Citation: Muñoz-Pérez, I.;
Varela-Sanz, A.; Lago-Fuentes, C.;
Navarro-Patón, R.; Mecías-Calvo, M.
Central and Peripheral Fatigue in
Recreational Trail Runners: A Pilot
Study. Int. J. Environ. Res. Public
Health 2023, 20, 402. https://
doi.org/10.3390/ijerph20010402
Academic Editor: Antonio Sousa
Received: 18 October 2022
Revised: 9 December 2022
Accepted: 22 December 2022
Published: 27 December 2022
Copyright:
© 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
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Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
International  Journal  of
Environmental Research
and Public Health
Article
Central and Peripheral Fatigue in Recreational Trail Runners:
A Pilot Study
Iker Muñoz-Pérez 1
, Adrián Varela-Sanz 2,*, Carlos Lago-Fuentes 3
, Rubén Navarro-Patón 4
and Marcos Mecías-Calvo 4,*
1
Facultad de Ciencias de la Educación y Deporte, Universidad de Deusto, 48007 Bilbao, Spain
2
Physical and Sports Education Department, Faculty of Sport Sciences and Physical Education,
University of A Coruna, 15179 A Coruna, Spain
3
Facultad de Ciencias de la Salud, Universidad Europea del Atlántico, 39011 Santander, Spain
4
Facultad de Formación del Profesorado, Universidade de Santiago de Compostela, 27001 Lugo, Spain
*
Correspondence: adrian.varela.sanz@udc.es (A.V.-S.); marcos.mecias@usc.es (M.M.-C.);
Tel.: +34-981167000 (ext. 4012) (A.V.-S.); +34-982821069 (M.M.-C.)
Abstract: Background: Understanding fatigue mechanisms is crucial for exercise performance. How-
ever, scientiﬁc evidence on non-invasive methods for assessing fatigue in trail running competitions
is scarce, especially when vertical kilometer trail running races (VK) are considered. The main
purpose of this study was to assess the autonomic nervous system (ANS) activity (i.e., central fa-
tigue) and the state of muscle activation (i.e., peripheral fatigue) before and after a VK competition.
Methods: A cross-sectional pilot study was performed. After applying inclusion/exclusion criteria,
8 recreational male trail runners (31.63 ± 7.21 yrs, 1.75 m ± 0.05 m, 70.38 ± 5.41 kg, BMI: 22.88 ± 0.48,
running experience: 8.0 ± 3.63 yrs, weekly training volume: 58.75 ± 10.35 km) volunteered to
participate and were assessed for both central (i.e., via heart rate variability, HRV) and peripheral
(via tensiomyography, TMG) fatigue before and after a VK race. Results: After the VK, resting heart
rate, RMSSD (p = 0.01 for both) and SDNN signiﬁcantly decreased (p = 0.02), while the stress score
and the sympathetic-parasympathetic ratio increased (p = 0.01 and p = 0.02, respectively). The TMG
analyses suggest that runners already suffered peripheral fatigue before the VK and that 20–30 min
are enough for muscular recovery after the race. In summary, our data suggest that participants
experienced a pre-competition fatigue status. Further longitudinal studies are necessary to investigate
the mechanisms underlying fatigue during trail running races, while training periodization and
tapering strategies could play a key role for minimizing pre-competition fatigue status.
Keywords: vertical kilometer; trail running; running performance; heart rate variability; muscular
fatigue; tensiomyography
1. Introduction
Vertical Kilometer (VK) running races are a trail running modality that has gained
importance in the last few years and are characterized by the great gradient (1000 m)
that runners have to cover over a distance of less than 5000 m (regulation of the Interna-
tional Skyrunning Federation), usually performed in mountainous areas. While the main
factors determining endurance running performance were exhaustively investigated in
scientiﬁc literature (i.e., maximum oxygen consumption -VO2max-, velocity associated to
VO2max-vVO2max-, lactate threshold -LT- and running economy -RE-) [1,2], the key factors
affecting trail running performance were scarcely studied until recently. In this regard,
studies have predominately focused on metabolic (e.g., VO2max, vVO2max, RE), biome-
chanical (e.g., vertical running speed, ground contact time and ﬂight time, stride length
and frequency, ground technicity) and neuromuscular (e.g., stiffness, lower-limb muscular
endurance and extensor muscles maximum strength) parameters during both uphill and
downhill running [3–8].
Int. J. Environ. Res. Public Health 2023, 20, 402. https://doi.org/10.3390/ijerph20010402
https://www.mdpi.com/journal/ijerph
Int. J. Environ. Res. Public Health 2023, 20, 402
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Considering the aforementioned key factors affecting trail running performance and
the speciﬁc characteristics of VK competitions (i.e., runners are used to face slopes of more
than 40%, while the duration of these challenges can range between 29 and 60 min), the
physiological and neuromuscular demands are maximized due to the accumulated gradient
in competition, since runners must displace their body upward against gravity, increasing
mechanical power in a manner proportionate to slope [9]. In this regard, scientiﬁc evidence
shows a time-dependent relationship for both the development of muscle damage [3,10–12]
and altered myocardial function [13] after ultramarathon races, even if the competition is
performed at low intensity [14]. Nevertheless, there is no study that determines the degree
of peripheral and central fatigue after shorter, more intense (>LT) competitions, such as
VK races.
Hence, knowing the degree of fatigue generated during trail running competitions is
crucial to establish the optimal recovery time before applying high-demand training loads.
On this point, a common approach to evaluate the acute changes in cardiac function and
athletes’ readiness for training is the autonomic nervous system (ANS) activity monitoring
via heart rate variability (HRV) evaluation. This method has proven to be valid and
reliable to control and monitor endurance training, avoiding the development of non-
functional overreaching and overtraining [15–18] by assessing the balance between the
parasympathetic (PNS) and sympathetic nervous system (SNS) [19–21], thus allowing the
establishment of optimal training conditions for supercompensation [22].
The use of HRV to assess the rest time required to restore ANS balance after competi-
tion has been reported in several studies [23,24], ranging from 1 to 3 days depending on
the competition characteristics (e.g., distance, race proﬁle, etc). However, athletes’ self-
perception of full recovery after a 24-h competition can be up to 12 days [25]. This difference
between subjective perception and objective evaluation of recovery (i.e., measured by ANS
activation) may be inﬂuenced by muscle damage associated with peripheral fatigue and
therefore, cannot be detected by HRV measurement. In this regard, simultaneous assess-
ment of muscle fatigue and HRV, both pre- and post-competition, could be a suitable
strategy to determine the degree of fatigue and the minimum time required for optimal
recovery and subsequent performance.
To date, there are few studies using non-invasive methods, such as maximal voluntary
contraction (MVC) determination and electrical stimulation, to assess the level of muscle
fatigue after an endurance trail running competition [10,12,26]. Therefore, the use of a non-
invasive method to assess muscle contractile capacity, such as tensiomyography (TMG),
may be a novel strategy to analyze muscle activity before and after competition in order to
establish the optimal relationship between activity and recovery [11,27–33].
Taking into account the beneﬁts and practical applications of using non-invasive
methods to evaluate performance variables (i.e., muscle fatigue and performance of ANS)
during a competitive race, to the best of our knowledge, no studies have implemented
these approaches in trail-mountain running races. For these reasons, the ﬁrst objective
of this study was to compare the state of activation of the ANS (via HRV measurement)
before and after a VK trail running race in recreational trail runners (i.e., central fatigue).
The second objective of the study was to assess the muscle fatigue caused by this type
of competition in recreational trail runners (i.e., peripheral fatigue). We hypothesize that
both HRV and TMG values will be negatively affected after the race (within 20–30 min
after completion) when compared to those registered previous to the competition, but the
magnitude of these changes will not be large.
2. Materials and Methods
2.1. Study Design
A cross-sectional study was conducted with pre- and post-competition evaluations
regarding central and peripheral fatigue in a group of experienced recreational trail runners
to determine the objectives of this investigation. The athletes took part in the Vertical
Int. J. Environ. Res. Public Health 2023, 20, 402
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Kilometer of Fuente Dé (2018), an uphill trail running race 2.6 km in distance and with a
positive slope of 972 m to reach an altitude of ~1877 m.
2.2. Participants
Eleven recreational trail runners (10 men and 1 woman), with competition experience
in these types of races of at least 3 years, voluntarily participated in this study.
The inclusion criteria for the present study were: (1) to complete all the records, (2) to
ﬁnish the competition, and (3) not to suffer any injury or illness during the measurements.
Once the inclusion criteria were applied, the ﬁnal sample consisted of 8 male participants
with the following characteristics (mean ± SD): age 31.63 ± 7.21 yrs, height 1.75 m ± 0.05 m,
body weight 70.38 ± 5.41 kg, BMI 22.88 ± 0.48, running experience 8.0 ± 3.63 yrs; weekly
training volume 58.75 ± 10.35 km.
The experimental procedures were explained in detail to all participants prior to the
beginning of the study and they were free to withdraw from the study at any time. All of
them signed a written informed consent form before the start of the study. The research
was approved by the Ethics Committee of the Universidad Europea del Atlántico (CEI
21/2018), under the standards established in the Declaration of Helsinki.
2.3. Measurements
2.3.1. Central Fatigue Assessment: HRV
To collect HRV data for each athlete and after a 1-min stabilization period, a 5-min
measurement protocol was performed in the supine position in a dim light room with
a temperature of 20–22 ◦C, with a relative humidity of 60–65% and after emptying their
urinary bladder, as previously recommended [19,34]. During the recordings the authors
encouraged participants to stay calm and not perform any movement throughout the
measurements. Respiratory rate was not controlled during recording, these previous studies
found only small differences between spontaneous and metronome-guided breathing on
HRV variables [35].
The R-R intervals were registered using an HR band (Polar H10 band, Polar V800,
Polar Electro Oy, Finland), with data downloaded using custom software (Polar Pro) and
dumped into a .txt ﬁle without applying any ﬁlter for correction. Once .txt ﬁles were
generated for each athlete and measurement (i.e., pre-post), these were imported into a
speciﬁc software (HRV Kubios Version 3.5, Kuopio, Finland) [36] to process HRV data with
artifact correction (i.e., settings: “custom” and “0.3”). The data processing conﬁguration
was carried out following the pre-established values by the Kubios software (Lambda = 500).
Each R-R series were corrected by applying the medium threshold for beat correction, as
suggested in the software. In this regard, the following variables were obtained for further
analyses [37]: the square root of the mean of the squared differences between successive
normal-to-normal intervals (RMSSD), the standard deviation of normal-to-normal intervals
(SDNN), and the percentage of successive RR intervals that differ by more than 50 ms
(pNN50) in the time domain. The stress score (SS), and the ratio that compares the activity
of the SNS -measured by SS- vs. the activity of the PNS-measured by the variable SD1-
(S/PS ratio), were obtained as non-linear measurements.
2.3.2. Peripheral Fatigue Assessment: Contractile Muscle Properties
The muscular response of the rectus femoris (RF), vastus lateralis (VL), vastus medialis
(VM), and gastrocnemius medialis (GM) of both legs was measured by TMG (TMG-100
System electrostimulator, TMG-BMC d.o.o., Ljubljana, Slovenia). All measurements were
performed under static conditions, and with the muscle totally relaxed. The RF, VL and
VM were measured with the participant in the supine position and the knee joint ﬂexed at a
40◦ angle by means of a wedge cushion designed for that purpose. The GM was measured
with the participant in the prone position and the knee joint bent at an angle of 15◦, also
through a specially-designed wedge cushion. A digital displacement transducer (Trans-Tek
DC-DC; GK 40, Panoptik d.o.o. Ljubljana, Slovenia) incorporating a spring of 0.17 N·m−1
Int. J. Environ. Res. Public Health 2023, 20, 402
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was used and placed perpendicular and directly on the skin at the area of maximal mus-
cle mass of each muscle (established visually and on palpation of the muscle during a
voluntary contraction), as previously described [38]. The two self-adhesive electrodes
(5 × 5 cm2) (Compex Medical SA, Ecublens, Switzerland) were placed symmetrically to
the sensor, following the arrangement of the ﬁbers [39]. The positive electrode (anode)
was placed in the proximal part and the negative (cathode) in the distal part, between
5–6 cm from the measurement point. The electrical stimulus (i.e., 1 ms) was applied with an
electrostimulator (TMG-S1; Furlan Co., & Ltd., Ljubljana, Slovenia), while the intensity was
varied (i.e., 50, 75 and 100 mAp). The intensity that reached the maximum response of the
radial displacement of the muscle belly was selected [40]. In addition, periods of 10 s were
established between consecutive measurements to minimize the possible effects of fatigue
or muscle enhancement [40,41]. All measurements were performed by the same researcher,
who had experience in collecting these types of measurements. None of the evaluated
subjects presented discomfort during electrical stimulation. Maximal radial muscle-belly
displacement (Dm); reaction or activation time (also known as time delay) between the
initiation and 10% of Dm (Td); contraction time between 10 and 90% Dm (Tc); sustain time
(Ts), as the interval in milliseconds (ms) between 50% of Dm on both the ascending and
descending sides of the curve; and relaxation time (Tr), as the interval between 90% and
50% Dm of muscle reaction of the RF, VL, VM and GM, were recorded using TMG. The
TMG-derived contraction velocity (Vc) was also calculated by dividing Dm by the sum
of Tc and Td [39,42]. In this regard, previous evidence supports the use of Vc as a sensi-
tive marker of acute variations in speed and power performance [39]. All TMG variables
used had demonstrated a high intraclass correlation coefﬁcient (ICC) (i.e., 0.86–0.98), as
described in previous studies [43,44].
Finally, to assess peripheral fatigue of the lower limb, both legs were individually
analyzed, and then the results obtained for each muscle of each leg were pooled, according
to García-Manso et al. [29].
2.4. Procedures
Both central and peripheral fatigue tests were performed the day before the com-
petition and immediately after it, within 20–30 min of the end of the race, as previously
described [38]. Each test lasted no more than 10 min.
Firstly, central fatigue was assessed by measuring HRV. Upon completion of this,
peripheral fatigue was assessed using the contractile properties of the RF, VL, VM and GM
of both legs. An experimental design scheme is presented in Figure 1.
2.5. Statistical Analysis
Statistical analysis of the data was performed using the Jamovi 1.6.16 software (Sydney,
Australia). The Shapiro-Wilk test was applied to establish whether the variances of the
different variables correspond to a normal and homogeneous distribution. A T-test was
performed for repeated samples, or its non-parametric counterpart, when applicable, to
detect signiﬁcant differences before and after the competition (i.e., pre-post) in the following
variables: (1) SDNN, Pnn50, RSSMD, SS, and S/PS ratio for central fatigue assessment; and
(2) Td, Tc, Ts, Tr, Dm, Vc for peripheral fatigue assessment. Cohen’s d was used to measure
the effect size (ES) of the parametric meanings, using the small (d = 0.2), medium (d = 0.5)
and large (d = 0.8) reference values, as Cohen suggested [45]. In the case of applying a
non-parametric test, the ES was determined using a biserial correlation analysis [46]. The
conﬁdence interval for the differences was established at 95%. The signiﬁcant difference for
the value of α was established with a value of p < 0.05.
Int. J. Environ. Res. Public Health 2023, 20, 402
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Int. J. Environ. Res. Public Health 2023, 20, x FOR PEER REVIEW 
5  of  12 
 
 
 
Figure 1. Experimental design scheme. VK: vertical kilometer; HRV: heart rate variability; RMSSD: 
square root of the mean of the squared differences between successive normal–to–normal intervals; 
SDNN: standard deviation of normal–to–normal intervals; pNN50: percentage of successive RR in‐
tervals that differ by more than 50 ms; SS: stress score; S/PS ratio: ratio between the sympathetic 
nervous system and the parasympathetic nervous system activity; TMG: tensiomyography; RF: rec‐
tus femoris; VL: vastus lateralis; VM: vastus medialis; GM: gastrocnemius medialis. 
2.5. Statistical Analysis 
Statistical analysis of the data was performed using the Jamovi 1.6.16 software (Syd‐
ney, Australia). The Shapiro‐Wilk test was applied to establish whether the variances of 
the different variables correspond to a normal and homogeneous distribution. A T‐test 
was performed for repeated samples, or its non‐parametric counterpart, when applicable, 
to detect significant differences before and after the competition (i.e., pre‐post) in the fol‐
lowing variables: (1) SDNN, Pnn50, RSSMD, SS, and S/PS ratio for central fatigue assess‐
ment; and (2) Td, Tc, Ts, Tr, Dm, Vc for peripheral fatigue assessment. Cohen’s d was used 
to measure the effect size (ES) of the parametric meanings, using the small (d = 0.2), me‐
dium (d = 0.5) and large (d = 0.8) reference values, as Cohen suggested [45]. In the case of 
applying a non‐parametric test, the ES was determined using a biserial correlation analy‐
sis [46]. The confidence interval for the differences was established at 95%. The significant 
difference for the value of α was established with a value of p < 0.05. 
3. Results 
3.1. Central Fatigue Assessment: HRV 
Table 1 shows the fluctuation of the observed variables, referring to the ANS activity. 
Resting heart rate (HR) significantly increased after the VK competition (⁓28%, p = 0.01). 
 
 
Figure 1.
Experimental design scheme.
VK: vertical kilometer; HRV: heart rate variability;
RMSSD: square root of the mean of the squared differences between successive normal–to–normal
intervals; SDNN: standard deviation of normal–to–normal intervals; pNN50: percentage of successive
RR intervals that differ by more than 50 ms; SS: stress score; S/PS ratio: ratio between the sympathetic
nervous system and the parasympathetic nervous system activity; TMG: tensiomyography; RF: rectus
femoris; VL: vastus lateralis; VM: vastus medialis; GM: gastrocnemius medialis.
3. Results
3.1. Central Fatigue Assessment: HRV
Table 1 shows the ﬂuctuation of the observed variables, referring to the ANS activity.
Resting heart rate (HR) signiﬁcantly increased after the VK competition (~28%, p = 0.01).
Table 1. Central fatigue before and after the VK trail running race.
Pre-VK
Post-VK
p-Value
95% CI
Cohen’s d
ES
95% CI
(Mean ± SD)
(Mean ± SD)
Lower
Upper
Lower
Upper
HR (bpm)
56.10 ± 6.96
71.90 ± 11.78
0.01
−26.99
−4.51
−1.17
−2.07
−0.23
Time domain
SDNN (ms)
58.10 ± 27.06
33.40 ± 18.99
0.02
4.79
44.52
1.04
0.14
1.89
pNN50 (%)
23.71 ± 12.56
8.26 ± 8.94
0.23
3.04
24.49
0.89
RMSSD (ms)
48.45 ± 19.29
27.21 ± 12.16
0.01
8.13
34.35
1.35
0.35
2.31
Non-linear measurements
SS (a.u.)
14.97 ± 7.76
28.42 ± 13.66
0.01
−22.31
−4.59
−1.27
−2.20
−0.30
S/PS ratio (a.u.)
0.58 ± 0.45
2.36 ± 2.32
0.02
−3.89
−0.21
−0.94
HR: Heart Rate; SDNN: standard deviation of normal-to-normal intervals; pNN50: percentage of successive RR
intervals that differ by more than 50 ms; RMSSD: square root of the mean of the squared differences between succes-
sive normal-to-normal intervals; SS: Stress Score; S/PS ratio: ratio between the sympathetic nervous system and the
parasympathetic nervous system activity; VK: vertical kilometer; SD: standard deviation; CI: conﬁdence interval.
The time domain variables showed a signiﬁcant decrease of the PNS activity after the
competition (Table 1). On the contrary, SS, an index related to the SNS activity, signiﬁcantly
increased after the race (Table 1).
Regarding autonomic balance, S/PS ratio increased, denoting a signiﬁcant predomi-
nance of the SNS over the PNS (Table 1).
Int. J. Environ. Res. Public Health 2023, 20, 402
6 of 11
3.2. Peripheral Fatigue Assessment: TMG
Concerning TMG measures, there were several changes in the muscular response after
the competition. Table 2 shows the differences between pre- and post-competition values
obtained from the analysis of both legs pooled and for each muscle group. Signiﬁcant dif-
ferences were found only in TdRF [mean difference: 1.94 (95% CI: 0.41–3.47), t (10) = 2.8310;
p = 0.018, d = 0.85)], TrVM [mean difference: 72.05 (95% CI: 18.01–126.1), t (10) = 2.9707;
p = 0.014, d = 0.89)] and TsGM [mean difference: −139.274 (95% CI: −239.41–39.11),
t (10) = −3.09; p = 0.011, d = 0.93)] when comparing pre- and post-competition values.
Table 2. Peripheral fatigue before and after the VK trail running race for both legs.
Muscle
Group
TMG
Pre-VK
(Mean ± SD)
Post-VK
(Mean ± SD)
Difference
p-Value
95% CI
Cohen’s d
ES
95% CI
(%)
Lower
Upper
Lower
Upper
Rectus
Femoris (RF)
Td
47.45 ± 2.93
45.34 ± 2.66
−4.7
0.02 *
0.41
3.47
0.85
0.14
1.54
Tc
57.19 ± 7.09
57.41 ± 8.40
0.4
0.88
−5.0
4.35
−0.05
−0.64
0.55
Ts
307.43 ± 599.85
143.03 ± 131.92
−114.9
0.17
−97.48
481.47
0.44
−0.19
1.06
Tr
121.65 ± 230.21
53.69 ± 55.11
−126.6
0.15
−33.34
188.73
0.47
−0.17
1.09
Dm
14.30 ± 3.43
15.60 ± 3.76
8.3
0.18
−3.10
0.68
−0.43
−1.04
0.20
Vc
0.27 ± 0.06
0.30 ± 0.06
9.2
0.12
−0.06
0.008
−0.50
−1.13
0.14
Vastus
Lateralis (VL)
Td
43.93 ± 2.31
42.81 ± 2.49
−2.6
0.09
−0.19
2.20
0.56
−0.09
1.19
Tc
45.85 ± 4.99
45.20 ± 5.88
−1.4
0.52
−1.22
2.24
0.19
−0.40
0.79
Ts
90.84 ± 47.23
89.07 ± 25.18
−2.0
0.81
−28.24
35.47
0.07
−0.52
0.67
Tr
38.82 ± 39.04
35.05 ± 17.45
−10.8
0.66
−21.23
32.03
0.13
−0.46
0.73
Dm
10.37 ± 1.82
11.16 ± 1.91
7.0
0.12
−1.68
0.23
−0.50
−1.13
0.13
Vc
0.23 ± 0.04
0.25 ± 0.04
9.2
0.06
−0.04
0.001
−0.63
−1.28
0.03
Vastus
Medialis (VM)
Td
42.83 ± 1.52
42.51 ± 1.01
−0.7
0.49
−0.72
1.38
0.21
−0.39
0.81
Tc
47.64 ± 4.52
48.15 ± 5.28
1.0
0.41
−1.89
0.83
−0.26
−0.86
0.34
Ts
411.90 ± 165.18
371.33 ± 59.77
−10.9
0.23
−34.64
127.56
0.38
−0.24
0.99
Tr
230.80 ± 65.42
151.23 ± 68.48
−52.6
0.01 *
18.01
126.10
0.89
0.17
1.59
Dm
15.58 ± 3.46
16.09 ± 3.38
3.2
0.24
−1.28
0.36
−0.37
−0.98
0.25
Vc
0.35 ± 0.09
0.36 ± 0.08
2.9
0.33
−0.03
0.01
−0.31
−0.91
0.30
Gastrocnemius
Medialis (GM)
Td
39.94 ± 1.91
37.72 ± 3.82
−5.9
0.10
−0.43
4.12
0.54
−0.10
1.17
Tc
43.22 ± 6.08
44.10 ± 13.28
2.0
0.66
−8.64
5.73
−0.13
−0.73
0.46
Ts
480.16 ± 242.51
622.28 ± 353.28
22.8
0.01 *
−239.42
−39.12
−0.93
−1.63
−0.20
Tr
175.05 ± 129.45
143.02 ± 118.09
−22.4
0.61
−96.87
157.16
0.15
−0.44
0.75
Dm
5.07 ± 2.17
4.76 ± 2.14
−6.6
0.65
−1.07
1.65
0.14
−0.46
0.73
Vc
0.12 ± 0.05
0.12 ± 0.05
−4.8
0.71
−0.02
0.04
0.11
−0.48
0.71
VK: vertical kilometer; SD: standard deviation; CI: conﬁdence interval; ES: effect size; TMG: variables derived
from the tensiomyography measurements; Td: time delay between the initiation and 10% of maximal radial
muscle-belly displacement; Vc: tensiomyography-derived contraction velocity; Tc: contraction time between 10%
and 90% of maximal radial muscle-belly displacement; Ts: sustain time, the interval in milliseconds (ms) between
50% of Dm on both the ascending and descending sides of the curve; Tr: relaxation time, the interval between 90%
and 50% Dm of muscle reaction. * p < 0.05.
Table 3 shows the statistically signiﬁcant differences obtained from the analysis of
each leg individually (lateral symmetry).
Table 3. Peripheral fatigue before and after the VK trail running race for each leg.
Side & Muscle
Group
TMG
Pre-VK
(Mean ± SD)
Post-VK
(Mean ± SD)
p-Value
95% CI
Cohen’s d
ES
95% CI
Lower
Upper
Lower
Upper
Right RF
Td
23.74 ± 1.70
22.38 ± 1.95
0.02
0.02
2.46
0.94
0.13
1.71
Vc
0.14 ± 0.04
0.16 ± 0.036
0.03
−0.04
−0.002
−0.86
−1.62
−0.07
Right VL
Td
22.08 ± 1.48
21.13 ± 1.12
0.02
0.20
1.71
0.97
0.15
1.75
Tc
23.1 ± 1.83
21.85 ± 2.19
0.02
0.24
2.27
0.95
0.13
1.73
Left VM
Tr
117.4 ± 48.07
67.18 ± 39.76
0.02
8.87
91.55
0.93
0.12
1.71
VK: vertical kilometer; SD: standard deviation; CI: conﬁdence interval; ES: effect size; TMG: variables derived
from the tensiomyography measurements; RF: rectus femoris; VL: vastus lateralis; VM: vastus medialis; Td: time
delay between the initiation and 10% of maximal radial muscle-belly displacement; Vc: tensiomyography-derived
contraction velocity; Tc: contraction time between 10% and 90% of maximal radial muscle-belly displacement;
Ts: sustain time, the interval in milliseconds (ms) between 50% of Dm on both the ascending and descending
sides of the curve; Tr: relaxation time, the interval between 90% and 50% Dm of muscle reaction.
Int. J. Environ. Res. Public Health 2023, 20, 402
7 of 11
4. Discussion
The main ﬁndings of our investigation were: (1) as expected, there was a signiﬁcant
increase in the SNS activity after the competition, which lasted up to 45–60 min; (2) simulta-
neously, post-exercise PNS activity was signiﬁcantly reduced; and (3) time-related variables
and Dm levels presented by our runners suggest pre-competition peripheral fatigue.
To the best of our knowledge, this is the ﬁrst study evaluating central (i.e., ANS
performance) and peripheral (i.e., muscle activity) fatigue before and immediately after
(i.e., within 20–30 min after ﬁnishing) a VK trail running race in recreational trail runners.
Although trail running is an emerging topic, the great majority of studies performed in the
past few years have focused on metabolic, biomechanical and neuromuscular parameters.
However, no studies have simultaneously assessed central and peripheral fatigue via
non-invasive methods, especially when uphill trail running is considered (e.g., VK races).
As expected, HRV parameters assessed after the competition showed signiﬁcantly
decreased values when compared to pre-competition levels. However, one interesting
point is that when values of the variables related to the modulation of PNS (Table 1) are
compared with previous studies [37,47,48], our runners showed lowered values before
the VK competition (previous 24–36 h). In this regard, it should be considered that HRV
is usually greater in active than sedentary individuals [21,47,49,50]. For instance, trained
athletes show higher RMSSD values [21], thus training characteristics might inﬂuence HRV
time and frequency domain measures [21].
The analyses of the pre-competition HRV time-domain variables (i.e., RMSSD, SDNN
and Pnn50) traditionally related to PNS activity [37,51] showed that RMSSD values of
our runners (48.45 ± 19.29 ms) would be considered within the average range (i.e., 50th
percentile) for the age group when compared with previous studies performed with healthy
non-athletes [48]. However, if our results are compared with another investigation carried
out in a group of athletes with similar daily activity patterns [47], our runners would
be located close to the 25th and 10th percentiles regarding RMSSD and SDNN values
respectively. Similarly, Pnn50 values drops to below the 25th percentile when our results
are compared with a previous work performed with professional athletes [37]. However,
during the present study, a measurement of PNS-related variables was not performed
continuously, establishing baseline values and the trend of these before the competition in
each participant [52]. Therefore, it was not possible to assess intrasubject PNS activity and
to determine a greater or lesser degree of PNS dominance in our runners in the hours prior
to competition based only on the HRV measurements performed.
Apart from that, all recorded variables related to time domain underwent a large
change after the race (ES = 0.89–1.35), indicating a clear downregulation of the PNS (Table 1,
time domain variables) and therefore, presumably, an upregulation of the SNS. Thus, it is
clear that a VK is a very demanding competition, even if the average speed during the race
is low. One of the limitations of this study was the lack of measurements throughout the
days after the race, which would allow us to know how much time is needed for the PNS
to reach its pre-race levels.
Regarding the pre-competition balance between the SNS and PNS assessed through
HRV non-linear measurements, our runners reported high SS values (14.97 ± 7.76) that
are beyond the 90th percentile, which is related to a high level of sympathetic stress [37].
Further, the runners of the present study showed a clear disbalance of autonomic activity,
reporting an S/PS ratio higher than 0.3 at rest (0.58 ± 0.45), which suggests an excess of the
SNS activity or a lack of recovery of the PNS activity [37]. In addition to this, considering
the variation of these two variables (i.e., S/PS and SS) before and after the competition, a
clear increase in their values can be observed and therefore, a greater dominance of the
SNS after the VK. This is in accordance with a lower activity of the PNS measured by time
domain variables (Table 1).
Taken together, in addition to the fatigue generated by the VK, both time domain
and nonlinear measures may represent a sensitive downward modulation in the PNS of
our runners and a lack of autonomic balance (i.e., central fatigue) prior to the race. This
Int. J. Environ. Res. Public Health 2023, 20, 402
8 of 11
interpretation could suggest that a supercompensation status was not attained, which
could be linked with fatigue. In this regard, some authors have suggested that recreational
endurance runners who do not properly recover between training sessions, or those ex-
periencing psychological stress or autonomic neuromuscular fatigue, are at higher risk
of developing the so-called “overtraining syndrome”, which impairs endurance perfor-
mance and leads to long-term fatigue [53]. Moreover, other studies have suggested that
recreational runners try to imitate training practices performed by professional athletes,
including high weekly volume (e.g., >70 km), which could lead to some health-related
problems (e.g., injuries, overtraining) [54]. With this in mind, we speculate that many
recreational runners are usually overtrained and therefore, unable to peak during the
competition period. However, the absence of previous works in the ﬁeld with runners
with similar characteristics to those in our study and the lack of continuous pre-race HRV
measurement, means it is not possible to draw a conclusion about the degree of stress and
fatigue that runners experienced in our study previous to the race.
Regarding peripheral fatigue, despite having analyzed six variables by TMG (i.e., Td,
Tc, Ts, Tr, Vc and Dm) in four muscle groups (i.e., RF, VL, VM and GM), there were
statistically signiﬁcant differences only in TdRF (p = 0.018; d = 0.85), TrVM (p = 0.014;
d = 0.89) and TsGM (p = 0.011; d = −0.93), when the pooled data of both legs were con-
sidered. These variations in Td, Tr and Ts might be connected to metabolic changes in
myoplasmic Ca2+ [55,56]. Some research in endurance sports suggests that the time-related
variables (Td, Tc, Ts, Tr and Vc) tend to decrease after competition, while Dm increases,
indicating a reduction of muscular stiffness and an increase of neuromuscular peripheral
fatigue [11,29,38,55,57], according to the different muscles evaluated. In this regard, our
results are partially in accordance with previous studies, since not all time-related variables
showed decreased values after the VK race. For instance, after the race, Tc increased in all
the muscles studied, except VL (−1.4%); Ts increased in GM (22.8%); and Vc increased in
all the muscles analyzed (RF: 9.2%; VL: 9.2%; VM: 2.9%), except in GM, which decreased
(−4.8%). Similarly, Dm increased in all the muscles studied (RF: 8.3%; VL: 7.0%; VM: 3.2%)
except in GM, where it decreased (−6.6%). On the other hand, when analyzing each leg
individually (i.e., lateral symmetry) we found statistically signiﬁcant differences in the
right leg in TdRF (0.023; ES = 0.94), VcRF (0.032; ES = 0.86), TdVL (0.02; ES = 0.97) and
TcVL (0.021; ES = 0.95); and in the left leg in TrVM (0.023; ES = 0.93). These differences
between segments may be due to ground surface irregularities, independent of differences
in muscle strength, which may predispose the athlete to temporary asymmetric stimuli
due to the activity being performed at a given time [58]. Based on our neuromuscular
results, and the lack of consistency of previous studies, we speculate that: (1) 20–30 min
is a sufﬁcient period for experienced trail runners to recover at neuromuscular level after
an intense effort with predominantly concentric contractions (i.e., uphill trail running);
and (2) regarding HRV tendency, recreational runners might have already experienced
peripheral fatigue before the race. Therefore, the peripheral fatigue generated during the
competition did not represent an important stimulus at neuromuscular level. On this point,
previous scientiﬁc evidence has suggested there is a link between central and peripheral
fatigue. Thus, endurance training induces central fatigue adaptations, leading to improved
tolerance of peripheral fatigue by the central nervous system [59].
One of the limitations of our study is the small sample size. However, it is important
to consider that VK competitions are highly-demanding trail running races with less
participation than other endurance running events. The complexity of assessing both
central and peripheral fatigue immediately after the race (i.e., in the mountains, within
20–30 min after ﬁnishing) makes it difﬁcult to evaluate a large number of participants.
Future longitudinal studies are guaranteed for investigating the mechanisms underlying
fatigue in endurance trail running events, especially when uphill running (i.e., VK races) is
considered. In this regard, it would be of great interest to assess recovery variables over
subsequent days after the competition to analyze recovery status evolution.
Int. J. Environ. Res. Public Health 2023, 20, 402
9 of 11
5. Conclusions
The present study demonstrates that a VK race affects the ANS system, downregulat-
ing the PNS and upregulating the SNS. However, regarding peripheral fatigue, only small
changes in the contractile capacity of speciﬁc muscle groups were detected. In addition, the
pre-race measurements of HRV could suggest trail runners experienced a lack of recovery
or non-functional overreaching before the race. Furthermore, while the neuromuscular
stimulus of the competition did not seem to infer a great peripheral fatigue in these athletes,
central fatigue signiﬁcantly increased after the race. Considering the link between both
central and peripheral fatigue, training periodization and new tapering strategies are called
to play a key role in minimizing pre-competition fatigue status, thus favoring running
performance. In this regard, monitoring HRV during the preparation period has been
shown to be an effective strategy to avoid pre-competition fatigue.
Author Contributions: Conceptualization, I.M.-P. and M.M.-C.; methodology, I.M.-P., R.N.-P. and
M.M.-C.; validation, I.M.-P., A.V.-S., C.L.-F., R.N.-P. and M.M.-C.; formal analysis, I.M.-P. and R.N.-P.;
investigation, I.M.-P., C.L.-F. and M.M.-C.; resources, I.M.-P. and M.M.-C.; data curation, I.M.-P. and
M.M.-C.; writing—original draft preparation, I.M.-P., A.V.-S. and M.M.-C.; writing—review and
editing, I.M.-P., A.V.-S., C.L.-F., R.N.-P. and M.M.-C.; visualization, I.M.-P., A.V.-S., C.L.-F., R.N.-P. and
M.M.-C.; supervision, I.M.-P., A.V.-S. and M.M.-C. All authors have read and agreed to the published
version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: The study was conducted in accordance with the Declaration
of Helsinki, and approved by the Ethics Committee of Universidad Europea del Atlántico (protocol
code CEI 21/2018 and 03/2018 of approval).
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: The data presented in this study are available on request from the
corresponding author. The data are not publicly available due to conﬁdentiality and anonymity of
study participants.
Conﬂicts of Interest: The authors declare no conﬂict of interest.
References
1.
Bassett, D.R.J.; Howley, E.T. Limiting factors for maximum oxygen uptake and determinants of endurance performance. Med. Sci.
Sports Exerc. 2000, 32, 70–84. [CrossRef] [PubMed]
2.
Casado, A.; González-Mohíno, F.; González-Ravé, J.M.; Foster, C. Training Periodization, Methods, Intensity Distribution, and
Volume in Highly Trained and Elite Distance Runners: A Systematic Review. Int. J. Sports Physiol. Perform. 2022, 17, 820–833.
[CrossRef] [PubMed]
3.
Lazzer, S.; Salvadego, D.; Taboga, P.; Rejc, E.; Giovanelli, N.; di Prampero, P.E. Effects of the Etna Uphill Ultramarathon on Energy
Cost and Mechanics of Running. Int. J. Sports Physiol. Perform. 2015, 10, 238–247. [CrossRef]
4.
Ehrström, S.; Tartaruga, M.P.; Easthope, C.S.; Brisswalter, J.; Morin, J.-B.; Vercruyssen, F. Short Trail Running Race: Beyond the
Classic Model for Endurance Running Performance. Med. Sci. Sports Exerc. 2018, 50, 580–588. [CrossRef]
5.
Björklund, G.; Swarén, M.; Born, D.-P.; Stöggl, T. Biomechanical Adaptations and Performance Indicators in Short Trail Running.
Front. Physiol. 2019, 10, 506. [CrossRef]
6.
Bascuas, P.J.; Bataller-Cervero, A.V.; Gutierrez, H.; Berzosa, C. Modiﬁcations of viscoelastic properties and physiological
parameters after performing uphill and downhill running trials. J. Sports Med. Phys. Fit. 2020, 61, 625–635. [CrossRef]
7.
Lemire, M.; Hureau, T.J.; Favret, F.; Geny, B.; Kouassi, B.Y.L.; Boukhari, M.; Lonsdorfer, E.; Remetter, R.; Dufour, S.P. Physiological
factors determining downhill vs uphill running endurance performance. J. Sci. Med. Sport 2021, 24, 85–91. [CrossRef]
8.
Nicot, F.; Sabater-Pastor, F.; Samozino, P.; Millet, G.Y.; Rupp, T. Effect of ground technicity on cardio-respiratory and biomechanical
parameters in uphill trail running. Eur. J. Sport Sci. 2021, 22, 1836–1846. [CrossRef]
9.
Berzosa, C.; Gutierrez, H.; Bascuas, P.J.; Arbones, I.; Bataller-Cervero, A.V. Muscle Tone and Body Weight Predict Uphill Race
Time in Amateur Trail Runners. Int. J. Environ. Res. Public Health 2021, 18, 2040. [CrossRef]
10.
Millet, G.Y.; Tomazin, K.; Verges, S.; Vincent, C.; Bonnefoy, R.; Boisson, R.-C.; Gergelé, L.; Féasson, L.; Martin, V. Neuromuscular
Consequences of an Extreme Mountain Ultra-Marathon. PLoS ONE 2011, 6, e17059. [CrossRef]
11.
Giovanelli, N.; Taboga, P.; Rejc, E.; Simunic, B.; Antonutto, G.; Lazzer, S. Effects of an Uphill Marathon on Running Mechanics
and Lower-Limb Muscle Fatigue. Int. J. Sports Physiol. Perform. 2016, 11, 522–529. [CrossRef] [PubMed]
Int. J. Environ. Res. Public Health 2023, 20, 402
10 of 11
12.
Millet, G.Y.; Martin, V.; Temesi, J. The role of the nervous system in neuromuscular fatigue induced by ultra-endurance exercise.
Appl. Physiol. Nutr. Metab. 2018, 43, 1151–1157. [CrossRef] [PubMed]
13.
Hewing, B.; Schattke, S.; Spethmann, S.; Sanad, W.; Schroeckh, S.; Schimke, I.; Halleck, F.; Peters, H.; Brechtel, L.; Lock, J.; et al.
Cardiac and renal function in a large cohort of amateur marathon runners. Cardiovasc. Ultrasound 2015, 13, 13. [CrossRef]
[PubMed]
14.
Marklund, P.; Mattsson, C.M.; Wåhlin-Larsson, B.; Ponsot, E.; Lindvall, B.; Lindvall, L.; Ekblom, B.; Kadi, F. Extensive inﬂamma-
tory cell inﬁltration in human skeletal muscle in response to an ultraendurance exercise bout in experienced athletes. J. Appl.
Physiol. 2012, 114, 66–72. [CrossRef]
15.
Kiviniemi, A.M.; Hautala, A.J.; Kinnunen, H.; Tulppo, M.P. Endurance training guided individually by daily heart rate variability
measurements. Eur. J. Appl. Physiol. 2007, 101, 743–751. [CrossRef]
16.
Pichot, V.; Roche, F.; Celle, S.; Barthélémy, J.-C.; Chouchou, F. HRVanalysis: A Free Software for Analyzing Cardiac Autonomic
Activity. Front. Physiol. 2016, 7, 557. [CrossRef]
17.
Varela-Sanz, A.; Tuimil, J.L.; Abreu, L.; Boullosa, D.A. Does Concurrent Training Intensity Distribution Matter? J. Strength Cond.
Res. 2017, 31, 181–195. [CrossRef]
18.
Vesterinen, V.; Nummela, A.R.I.; Heikura, I.D.A.; Laine, T.; Hynynen, E.S.A.; Botella, J.; Häkkinen, K. Individual Endurance
Training Prescription with Heart Rate Variability. Med. Sci. Sports Exerc. 2016, 48, 1347–1354. [CrossRef]
19.
Task Force of the European Society of Cardiology the North American Society of Pacing and Electrophysiology. Heart Rate
Variability. Standards of measurement, physiological interpretation and clinical use. Circulation 1996, 93, 1043–1065. [CrossRef]
20.
Carrasco-Poyatos, M.; González-Quílez, A.; Martínez-González-Moro, I.; Granero-Gallegos, A. HRV-Guided Training for
Professional Endurance Athletes: A Protocol for a Cluster-Randomized Controlled Trial. Int. J. Environ. Res. Public Health 2020,
17, 5465. [CrossRef]
21.
Altini, M.; Plews, D. What Is behind Changes in Resting Heart Rate and Heart Rate Variability? A Large-Scale Analysis of
Longitudinal Measurements Acquired in Free-Living. Sensors 2021, 21, 7932. [CrossRef] [PubMed]
22.
Pichot, V.; Busso, T.; Roche, F.; Garet, M.; Costes, F.; Duverney, D.; Lacour, J.-R.; Barthélémy, J.-C. Autonomic adaptations to
intensive and overload training periods: A laboratory study. Med. Sci. Sports Exerc. 2002, 34, 1660–1666. [CrossRef] [PubMed]
23.
Gratze, G.; Rudnicki, R.; Urban, W.; Mayer, H.; Schlögl, A.; Skrabal, F. Hemodynamic and autonomic changes induced by
Ironman: Prediction of competition time by blood pressure variability. J. Appl. Physiol. 2005, 99, 1728–1735. [CrossRef] [PubMed]
24.
Hautala, A.; Tulppo, M.P.; Mäkikallio, T.H.; Laukkanen, R.; Nissilä, S.; Huikuri, H.V. Changes in cardiac autonomic regulation
after prolonged maximal exercise. Clin. Physiol. 2001, 21, 238–245. [CrossRef] [PubMed]
25.
Nicolas, M.; Banizette, M.; Millet, G.Y. Stress and recovery states after a 24 h ultra-marathon race: A one-month follow-up study.
Psychol. Sport Exerc. 2011, 12, 368–374. [CrossRef]
26.
Easthope, C.S.; Hausswirth, C.; Louis, J.; Lepers, R.; Vercruyssen, F.; Brisswalter, J. Effects of a trail running competition on
muscular performance and efﬁciency in well-trained young and master athletes. Eur. J. Appl. Physiol. 2010, 110, 1107–1116.
[CrossRef]
27.
Carrasco, L.; Sañudo, B.; de Hoyo, M.; Pradas, F.; Da Silva, M.E. Effectiveness of low-frequency vibration recovery method on
blood lactate removal, muscle contractile properties and on time to exhaustion during cycling at VO2max power output. Eur. J.
Appl. Physiol. 2011, 111, 2271–2279. [CrossRef]
28.
García-García, O.; Cuba-Dorado, A.; Riveiro-Bozada, A.; Carballo-López, J.; Álvarez-Yates, T.; López-Chicharro, J. A Maximal
Incremental Test in Cyclists Causes Greater Peripheral Fatigue in Biceps Femoris. Res. Q. Exerc. Sport 2020, 91, 460–468. [CrossRef]
29.
García-Manso, J.M.; Rodríguez-Ruiz, D.; Rodríguez-Matoso, D.; de Saa, Y.; Sarmiento, S.; Quiroga, M. Assessment of muscle
fatigue after an ultra-endurance triathlon using tensiomyography (TMG). J. Sports Sci. 2011, 29, 619–625. [CrossRef]
30.
García-Manso, J.M.; Rodríguez-Matoso, D.; Sarmiento, S.; de Saa, Y.; Vaamonde, D.; Rodríguez-Ruiz, D.; Da Silva-Grigoletto, M.E.
Effect of high-load and high-volume resistance exercise on the tensiomyographic twitch response of biceps brachii. J. Electromyogr.
Kinesiol. 2012, 22, 612–619. [CrossRef]
31.
Macgregor, L.J.; Ditroilo, M.; Smith, I.J.; Fairweather, M.M.; Hunter, A.M. Reduced Radial Displacement of the Gastrocnemius
Medialis Muscle After Electrically Elicited Fatigue. J. Sport Rehabil. 2016, 25, 241–247. [CrossRef] [PubMed]
32.
Raeder, C.; Wiewelhove, T.; Simola, R.Á.D.P.; Kellmann, M.; Meyer, T.; Pfeiffer, M.; Ferrauti, A. Assessment of Fatigue and
Recovery in Male and Female Athletes After 6 Days of Intensiﬁed Strength Training. J. Strength Cond. Res. 2016, 30, 3412–3427.
[CrossRef] [PubMed]
33.
Wiewelhove, T.; Raeder, C.; Meyer, T.; Kellmann, M.; Pfeiffer, M.; Ferrauti, A. Markers for Routine Assessment of Fatigue and
Recovery in Male and Female Team Sport Athletes during High-Intensity Interval Training. PLoS ONE 2015, 10, e0139801.
[CrossRef] [PubMed]
34.
González-Fimbres, R.A.; Hernández-Cruz, G.; Flatt, A.A. Ultrashort Versus Criterion Heart Rate Variability Among International-
Level Girls’ Field Hockey Players. Int. J. Sports Physiol. Perform. 2021, 16, 985–992. [CrossRef] [PubMed]
35.
Bloomﬁeld, D.M.; Magnano, A.; Bigger, J.T.; Rivadeneira, H.; Parides, M.; Steinman, R.C. Comparison of spontaneous vs.
metronome-guided breathing on assessment of vagal modulation using RR variability. Am. J. Physiol. Circ. Physiol. 2001, 280,
H1145–H1150. [CrossRef] [PubMed]
36.
Tarvainen, M.P.; Niskanen, J.-P.; Lipponen, J.A.; Ranta-aho, P.O.; Karjalainen, P.A. Kubios HRV—Heart rate variability analysis
software. Comput. Methods Programs Biomed. 2014, 113, 210–220. [CrossRef] [PubMed]
Int. J. Environ. Res. Public Health 2023, 20, 402
11 of 11
37.
Naranjo Orellana, J.; de la Cruz Torres, B.; Sarabia Cachadiña, E.; de Hoyo, M.; Domínguez Cobo, S. Two New Indexes for the
Assessment of Autonomic Balance in Elite Soccer Players. Int. J. Sports Physiol. Perform. 2015, 10, 452–457. [CrossRef]
38.
Gutiérrez-Vargas, R.; Martín-Rodríguez, S.; Sánchez-Ureña, B.; Rodríguez-Montero, A.; Salas-Cabrera, J.; Gutiérrez-Vargas, J.C.;
Simunic, B.; Rojas-Valverde, D. Biochemical and Muscle Mechanical Postmarathon Changes in Hot and Humid Conditions.
J. Strength Cond. Res. 2020, 34, 847–856. [CrossRef]
39.
Pereira, L.A.; Ramirez-Campillo, R.; Martín-Rodríguez, S.; Kobal, R.; Abad, C.C.C.; Arruda, A.F.S.; Guerriero, A.; Loturco, I. Is
tensiomyography-derived velocity of contraction a sensitive marker to detect acute performance changes in elite team-sport
athletes? Int. J. Sports Physiol. Perform. 2020, 15, 31–37. [CrossRef]
40.
Rey, E.; Padrón-Cabo, A.; Barcala-Furelos, R.; Mecías-Calvo, M. Effect of High and Low Flexibility Levels on Physical Fitness and
Neuromuscular Properties in Professional Soccer Players. Int. J. Sports Med. 2016, 37, 878–883. [CrossRef]
41.
Rey, E.; Lago-Peñas, C.; Lago-Ballesteros, J. Tensiomyography of selected lower-limb muscles in professional soccer players.
J. Electromyogr. Kinesiol. 2012, 22, 866–872. [CrossRef]
42.
Loturco, I.; Pereira, L.A.; Kobal, R.; Kitamura, K.; Ramírez-Campillo, R.; Zanetti, V.; Abad, C.C.C.; Nakamura, F.Y. Muscle
contraction velocity: A suitable approach to analyze the functional adaptations in elite soccer players. J. Sports Sci. Med. 2016,
15, 483. [PubMed]
43.
De Paula Simola, R.Á.; Harms, N.; Raeder, C.; Kellmann, M.; Meyer, T.; Pfeiffer, M.; Ferrauti, A. Assessment of Neuromuscular
Function After Different Strength Training Protocols Using Tensiomyography. J. Strength Cond. Res. 2015, 29, 1339–1348.
[CrossRef]
44.
De Paula Simola, R.Á.; Raeder, C.; Wiewelhove, T.; Kellmann, M.; Meyer, T.; Pfeiffer, M.; Ferrauti, A. Muscle mechanical properties
of strength and endurance athletes and changes after one week of intensive training. J. Electromyogr. Kinesiol. 2016, 30, 73–80.
[CrossRef] [PubMed]
45.
Ellis, P.D. The Essential Guide to Effect Sizes; Cambridge University Press: Cambridge, UK, 2010; ISBN 9780511761676.
46.
Kerby, D.S. The Simple Difference Formula: An Approach to Teaching Nonparametric Correlation. Compr. Psychol. 2014, 3, 11.
[CrossRef]
47.
Medina Corrales, M.; de la Cruz Torres, B.; Garrido Esquivel, A.; Garrido Salazar, M.A.; Naranjo Orellana, J. Normal values of
heart rate variability at rest in a young, healthy and active Mexican population. Health 2012, 4, 377–385. [CrossRef]
48.
Sammito, S.; Böckelmann, I. Reference values for time- and frequency-domain heart rate variability measures. Heart Rhythm 2016,
13, 1309–1316. [CrossRef] [PubMed]
49.
Melanson, E.L. Resting heart rate variability in men varying in habitual physical activity. Med. Sci. Sports Exerc. 2000,
32, 1894–1901. [CrossRef]
50.
Sandercock, G.R.H.; Hardy-Shepherd, D.; Nunan, D.; Brodie, D. The relationships between self-assessed habitual physical activity
and non-invasive measures of cardiac autonomic modulation in young healthy volunteers. J. Sports Sci. 2008, 26, 1171–1177.
[CrossRef]
51.
Hoshi, R.A.; Pastre, C.M.; Vanderlei, L.C.M.; Godoy, M.F. Poincaré plot indexes of heart rate variability: Relationships with other
nonlinear variables. Auton. Neurosci. 2013, 177, 271–274. [CrossRef]
52.
Plews, D.J.; Laursen, P.B.; Kilding, A.E.; Buchheit, M. Heart rate variability in elite triathletes, is variation in variability the key to
effective training? A case comparison. Eur. J. Appl. Physiol. 2012, 112, 3729–3741. [CrossRef] [PubMed]
53.
Nicoll, J.X.; Hatﬁeld, D.L.; Melanson, K.J.; Nasin, C.S. Thyroid hormones and commonly cited symptoms of overtraining in
collegiate female endurance runners. Eur. J. Appl. Physiol. 2018, 118, 65–73. [CrossRef] [PubMed]
54.
Boullosa, D.; Esteve-Lanao, J.; Casado, A.; Peyré-Tartaruga, L.A.; da Rosa, R.; Del Coso, J. Factors Affecting Training and Physical
Performance in Recreational Endurance Runners. Sports 2020, 8, 35. [CrossRef]
55.
Rodríguez-Matoso, D.; Mantecón, A.; Barbosa-Almeida, E.; Valverde, T.; García-Manso, J.M.; Rodríguez-Ruiz, D. Mechanical
response of knee muscles in high level bodyboarders during performance. Rev. Bras. Med. Esporte 2015, 21, 144–147. [CrossRef]
56.
Westerblad, H.; Lännergren, J.; Allen, D.G. Slowed Relaxation in Fatigued Skeletal Muscle Fibers of Xenopus and Mouse:
Contribution of [Ca2+]i and Cross-bridges. J. Gen. Physiol. 1997, 109, 385–399. [CrossRef] [PubMed]
57.
García-García, O.; Cuba-Dorado, A.; Álvarez-Yates, T.; Carballo-López, J.; Iglesias-Caamaño, M. Clinical utility of tensiomyogra-
phy for muscle function analysis in athletes. Open Access J. Sports Med. 2019, 10, 49–69. [CrossRef]
58.
Carpes, F.P.; Mota, C.B.; Faria, I.E. On the bilateral asymmetry during running and cycling—A review considering leg preference.
Phys. Ther. Sport 2010, 11, 136–142. [CrossRef]
59.
Zghal, F.; Cottin, F.; Kenoun, I.; Rebaï, H.; Moalla, W.; Dogui, M.; Tabka, Z.; Martin, V. Improved tolerance of peripheral fatigue by
the central nervous system after endurance training. Eur. J. Appl. Physiol. 2015, 115, 1401–1415. [CrossRef]
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Central and Peripheral Fatigue in Recreational Trail Runners: A Pilot Study.
Muñoz-Pérez, Iker,Varela-Sanz, Adrián,Lago-Fuentes, Carlos,Navarro-Patón, Rubén,Mecías-Calvo, Marcos