evaluated, highlighting their capacity to maintain the
breathing motion pattern during the whole test. The reduced coordination between
superior thorax and abdomen can be motivated by the limited mechanical linkage
between them, which is intentional to minimize elastic work when moving the chest
wall [42 ]. We may argue that another possible
explanation is the difference in the inspiratory and expiratory reserve volumes,
which are relatively greater in the rib cage and abdominal compartment [18 ], respectively, and prevent paradoxical motion of
any chest wall compartment during exercise [21 ]. The
greater percentage of contribution of the inferior thorax found in our results, and
the long-lasting pattern due to training, was further supported by literature
outcomes about untrained individuals performing cycling [12 ]
[18 ]
[43 ]. All studies pointed out the major contribution of superior thorax to the
breathing pattern. In trained cyclists, it can be hypothesized that the inferior
thorax mechanics have a greater role in intra-abdominal pressure regulation,
contributing the most to spinal and pelvic floor stabilization [44 ]
[45 ], respiratory
function, and power output [41 ]
[45 ]
[46 ]. As a result,
we argue that the planning of specific inspiratory muscle training is to be
considered to further improve the performance of competitive cyclists, in agreement
with [5 ] who suggested that inspiratory muscle
training attenuates the perceptual response to maximal incremental exercise.
While our findings provide valuable insights into the differences between cyclists
and non-cyclists, it is essential to acknowledge the inherent limitations of our
approach. As our study employed a cross-sectional design, we lacked information
about cyclists' breathing patterns from the inception of their training
journey. Additionally, we did not track cyclists over the course of their
progression from novice to experienced cyclists or monitor changes in their
breathing patterns over time. To gain a more comprehensive understanding of the
impact of cycling training on the thoracoabdominal motion pattern, future research
should incorporate longitudinal designs. Longitudinal studies would allow for
tracking the trajectory of individuals' thoracoabdominal motion patterns from
the beginning of cycling training, potentially capturing the magnitude of its
influence and uncovering causal relationships. Additionally, differentiating
patterns between novice and experienced cyclists warrants investigation. Such
studies would significantly contribute to a more in-depth exploration of the dynamic
nature of thoracoabdominal motion in the context of cycling training.
In summary, cyclists demonstrated a specific thoracoabdominal motion pattern
characterized by an increased role of the inferior thorax compartment with respect
to the superior thorax. In addition, the inferior thorax contribution showed higher
coordination with the abdomen. During the simulated 20-km time trial, the
significant participation of the abdomen and inferior thorax was retained during all
the tests, which can be associated with the cyclists’ capacity to maintain during
the effort imposed. Finally, the breathing mechanics outlined in this study can
assist in designing specific programs aimed at improving the performance of
competitive cyclists by enhancing the functionality of their inspiratory muscles,
mainly the diaphragm.
