Abstract
races (OCR) have experienced significant growth in recent years, with millions of participants worldwide. However, there is limited research on the specific physiological demands and injury prevention strategies required for these events. This study aimed to analyze the physiological responses and injury risks in participants of a 5 km (Sprint) and 13 km (Super) OCR. Sixty-eight participants were assessed for cortical arousal, leg strength, isometric handgrip strength, blood lactate, heart rate, blood oxygen saturation, body temperature, urine composition, spirometry values, hamstring flexibility, lower limb stability, foot biomechanics, and scapular kinematics, one hour before and immediately after the races. The results showed a significant decrease in leg strength (Sprint: r =−0.56,p< 0.01; Super: r =−0.54,p= 0.01) and urine pH (Sprint: r =−0.70,p= 0.03; Super: r =−0.67,p= 0.01) in both distances, with increases in urine colour, protein, and glucose (Sprint:p< 0.04). In the 13 km race, lower limb stability decreased significantly post-race (r =−0.53, p= 0.01). Positive correlations were found between
results showed a significant decrease in leg strength (Sprint: r =−0.56,p< 0.01; Super: r =−0.54,p= 0.01) and urine pH (Sprint: r =−0.70,p= 0.03; Super: r =−0.67,p= 0.01) in both distances, with increases in urine colour, protein, and glucose (Sprint:p< 0.04). In the 13 km race, lower limb stability decreased significantly post-race (r =−0.53, p= 0.01). Positive correlations were found between performance and pre-race handgrip strength (Sprint: r = 0.71,p= 0.001; Super: r = 0.72,p= 0.01) and spirometry values (FVC, FEF 25–75%, FEV1) (Sprint: r = 0.52,p= 0.031; Super: r = 0.48,p= 0.035). Thermoregulation capacity, reflected in a higher pre-race body temperature and lower post-race body temperature, also correlated with improved performance (r = 0.49,p= 0.046). Injury risk increased post-race, with a significant decline in lower limb stability (p< 0.05). These findings highlight the importance of targeted training programs, focusing on grip strength, leg strength, respiratory muscle training, and hydration strategies to optimize performance and reduce injury risk in OCR athletes. Keywords:physiology; training; physiotherapy; performance; running; strength 1. Introduction Obstacle course races (OCR) have experienced a large growth in the number of par- ticipants and competitions in the last few years. It is estimated that more than 4.2 million people participated in an OCR in 2014 in more than 30 countries all over the world [1]. The races manage distances from 5 km to marathon lengths with 10 to over 100 obstacles, respectively. The most popular challenges cover 5 km, with 10–15 obstacles, and 13 km with 15–25 obstacles. The obstacles include climbing ropes and walls, crawling through mud pits with barbed wires, running through fire, swimming in freezing waters, carrying heavy loads, and even sometimes passing through electrified cables. The physiological requirements for this new sports modality are based on a combina- tion of anaerobic and aerobic pathways [1,2]. The maximum volume of oxygen (VO2max) is Appl. Sci.2024,14, 9604.
Appl. Sci.2024,14, 9604 2 of 15 highly correlated with aerobic performance, especially when they involve passing through obstacles and carrying heavy loads [2,3] because it allows individuals to keep a greater running pace and recover quickly after the obstacles [2–4]. The VO2max is also a per- formance indicator for lactic anaerobic efforts [4]. This is important because lactic and phosphocreatine organic systems are challenged when passing obstacles. Running econ- omy and lactate threshold are also well correlated with endurance performance, so it is important to include specific training sessions in their development [5,6]. Thus, muscular strength (both lower and upper body), speed, and endurance, added to the high demands of balance and coordination, which are all related to the maintenance of optimal cortical arousal [7], are determinant factors related to OCR performance [1]. Other crucial factors are optimal hydration levels, since this OCR probe could take more than 1 h to complete, and the nitrates, pH, protein, and glucose in the urine, which can be very influenced by the requirements of the race [8]. To successfully complete an OCR, athletes must undergo highly intense and cross- training programs [1]. It has been demonstrated that large training loads make competitors vulnerable to injuries [9], and experienced athletes are less affected by injuries [10]. The most predominant occur in the lower limbs because of overuse and/or altered biomechanics during running, and the risk of suffering them can be predicted by conducting some simple field tests such as one-leg standing test [11], navicular drop test [12], Jack’s test [13], and active straight leg raise test [14]. But, upper extremities injuries can also be present in this sports modality because of the suspension obstacles, so the assessment of scapular kinematics [15] and isometric hand grip strength could be interesting [16,17]. When analysing the previous literature on OCR, we found little evidence to support specific training for them based on the real physiological requirement of these special probes. Thus, we propose the present research to study the physiological and psychological demands and injury risk in OCR with the following objectives: i. to study the physiological
grip strength could be interesting [16,17]. When analysing the previous literature on OCR, we found little evidence to support specific training for them based on the real physiological requirement of these special probes. Thus, we propose the present research to study the physiological and psychological demands and injury risk in OCR with the following objectives: i. to study the physiological response in a Sprint (5 km) and Super (13 km) obstacle course race; ii. to analyse the differences in the physiological response between Sprint and Super OCR; iii. to analyse injury patterns in Sprint and Super OCR; iv. to analyse the correlation between the physio- logical and injury risk variables with performance in Sprint and Super OCR. The initial hypothesis was that Sprint OCR would achieve a higher anaerobic metabolism activation, and Super OCR would achieve higher degrees of stress manifestations and dehydration. This information could be used by trainers and health care professionals to improve the efficient design of training programs and injury prevention plans. 2. Materials and Methods 2.1. Experimental Approach to the Problem The physiological and physical exertion in OCR can influence the final performance of the athletes and the risk of injuries. Therefore, the current descriptive study aimed to analyse the physiological requirements and injury-related parameters of the probe to better understand the demands of obstacle course races so practitioners could design efficient and effective interventions for athletes. To our knowledge, no research exists in this area that has a deep analysis of physiological and physical variables. 2.2. Subjects We analysed 66 volunteer participants in a Spartan Race competition of 5 km (Sprint distance) and 13 km (Super distance) in Valencia, Spain. Twelve men (30±8 years, 179±9.86 cm, 76±9.21 kg, and 23.56±1.41 BMI) and eleven women (32±7 years, 165±5.54 cm, 58±9.2 kg, and 21.29±2.63 BMI) took part in the Sprint distance, and thirty-six men (30±6 years, 176±5.8 cm, 77±7.56 kg, and 24.68±2.05 BMI) and seven women (31±7 years, 163±5.26 cm, 59±8.43 kg, and 22.1±2.5 BMI) took part in the super distance.
(32±7 years, 165±5.54 cm, 58±9.2 kg, and 21.29±2.63 BMI) took part in the Sprint distance, and thirty-six men (30±6 years, 176±5.8 cm, 77±7.56 kg, and 24.68±2.05 BMI) and seven women (31±7 years, 163±5.26 cm, 59±8.43 kg, and 22.1±2.5 BMI) took part in the super distance.
Appl. Sci.2024,14, 9604 3 of 15 2.3. Procedures Prior to starting the research, the experimental procedures were explained to the participants, who gave their voluntary written informed consent in accordance with the Helsinki Declaration, ensuring ethical standards were met, with ethical approval granted by the European University’s Bioethics Committee under code CIPI/18/074. Then, the following variables were measured the hour before the race and immediately after finishing the race. Cortical arousal was analysed through the critical flicker fusion threshold (CFFT) in a viewing chamber which was constructed to control extraneous factors that might distort CFFT values (Lafayette Instrument Flicker Fusion Control Unit Model 12021) following the procedures conducted in previous studies [18,19]. An increase in CFFT suggests an increase in cortical arousal and information process; by contrast, when the values fall below the baseline, it suggests a reduction in the efficiency of processing information and fatigue of the central nervous system [7]. Blood oxygen saturation (BOS) and heart rate (HR) were measured with a pulse oximeter (PO 30 Beurer Medical) following previous procedures [20]. Body temperature (BT) was measured with a digital infrared thermometer (Temp Touch; Xilas Medical, San Antonio, TX, USA) following previous authors [21,22]. Lower body muscular strength manifestation was analysed using a vertical jump test. We used the Sensorize FreePowerJump system (SANRO Electromedicina, Madrid, Spain), which recorded flight time (s) and jump height (cm) to evaluate 2 vertical types of jump following previous protocols [23,24]. Participants performed 2 Contramovement Jumps (CMJ) and 2 Abalakov jumps (ABK), as in previous research [19,25]. The coordinative capabilities and arms utilization capacity (CCA) were also analysed using the Bosco formula (1999) (CCA= ABK−CMJ) Upper body muscular strength manifestation was analysed by testing the isometric hand strength (IHS) with a grip dynamometer (Takei Kiki Kogyo, Tokyo, Japan). Two repetitions were performed with the dominant hand, and the best one was chosen in line with previous research where performance was measured [26,27]. The blood lactate concentration was measured by taking a sample of 5µL of capillary blood from a finger and analysed with the validated Lactate Pro Arkay, Inc. system (Kyoto, Japan) according
(Takei Kiki Kogyo, Tokyo, Japan). Two repetitions were performed with the dominant hand, and the best one was chosen in line with previous research where performance was measured [26,27]. The blood lactate concentration was measured by taking a sample of 5µL of capillary blood from a finger and analysed with the validated Lactate Pro Arkay, Inc. system (Kyoto, Japan) according to previous research [28]. Urine samples were collected to analyse dehydration levels, which were examined against a urine colour chart (colour range 1–8, where 1 = very pale yellow urine, which reflected a good level of hydration, and 8 = very dark yellowish brown, which reflected a significant level of dehydration); the number closest to the sample colour was recorded [29]. Urine nitrates, protein, glucose, and pH were measured with the Orine Combur Test (Roche, Madrid, Spain) stripes. The rate of perceived exertion (RPE) was measured with a Borg 6–20 scale [30]. Spirometry values of forced vital capacity (FVC), forced expiratory volume in 1 s (Fev1), and forced expiratory flow 25–75% (FEF25) were measured using a CPX device (Medical Graphics Corporation, St. Paul, MN, USA) performing maximum inhale-exhale– inhale cycle as previous research [31]. Kinematics of the scapula were measured with a scapular dyskinesis test. Participants performed 3 shoulder 180 ◦ flexions and 3 shoulder 180 ◦ abductions. Values were classified into 3 grades: normal pattern, subtle dyskinesia, and evident dyskinesia [15]. Lower limb stability was measured with the 1-leg standing test (1LST). Participants performed one repetition of 30 s with each leg at 90 ◦ of hip and knee flexion with their eyes closed. Values were also classified into 3 grades: I. The athletes remained the whole 30 s without falling, collapsing their hips (Trendelemburg sign) or pivoting with their feet, which indicates good stability; II. The athletes remained the whole 30 s, but their hips collapsed and/or feet pivoting occurred, which indicates poor stability; III. The athletes were unable to hold the position for 30 s, which indicates very poor stability.
their feet, which indicates good stability; II. The athletes remained the whole 30 s, but their hips collapsed and/or feet pivoting occurred, which indicates poor stability; III. The athletes were unable to hold the position for 30 s, which indicates very poor stability.
Appl. Sci.2024,14, 9604 4 of 15 Plantar arch stability was measured with the navicular drop test [12]. Navicular height was measured with a 66 fit commercial house goniometer on both feet in a seated position and in a standing position. The difference between the seated and standing positions indicates the navicular drop in charge. The windlass mechanism was measured using Jack’s test [13]. The athletes performed 2 trials of active dorsiflexion of the first metatarsophalangeal joint, and we evaluated its range of movement (ROM) with a 66 fit commercial house goniometer, the increment of the plantar arch’s height, and the reposition of the tibia over the astragalus. Lower limb posterior chain flexibility was measured with an adaptation of the active straight leg raise test [14]. Participants performed one repetition with each leg of active hip flexion with knee extension, and the range of movement was measured with the 66 fit commercial house goniometer. 2.4. Statistical Analysis Data were analysed using the Statistical Package for the Social Sciences (SPSS) ver- sion 21 (SPSS Inc., Chicago, IL, USA). Means and SDs were calculated using traditional statistical techniques. Normality and homoscedasticity assumptions were tested with the Kolmogorov–Smirnov test. A one-factor ANOVA for intergroup comparisons and a Stu- dent’st-test for intragroup comparisons, since variables presented a parametric distribution, were conducted. The effect size was calculated using Cohen’s d formula, which is defined as the difference between two group means divided by the pooled standard deviation. In terms of the variables presented in the charts, this calculation allows us to quantify the magnitude of differences between the pre- and post-race measures for both the control (baseline) and experimental (post-race) conditions. A positive effect size indicates that the mean post-race value is higher than the pre-race value, signifying an increase in the variable (e.g., blood lactate or rate of perceived exertion) as a result of the race. Conversely, a negative effect size indicates a decrease in the post-race mean compared to the pre-race mean, such as the observed decreases in leg strength or stability. In terms of interpreting the magnitude of effect sizes, we followed the
value, signifying an increase in the variable (e.g., blood lactate or rate of perceived exertion) as a result of the race. Conversely, a negative effect size indicates a decrease in the post-race mean compared to the pre-race mean, such as the observed decreases in leg strength or stability. In terms of interpreting the magnitude of effect sizes, we followed the conventional benchmarks where an effect size of 0.2 represents a small effect, 0.5 is a medium effect, and 0.8 or greater is a large effect. For example, in the sprint race, the decrease in leg strength had a medium to large effect (d =−0.56), while the increase in urine glucose showed a large effect (d = 3.00). The significance level was set atp< 0.05. In addition, A post hoc correlation analysis was conducted to assess the relationships between key pre- and post-race physiological variables. Pearson’s correlation coefficients were calculated to determine the strength and direction of associations between pre-race and post-race measures, including countermovement jump (CMJ), Abalakov jump (ABK), upper body coordination and grip strength (CCUB), forced vital capacity (FVC), and forced expiratory volume (Fev1). The analysis was performed using a two-tailed significance level ofp< 0.05. This approach allowed us to explore how pre-race performance indicators predicted post-race outcomes, providing deeper insight into the physiological responses of athletes during obstacle course racing. Finally, a linear regression analysis was conducted to assess the predictive power of pre-race physiological measures on post-race countermovement jump (CMJ) performance. The independent variables included pre-race CMJ, Abalakov jump (ABK), upper body coordination and grip strength (CCUB), forced vital capacity (FVC), forced expiratory volume (Fev1), and FER25. The dependent variable was post-race CMJ. The dataset was split into training (80%) and testing (20%) sets, and the model’s goodness of fit was evaluated using R-squared. The regression coefficients were analyzed to determine the influence of each predictor on post-race performance. 3. Results In the SPRINT distance, there was a significant decrease in CMJ, ABK, pH and RJack. On the contrary, heart rate, lactate, RPE and urine colour, protein, and glucose presented
goodness of fit was evaluated using R-squared. The regression coefficients were analyzed to determine the influence of each predictor on post-race performance. 3. Results In the SPRINT distance, there was a significant decrease in CMJ, ABK, pH and RJack. On the contrary, heart rate, lactate, RPE and urine colour, protein, and glucose presented
Appl. Sci.2024,14, 9604 5 of 15 a significant increase (Table). In the SUPER distance, we found significant decreases in CMJ, ABK, CCA, Fev1, pH, and RDrop and significant increases in heart rate, lactate, RPE, LST, RST, and colour (Table). Table 1.Pre-post results of Sprint OCR (5 km). Pre Post Cohen’s D 95% Confidence Interval Variable (Unit) Mean±SD Mean ±SD % Change Inferior Superior T p CMJ (m) 36.88 ±6.37 33.29±4.63 −9.73 −0.56 2.25 4.92 5.69 0.00 ABK (m) 42.59 ±9.02 39 ±5.48 −8.43 −0.40 0.9 6.28 2.83 0.01 CCA 5.71 ±4.33 5.71 ±3.04 0.00 0.00 −1.98 1.98 0 1 FVC (mL) 4.91 ±1.61 4.28 ±1.2 −12.83 −0.39 −0.05 1.32 1.98 0.07 Fev1 (mL) 4.06 ±3.26 3.59 ±1.19 −11.58 −0.14 −0.95 1.91 0.71 0.49 FEF25 (mL) 7.41 ±3.35 8.27 ±3.69 11.61 0.26 −2.72 1.01 −0.97 0.35 HR (bpm) 83.12 ±16.44 103±16.51 23.92 1.21 −32.93 −6.83 −3.23 0.01 BOS (%) 97.41 ±1.5 97.18±2.43 −0.24 −0.15 −1.06 1.53 0.39 0.71 BT ( ◦ C) 35.78 ±1.81 30.64±10.84 −14.37 −2.84 −0.76 11.03 1.85 0.08 IHS (kg) 39.53 ±12.07 38.12±14 −3.57 −0.12 −1.63 4.45 0.98 0.34 Color (au) 3.92 ±1.94 7 ±0.91 78.57 1.59 −4.17 −1.99 −6.16 0 Ph 5.62 ±0.77 5.08 ±0.28 −9.61 −0.70 0.07 1.01 2.5 0.03 Protein (mg/dL) 0.08±0.28 0.92 ±0.64 1050.00 3.00 −1.33 −0.36 −3.81 0 Glucose (mg/dL) 0±0 0.31 ±0.48 inf 0.10 −0.6 −0.02 −2.31 0.04 RPE 6 ±0 13.06 ±2.1 117.67 0.10 −8.1 −6.01 −14.26 0 Lac (mmol) 0.56 ±0.26 9.44 ±4.97 1585.71 34.15 −11.47 −6.3 −7.29 0 LST 1.82 ±0.39 1.71 ±0.69 −6.04 −0.28 −0.32 0.56 0.57 0.58 RST 1.77 ±0.56 1.71 ±0.69 −3.39 −0.11 −0.44 0.56 0.25 0.81 LFlex ( ◦ ) 78.88 ±12.61 77.47±12.64 −1.79 −0.11 −3.93 6.75 0.56 0.58 RFlex ( ◦ ) 80.65 ±14.33 76±11.73 −5.77 −0.32 −1 10.3 1.74 0.1 LDisk 1 ±0.82 1.63 ±2.13 63.00 0.77 −1.67 0.42 −1.27 0.22 RDisk 1 ±0.87 1.59 ±2.06 59.00 0.68 −1.69 0.52 −1.13 0.28 LJack 0.65 ±0.49 0.47 ±0.52 −27.69 −0.37 −0.38 0.03 −1.85 0.08 RJack 0.65 ±0.49 0.41 ±0.51 −36.92 −0.49 0.01 0.46 2.22 0.04 LDrop (cm) 0.44 ±0.19 0.44 ±0.14 0.00 0.00 −0.13
Description
The research investigates physiological demands and injury risks in obstacle course racing.