sensors
Article
Muscle Activation in Middle-Distance Athletes with
Compression Stockings
Diego Moreno-Pérez 1, Pedro J. Marín 2, Álvaro López-Samanes 3,*
, Roberto Cejuela-Anta 4 and
Jonathan Esteve-Lanao 5
1
Departament of Education, Research and Evaluation Methods, Comillas Pontiﬁcal University, 28015 Madrid,
Spain; dmperez@comillas.edu
2
CYMO Research Institute, 47140 Valladolid, Spain; pjmarin@checkyourmotion.com
3
School of Physiotherapy, Faculty of Health Sciences, Universidad Francisco de Vitoria, 28223 Madrid, Spain
4
Department of Physical Education and Sports, University of Alicante, 03690 Alicante, Spain;
roberto.cejuela@ua.es
5
All in Your Mind Training System, Mérida 97134, Mexico; jonathan.esteve@allinyourmind.es
*
Correspondence: alvaro.lopez@ufv.es; Tel.: +34-91-709-14-00 (ext. 1955)
Received: 18 January 2020; Accepted: 24 February 2020; Published: 26 February 2020


Abstract: The aim of this study was to evaluate changes in electromyographic activity with the
use of gradual compression stockings (GCSs) on middle-distance endurance athletes’ performance,
based on surface electromyography measurement techniques. Sixteen well-trained athletes were
recruited (mean ± SD: age 33.4 ± 6.3 years, VO2max 63.7 ± 6.3 mL·kg−1·min−1, maximal aerobic speed
19.7 ± 1.5 km·h). The athletes were divided into two groups and were assigned in a randomized
order to their respective groups according to their experience with the use of GCSs. Initially, a
maximum oxygen consumption (VO2max) test was performed to standardize the athletes’ running
speeds for subsequent tests. Afterward, electromyographic activity, metabolic, and performance
variables for each group were measured with surface electromyography. In addition, blood lactate
concentration was measured, both with and without GCSs, during 10 min at 3% above VT2 (second
ventilatory threshold), all of which were performed on the track. Next, surface electromyography
activity was measured during a 1 km run at maximum speed. No signiﬁcant changes were found in
electromyography activity, metabolic and performance variables with GCSs use (p > 0.164) in any
of the variables measured. Overall, there were no performance beneﬁts when using compression
garments against a control condition.
Keywords: surface electromyography; compression garment; gradual-elastic compression stockings;
muscular fatigue; endurance athletes
1. Introduction
Graduated compression stockings (GCSs) are socks that create a compressive pressure around
the muscle, bone, and connective tissue, with this pressure higher in the ankle area and gradually
decreasing until the knee [1]. In addition, compression garments were originally used to treat deep vein
thrombosis [2] and venous insuﬃciencies [1,3]. Thus, several studies have demonstrated an increase in
the venous velocity, a reduction of venous pooling, and improvement in venous return in hospital
patients who wore GCSs [1,4]. Although there are no signiﬁcant changes in heart rate associated with
the use of GCSs in endurance events, [5–8] the interest in the sports sciences ﬁeld in GCS application
and commercialization is highly increasing [9].
A decrease in the concentration of metabolites associated with compression garment use may
have beneﬁts during submaximal running eﬀorts [10]. Berry and McMurray [11] hypothesized that a
Sensors 2020, 20, 1268; doi:10.3390/s20051268
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reduced blood lactate concentration associated with the use of compression stockings could be due
to greater blood ﬂow removal during exercise with CGSs. In addition, it has been described that
GCSs lead to improvements in blood lactate concentration clearance during continuous sports, such as
cycling [12]. However, other studies reported no changes in blood lactate concentration with the use of
GCSs in endurance eﬀorts [6–8,13]. The diﬀerences between studies could be attributed to the diﬀerent
methodologies used [6,7]. Thus, the beneﬁts of using GCSs while running are not entirely clear at the
metabolic and cardiovascular levels. Moreover, the possible improvements in muscle recruitment with
the use of GCSs during dynamic actions are unknown.
One method to measure the ﬁber recruitment is electromyography (EMG). This type of
measurement technique comprises the sum of the electrical contributions made by the active motor
units (MUs), that are detected by electrodes placed on the skin overlying the muscle. The information
extracted from the surface EMG is often considered a global measure of MU activity because of the
inability of the traditional (two electrode) recording conﬁguration to detect activity at the level of
single MUs, which allows the measurement of the electrical signal during a muscular action [14].
Raez et al. [15] deﬁned EMG as the acquisition, recording, and analysis of electrical activity produced
by nerves and muscles through electrode surface electromyography, which is a noninvasive method
that allows the evaluation of muscle recruitment during dynamic eﬀorts [16]. Most recent research
on compression garments by means of EMG has mainly been focused on the relationship between
intramuscular pressure and EMG responses during concentric isokinetic muscle contractions [17].
Likewise, previous research underlined the relationship between the use of compression garments and
the perception of lower muscle pain [5,18], greater comfort, and a lower subjective perception of eﬀort
(RPE; rating of perceived exertion) [18]. According to Varela-Sanz [13] there seems to be a tendency to
run faster with a lower perception of eﬀort. If there are no clear metabolic or cardiovascular beneﬁts,
the beneﬁts may be found in a change in muscle recruitment between the thigh muscle and the leg (i.e.,
triceps surae).
The aim of this study is to evaluate changes in electromyographic activity with the use of the
use of gradual compression stockings (GCSs) on middle-distance endurance athletes’ performance,
based on surface electromyography measurement techniques. We hypothesize an improvement in
submaximal (i.e., 10 min at 3% above VT2 (second ventilatory threshold)) and maximal conditions (i.e.,
1 km at full speed) with the use of gradual compression stockings compared to control condition.
2. Materials and Methods
2.1. Participants
Fourteen male and two female athletes reported to the laboratory three times with 72 h hours
between protocols (mean ± SD, age 33.2 ± 7.2 years, VO2max 63.7 ± 6.3 mL·kg−1·min−1, maximal
aerobic speed 19.7 ± 1.5 km·h−1, 4 min and 18 s at 1500 mL). All athletes had competed in the
Spanish Track and Field Championships, and some of them had won medals at the National Track
Veterans’ Championships.
Before the beginning of the study, all subjects gave written informed consent in accordance with
the Declaration of Helsinki [19]. The protocol was approved by the Ethics Committee of the University.
The athletes were randomly assigned to either an experimental group, with GCSs (EXP), or control
group without CGSs (CNT). There were no signiﬁcant changes in the descriptive variables between
groups (p < 0.050).
2.2. Experimental Design
Day 1: A maximum oxygen consumption (VO2max) test was performed in order to deﬁne the
subjects’ running speeds for consecutive tests. After a standardized warm-up of 20 min of continuous
running on a treadmill (Technogym Run Race 1400 HC, Gambettola, Italy) at 60% of their maximum
heart rate and a block of dynamic warm-up [20], subjects performed a VO2max test with a gas analyzer
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(VO2000, Medical Graphics Corporation, St. Paul, MN, USA). The variables that were measured were
oxygen uptake (VO2), pulmonary ventilation (VE), ventilatory equivalents for oxygen (VE·VO2−1) and
carbon dioxide (VE·CO2 VE·VO2−1), and end-tidal partial pressure of oxygen (PETO2) and carbon
dioxide (PETCO2). VO2max was recorded as the highest VO2 value obtained for any continuous 30 s
period during the test. The VT1 was determined using the criteria of an increase in both VE·VO2−1
and PETO2 with no increase in VE·VCO2−1, whereas the VT2 was determined using the criteria of an
increase in both VE·VO2−1and VE·VCO2−1 and a decrease in PETCO2 [21]. Two independent observers
detected VT1 and VT2. If there was disagreement, a third investigator was consulted. The maximal
aerobic speed was associated with the last completed 30 s stage before the exhaustion, which was
associated with VO2max [21]. The protocol started with a gradient of 1% at a speed of 10 km·h−1, with
increments of 0.3 km·h−1 every 30 s until the maximum exhaustion [21]. The tests were performed in
the Exercise Physiology Laboratory of the Universidad Europea de Madrid (i.e., 600 m altitude). All
evaluations were performed at the same time of day (i.e., evening, between 7:00 p.m. and 9:00 p.m.)
and under similar environmental conditions (i.e., 20–22 ◦C temperature, 60–65% relative humidity) to
avoid eﬀects associated with circadian rhythms on performance [22].
Days 2 and 3: Each group had to perform the same training session with compression garments
(EXP) and without GCSs (CNT), with a recovery period of 72 h between the two sessions. One group
was assigned to use GCSs only on the ﬁrst day, and the other group was assigned to use GCSs only
on the second day (the athletes served as their own controls). On the day that GCSs were not used,
the athletes used traditional socks. The participants wore GCSs (Medilast Sport, Lleida, Spain) with
degressive pressure (15–20 mm Hg at the ankle; 88% Polyamid, 12% Elasthane) from the ankle to the
calf area (always under the supervision of a member of the investigators’ team.). The compression was
similar to that used in the medical ﬁeld [23].
2.3. Surface Electromyographic Activity (EMG)
EMG was measured according to the electrical activity (EA) recorded with a telemetric system
(BTS Pocket EMG, Garbagnate M.se, Italy). The information extracted from the surface EMG give
global and, rarely, individual indications of motor units activity [24]. A sampling frequency of 1 kHz
was used. Preampliﬁers placed next to the measuring electrode allowed ruling out the inﬂuence of
likely movements of the wires on the measurement. Signals from the EMG were band-pass ﬁltered
(10–400 Hz), and the root mean square (RMS) was analyzed. Bipolar surface EMG electrodes (Al/AgCl,
discs of 10 mm diameter) with an inter-electrode distance of 24 mm were placed on the bellies of the
vastus lateralis (VAL), vastus medialis (VAM), rectus femoris (RF), biceps femoris (BF), gastrocnemius
(GAM), and soleus (SOL) in accordance with the Surface EMG for Non-invasive Assessment of
Muscles [25].
We evaluated EMG during two footraces: (a) 10 min at 3% above VT2 (t > VT2) (i.e., represents a
submaximal eﬀort), (b) 1 km at full speed (t1km) (i.e., represents a maximal eﬀort). These runs were
performed on the athletics track with a 3 min break in between. All evaluations were performed at the
same time of day (i.e., evening, between 7:00 p.m. and 9:00 p.m.) and under similar environmental
conditions (i.e., 22–24 ◦C temperature, 55% relative humidity).
2.4. Metabolic, Perceptual and Performance Variables
The concentration of blood lactate concentration (mmol/L−1) was measured at t > VT2 with a
blood lactate analyzer (Lactate Pro Arkray INIC, Amstelveen, NED). The subjective perception of eﬀort
(RPE) was measured using the Borg scale [26]. For the performance variable, a stopwatch was used to
measure the time (min) subjects took to run t1km.
2.5. Statistical Analysis
The data set obtained was analyzed with the SPSS Statistics 19 software (SPSS Inc., Chicago,
IL, USA). T-tests were applied to related samples, both to verify that there were no diﬀerences in
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matching the subjects and to observe the diﬀerences in the sports performance variables. All data were
expressed as mean (M) and standard deviation (SD). Homogeneity of variance was tested with the use
of a Kolmogorov–Smirnov test and Lilliefors correction. The level of statistical signiﬁcance was set at
p < 0.05. The signiﬁcance level was set at 0.05. Cohen’s formula for eﬀect size (ES) was used and the
results were based on the following criteria: trivial (0–0.19), small (0.20–0.49), medium (0.50–0.79), and
large (0.80 and greater) [27].
3. Results
3.1. Metabolic and Perceptual Variables at Submaximal Eﬀorts (t > VT2)
According to the metabolic demands and perceptual variables, no statistical diﬀerences were
founded in the diﬀerent conditions measured in the study between GCSs and CNT conditions, such as
heart rate (182.6 ± 10.1 versus 182.6 ± 10.0, p = 1.000, ES < 0.01, trivial), blood lactate concentration
(mmol·L−1) (8.3 ± 2.1 versus 7.9 ± 2.4, p = 0.476, ES = 0.20, small), and RPE (8.5 ± 1.0 versus 9.0 ± 0.6,
p = 0.301, ES = 0.33, small).
3.2. Perceptual and Performance Variables at Maximal Eﬀort (t1km)
Perceptual and performance variables did not reach statistical signiﬁcance during t1km—GCS
versus CNT: RPE (9.9 ± 0.3 versus 10.0 ± 0.0; p = 0.164; ES = 0.39, small), speed (19.2 ± 1.7 versus 19.1 ± 1.7
km·h−1; p = 0.847; ES = 0.00, trivial), % maximal aerobic speed (97.2 ± 3.0 versus 97.0 ± 3.6 km·h−1;
p = 0.823; ES = 0.00, trivial).
3.3. Surface Electromyography
Muscular activity did not any reach statistical signiﬁcance (Table 1). However, according to eﬀect
sizes the electromyographic activity was greater in the calf musculature when not using GCSs while
running at submaximal eﬀort, while descriptive changes were observed in eﬀect size (ES). EA was
lower in the leg during submaximal eﬀorts (GAM and SOL, ES = 0.10, trivial) compared to in the thigh
(VAL, VAM, BF, and RF, ES = 0.24, small) with GCS use versus CNT; leg (ES = 0.25, small) and leg
(ES = 0.25, small).
Table 1. EMG (electromyography) variables base on wearing or not wearing a graduate compression
garment during the t > Vt2 and t1km.
EMGrms (mV)
GCSs
CNT
t > VT2
t1km
t > VT2
t1km
BF
0.320
±
0.181
0.314
±
0.196
0.323
±
0.104
0.304
±
0.304
RF
0.199
±
0.058
0.194
±
0.081
0.220
±
0.085
0.192
±
0.094
VAL
0.176
±
0.075
0.168
±
0.087
0.195
±
0.086
0.189
±
0.088
VAM
0.223
±
0.053
0.212
±
0.077
0.212
±
0.077
0.235
±
0.064
SOL
0.196
±
0.116
0.164
±
0.062
0.296
±
0.115
0.295
±
0.141
GAM
0.388
±
0.289
0.331
±
0.257
0.268
±
0.101
0.233
±
0.092
Abbreviations: vastus lateralis (VAL), vastus medialis (VAM), rectus femoris (RF), biceps femoris (BF), gastrocnemius
(GAM), and soleus (SOL).
During running maximal eﬀort (i.e., t1km), EA was higher in the leg (GAM and SOL, ES = 0.24,
small) compared to in the thigh (VAL, VAM, BF, and RF, ES = 0.11, trivial) with GCS use versus CNT:
thigh (ES = 0.17, trivial) and leg (ES = 0.18, trivial). (Table 1)
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4. Discussion
The aim of this study was to evaluate changes in electromyographic activity with the use of the
use of gradual compression stockings (GCSs) on endurance athletes’ performance, with the use of
electromyography techniques. At the metabolic domain, our results are consistent with previous
studies, where GCS use did not aﬀect blood lactate concentration measurement [6–8,13]. Thus,
performance variables during t1km reported no beneﬁts from GCS use with regards to the time
required to run the speciﬁed distance, and our data are in agreement with studies previously published
by Ali [5,7], in which no diﬀerences were found in a 10 km test. Therefore, in our study there were no
signiﬁcant changes in the perceptual variables with GCS use, and small eﬀect sizes were found for
GCS use during submaximal eﬀorts (t > VT2; ES = 0.33) and maximal eﬀorts (t1km; ES = 0.39). This is
consistent with previous studies, where lower muscle pain [5,18], greater comfort [6,7], and a lower
subjective perception of eﬀort [13,18,28] were observed.
With regard to electromyography activity, it was evaluated by means of changes in amplitude
(electromyography amplitude), since it has been described as a variable that provides knowledge
on the degree of muscle fatigue [29]. No signiﬁcant changes were found between treatments when
measuring EA by surface electromyography, null and small eﬀect sizes were observed (ES = 0.11–0.25),
so that muscle activation changed according to whether CGSs were used or not. Thus, in submaximal
eﬀorts (t > VT2), the activation of the calf muscles (GAM, SOL) was observed to be less than that of the
thigh muscles (VAL, VAM, RF, BF). This would have advantages for performance, as fatigue in the
leg muscles frequently tends to limit performance more than that of the thigh muscles, because the
gastrocnemius and soleus are the greatest contributors to propulsion and support during submaximal
running [30]. According to our data, Lucas-Cuevas AG [31] reported a decrease in the muscular
contribution of the GAM using GCSs in the rest situation and at the beginning of submaximal eﬀort in
the running race. However, one limitation of this study was that they did not analyze the possible
changes in muscle recruitment between the thigh and muscular fatigue between GAM and SOL that
we measured in our study.
During maximal eﬀorts (t1km), muscle recruitment diﬀers from that of submaximal eﬀorts
(t > VT2). The use of GCSs reduces electromyographic activity in the thigh and increases in the
gastrocnemius muscles (sum GAM and SOL). If we analyze EA by muscle group in our study during
the maximum eﬀort of race (t1km), there was less recruitment using GCSs in both the BF and the
RF than where there was no use of GCSs. This could be beneﬁcial since in foot race eﬀorts—as the
speed increases, the RF and BF are the muscle groups that increase their muscular contribution the
most [32,33].
During the present study, the study subjects realized a protocol for detecting electromyography
activity performance, and eﬀort perception variables during two diﬀerent endurance tests. There are
a few limitations that need to be addressed. First, it could be that the diﬀerent tests used could not
represent the endurance eﬀort made by the athletes in a race. Thus, it could be necessary to gather more
studies. Also, the individual data were variable, and the sample size was small and medium. Therefore,
the lack of statistical signiﬁcance could be due to a Type II error. Secondly, quantiﬁcation of muscle
activity from surface EMG signals is problematic when movement is involved and motion artifacts
and other electromagnetic noises may inﬂuence the signal levels. We tried to minimize disturbances
by the applied signal processing and ﬁltering routines; still, artifacts may have small impacts on the
derived maximum muscle activations. Thirdly, we could not use a multi-channel approach to provide
access to a set of physiologically relevant variables on the global muscle level or on the level of single
motor units, opening new fronts for the study of muscle fatigue; however, in the present study, we did
not have this electromyographic analysis technology.
From our point of view, the various ﬁndings reported on previous studies with CGSs may be due
to the diﬀerent gradual compression socks used [5–8,12]. Other authors did not specify which type of
GCSs they used in their studies; most likely they used stockings with a uniform degree of compression.
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All the studies conducted to date assessed the potential beneﬁts of using compression means
(GCS) during the eﬀorts in the race on metabolic or cardiovascular variables. According to a study
developed by Varela-Sanz et al. [13], there seems to be a tendency to run faster with a lower perception
of eﬀort. If there are no clear metabolic or cardiovascular beneﬁts, the beneﬁts can be found in a
change in muscle recruitment between the thigh and leg muscle (i.e., triceps surae). Only a previous
study developed by Lucas Cuevas et al. [31] analyzed muscle fatigue using GCSs by EMG techniques.
However, Lucas Cuevas et al. [31] did not compare the possible recruitment changes between the
muscles of the thigh; the study was realized with lower performance level athletes and lower intensities
(75% maximal speed). The novelty of our study is the analysis of EMG recruitment on thigh muscle in
high-level athletes at submaximal/maximal intensities.
5. Conclusions
Endurance athletes perform much of their training and competitions at submaximal intensities
Contrary to our initial hypothesis, no diﬀerences were reported on any of the variables analyzed on
this study in EMG recruitment in well-trained athletes between the diﬀerent conditions (GCSs versus
CNT) at submaximal/maximal eﬀorts. Future studies should be developed based on research ﬁndings
to conﬁrm our data or even explore other possibilities, such as the analysis of EMG recruitment during
the recovery process, that are essential for performance in high-level athletes.
Author Contributions: Conceptualization, D.M.-P. and J.E.-L.; methodology, D.M.-P. and J.E.-L.; software, P.J.M.;
validation, D.M.-P., P.J.M. and J.E.-L.; formal analysis, D.M.-P. and P.J.M.; investigation, D.M.-P., R.C.-A. and
J.E.-L.;; resources, Á.L.-S.; data curation, D.M.-P., Á.L.-S.; writing—original draft preparation, D.M., Á.L.-S. and
J.E.-L.; writing—review and editing, D.M., P.J.M., Á.L.-S., R.C.-A., and J.E.-L.; visualization, D.M.-P. and J.E.-L.;
supervision, P.J.M. and J.E.-L.; project administration, D.M.-P. and J.E.-L.; funding acquisition, D.M.-P. All authors
have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Acknowledgments: We would like to thank the “Allinyourmind training group” for their participation in this
project. We also acknowledge Medilast S. A. (Lleida, Spain), who supplied GCSs for the study participants. The
authors of this study contacted the brand Medilast Sport (Lleida, Spain) with the sole purpose of supplying sports
garments to carry out the investigation.
Conﬂicts of Interest: The authors declare no conﬂict of interest.
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Muscle Activation in Middle-Distance Athletes with Compression Stockings.
Moreno-Pérez, Diego,Marín, Pedro J,López-Samanes, Álvaro,Cejuela, Roberto,Esteve-Lanao, Jonathan