Central and Peripheral Fatigue in Recreational Trail Runners: A Pilot Study

derstanding fatigue mechanisms is crucial for exercise performance. How- ever, scienti c 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-sectionalpilot study was performed. After applying inclusion/exclusion criteria, 8 recreationalmale 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 signi cantly decreased (p= 0.02),

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 signi cantly decreased (p= 0.02), while the stress score and the sympathetic-parasympathetic ratio increased (p= 0.01 andp= 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 scienti c 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 ight 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 Health2023,20, 402.

Int. J. Environ. Res. Public Health2023,20, 402 2 of 11 Considering the aforementioned key factors affecting trail running performance and the speci c 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, scienti c 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 pro le, 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 in uenced by muscle damage associated with peripheral fatigue and therefore, cannot be detected by HRV measurement. In this regard, simultaneous assess- ment

le, 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 in uenced 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 bene ts 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 rst 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

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 Health2023,20, 402 3 of 11 Kilometer of Fuente D²(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 nish the competition, and (3) not to suffer any injury or illness during the measurements. Once the inclusion criteria were applied, the nal 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 Atl¡ntico (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 le without applying any lter for

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 le without applying any lter for correction. Once .txt les were generated for each athlete and measurement (i.e., pre-post), these were imported into a speci c 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 con guration 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 exed 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

and the knee joint exed 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 of0.17 N m 1

Int. J. Environ. Res. Public Health2023,20, 402 4 of 11 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 cm 2 ) (Compex Medical SA, Ecublens, Switzerland) were placed symmetrically to the sensor, following the arrangement of the bers [39]. The positive electrode (anode) was placed in the proximal part and the negative (cathode) in the distal part, between 5–6 cmfrom 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 coef cient (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

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 coef cient (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 Garc½a-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. 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 signi cant 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'sdwas 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 con dence interval for the differences was established at 95%. The signi cant difference for the value of was established with a value ofp <0.05.

was determined using a biserial correlation analysis [46]. The con dence interval for the differences was established at 95%. The signi cant difference for the value of was established with a value ofp <0.05.

Int. J. Environ. Res. Public Health2023,20, 402 5 of 11Int. 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: squareroot 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

This study evaluates central and peripheral fatigue in recreational trail runners during a vertical kilometer race.