Abstract
his study explores the stability of biomechanical parameters of the running stride of male trained athletes during a half-marathon competition. Using a field-based descriptive design, eight male athletes from a local training group were monitored throughout an official half-marathon race under identical conditions, assessing biomechanical parameters including ground contact time (GCT), leg spring stiffness (LSS), vertical oscillation (VO), and stride length (SL) recorded via the Stryd Summit Power Meter. A repeated measures analysis of variance (RM ANOVA) was conducted to detect significant changes in biomechanical parameters as the race progressed. Results demonstrated minimal changes in all parameters, with no significant differences observed for GCT (F= 0.96,p= 0.38), VO (F= 0.23,p= 0.87), and SL (F= 1.07,p= 0.35), and a small (η 2= 0.004) yet statistically significant difference in LSS (F= 5.52,p= 0.03) between the first and second segments, indicating that athletes were able to maintain stable biomechanical parameters throughout the race. The conclusion highlights the need for personalized training programs tailored to the unique biomechanical adaptations and demands of endurance running. Keywords:athletes; competition; Stryd; long-distance running 1. Introduction Maintaining consistent movement patterns throughout a running event presents a significant challenge [1]. Deviations from these patterns can not only lead to decreased performance but also exponentially increase risk factors and contribute to injuries [2]. Fatigue stands out as one of the major factors
the unique biomechanical adaptations and demands of endurance running. Keywords:athletes; competition; Stryd; long-distance running 1. Introduction Maintaining consistent movement patterns throughout a running event presents a significant challenge [1]. Deviations from these patterns can not only lead to decreased performance but also exponentially increase risk factors and contribute to injuries [2]. Fatigue stands out as one of the major factors behind this performance decrement and the ensuing issues [3]. From a physiological perspective, the metabolic cost of running at a submaximal constant speed is influenced by various factors, including fatigue and associated kinematic alterations [4]. The influence of fatigue on biomechanical parameters in endurance sports has been shown to modify stride frequency (cadence) and length (SL), ground contact time (GCT), vertical oscillation (VO), and leg spring stiffness (LSS), with variations being multiple and variable between athletes and conditions. However, the most relevant variations are primarily found in these parameters [5]. Regarding SL, several authors [6–8] have observed a reduction in fatigued states compared to non-fatigued states. Numerous studies [2,6,7] show an increase in GCT in the presence of fatigue, potentially due to a reduction in muscle stiffness or LSS [6,9,10]. Concerning VO, a decrease has been noted [7], with some authors [11] suggesting that this reduction may result from diminishing strength capacity of the main muscles in the lower extremities. As a strategy to enhance running economy, Moore [12] proposed that reducing GCT while maintaining stride frequency could lead to greater LSS, larger stride angles, and shorter swing times, thereby optimizing running technique. The analysis of biomechanical parameters in half-marathon and marathon races is particularly relevant—for example, studies on predictive variables of half-marathon perfor- mance for male runners [13], the comparison of half-marathon runners according to their Appl. Sci.2024,14, 4807.
Appl. Sci.2024,14, 4807 2 of 9 performance level [14], and the concurrent evolution of biomechanical and physiological parameters with acute fatigue induced by running [2]. In fatigued states, trained runners have been shown to reduce their stride frequency compared to a non-fatigued state [4,6,8], likely due to the muscle’s reduced force generation capacity [4]. Conversely, other stud- ies [11,15,16] found increases in stride frequency, with Matta et al. [7] and Morin et al. [11] observing no significant differences. With the emergence of portable and low-cost equipment that allows data collection in field sessions, training, and competitions, alternatives are offered over older methods that require well-equipped research laboratories [17]. In this way, information more applicable to current sports practice is discovered as researchers can move participants to more sport- specific environments [18]. This alternative highlights the potential of these devices for gait analysis but without the corresponding limitations of traditional laboratory technology [19]. For assessing spatiotemporal parameters, the Stryd Running Power Meter has been widely studied in athletes to analyze the absolute reliability and concurrent validity of the Stryd system for the assessment of running stride kinematics at different velocities [20], as well as the agreement between spatiotemporal gait parameters from two different wearable devices and high-speed video analysis [21]. Additionally, the influence of biomechanical parameters on performance in elite triathletes has been analyzed [22]. Therefore, the main purpose of the present research is to analyze the stability of biomechanical parameters of the running stride of male trained athletes during a half- marathon competition, focusing on changes in SL, LSS, VO, and GCT. This approach offers new perspectives for optimizing the training of biomechanical parameters in running. 2. Materials and Methods 2.1. Participants This study was conducted with eight trained male athletes who ran a half-marathon race spanning 21,097 m (race times 1:23:34±00:10:12 h:m:s), all members of the same local training group. To be selected as trained athletes, participants were required to meet the following criteria [23]: (1) engagement in competition at the local level, (2) adherence to a regular training regime of more than three times per week, (3) specialization
athletes who ran a half-marathon race spanning 21,097 m (race times 1:23:34±00:10:12 h:m:s), all members of the same local training group. To be selected as trained athletes, participants were required to meet the following criteria [23]: (1) engagement in competition at the local level, (2) adherence to a regular training regime of more than three times per week, (3) specialization in the same sports specialty, and (4) a focus on training specifically for competitive events. The number of participants was determined through a statistical power analysis. Specifically, an a priori power calculation was conducted using G*Power 3.1 (Heinrich Heine Universität, Düssel- dorf, Germany) [24] for the repeated measures ANOVA design with four measurements (representing the four race segments). Based on an effect size via eta squared of approxi- mately 0.04 (estimated from a Cohen’sdof 0.2), an alpha level of 0.05, a desired power of 0.8, and an expected high correlation (>0.9) among the repeated biomechanical parameter measurements based on previous field measurements from the authors, the recommended total sample size was eight participants. While this sample size may be considered small, it is in line with similar studies in the field that have investigated biomechanical changes during endurance running events [25]. Prior to participation, all individuals read and provided written informed consent, acknowledging their understanding of the objectives of the study and the exclusively scientific application of the data collected, in alignment with the World Medical Association (WMA) Declaration of Helsinki; Ethical Principles for Medical Research Involving Human Subjects 1975 (revised in Fortaleza, Brazil in 2013). The ethical approval for this study was granted by the Ethics Committee of the University of Alicante (UA-2023-02-04). Table The procedure for obtaining the descriptive data of the participants, obtained through the International Society for the Advancement of Kinanthropometry (ISAK) protocol, is shown below.
Appl. Sci.2024,14, 4807 3 of 9 Table 1.Descriptive characteristics of the athletes. Mean (M) Standard Deviation (SD) Age (years) 38.8 5.4 Body Mass (kg) 70.2 4.7 Body Height (m) 1.77 0.05 ∑8 skinfolds (mm) 67.2 19.7 Muscle Mass (kg) [26] 22.6 11.8 Fat Mass (kg) [27] 6.86 2.03 Fat Mass (%) [28] 9.7 2.7 2.2. Procedure A descriptive study was conducted through a field session, wherein all participants competed in the same official race under identical weather conditions and at the same time of day. To record biomechanical parameters, each athlete was equipped with the Stryd Summit Power Meter. This device was attached to the shoelace of the right foot and calibrated before the race started, following the manufacturer’s guidelines. The field session, the “International Half-Marathon Villa de Santa Pola”, was held on Sunday, 22 January 2023. This event is sanctioned by the Royal Spanish Athletics Federation and spans 21,097 m. The race features a total elevation change of 72 m, unfolding over a two-lap, urban circular route. Weather conditions on the day of the competition were optimal, with a temperature of 9 ◦ C, relative humidity of 28%, wind speed of 3.5 m/s, and no precipitation (0.0 l/m 2 ). For the analysis of biomechanical parameters and subsequent statistical analysis, the race was segmented as follows: Segment 1 data were collected from the race start to the first quarter (5274 m), Segment 2 data spanned from 5275 m to the midpoint (10,548 m), Segment 3 data covered from 10,549 m to the three-quarter mark (15,822 m), and Segment 4 data were gathered from 15,823 m to the race’s conclusion. 2.3. Anthropometric Characteristics The body composition of the athletes was estimated using precise anthropometric measurements, conducted between 48 and 72 h prior to the race at the Motion Analysis Laboratory of the University of Alicante (0001P1006). These measurements were performed by a Level 1 certified anthropometrist of the International Society for the Advancement of Kinanthropometry (ISAK). To ensure consistency and accuracy, the Ross and Marfell-Jones protocol [29] was adhered to, focusing on basic physical characteristics such as age, body mass,
h prior to the race at the Motion Analysis Laboratory of the University of Alicante (0001P1006). These measurements were performed by a Level 1 certified anthropometrist of the International Society for the Advancement of Kinanthropometry (ISAK). To ensure consistency and accuracy, the Ross and Marfell-Jones protocol [29] was adhered to, focusing on basic physical characteristics such as age, body mass, and height. For the measurements, a suite of approved equipment was employed, including a Holtain skinfold caliper, a Holtain bone breadth caliper (both from Holtain Ltd., Crymych, UK), scales and a stadiometer for height measurement, and an anthropometric tape (SECA Ltd., Hamburg, Germany) for circumferential measurements. Each participant underwent three repeated measurements for enhanced reliability, covering the biepicondy- lar humerus, bi-styloid, and biepicondylar femur breadths, as well as girths of the relaxed arm, flexed and tensed arm, waist, hip, and calf. Additionally, skinfold thickness was as- sessed at eight specific sites: triceps, subscapular, biceps, iliac crest, supraspinal, abdominal, thigh, and calf. The estimation of muscle mass was derived using the Lee equation [26], fat mass was calculated according to the Withers equation [27], and bone mass was determined using the Döbeln equation, modified by Rocha [28]. 2.4. Biomechanical Parameters The Stryd Summit Power Meter has been recognized as a reliable and valid instrument for the measurement of spatiotemporal biomechanical parameters during running [30], having been extensively utilized in research for this purpose [20–22]. The device, a carbon fiber-reinforced foot pod, is affixed to the toe cap of the right shoe. Weighing 9.1 g, it incorporates a 6-axis inertial measurement unit (IMU), comprising a 3-axis gyroscope and a 3-axis accelerometer [21]. This device enables the accurate capture of key biomechanical parameters, including GCT, LSS, VO, and SL. During the official competition, the device
Appl. Sci.2024,14, 4807 4 of 9 recorded the biomechanical parameters for each athlete across the entire race, which was divided into four race segments for analysis. The following spatial variables in the four race segments were analyzed through the Stryd “Power Center”. This approach allowed for a detailed examination of how these parameters might vary within the context of race progression. 2.5. Statistical Analysis Data are presented as the mean, standard deviation (SD), and 95% confidence interval (CI) for each race segment of the four parameters. The standard error (SE) and coefficient of variation (CV) were also calculated. Normality was checked through the Shapiro–Wilk test. A repeated measures ANOVA (within-subject factor: race segment) was used to test for any statistically significant differences in GCT, LSS, VO, and SL parameters across the different parts of the race. The effect size was computed using eta squared (η 2), with threshold values interpreted as small (0.01), medium (0.06), and large (0.14). To assess the consistency in the biomechanical parameters across the four race segments, the coefficient of variation (CV) was used, assessed as very good (<10%), good (10–20%), acceptable (20–30%), and not acceptable (>30%) [31]. In cases where statistical significance was found, Bonferroni post-hoc pairwise comparisons were computed to determine which specific segments differed from each other. Sphericity was checked using Mauchly’sWtest, and the Greenhouse–Geisserεcorrections were applied if sphericity was violated. All statistical analyses were performed using Jamovi version 2.3.28 (The Jamovi Project, 2022). 3. Results Figure segments with lines. The different colors for the data points and lines represent different athletes from the sample. The lack of a clear trend in any of the four plots suggests that the measured variables (GCT, LSS, VO, SL) do not consistently increase or decrease throughout the segments of the race for the individuals.Appl. Sci. 2024, 14, x FOR PEER REVIEW 5 of 10 Figure 1. Individual variation in biomechanical parameters GCT, LSS, VO, and SL across four race segments. Each colored point represents a different athlete. Solid black lines indicate mean value of each parameter for each segment. The descriptive statistics of four biomechanical parameters
the segments of the race for the individuals.Appl. Sci. 2024, 14, x FOR PEER REVIEW 5 of 10 Figure 1. Individual variation in biomechanical parameters GCT, LSS, VO, and SL across four race segments. Each colored point represents a different athlete. Solid black lines indicate mean value of each parameter for each segment. The descriptive statistics of four biomechanical parameters measured across all four race segments are shown in Table 2. The mean GCT is relatively stable, ranging narrowly from 208.5 to 210.0 ms; SD, which measures the spread of the data around the mean, de- creases slightly across the segments, from 17.4 ms in the first segment to 14.1 ms in the last, suggesting less variability in GCT as the race progresses. The CV, which is the SD expressed as a percentage of the mean, also decreases, although within the threshold of very good consistency (CV < 10%). Regarding LSS, the mean value decreases slightly throughout the race from 11.28 to 11.10 kN/m, while SD and SE show minimal changes, suggesting consistent variability and precision across race segments, with good CV values for all segments. The mean VO is very stable, showing minimal fluctuation between 7.36 and 7.41 cm across the race segments, and the SD and SE are relatively constant, suggest- ing that the amount of spread and precision in measuring VO does not change much through the race. CV is also good for all race segments (CV < 10%). Finally, the mean SL shows negligible variation, with a slight decrease in the third segment before returning to the initial value in the last segment, while the SD decreases as the race goes on, suggesting SL becomes more consistent among the athletes for a measure of centimeters. The CV for all parameters remained relatively low and constant across all race segments, implying that the athletes maintained a steady and consistent pattern across the race. Overall, the results suggest that the absolute values for each of these variables do not change dramat- ically across the race segments. Table 2. Comparison of biomechanical characteristics as within-subjects factors across four race
CV for all parameters remained relatively low and constant across all race segments, implying that the athletes maintained a steady and consistent pattern across the race. Overall, the results suggest that the absolute values for each of these variables do not change dramat- ically across the race segments. Table 2. Comparison of biomechanical characteristics as within-subjects factors across four race seg- ments: GCT, LSS, VO, and SL measurements. Factors Race Segment Mean SD 95% CI SE CV (%) GCT (ms) 1 208.6 17.4 194.1–223.1 6.14 8.3 2 208.5 16.1 195.0–222.0 5.71 7.7 3 210.0 14.6 197.8–222.2 5.15 6.9 4 209.6 14.1 197.8–221.4 4.98 6.7 Figure 1.Individual variation in biomechanical parameters GCT, LSS, VO, and SL across four race segments. Each colored point represents a different athlete. Solid black lines indicate mean value of each parameter for each segment.
Appl. Sci.2024,14, 4807 5 of 9 The descriptive statistics of four biomechanical parameters measured across all four race segments are shown in Table. The mean GCT is relatively stable, ranging narrowly from 208.5 to 210.0 ms; SD, which measures the spread of the data around the mean, decreases slightly across the segments, from 17.4 ms in the first segment to 14.1 ms in the last, suggesting less variability in GCT as the race progresses. The CV, which is the SD expressed as a percentage of the mean, also decreases, although within the threshold of very good consistency (CV < 10%). Regarding LSS, the mean value decreases slightly throughout the race from 11.28 to 11.10 kN/m, while SD and SE show minimal changes, suggesting consistent variability and precision across race segments, with good CV values for all segments. The mean VO is very stable, showing minimal fluctuation between 7.36 and 7.41 cm across the race segments, and the SD and SE are relatively constant, suggesting that the amount of spread and precision in measuring VO does not change much through the race. CV is also good for all race segments (CV < 10%). Finally, the mean SL shows negligible variation, with a slight decrease in the third segment before returning to the initial value in the last segment, while the SD decreases as the race goes on, suggesting SL becomes more consistent among the athletes for a measure of centimeters. The CV for all parameters remained relatively low and constant across all race segments, implying that the athletes maintained a steady and consistent pattern across the race. Overall, the results suggest that the absolute values for each of these variables do not change dramatically across the race segments. Table 2.Comparison of biomechanical characteristics as within-subjects factors across four race segments: GCT, LSS, VO, and SL measurements. Factors Race Segment Mean SD 95% CI SE CV (%) GCT (ms) 1 208.6 17.4 194.1–223.1 6.14 8.3 2 208.5 16.1 195.0–222.0 5.71 7.7 3 210.0 14.6 197.8–222.2 5.15 6.9 4 209.6 14.1 197.8–221.4 4.98 6.7 LSS (kN/m) 1 11.28 1.31 10.19–12.39 0.47 11.6 2
biomechanical characteristics as within-subjects factors across four race segments: GCT, LSS, VO, and SL measurements. Factors Race Segment Mean SD 95% CI SE CV (%) GCT (ms) 1 208.6 17.4 194.1–223.1 6.14 8.3 2 208.5 16.1 195.0–222.0 5.71 7.7 3 210.0 14.6 197.8–222.2 5.15 6.9 4 209.6 14.1 197.8–221.4 4.98 6.7 LSS (kN/m) 1 11.28 1.31 10.19–12.39 0.47 11.6 2 11.21 1.31 10.11–12.31 0.47 11.7 3 11.11 1.23 10.09–12.18 0.43 11.0 4 11.10 1.28 10.04–12.16 0.44 11.5 VO (cm) 1 7.40 0.58 6.92–7.88 0.20 7.8 2 7.41 0.58 6.92–7.90 0.21 7.9 3 7.38 0.58 6.89–7.87 0.21 7.9 4 7.36 0.59 6.86–7.86 0.21 8.1 SL (m) 1 1.42 0.17 1.29–1.56 0.06 11.4 2 1.43 0.16 1.30–1.56 0.06 11.0 3 1.41 0.14 1.30–1.54 0.05 9.9 4 1.43 0.13 1.31–1.53 0.05 9.4 Table race segments for four different measurements: GCT, LSS, VO, and SL. Mauchly’sWtest indicated that sphericity is violated for GCT, LSS, and SL (p< 0.05), and therefore the df,MS, andpvalues of the RM ANOVA are corrected with theεGreenhouse–Geisser correction. There were no significant effects of race segment on GCT (F= 0.96,p= 0.38), VO (F= 0.23,p= 0.87), and SL (F= 1.07,p= 0.35), all with a small effect size (η 20.001 to 0.002). Regarding the LSS, the RM ANOVA result showed a significant effect of race segment (F= 5.52,p= 0.03) with a small effect size (η 2 = 0.004).
Appl. Sci.2024,14, 4807 6 of 9 Table 3.Repeated measures (RM) ANOVA results for GCT, LSS, VO, and SL measurements. Sphericity RM ANOVA W p ε SS df MS F p η 2 GCT 0.13 0.04 0.46 13.12 1.37 1 9.55 1 0.96 0.38 1 0.002 LSS 0.02 <0.001 0.47 0.19 1.41 1 0.13 1 5.52 0.03 1 0.004 VO 0.18 0.09 0.52 0.01 3 0.005 0.23 0.87 0.002 SL 0.08 0.01 0.45 0.0009 1.34 1 0.007 1 1.07 0.35 1 0.001 1 Greenhouse–Geisser corrected values as the sphericity assumption is violated. The Bonferroni post hoc test reveals a statistically significant difference between the first and second race segments for LSS, with a mean difference of 0.07 kN/m and a p-valueof 0.015. This indicates that the difference in LSS between these two segments is likely not due to random chance and is statistically significant after adjusting for multiple comparisons. The RM ANOVA analysis suggests that while most of the measurements did not significantly vary across race segments, LSS did show significant variability. However, the Bonferroni-corrected post hoc analysis specifies that this significant variability is only present between the first and second segments. The eta squared (η 2) values, which represent the effect size, are relatively small for all measures, indicating that even where significant effects are found, the proportion of variance explained by race segment differences is small. 4. Discussion The primary aim of this study is to analyze the stability of the biomechanical parame- ters of the running stride among male trained athletes for endurance, specifically within the context of a half-marathon race. Previous research has predominantly utilized laboratory settings to predict perfor- mance outcomes based on biomechanical analyses, employing various protocols [13,14]. In contrast, the investigation presented herein investigates the effects of fatigue on these parameters in the inherently dynamic and uncontrolled environment of an official half- marathon event. Findings are discussed with an emphasis on GCT, LSS, VO, and SL, delineating how fatigue alters these key performance indicators. In the examination of GCT, no significant differences were found (p= 0.38) and only small effect sizes were observed (η
Description
The research analyzes biomechanical stability in trained male runners during a half-marathon.