Metastable Pain-Attention Dynamics during Incremental Exhaustive Exercise

Abstract

Background: Pain attracts attention on the bodily regions. Attentional allocation toward pain results from the neural communication across the brain-wide network “connectome” which consists of pain-attention related circuits. Connectome is intrinsically dynamic and spontaneously fluctuating on multiple time-scales. The present study delineates the pain-attention dynamics during incremental cycling performed until volitional exhaustion and investigates the potential presence of nested metastable dynamics.
Method: Fifteen young and physically active adults completed a progressive incremental cycling test and reported their discomfort and pain on a body map every 15 s.
Results: The analyses revealed that the number of body locations with perceived pain and discomfort increased throughout five temporal windows reaching an average of 4.26 ± 0.59 locations per participant. A total of 37 different locations were reported and marked as painful for all participants throughout the cycling task. Significant differences in entropy were observed between all temporal windows except the fourth and fifth windows. Transient dynamics of bodily locations with perceived discomfort and pain were spanned by three principal components. The metastable dynamics of the body pain locations groupings over time were discerned by three time scales: (1) the time scale of shifts (15 s); (2) the time scale of metastable configurations (100 s), and (3) the observational time scale (1000 s).
Conclusion: The results of this study indicate that body locations perceived as painful increase throughout the incremental cycling task following a switching metastable and nested dynamics. These findings support the view that human brain is intrinsically organized into active, mutually interacting complex and nested functional networks, and that subjective experiences inherent in pain perception depict identical dynamical principles to the neural tissue in the brain.

Full text

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ORIGINAL RESEARCH
published: 06 January 2017
doi: 10.3389/fpsyg.2016.02054
Edited by:
Tadhg Eoghan MacIntyre,
University of Limerick, Ireland
Reviewed by:
John Toner,
University of Hull, UK
Noel E. Brick,
Ulster University, Ireland
*Correspondence:
Agn ˙
e Slapšinskait ˙
e
agneslapsinskaite@gmail.com
Specialty section:
This article was submitted to
Movement Science and Sport
Psychology,
a section of the journal
Frontiers in Psychology
Received: 31 October 2016
Accepted: 19 December 2016
Published: 06 January 2017
Citation:
Slapšinskait ˙
e A, Hristovski R,
Razon S, Balagué N and
Tenenbaum G (2017) Metastable
Pain-Attention Dynamics during
Incremental Exhaustive Exercise.
Front. Psychol. 7:2054.
doi: 10.3389/fpsyg.2016.02054
Metastable Pain-Attention Dynamics
during Incremental Exhaustive
Exercise
Agn ˙
e Slapšinskait ˙
e1*, Robert Hristovski2, Selen Razon3, Natàlia Balagué1and
Gershon Tenenbaum4
1Complex Systems in Sport Research Group, INEFC Barcelona University, Barcelona, Spain, 2Saints Cyril and Methodius
University of Skopje, Skopje, Macedonia, 3West Chester University, West Chester, PA, USA, 4Florida State University,
Tallahassee, FL, USA
Background: Pain attracts attention on the bodily regions. Attentional allocation
toward pain results from the neural communication across the brain-wide network
“connectome” which consists of pain-attention related circuits. Connectome is
intrinsically dynamic and spontaneously fluctuating on multiple time-scales. The present
study delineates the pain-attention dynamics during incremental cycling performed
until volitional exhaustion and investigates the potential presence of nested metastable
dynamics.
Method: Fifteen young and physically active adults completed a progressive
incremental cycling test and reported their discomfort and pain on a body map every
15 s.
Results: The analyses revealed that the number of body locations with perceived pain
and discomfort increased throughout five temporal windows reaching an average of
4.26 ±0.59 locations per participant. A total of 37 different locations were reported and
marked as painful for all participants throughout the cycling task. Significant differences
in entropy were observed between all temporal windows except the fourth and fifth
windows. Transient dynamics of bodily locations with perceived discomfort and pain
were spanned by three principal components. The metastable dynamics of the body
pain locations groupings over time were discerned by three time scales: (1) the time
scale of shifts (15 s); (2) the time scale of metastable configurations (100 s), and (3) the
observational time scale (1000 s).
Conclusion: The results of this study indicate that body locations perceived as painful
increase throughout the incremental cycling task following a switching metastable and
nested dynamics. These findings support the view that human brain is intrinsically
organized into active, mutually interacting complex and nested functional networks,
and that subjective experiences inherent in pain perception depict identical dynamical
principles to the neural tissue in the brain.
Keywords: non-linear dynamics, pain, attention, accumulated effort, exercise, metastability
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INTRODUCTION
Humans have the capacity to distract from and also differentiate
between various sensations related to physical exercise such as
exercise-related effort and pain (O’Connor and Cook, 2001;
Pageaux, 2016). Perception of effort is defined as “the conscious
sensation of how hard, heavy, and strenuous a physical task is”
(Marcora, 2010). Perception of pain on the other hand is defined
as the perception of a distressing experience associated with
actual or potential tissue damage that entails sensory, cognitive,
emotional, and social components (Williams and Craig, 2016).
From a standpoint of measurement accuracy within self-report
settings, the instructions provided by the test administrator play
an important role for distinguishing between perception of effort
and pain (Pageaux, 2016).
Of specific interest herein, perception of pain typically
requires attentional allocation. To that end, attention focus is
a key cognitive mechanism for increasing or decreasing the
perception of pain (Legrain et al., 2009;Slapsinskaite et al.,
2015). Even in the presence of unchanging nociceptive input
and regardless of on-going task demands the attentional state
seems waxing and waning spontaneously (Peters and Crombez,
2007). Consequently, shifting of attention could be due to the
pain-attention related processes that are intrinsically dynamic
and spontaneously fluctuating on multiple time-scales (Bressler
and Kelso, 2001, 2016;Deco et al., 2013). Pervasiveness of
such attentional fluctuations, their intrinsic nature, and their
relevance to subjective experience, such as pain, are further
supported by the evidence provided from studies of spontaneous
brain dynamics and the impact of pre-existing brain state
on subsequent perception (Kucyi and Davis, 2015). Within
such a framework, attentional fluctuations away from non-
painful modalities and their neural mechanisms can be termed
“perception decoupling” or “disengagement of attention” from
perception (Schooler et al., 2011). Spontaneous attentional
fluctuations toward and “away from pain” and individual
differences in this regard are represented in the very brain
network structure and dynamics.
The dynamical system theory (DST) is a sub field of
mathematics that aims at understanding and describing the
dynamical changes that occur over time. Specifically, DST
establishes a series of principles that govern the system’s
dynamical changes. In the last few decades, DST has
demonstrated that painful experiences are emergent phenomena
resulting from self-organized processes, and that pain-attention
interaction can be understood as a virtue of such dynamics (Lutz
et al., 2008). To that end, it is known that non-linear dynamic
mechanisms are involved in the modulation of attentional
focus during physical activity (Balagué et al., 2012;Slapsinskaite
et al., 2016). Indeed, the DST framework that captures pain in
terms of spatiotemporal trajectories of neural activity emerging
from complex non-linear neural interactions, provides a novel
approach to the study of pain-attention dynamics (Freeman,
1992).
With regards to the perception of painful sensations also
known as nociception (Pfaff, 2013), the brain is intrinsically
organized into active, mutually interacting complex and
functional networks. There is also a consensus that the pain
experience is both highly subjective and top-down modulated
(Garland, 2013). To that end, evidence indicates that non-linear
dynamical processes form the basis of a number of neural
(Izhikevich, 2010) and higher order processes. Specifically, non-
linear processes are defined as those with non-proportionality
between the input and the output and with occasional reduction
to linear processes (Kelso, 1997).
Findings from research that focused on subjective experiences
have revealed fluctuating and metastable dynamics inherent to
effort perception and within different types of exercise setting
(Balagué et al., 2012, 2015;Aragonés et al., 2013;Garcia et al.,
2015;Slapsinskaite et al., 2016). Metastability can be seen as a
property related to the existence of multiple separated timescales
(Bovier and Den Hollander, 2016). At short time-scales, the
system appears to be in equilibrium, but in fact, explores only a
limited part of its available state space. At longer timescales, it
undergoes transitions between numbers of metastable states.
From a broader standpoint, overall cognition is also facilitated
through the dynamical phenomenon of chunking (i.e., larger
sequence of information is managed into its smaller units).
Indeed, chunking has been shown to be involved in a range of
perception and cognition-related processes in humans (Gobet
et al., 2001;Rabinovich et al., 2014). Thus, the notion that mental
function is based on the dynamical and ongoing interaction of a
number of neural and bodily parts that produce complex patterns
has gained acceptance (Thompson and Varela, 2001;Rabinovich
et al., 2008;Rabinovich and Muezzinoglu, 2010).
To date, it is known that brain exhibit periods of stability and
instability in both behavioral and neural levels. The transition
from stable to unstable patterns comes as a response to the
changes in control parameters as those govern the system’s
properties (stability and instability) (Haken, 1987). The neural
dynamics of the brain, based upon metastability and dwelling
on different time scales, flexibly reorganize pain-attention on a
moment to moment basis. Consequently, the processes that are
more stable dwell over longer time scales and naturally tend to
correlate with the pain-attention configurations that emerge over
shorter time scales. These dynamics, in turn, are reflected in the
sequential switching in between the temporally and structurally
nested metastable states during trials (Rabinovich and Varona,
2011).
Of specific interest herein, the link among the dynamic
principles of spontaneous attention fluctuations, brain networks
dynamics and the neural processing of pain observed through
subjective experiences of pain may be essential for the
understanding of attentional modulation and its involvement
in the perception of pain. To that end, exercise settings can
provide a particularly adequate context because during exercise
the sensory, cognitive, emotional, and physical conditions change
continuously. On a practical note, capturing pain-attention
interaction dynamics through subjective experiences can also
contribute to designing non-invasive approaches to ultimately
control pain during exercise or beyond. The purpose of this
study was to delineate the pain-attention dynamics during
incremental cycling performed until volitional exhaustion.
Specifically, drawing upon a DST approach, we hypothesized
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that pain-attention relationship during exercise would display
hierarchically or nested metastable dynamics.
MATERIALS AND METHODS
Study Design and Participants
Fifteen young and physically active adults (10 women, 5 men,
Mage =22.5 years, SEM =0.43, age range: 20–25 years, and
BMI =22.84, SEM = ± 0.77) who engaged in a wide range of
aerobic activities (e.g., jogging, swimming, dancing) at least three
times a week, participated in this study. None of the participants
had previous history of chronic pain or musculoskeletal injuries
at the time of the study. Prior to the onset of the study,
participants completed a health history questionnaire, as well as
an informed consent form, which was approved by the Clinical
Research Ethics Committee of the Sports Administration of
Catalonia (registration number 072015CEICEGC). This study
was carried out in accordance with the Declaration of Helsinki.
Discomfort and Pain Monitoring
To detect pain dynamics and corresponding bodily regions, a
body map (see Figure 1) was verbally explained to participants
prior to the baseline test and experimental tasks. Using the
map, every 15 s during exercise, upon the researcher’s prompts,
participants reported bodily regions with discomfort and pain.
The instructions provided to the participants included the
following:
“When prompted, we ask you to report the locations of discomfort
and pain (if you feel it, independently of its magnitude) using the
numbers on the body map placed in front of you.”
Familiarization Procedures
All participants were already familiar with cycle ergometer
testing. One week prior to the tests, they received instructions
on how to use the body map during the tests. To ensure
their competence, they practiced a submaximal version of the
incremental cycling test (see below) and using the body map,
reported bodily regions with pain every 15 s upon the researcher’s
prompts. All participants displayed adequate competence of the
study protocols following one single trial.
Incremental Cycling Test
Following a 2 min rest period, participants performed a
progressive incremental test on a cycle ergometer (Sport
Excalibur 925900) with saddle and handlebar specifications
adjusted to their preference. For the purposes of the test, they
were instructed to pedal at 60 rpm with an initial load of 30 W
and increases of 25 W/min for female and 30 W/min for male,
until they could no longer maintain the pedaling rate for five
consecutive seconds while in the sitting position. Participants
performed the test with no verbal communication except for
indicating bodily locations with pain after the researcher’s
prompts. Heart rate was continuously monitored (Polar RS 400)
to assure that participants reached at least 170 beats/min at the
point of exhaustion. Upon task completion, using an 11-point
FIGURE 1 | Body map. Head (areas 1, 2, 23, or 24); neck (areas 3 or 25), shoulders (areas 4, 5, 26, 27); arms (areas 6, 7, 8, 9, 28, 29, 30, or 31); hand (areas 10,
11, 32, 33); ribs or chest (areas 12 or 13); abdomen (areas 14 or 15), back (areas 34, 35, 36, 37), buttocks or hips (areas 38 or 39); genitalia (area 16), legs (areas
17, 18, 19, 20, 40, 41, 42, or 43); feet (areas 21, 22, 44, or 45). Adapted from Margolis et al. (1986).
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Likert-type scale with anchors ranging from 0 (not at all) to 10
(greatly), participants answered two questions to measure task
commitment: (a) “Have you pedaled as long as you can, achieving
your exhaustion point?” and, commitment to the protocol (b)
“Have you reported all the changes in your discomfort and pain-
locations when required?”
Statistical Analysis
The reported number of locations with discomfort and pain
during the test were plotted for each participant. Each time
series was divided into five non-overlapping temporal windows
(time to volitional exhaustion of the participant/5). Mean value
of the number of locations with discomfort and pain in total
were computed for each time window. The changes of entropy
were computed within five temporal windows. A median was
calculated for each window from all participants’ mean value of
the number of locations with discomfort and pain frequencies of
painful bodily locations.
These collected data with painful locations obtained from the
body maps were then used to form 17 Boolean m×ndata
matrices where msignifies the number of body locations and n
the number of time samples (Casari et al., 1995;Gogos et al.,
2000;Jolliffe, 2014). Visual observation of the structure of the
data matrices helped distinguish between two types of reported
locations: locations that were persistent throughout the entire
exercise bout (i.e., long-term and stable locations), and locations
that were inconsistent (i.e., short-term and unstable locations).
In other words, the data matrix either depicted long-term bodily
locations that were stable on the time scale of 10s of minutes, or
other short-term ones that were stable on the time scale of 10s of
seconds or minutes.
Collective variables were then determined by means of a
principal component analysis (PCA) (Jirsa et al., 1994). The
Kaiser-Guttmann criterion (eigenvalue λ≥1) was used to define
the number of salient PCs of the first order (Yeomans and Golder,
1982). The hierarchical analysis of oblique principal components
(hPCA) (Fabrigar et al., 1999) was subsequently used to check for
the presence of collective variables of higher order, and to obtain
a maximal dimensional reduction of the data. For the purposes of
the hPCA analysis, the software package Statistica 5.0 was used.
The null hypothesis of a constant median (with no significant
differences) over time was tested using non-parametric repeated-
measures Friedman ANOVA. Effect sizes (Cohen’s d) were
computed to demonstrate the magnitude of standardized
differences in medians where effect sizes neared p<0.05 level.
RESULTS
During incremental cycling, the reached maximal load
corresponded to 228 ±17 and 240 ±30 W for females
and males, respectively. On average, participants’ heart rate
reached 180 ±9.5 bpm at the exhaustion point. The Friedman
ANOVA revealed a significant effect of time for the total number
of locations with discomfort and pain, χ2(15,4) =49.249,
p<0.001, during the incremental cycling test. Figure 2 depicts
the changes in the number of locations with discomfort and pain
throughout the five temporal windows. The number of locations
resulted in a significant difference between temporal windows:
first vs. third time intervals, Z= −2.97; p<0.05, d=1.59, 95%
CI [0.65, 2.04]); third vs. fifth time intervals, Z= −3.26; p<0.05,
d=0.81, 95% CI [−0.35, 1.73]), and first vs. fifth time intervals,
Z= −2.17; p<0.05, d=2.27, 95% CI [1.11, 2.74]).
Figure 3 illustrates the frequencies of locations with
discomfort and pain during the incremental cycling test.
The number of locations and the probability of experiencing
FIGURE 2 | The number of locations with discomfort and pain throughout the five temporal windows.
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FIGURE 3 | Locations with pain and/or discomfort. The group pulled probabilities of locations with pain or discomfort during cycling tasks in five temporal
windows in a given sample (n= 15). As time on task increases (vertical axis) the number of locations and the probability of experiencing pain and discomfort at
selective locations also increase (darker shades of gray) on average. Legend: the probability of experiencing discomfort and pain.
discomfort and pain at select locations (depicted in darker shades
of gray), increased during the test until reaching 4.26 ±0.59 in the
fifth temporal window. The dominant locations with discomfort
and pain at exhaustion included left and right quadriceps, lower
back and left ankle. Both the waxing and waning experience of
pain were also identified. Depicted in shades of gray in Figure 3,
exertive pain exhibited metastable dynamics, dwelling around
select bodily regions for some time to transition into another
one quickly after. A total of 37 different areas were reported and
marked as painful for all participants throughout the cycling test.
The Friedman ANOVA revealed a significant effect of time for
the pain entropy, χ2(15,4) =49.77, p<0.001 in incremental
cycling (see Figure 4). The entropy of exertive pain showed
significant difference between all temporal windows except the
fourth and fifth windows. first vs. second time intervals, Z=2.93;
p<0.05, d=1.04, 95% CI [1.02, 1.05]; first vs. third time
intervals, Z=3.29; p<0.001, d=2.07, 95% CI [2.06, 2.08];
first vs. fourth time intervals, Z=3.4; p<0.001, d=1.62, 95%
CI [1.61, 1.63); first vs. fifth time intervals, Z=3.4; p<0.001,
d=2.44, 95% CI [2.42, 2.45]; second vs. third time intervals,
Z=3.17; p<0.001, d=1.04, 95% CI [1.02, 1.05]; second vs.
fourth time intervals, Z=3.17; p<0.001, d=0.81, 95% CI
[0.8, 0.82]; second vs. fifth time intervals, Z=3.26; p<0.001,
d=1.62, 95% CI [1.61, 1.63]; third vs. fourth time intervals,
Z=2.07; p<0.05, d=0.00, 95% CI [−0.01, 0.02]; and third
vs. fifth time intervals, Z=3.26; p<0.05, d=0.81, 95% CI
[0.8, 0.82]. Kendall’s W was equal to 0.83 with an average rank
r=0.82. In general, relative to participants who started with low
entropy, participants with higher entropy kept and ended with
higher entropy.
FIGURE 4 | The entropy of exertive pain throughout the five temporal windows.
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FIGURE 5 | Transient dynamics of bodily locations with perceived pain
spanned by three PCs.
Figure 5 depicts an example of transient dynamics of bodily
locations with perceived discomfort and pain in the space
spanned by three PCs. From a chunk sequence – trajectory
within three PCs a dwelling time around select region (e.g., PC1)
and a transitional trajectory to another state “temporal winner”
PC2 and a final rapid switch to the next metastable state can
be identified. This example illustrates a metastability of sensory
interactions and integration of information from the painful
locations.
Figure 6 presents an integration-segregation picture of
exertive pain through three PCs joined hierarchically into one
second-order PC. Through hierarchical PC of time series of
exertive pain configurations chunks on different time scales can
be detected. The trajectory dwells for some time in space then
wanes to finally dwell again. The persistent (longest dwell time)
painful locations over all time resulted in the emergence of super-
chunks. These group-clusters of persistent locations formed
a skeleton-like figure on which other less persistent (shorter
dwell time) motives join and dissolve following which further
short-lived (shortens dwell time) painful locations emerged. The
metastable dynamics of the body pain locations groupings over
time projected on the secondary PC. The system dwells for some
time in one configuration state then quickly shifts to another one.
At least three time scales can be discerned: (1) the time scale
of shifts (15 s), (2) the time scale of metastable configurations
(100 s), and (3) the observational time scale (1000 s).
DISCUSSION
The present findings advance the understanding of generated
sequential switching nature of attention-pain dynamics, and
further support the view that human brain is intrinsically
organized into active, mutually interacting complex and
nested functional networks (Rabinovich and Varona, 2011).
Surprisingly, our results draw attention to a potential link
between pain-attention dynamic states and modeled sequences
of two psychological components of effort, namely cognition and
emotion (Rabinovich et al., 2010, 2015), as well as the dynamical
signatures of several brain functions and mental diseases
(Rabinovich and Varona, 2011). This is an important finding
in that it demonstrates that the phenomenological subjective
experiences, so called qualia (Chalmers, 1996), are grounded in
identical dynamical principles to the neural tissue, i.e., the brain.
Consequently, perception of pain and attention to pain seem
to be multidimensional and interrelated through hierarchical
FIGURE 6 | The metastable dynamics of the configuration of body pain locations over time projected on the secondary PC.
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dynamical processes that highly depend on sensory cues (i.e.,
painful locations) (Rabinovich et al., 2015). In the light of
these findings, it is important to note that attention does
not appear to be a static capacity but rather a process that
involves the attentional “reorienting” from one input (i.e., one
painful location) or modality (i.e., pain intensity or quality)
to another. Therefore, the perception process requires a short-
term integration between a number of continuously interplaying
components such as the environmental cues, the body and the
brain itself (Rabinovich et al., 2015).
The present study focused on the evolving interactions among
pain-attention dynamics, where perceived pain throughout
a cycling task was considered a bi-product of the mutual
interaction between attention and distinct psychophysiological
process, and not of select singular mechanisms. In fact,
disengagement of attention from perception within ongoing
dynamics of pain-attention or so called perceptual decoupling
could also be responsible for the metastable dynamics observed
in this study. Studies on neural mechanisms of spontaneous
attentional fluctuations on multiple timescales and pain
variability have already underlined the importance of dynamics
of pain-attention interactions, and its mutual influence on each
other. Upcoming work must consider this phenomenon (Kucyi
and Davis, 2015).
The present findings also help detect and confirm the
dynamical phenomenon of chunking that the biological-
cognitive system uses to manage larger sequence of information
into smaller units to facilitate information processing. Indeed, the
presence of interconnected pieces that are prevalent over long
periods of time supports the notion of a hierarchical organization
of neural processing, which is the basis for understanding
chunking dynamics (Rabinovich et al., 2014). Specifically, in the
present study we observed the produced hierarchical chunking
of locations with pain sequences, and to our knowledge for the
first time, demonstrated how dynamics of mental hierarchies
may be established on component perceptions that dwell over
different time scales. Our data suggest that basic functions,
such as focusing on the painful locations, and chunking of the
information evolve through dynamic and not static interactions.
That is, while forming a chunking network individuals tend to
transform the chain of metastable states along with transient
process to the chain of groups of such states. Therefore, within the
present framework, it was considered that the chunks operate on
an heteroclinic cycle of metastable states where each metastable
state itself is a heteroclinic cycle of basic information items
(Rabinovich et al., 2014). Altogether, the set of informational
items (i.e., painful locations) can be interpreted as sequences.
Consequently, conceptualizing pain and attention-related brain
and body network processes from the standpoint of a concurrent
activation of sensory cues emanating from the body and multiple
other sources within a distributed brain network can prove
beneficial.
Several limitations to our study should be noted. First, we
have not studied all the spatiotemporal mental fields (e.g.,
alternative facets of perception, cognition, emotion, mental
resources) and their dynamics within the exercise setting.
Second, magnetic resonance imaging was not used to capture
the neurophysiological mechanisms behind the interaction of
attention-pain and this may have shed more light on the pain
dynamics. Third, pre-existing brain state was not measured and
finally, participants’ personal beliefs or expectations about pain
were not evaluated. Finally, prompting protocol used herein
may also present a limitation. It is plausible that, due to the
reporting task, participants’ attention focus was somewhat biased.
This protocol was implemented, however, to follow a systematic
and regularly imposed rating strategy. Traditionally, the data
of pain ratings were obtained in lower recording frequency,
for instance varying between 1 and 3 min intervals (Angius
et al., 2015), before and after physical activity (Choi et al.,
2013), or once per day (Burnett et al., 2010). Some researchers
have also recorded pain ratings at high frequencies with use
of 30 s (Cook et al., 1998) or 15 s (Slapsinskaite et al.,
2015). Intra-individual changes are, however, better captured
through short frequency recording of self-reports precisely
because participants may not be able to attend and report
all changes within lower recording frequency settings. To that
end, with regards to the present study, it is important to
note that the test administrator was frequently prompting the
self-report with no prompting of any particular pain location
per se.
CONCLUSION
To the best of our knowledge, this study remains a first attempt to
illustrate and explain the pain-attention information processing
dynamics within an exercise setting. Finally, from a translational
standpoint greater knowledge into pain dynamics during exercise
can help practitioners design effective strategies to cope with
painful sensations during effort. This is important in that
within effortful settings, somatic pain is associated with negative
affective responses to exercise and eventual lack of exercise-
adherence (Ekkekakis et al., 2011). Consequently, strategies to
allow improved pain management during activity are likely to
facilitate exercise-related enjoyment, and help long term exercise-
engagement (Saanijoki et al., 2015).
AUTHOR CONTRIBUTIONS
Conceived and designed the experiments: NB, RH, AS; performed
the experiments: AS, NB; analyzed the data: AS, NB, RH, GT, SR;
contributed reagents/materials/analysis tools: RH, AS, NB, SR,
GT; wrote the paper: AS, SR, NB, RH.
FUNDING
This study was supported by the Institut Nacional d’Educacio
Física de Catalunya (INEFC), the Generalitat de Catalunya. AS
is the recipient of a predoctoral fellowship from the Institut
Nacional d’Educacio Física de Catalunya (INEFC). We would like
to thank TECNO SPORT for the technical support. The funders
had no role in study design, data collection and analysis, decision
to publish, or preparation of the manuscript.
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Conflict of Interest Statement: The authors declare that the research was
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be construed as a potential conflict of interest.
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e, Hristovski, Razon, Balagué and Tenenbaum. This
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