Physiological, Biomechanical, and Thermographic Responses in Male Athletes during an Ultra-Endurance Race

investigates the comprehensive physiological, biomechanical, and thermo- graphic responses of male athletes during an ultra-endurance race, the Santander Four Days (S4D). Involving a 160 km race over four consecutive days with a 10 kg backpack, the study focuses on key aspects such as body mass, cortical arousal, handgrip strength, heart-rate variability, hydration status, blood glucose and lactate concentrations, and thermographic responses. The results indicate changes in heart-rate variability, indicating increased cardiovascular strain, consistent neuromuscular performance, significant body-weight reduction possibly due to dehydration and energy use, stable pH and glucose, but increased protein in urine suggesting renal stress, and varied body temperatures reflecting physical exertion and environmental factors. These findings highlight the body’s adaptive mechanisms and the importance of specialized training and recovery strategies in such physically demanding events. Keywords:ultra-endurance; thermography; heart-rate variability; stress; cortical arousal; strength 1. Introduction Ultra-endurance events are athletic competitions that significantly exceed the duration of traditional endurance events, typically lasting six hours or more. These events include activities such as ultramarathons, Ironman triathlons, and long-distance cycling

the body’s adaptive mechanisms and the importance of specialized training and recovery strategies in such physically demanding events. Keywords:ultra-endurance; thermography; heart-rate variability; stress; cortical arousal; strength 1. Introduction Ultra-endurance events are athletic competitions that significantly exceed the duration of traditional endurance events, typically lasting six hours or more. These events include activities such as ultramarathons, Ironman triathlons, and long-distance cycling races. They are defined as those lasting more than six hours, often requiring sustained physical exertion and exceptional stamina [1,2]. Participation in these events has increased in the last 25 years. The most popular are ultramarathon races, ultratriathlons, ultradistance swimming, ultracycling, and cross-country skiing [3]. There is also an increasing number of ultra-endurance mountain races, which are demanding events that produce a high impact on the psychophysiological response of participants [4]. Specifically, ultra-endurance athletes report a large rate of perceived exertion (RPE) [4], a decrease in blood glucose values [5], and an increase in sympathetic modulation analyzed by heart-rate variability (HRV), heart rate [6], muscular pain, and a decrease in cortical arousal [7]. Also, these long-duration events produce dehydration and a decrease in leg muscle strength [4]. They are performed at intensities lower than the anaerobic threshold [8] but produce a large muscular breakdown, as shown by increased creatinine kinase values [9]. Previous authors have found that different parameters are related to performance in ultra-endurance races [2,4]. Regarding training programs, traditionally, high-volume training has been the principal paradigm; however, the advantages of implementing high- intensity programs for ultra-endurance athlete performance have been shown [2,4]. In Appl. Sci.2024,14, 6511.

Appl. Sci.2024,14, 6511 2 of 14 addition, complementary strength training is also related to increased performance and injury prevention [4]. Specifically, in ultra-endurance mountain races, previous authors have shown how stress levels and general mental health are important performance predic- tors [10]. Regarding body composition, lower levels of body fat are a key factor for finishing ultra-endurance mountain races [10]. In line with this, hydration and nutritional patterns have also been shown to be crucial in these extreme events. For example, athletes that are more hydrated at the start line presented higher performance in ultra-endurance mountain races [2]. Ultra-endurance events often result in substantial energy deficits, producing a catabolic state [11]. Therefore, it is necessary to adapt the body to use fats with principal substrate energy [2]. Ultra-endurance athletes often face challenges such as hyperthermia that can hinder performance and even jeopardize their health during ultra-endurance events [4]. Heat acclimatization, maintaining proper hydration, and athlete morphology play pivotal roles in preventing hyperthermia [12]. In this context, infrared thermography has emerged as an effective tool for analyzing athletes’ thermal responses during movement and for injury- prevention interventions [13,14]. Specifically, in soccer players, an increase of 1.0–1.5 ◦ C is associated with a high injury risk, and when the values exceed 1.5 ◦ C, the risk becomes even more pronounced [15]. Moreover, in endurance sports, a rise in temperature is expected at the end of prolonged exercise due to the heat produced by muscular contraction [16,17]. Additionally, ultra-endurance sports induce an inflammatory response that correlates with the duration of physical exertion [18]. Therefore, the application of thermography is crucial for anticipating and preventing potential injury [19]. However, this has not yet been studied in the context of ultra-endurance events. Therefore, to better understand the psychophysiological variables related to perfor- mance in these events carried out on several consecutive days, as well as the effect of these extreme races on the body, we conducted this research with the primary aim of assessing the physiological, biomechanical, and thermographic responses of male athletes participating in an ultra-endurance race of 4 days. 2. Materials and Methods

understand the psychophysiological variables related to perfor- mance in these events carried out on several consecutive days, as well as the effect of these extreme races on the body, we conducted this research with the primary aim of assessing the physiological, biomechanical, and thermographic responses of male athletes participating in an ultra-endurance race of 4 days. 2. Materials and Methods 2.1. Participants In this study, the small sample size of three male volunteer athletes can be justified by the difficulty in recruiting participants for such extreme events. The participants had an average age of 38 years, a height of 173.6 cm, a weight of 67 kg, and a body-mass index (BMI) of 22.2 kg/m 2 . To recruit the participants, we contacted the organizers of the S4D event and arranged for the study to be conducted on the day of the event. Participants were given the opportunity to voluntarily participate in the study. The inclusion criteria required participants to be healthy individuals and volunteers willing to participate. The exclusion criteria included any participants who were taking medication, had any known pathologies or did not sign the informed consent form. The participants had over 20 years of experience in aerobic endurance training, with a background in competing in endurance and ultra-endurance events such as triathlons, trail running, mountain races, and cycling races. They typically trained five days per week, averaging 45–90 min per session, with an average weekly training volume of approximately 6 h, including strength training 1–2 times per week. Prior to commencing the research, the experimental procedures were thoroughly explained to the participants, who then provided their voluntary written informed consent in accordance with the Declaration of Helsinki. The methods employed in this study were developed and approved by the University Ethics Committee (CIPI/002/17). 2.2. Ultra-Endurance Event The ultra-endurance event was held in Santander as part of the Santander Four Days (S4D) civic–military race (noncompetitive event). Participants were required to walk 40 km per day for four consecutive days, carrying a 10 kg backpack. The total distance covered during the event was 160 km.

the University Ethics Committee (CIPI/002/17). 2.2. Ultra-Endurance Event The ultra-endurance event was held in Santander as part of the Santander Four Days (S4D) civic–military race (noncompetitive event). Participants were required to walk 40 km per day for four consecutive days, carrying a 10 kg backpack. The total distance covered during the event was 160 km.

Appl. Sci.2024,14, 6511 3 of 14 2.3. Design and Procedure The probe began at 7:00 a.m., with the participating athlete having his last meal before 9:00 pm the previous evening. In the hour preceding the start and immediately after the conclusion of the ultra-endurance event, we assessed the following parameters, drawing on previous research conducted using ultra-endurance probes [2,4,10]. Body mass was assessed using a SECA model 711 scale (SECA GmbH & Co. KG, Hamburg, Germany), which has a precision of 100 g and a measurement range of 0.1 to 130 kg. The scale was situated on a flat, smooth surface and calibrated to zero. Participants, barefoot and wearing minimal clothing, stood at the center of the platform. They avoided contact with any surrounding objects, ensuring their weight was evenly distributed on both feet while facing forward. Cortical arousal was evaluated using the Critical Flicker Fusion Threshold (CFFT) within a viewing chamber (Lafayette Instrument Flicker Fusion Control Unit Model 12021), adhering to established procedures from previous studies. An increase in CFFT indicated enhanced cortical arousal and information-processing capabilities, whereas values below the baseline suggested diminished efficiency in information processing and central nervous system fatigue. CFFT is a measure used to evaluate cortical arousal and information- processing capabilities. Variations in CFFT values were interpreted as follows: an increase in CFFT indicates enhanced cortical arousal and improved information-processing capabili- ties, reflecting central nervous system (CNS) activation. Conversely, a decrease in CFFT suggests diminished efficiency in information processing, which can be associated with CNS fatigue [20,21]. Isometric handgrip strength was measured using a TKK 5402 dynamometer (Takei Scientific Instruments Co., Ltd., Niigata City, Japan). The measurement was taken on the athlete’s dominant hand. The athlete was seated with the shoulder at 0 ◦ flexion, the elbow at 90 ◦ flexion, and the forearm in a neutral position. The highest value obtained from two trials was recorded. Lower limb strength was assessed using a horizontal jump test. The athlete stood behind a marked line on the ground, with feet shoulder-width apart. Three attempts were made, and the highest result was taken for analysis. Heart-Rate

flexion, the elbow at 90 ◦ flexion, and the forearm in a neutral position. The highest value obtained from two trials was recorded. Lower limb strength was assessed using a horizontal jump test. The athlete stood behind a marked line on the ground, with feet shoulder-width apart. Three attempts were made, and the highest result was taken for analysis. Heart-Rate Variability (HRV) was monitored using a Polar V800 HRV monitor (Kem- pele, Finland). Measurements commenced a few minutes before the event started and concluded at the event’s end. Following previous protocols [2,4,10], the following parameters were analyzed imme- diately after the event: Hydration status was evaluated using a colorimetry procedure with a urine color chart, which identified pH status and the presence of glucose, nitrites, protein, and glucose on the urine strip. Blood glucose concentration was determined by analyzing 5µL of capillary blood from the finger using a portable analyzer (One Touch Basic, LifeScan Inc., Madrid, Spain). Blood lactate concentration was measured by collecting a 5µL sample of capillary blood from the subject’s finger and analyzing it with the Lactate Pro II system (Arkay, Inc., Kyoto, Japan). Thermography response: Thermal images were captured using a second-generation FLIR One iOS Thermal Camera Smartphone Module (FLIR Systems, Wilsonville, OR, USA) through the Thermal Camera+ for the FLIR One app installed on an Apple iPhone 6. The emissivity setting in the app was adjusted to “matte: 95%” to closely match the human skin emissivity of 98% [22]. Before capturing each thermal image, the camera was recal- ibrated using the app’s built-in function to reduce noise. The thermal images captured with the FLIR One have a resolution of 160×120 pixels in a “compressed image format” to reduce storage size, and the accompanying software automatically upscales them to 640×480 pixels . The corresponding visible spectrum images are taken at a resolution of 1440×1080 pixels . According to the FLIR One specifications, it can measure object temper- atures ranging from−20 ◦ C to 400 ◦ C (−4 ◦ F to 752 ◦ F) with an accuracy of±3 ◦ C (5.4 ◦ F)

upscales them to 640×480 pixels . The corresponding visible spectrum images are taken at a resolution of 1440×1080 pixels . According to the FLIR One specifications, it can measure object temper- atures ranging from−20 ◦ C to 400 ◦ C (−4 ◦ F to 752 ◦ F) with an accuracy of±3 ◦ C (5.4 ◦ F)

Appl. Sci.2024,14, 6511 4 of 14 or±5%. All thermal images were collected in compliance with the recommendations of the European Association of Thermology (Ring & Ammer, 2012). The thermograms were obtained in a room with a controlled and constant temperature of 20 ◦ C and 40% humidity. Following protocols of previous studies, the thermograms were performed on the face, chest, abdomen, right and left arm, and right and left leg. The analysis of the skin surface temperature was conducted by locating the middle point of each anatomical region and through a circle or rectangle at the center of each region, as shown in Figure23,24].Appl. Sci. 2024, 14, 6511 4 of 14 human skin emissivity of 98% [22]. Before capturing each thermal image, the camera was recalibrated using the app’s built-in function to reduce noise. The thermal images cap- tured with the FLIR One have a resolution of 160 × 120 pixels in a “compressed image format” to reduce storage size, and the accompanying software automatically upscales them to 640 × 480 pixels. The corresponding visible spectrum images are taken at a reso- lution of 1440 × 1080 pixels. According to the FLIR One specifications, it can measure ob- ject temperatures ranging from −20 °C to 400 °C (−4 °F to 752 °F) with an accuracy of ±3 °C (5.4 °F) or ±5%. All thermal images were collected in compliance with the recommen- dations of the European Association of Thermology (Ring & Ammer, 2012). The thermo- grams were obtained in a room with a controlled and constant temperature of 20 °C and 40% humidity. Following protocols of previous studies, the thermograms were performed on the face, chest, abdomen, right and left arm, and right and left leg. The analysis of the skin surface temperature was conducted by locating the middle point of each anatomical region and through a circle or rectangle at the center of each region, as shown in Figure 1 [23,24]. Figure 1. Thermographic analysis model. Figure 1.Thermographic analysis model. 2.4. Statistical Analyses Statistical analyses were performed using SPSS version 21.0. The data were first checked for normality using

skin surface temperature was conducted by locating the middle point of each anatomical region and through a circle or rectangle at the center of each region, as shown in Figure 1 [23,24]. Figure 1. Thermographic analysis model. Figure 1.Thermographic analysis model. 2.4. Statistical Analyses Statistical analyses were performed using SPSS version 21.0. The data were first checked for normality using the Shapiro–Wilk test. Descriptive statistics, including means and standard deviations, were calculated for all variables. A Wilcoxon signed-rank test was used to analyze differences in cortical arousal, handgrip, weight, horizontal jump, glucose, lactate, and urine values. In addition, Cohen’s d was calculated for heart-rate variability and thermographic values to determine the effect size of the differences observed. For all tests, a significance level ofp< 0.05 was used.

Appl. Sci.2024,14, 6511 5 of 14 3. Results The heart-rate variability (HRV) metrics exhibited significant alterations during the ultra-endurance race. In the time-domain analysis, the mean heart rate escalated, with marked fluctuations over the four days. Notably, the RMSSD and pNN50 metrics sub- stantially decreased from their pre-event values on Day 1. In the frequency domain, LF Power increased while HF Power showed a decline, resulting in a notable elevation in the LF/HF ratio, especially on Day 2. The nonlinear domain also displayed shifts, with SD1 and SD2 metrics notably decreasing from pre-event values. Overall, the data reflects the physiological challenges and adaptations experienced by participants during the event (Table). Table 1.Heart-rate variability results before and during the event. Pre-Event Day 1 Day 2 Day 3 Day 4 Cohen’s d (Pre vs. Day 4) Time-domain Mean HR (bpm) 70.0 ±15.2 91.9±15.3 95.8±18.3 88.3±10.7 95.1±11.4 1.65 Min HR (bpm) 53.0 ±14.3 62.9 ±8.9 64.1 ±8.5 73.6 ±10.2 65.2±12.6 0.85 Max HR (bpm) 80.4 ±9.3 126.6±20.3 145.8±12.2 137.1±18.3 147.6±14.6 7.23 RMSSD (ms) 64.4 ±20.3 16.5±18.6 18.4±14.3 15.3±12.4 18.5±15.2 −2.26 pNN50 (%) 28.3 ±9.9 1.3 ±3.3 1.7 ±4.3 1.2 ±5.2 1.3 ±4.8 −2.73 Frequency domain LF Power (n.u) 70.8 ±6.2 83.2 ±7.8 92.2 ±4.1 85.3 ±6.3 89.1 ±5.9 2.95 HF Power (n.u) 29.2 ±6.7 16.7 ±5.0 7.8 ±3.2 14.7 ±6.8 10.9 ±4.6 −2.73 Ratio LF/HF 3.7 ±0.9 5.0 ±1.1 11.8 ±2.0 5.8 ±1.8 8.2 ±2.3 5.00 Nonlinear SD1 (ms) 45.6 ±9.6 11.7 ±8.9 13.0 ±11.3 10.8±12.4 13.1±10.8 −3.39 SD2 (ms) 95.2 ±25.6 29.0±10.5 42.6±21.3 34.2±18.3 34.4±14.5 −2.38 ApEn 1.2 ±0.2 1.4 ±0.6 1.2 ±0.5 1.2 ±0.4 1.3 ±0.2 0.50 SampEn 1.3 ±0.4 1.5 ±0.3 1.2 ±0.3 1.3 ±0.3 1.3 ±0.2 0.00 HR: Heart Rate; bpm: beats per minute; Mean HR: Average Heart Rate; Min HR: Minimum Heart Rate; Max HR: Maximum Heart Rate; RMSSD: Root Mean Square of the Successive Differences; pNN50: Percentage of successive RR intervals that differ by more than 50 ms; LF: Low Frequency; HF: High Frequency; n.u: normalized units; Ratio LF/HF: Ratio of low-frequency power to high-frequency power; SD1: Standard deviation of instantaneous beat- to-beat variability; SD2: Standard deviation

Min HR: Minimum Heart Rate; Max HR: Maximum Heart Rate; RMSSD: Root Mean Square of the Successive Differences; pNN50: Percentage of successive RR intervals that differ by more than 50 ms; LF: Low Frequency; HF: High Frequency; n.u: normalized units; Ratio LF/HF: Ratio of low-frequency power to high-frequency power; SD1: Standard deviation of instantaneous beat- to-beat variability; SD2: Standard deviation of long-term continuous RR interval variability; ApEn: Approximate Entropy; SampEn: Sample Entropy. Figure zontal jump. There were no significant differences in flicker fusion (p= 0.121), handgrip (p= 0.956), or horizontal jump (p= 0.090). However, a significant difference was found in weight (p= 0.020), lowering from the first 67.00±6.44 kg to 64.77±5.77 kg in the fourth stage.

The study focuses on physiological and thermographic responses in male athletes during a 160 km ultra-endurance race.