Body Composition and Gait Parameters in Young Male Athletes: A Cross-Sectional Observational Study Compared with Sedentary Controls

Body Composition and Gait Parameters in Young Male Athletes: A Cross-Sectional Observational Study Compared with Sedentary Controls | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Body Composition and Gait Parameters in Young Male Athletes: A Cross-Sectional Observational Study Compared with Sedentary Controls Ilknur AKKUS, Ercan GUR, Cengiz ARSLAN, Mustafa GUR This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8844166/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract Background This study aimed to investigate and compare differences in segmental body composition and gait characteristics (including plantar pressure distribution and spatio-temporal parameters) between athletes engaged in sports with a predominance of vertical jump (football, basketball, volleyball, and boxing) and sedentary young men using the gait analysis platform and body composition analyzer. Methods This cross-sectional observational study included 63 healthy young men aged 16–25 years (33 athletes and 30 individuals with sedentary lifestyles). Segmental body composition was assessed using a Tanita Jawon AVIS 333 Plus analyzer, and gait parameters, including spatiotemporal and plantar pressure data, were recorded using the Win-Track platform. T-tests and ANOVA were used to determine intergroup differences, with a significance level of p < 0.05. Results Athletes exhibited significantly lower body fat percentage (PBF) and higher skeletal muscle mass (SLM) in both lower extremities and trunk compared to sedentary individuals (PBF: 15.21 ± 5.47, p = 0.001; Trunk SLM: 29.25 ± 3.72, p = 0.014; Rt.Leg SLM: 11.42 ± 1.57, p = 0.002; Lt.Leg SLM: 11.46 ± 1.54, p = 0.007, respectively). Sedentary participants showed higher mean swing velocity and maximum plantar pressure (Right and Left Max. Plantar Pressure: 478.53 ± 97.47, p = 0.05). Conclusions Regular participation in sports involving frequent dynamic movements can have positive effects on body composition and gait biomechanics. Compared to sedentary individuals, athletes exhibit better muscle distribution, postural control, and gait efficiency; this underscores the importance of staying physically active to support healthy muscle function and movement control. Trial registration not applicable. gait analysis spatiotemporal parameters plantar pressure segmental body composition athletes sedentary individuals Figures Figure 1 INTRODUCTION The use of body composition parameters to assess an individual's overall fitness, nutritional quality, and health has become widespread in recent times ( 1 ). The ratio of body composition components may vary depending on an individual's age, gender, ethnicity, and physical activity level. BC (body composition) can be measured as a whole body (total values) or segmentally (by dividing into various anatomical regions such as arms, legs, and torso). Segmental BC provides detailed data on how body mass is distributed across different regions and helps identify potential asymmetries ( 2 ). The assessment and regular monitoring of body composition is directly related to athletic performance. This process contributes to setting goals, determining the athlete's level of development, and creating subsequent training or work plans ( 3 ). Athletes and coaches know that body composition plays a critical role in both performance and injury risk management. Specifically, while muscle mass is positively associated with strength and agility, a high fat percentage can negatively impact athletic performance ( 2 , 4 ). Gait analysis is one of the fundamental methods used to examine and evaluate individuals' walking patterns. Instrumented gait analyses (e.g., Win-Track) enable detailed evaluation of walking patterns by measuring kinetic, kinematic, and spatio-temporal variables ( 5 ). Monitoring the spatio-temporal parameters of walking is used both to track performance and to detect gait abnormalities, and may help predict the risk of overuse injuries in athletes ( 6 ). Therefore, comparing spatio-temporal parameters and body composition is important for understanding both performance potential and injury risks. In the literature, there are very few studies comparing gait analysis and segmental body composition between young athletes at the amateur level and sedentary individuals with similar sociodemographic characteristics. This study aims to examine the differences in plantar pressure, spatio-temporal walking data, and segmental body composition between male athletes aged 16–25 (playing sports that require jumping power, such as volleyball, basketball, soccer, and boxing) and a sedentary group. We hypothesized that athletes would demonstrate lower maximum plantar pressure and more efficient spatiotemporal gait patterns compared to sedentary individuals, and that sport-specific differences in gait parameters and segmental body composition would be observed among athletes engaged in jump-intensive disciplines. METHODS Study Design And Ethics This study is a cross-sectional observational study and has been reported in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines (7). This study was conducted in accordance with the Helsinki Declaration. Local ethics committee approval was obtained from the Fırat University Faculty of Medicine Non-Interventional Clinical Research Ethics Committee (Ethics Committee Approval No: 14.05.2025-34456). Written informed consent forms were obtained from all participants. Participants The study included participants with a sociodemographically homogeneous distribution (Group 1 age: 21.47 ± 2.53 years old, Group 2 age: 20.87 ± 2.35 years old; Group 1 height: 179. 53 ± 6.28, Group 2 height: 178.87 ± 4.87; Group 1 body mass: 75.50 ± 12.10 kg, Group 2 body mass: 76.73 ± 10.98 kg; mean and standard deviation, both groups of participants were male). Male young athletes at the amateur level aged 16-25 (n=32) were designated as Group 1, and sedentary male participants (n=30) were designated as Group 2. The plantar pressures, spatiotemporal gait parameters, and static balance parameters of all participants included in the study were recorded using the Win-Track platform. Segmental body composition analyses were performed using the Jawon Segmental Body Composition Analysers model AVIS 333 Plus (AVIS) device. In this analysis, muscle mass and fat percentages were evaluated for the torso, arms, and legs. Along with all measurement results, participants' age, gender, height, weight, body mass index (BMI) (kg/m²), educational status, tobacco and alcohol consumption, sports disciplines and sports ages for athlete participants were recorded in participant forms. Young athletes at the amateur level group (Group 1) This group included athletes aged 16-25 who had been competing at an young athletes at the amateur level in volleyball, soccer, basketball, or boxing for at least 3 years. These athletes also practiced sport-specific exercises at least five days a week and participated in national leagues. Exclusion criteria were defined as follows: being an young athletes at the amateur level for less than 3 years, having a neuromuscular or orthopedic disease, having a cardiopulmonary disease, having developmental hip dysplasia, scoliosis, or foot deformity, having a BMI >35 kg/m2, having undergone trauma or surgery within the last year, or being unwilling to participate in the study. Sedentary group (Group 2) For the study, male participants who had not been members of any sports club in the past 6 months, had no previous professional or amateur sports history, had a weekly level of regular physical activity of less than 1 day per week, were desk-bound workers or students, were defined as sedentary group participants. Exclusion criteria were defined as having a neuromuscular or orthopedic disease, having a cardiopulmonary disease, having developmental hip dysplasia, scoliosis, or foot deformity, having a BMI >35 kg/m2, having undergone trauma or surgery within the last year, or being unwilling to participate in the study. Data Collection Spatio-Temporal Parameters: The Win-Track platform (MEDICAPTEURS Technology, France) is a tool used to measure plantar pressures and spatio-temporal parameters during barefoot walking (8). The platform's length, width, and height measurements are 1610 mm x 652 mm x 30 mm, respectively. The platform is 9 mm thick and consists of 12,288 resistive sensors (8). The Win-Track platform is used to measure the patient's static posture and dynamic gait parameters. In this system, data is loaded onto a computer that performs automatic footprint recognition and parameter calculations. In this study, participants' spatio-temporal gait parameters, plantar pressure distribution, and static balance measurements were evaluated using the Win-Track pressure-sensitive gait platform (Figure 1). Since the 3-step protocol has been shown to provide better test-retest reliability compared to the 1-step protocol, the 3-step protocol was applied barefoot in this study (9). To ensure the 3-step protocol was correctly understood and implemented, participants were briefed on the platform before all measurements and performed three trial runs with at least three steps correctly placed on the platform. Participants were asked to look ahead and walk at a comfortable pace on the platform to ensure the most accurate measurement was recorded. The analyzed spatiotemporal parameters are as follows: (9) • Cadence (spm-steps/minute): Cadence refers to the number of steps taken per minute and is one of the parameters that indicates walking speed. • Step duration (ms): This indicates the total time from when one foot first makes contact with the ground to when the opposite foot first makes contact with the ground. • Swing phase (ms): Refers to the time the foot is off the ground and in the air. • Step length (mm): The distance between the moment one foot makes contact with the ground and the moment the other foot makes contact with the ground (5). • Gait Cycle Length (mm): The distance between two consecutive heel contact points of the same foot. • Foot angle (degrees): The angle between the direction of foot progression and the longitudinal axis of the foot during walking. Maxımum Plantar Pressure (g/cm²) This term refers to the maximum amount of pressure generated at a point on the plantar region during walking (9). In addition, the weight transfer (%) to the right and left feet of all participants was recorded. Statıc Balance Measurement A static balance measurement, namely the average oscillation speed (mm/s), was performed by having participants remain motionless for 30 seconds while looking fixedly at the opposite wall on the platform. Body Composition Analysis Body segmental analyses were performed according to a standard protocol using the Jawon Segmental Body Composition Analysers model AVIS 333 Plus (AVIS, Korea) device, which has segmental analysis capabilities based on the multi-electrode bioimpedance (BIA) principle. Before starting the data collection process, the measuring instruments were checked and calibrated. Participants' measurements were taken between 8 and 12 in the morning, without fluid or food intake, wearing similar clothing, and with jewelry and metals removed. All participants were asked to stand upright on the device's platform with bare feet and grasp the hand electrodes. Segmental fat and muscle masses (trunk, leg, arm); Body mass index (BMI) (kg/m2), body fat percentage (PBF) (%), body fat weight (MBF) (kg), and total body water (TBW) (kg) were recorded using a computer connected to a bioelectrical impedance analysis device (Jawon Segmental Body Composition Analysers model AVIS 333 Plus). Statistical Analysis Statistical analysis of the data obtained from the study was performed using the IBM SPSS 26.0 Statistics software package. Descriptive statistics for continuous variables are presented as mean ± standard deviation (Mean ± SD). Before proceeding with the analyses, outliers in the dataset were checked, the normality assumption of continuous variables was evaluated with the Shapiro–Wilk test and histograms, q–q plots; and homogeneity of variance was evaluated with the Levene test. In cases where the assumptions of normality and homogeneity of variance were met, the independent samples t-test was used to compare two independent groups (athletes and sedentary). One-way analysis of variance (ANOVA) was applied to compare body composition, lower extremity, balance, plantar pressure, and gait parameters according to sports branches (football, basketball, volleyball, boxing, and sedentary). For variables where a significant difference was found as a result of ANOVA, post-hoc multiple comparison tests with Bonferroni correction were performed to determine between which groups the difference occurred. Categorical variables (if any) were summarized as numbers and percentages [n (%)], and the chi-square (χ²) test was used for intergroup comparisons. A significance level of p < 0.05 was accepted for all statistical tests. An a priori power analysis was conducted using G*Power software (version 3.1.9.7; Heinrich Heine University, Düsseldorf, Germany). A total sample size of 63 participants (33 athletes and 30 sedentary individuals) was sufficient to achieve approximately 80% statistical power to detect a moderate effect size (Cohen’s d = 0.5) at an alpha level of 0.05 for between-group comparisons. RESULTS In our study, when comparing the body composition and basic anthropometric characteristics of athletes and sedentary individuals, significant differences were observed in many variables between the groups. No significant difference was found between the two groups in terms of age, height, and weight (p > 0.05). However, significant differences were observed in muscle and fat distribution variables. The athlete group was found to have significantly higher trunk and leg muscle mass compared to the sedentary group (p < 0.05). In contrast, the PBF, MBF, and trunk and leg fat masses of sedentary individuals were found to be significantly higher than in the athlete group (p < 0.05). In addition, the TBW amount was found to be higher in athletes (46.82±5.77, p = 0.028) (see Table 1). When lower extremity pressure and balance parameters were examined, it was found that individuals with a sedentary lifestyle had significantly higher plantar pressure and postural sway velocity compared to athletes. In sedentary individuals, the maximum plantar pressure values were higher on the right (478.53±97.47, p<0.001) and left (431.30±52.88, p=0.002). Similarly, the mean swing velocity was also significantly higher in sedentary individuals compared to athletes (6.35±3.08, p=0.034) (see Table 2). According to the results of the spatio-temporal parameters of the gait analysis, the walking performance of the athlete group was found to be significantly better compared to sedentary individuals. The athletes had shorter right and left step duration (605.78±62.86, p=0.002 and 614.41±73.86, p=0.044), while their step lengths were significantly longer (544.09±52.01, p=0.015 and 540.34±53.10, p<0.001). Additionally, the athletes had higher cadence values (96.30±10.49, p=0.002) and greater gait cycle length (1089.28±51.98, p=0.017). On the other hand, sedentary individuals were found to have wider right (7.12±6.21, p=0.022) and left (9.34±5.84, p=0.023) foot angles (see Table 3). One-way analysis of variance results showed significant differences in age and right leg fat mass among sports branches. The effect size for age was significant (p=0.040). According to multiple comparisons with Bonferroni correction, the average age of volleyball players was found to be significantly higher than that of football players. The difference between branches was also significant for right leg fat mass (p=0.019); Bonferroni results showed that volleyball players had significantly higher right leg fat mass values than football players. In all other variables (height, weight, trunk muscle mass, trunk fat mass, PBF, MBF, TBW, and BMI), the differences between the means were not statistically significant. (See Table 4) Table 5 examines the weight transfer, swing phase, maximum plantar pressure, and mean swing velocity values of athletes according to their sports branches. As a result of the analyses, no statistically significant difference was found between branches in all variables (p>0.05). The weight transfer values to the right and left feet of football and basketball players were quite similar, and it was observed that participants generally distributed their body weight equally to both feet (p=0.728 for right weight transfer; p=0.738 for left weight transfer). Similarly, there was no significant difference between the groups in the swing phase values (p=0.177 for right swing phase; p=0.166 for left swing phase). When maximum plantar pressure values were examined, it was seen that the average pressure values for both the right and left feet of basketball players were relatively higher compared to other branches, but this difference was not statistically significant (right foot p=0.512; left foot p=0.707). No significant difference was observed between branches in the average swing speed variable (p=0.768). Table 6 presents the findings regarding walking parameters of athletes according to their sport. According to the results of the one-way analysis of variance, no statistically significant difference was found between branches in the variables of right and left step duration, step length, foot angle, and gait cycle length. (p>0.05). DISCUSSION This study demonstrated that regular participation in sports involving frequent dynamic and jump-related activities is associated with favorable adaptations in both body composition and gait characteristics. Compared with sedentary individuals of similar anthropometric profiles, athletes exhibited lower fat mass and body fat percentage, higher lean muscle mass, and more efficient spatiotemporal gait parameters. In contrast, no significant differences were observed among athletes from different sports branches, suggesting that participation in jump-intensive sports may induce comparable neuromuscular and biomechanical adaptations during walking. A review of the literature reveals that studies comparing gait analysis and segmental body composition using the Win-Track platform are quite limited between athletes and sedentary individuals. The findings of this study suggest that in sports branches (such as football, volleyball, basketball, and boxing) where there is intense competition, usually culminating in a vertical jump after the walking and running phases, gaining new knowledge about the correct biomechanics of movement, developing positive attitudes, contributing to sports training, optimizing training and competition activities, and reducing susceptibility to injuries can be beneficial. Looking at the literature on this subject, it shows that observing an athlete's gait style, continuously training for improvement, and identifying any gait deformities will increase performance effectiveness and reduce the risk of injury (10,11). To avoid bias in the evaluation of gait, balance parameters, and weight transfer, the study was conducted in a population where the physical characteristics of athletes and sedentary individuals were similar in terms of age, height, weight, and BMI; therefore, no significant difference was found between these physical characteristics (p>0.05, Table 1). Regarding body composition characteristics (PBF, MBF, TBW, TrunkMBF, TrunkSLM, Rt.LegMBF and SLM, Lt.LegMBF and SLM), the results favoring the athlete group were found to support high physical activity (p<0.05). These results support the effect of regular exercise on reducing body fat percentage by increasing muscle hypertrophy. Similarly to our study, in 2024, Toskic et al. reported that muscle mass was significantly higher in athletes with different training profiles, while total fat mass was significantly increased in sedentary individuals (12). Furthermore, the findings of our study are consistent with studies in the literature that show significant differences in body composition parameters between active and inactive individuals (13,14). The more unfavorable segmental profile in our sedentary group is therefore not surprising and confirms once again that regular training improves not only cardiorespiratory but also morphological adaptation (15). In our study, the maximum plantar pressure (g/cm2) and mean oscillation velocity (mm/s) in both the right and left extremities, recorded using the Win Track walking platform, were higher in sedentary groups compared to athletes (p<0.05), indicating that regular physical activity has positive effects on balance control and postural stability (See Table 2). Similarly, Paillard (2017) showed that balance performance is better in individuals who exercise regularly (16). This situation can be explained by the fact that athletes have better developed neuromuscular adaptations. In a study comparing the gait kinetics, postural balance, and quality of life of CrossFit practitioners and those with sedentary lifestyles in 2025, the group that exercised reported better postural control and stronger gait kinetics (17). In our group of athletes, a similar mechanism may have been at play, with sufficient lean mass, particularly in the lower extremity segments, mechanically facilitating the stride cycle. Furthermore, improved balance and higher muscle mass can enhance athletic performance in athletes, while also reducing sports injuries, especially those originating in the lower extremities (18). It has been previously reported in various sports that physically active individuals outperform their sedentary peers in key spatiotemporal parameters such as stride length; a study comparing netball players with inactive youth showed that those who were physically active had increased stride length and more controlled internal hip rotation (19). This finding supports our view that individuals who train regularly exhibit better coordination, speed, and control in their walking patterns. On the other hand, the longer gait distance observed in the athlete group likely reflects the combined effect of muscle strength, range of motion, and neuromuscular coordination gained through regular exercise. The more stable walking kinetics and higher cadence values identified in individuals who engage in CrossFit and resistance-based exercises support this trend (17). The combination of a shorter stride duration and an extended stride distance indicates that energy transfer occurs more efficiently, thus making walking more mechanically economical (20,21). The wider foot angles observed in sedentary individuals may indicate a tendency to create a wider base during walking to maintain stability. This compensatory mechanism can be associated with low muscle strength and weak proprioceptive sensation. In this study, comparisons made according to sports branches revealed differences between branches in body composition and walking parameters. This shows that the physical performance components required by each sport (jumping, agility, endurance, strength) shape muscle and soft tissue adaptations in different ways. Since volleyball heavily relies on jumping and landing movements, it increases lower extremity muscle hypertrophy and optimizes plantar pressure distribution. In our findings, the right lower extremity leg muscle mass in volleyball players is higher than in other branches with an average of 12.27±0.80. In addition, the right leg max. plantar pressure is lower than in other branches with a value of 381.22±45.05. The fact that our findings did not show a statistically significant difference may be due to our sample size. Indeed, proprioceptive training has been reported to make plantar pressure distribution more homogeneous and reduce loading on the heel area in volleyball players (22). Similarly, significant relationships have been found between foot posture and functional jumping performance in volleyball players, showing that asymmetry in foot posture can negatively affect jumping and single-leg balance performance (23). Our study showed that the football player group had the lowest levels of fat mass in both the right and left lower extremities compared to other groups. This supports our hypothesis that the anthropometric characteristics of football players have led to sport-specific adaptations. Because football players are constantly exposed to asymmetrical movements such as changing direction, accelerating, and kicking the ball, it has been reported that plantar pressure distribution, particularly the loading on the dominant foot, is greater (24). Our study found no statistically significant difference in maximum plantar pressure and spatiotemporal parameters among athletes of different sports branches. This can be explained by several factors, including the athletes' relatively short sporting years, their lack of professionalism to participate in advanced competitions, and the small number of participants. Our findings show that basketball players have higher maximum plantar pressures on both the right and left feet compared to other groups. The tall stature, high body weight, and the combination of jumping and turning in basketball players' playing style may cause the plantar pressure center to shift to the forefoot region, leading to a different loading pattern in balance control. Indeed, it has been shown that taller handball players have higher forefoot pressure and lower heel loading (25). Boxers, due to their training which typically involves rhythmic step movements and agility, are expected to have higher cadence values compared to other sports. This result can be explained by their ability to maintain stability by constantly and dynamically shifting their center of gravity. When these findings are considered generally, it can be said that each sport creates specific adaptations in lower extremity muscle strength, plantar load distribution, and gait dynamics depending on its own motoric and biomechanical demands. In particular, plantar pressure is more evenly distributed in jump-based sports (volleyball, basketball), while more asymmetrical load distributions are observed in change-of-direction and contact-focused sports (football, boxing). These differences are important for understanding the effects of sport-specific training on postural control and gait biomechanics. Therefore, when segmental body composition and spatiotemporal gait parameters are examined together, sport-specific neuromuscular adaptation patterns can be distinguished. The scarcity of studies in this field focusing on the football, basketball, volleyball, and boxing athletes included in our research makes direct comparisons between athletes difficult. While this limits our study to some extent, it creates scientific potential and helps open new horizons for researchers. Limitations This study has several limitations. Firstly, the number of participants included in the study is relatively limited. Specifically, separating the athlete group into different branches may have reduced the sample size for each branch and limited the statistical power in inter-branch comparisons. This may have contributed to the fact that some observed differences in certain parameters did not reach statistical significance. The cross-sectional design of the study does not allow for a cause-and-effect interpretation of the findings. Therefore, the results only reflect the existing differences between athletes and sedentary individuals and do not reveal the changes caused by physical activity over time. Furthermore, the study was conducted only with young adult males. This limits the generalization of the results to female athletes or different age groups. Similarly, while the devices used during the assessments (Win-Track and segmental BIA analyzer) provide reliable and valid measurements, they do not provide more detailed biomechanical data such as three-dimensional motion analysis or electromyographic measurements. Another important limitation is that although segmental BIA is a practical and non-invasive method, it is less sensitive than dual-energy X-ray absorptiometry (DXA), particularly in athletic populations. Future studies using larger sample sizes, including different age and gender groups, preferably with longitudinal designs, and evaluating additional biomechanical and neuromuscular variables associated with performance and injury risk, will contribute to a more comprehensive interpretation of the findings from clinical and sporting perspectives. CONCLUSION This study demonstrated significant differences in body composition and gait parameters between young men who regularly engage in sports and sedentary individuals. The athlete group exhibited a lower body fat percentage and higher muscle mass, along with more balanced plantar pressure distribution and spatiotemporal gait parameters. In contrast, higher maximum plantar pressure values ​​and increased swing velocity in sedentary individuals suggest that postural control may be more limited. These findings suggest that regular physical activity not only creates morphological changes in the musculoskeletal system but also has positive effects on gait biomechanics and load distribution. In particular, sufficient muscle mass in the lower extremity segments may contribute to a more homogeneous distribution of load during walking and a reduction in mechanical stress. From a clinical perspective, the more balanced plantar pressure distribution and gait patterns observed in the athlete group can be interpreted as a protective mechanism against overuse injuries originating from the lower extremities. Therefore, gait analysis and segmental body composition assessments should be used not only for performance monitoring but also for other purposes. It can also be said that it is valuable in terms of risk assessment and preventive approaches in healthy individuals. In conclusion, regular physical activity in young adult males is associated with a more favorable body composition and more efficient gait biomechanics. These findings reveal that maintaining a physically active lifestyle is important for both preserving functional movement quality and supporting musculoskeletal health. Abbreviations AVIS: Advanced Visceral Impedance System BIA: Bioelectrical Impedance Analysis BMI: Body Mass Index MBF: Mass of Body Fat PBF: Percent Body Fat SLM: Soft Lean Mass TBW: Total Body Water Declarations Ethics approval and consent to participate: This study was conducted local ethics committee approval was obtained from the Fırat University Faculty of Medicine Non-Interventional Clinical Research Ethics Committee (Ethics Committee Approval No: 14.05.2025-34456). Written informed consent forms were obtained from all participants. Consent for publication: Not applicable. Availability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests: The authors declare that they have no competing interests. Funding: No financial support was received for the conceptual framework development, design, data collection, analysis, publication decision, or preparation of the article. Authors' contributions: I.A. contributed to the study conception and design, data collection, data analysis, and drafting of the manuscript. C.A. contributed to the study design and critically revised the manuscript. E.G. contributed to data collection and manuscript revision. M.G. contributed to manuscript revision and final approval of the submitted version. All authors read and approved the final manuscript. Acknowledgements: Not applicable. References Kutáč P, Bunc V, Sigmund M. 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Biomechanics. 2023;3:220–230. doi:10.3390/biomechanics3020019 Marotta N, Moggio L, Calafiore D, Prestifilippo E, Spanó R, Tasselli A, et al. Efficacy of proprioceptive training on plantar pressure and jump performance in volleyball players: a proof-of-principle study. Sensors (Basel). 2023;23(4):1906. doi:10.3390/s23041906 Kahraman Y. Is there any weakness in sports performance in volleyball athletes regarding the correlation between foot posture index and lower limb functional hopping performance? J Foot Ankle. 2023;17(1):8–15. doi:10.30795/jfootankle.2023.v17.1678 Hawrylak A, Brzeźna A, Chromik K. Distribution of plantar pressure in soccer players. Int J Environ Res Public Health. 2021;18(8):4173. doi:10.3390/ijerph18084173 Abdel-aziem AA, Ameer MA. The relationship between body height and plantar pressure distribution in adult handball players: a cross-sectional study. J Orthop Trauma Rehabil. 2024;31(2):160–166. doi:10.1177/22104917231208214 Tables Table 1. Comparison of Participants' Anthropometric Characteristics by Group. Parameters Athletes (Mean ± SD) Sedentary (Mean ± SD) p Age (years) 21.47 ± 2.53 20.87 ± 2.35 0.334 Height (cm) 179.53 ± 6.28 178.87 ± 4.87 0.642 Weight (kg) 75.50 ± 12.10 76.73 ± 10.98 0.675 Trunk SLM (kg) 29.25±3.72 27.03±3.20 0.014 Rt.Leg MBF (kg) 2.48±1.33 3.18±1.18 0.033 Rt.Leg SLM (kg) 11.42±1.57 10.20±1.42 0.002 Lt.Leg MBF (kg) 2.39±1.28 3.47±1.42 0.003 Lt.Leg SLM (kg) 11.46±1.54 10.45±1.31 0.007 Trunk MBF (kg) 6.11±2.10 10.16±3.48 <0.001 PBF (%) 15.21±5.47 19.84±5.47 0.001 Total MBF (kg) 11.94±5.93 15.62±6.75 0.027 TBW (kg) 46.82±5.77 43.36±6.25 0.028 BMI (kg/m²) 23.48±4.00 24.45±3.97 0.340 SD: Standard Deviation; SLM: Soft Lean Mass; MBF: Mass of Body Fat; PBF: Percent Body Fat; TBW: Total Body Water; BMI: Body Mass Index; Rt.: Right; Lt.: Left; m: meter; cm: centimeter; kg: kilogram; %: percent; p: p-value . Data are presented as mean ± standard deviation (SD). Independent samples t-test was used to compare groups. Statistical significance was set at p < 0.05. Significance levels: p < 0.05 (significant), p < 0.01 (highly significant), p < 0.001 (extremely significant). Table 2. Comparison of Lower Extremity Pressure & Balance Parameters Between Athletes and Sedentary Groups. Parameter Athletes (Mean ± SD) Sedentary (Mean ± SD) p Right Max. Plantar Pressure (g/cm 2 ) 392.56±35.60 478.53±97.47 <0.001 Left Max. Plantar Pressure (g/cm 2 ) 393.22±39.87 431.30±52.88 0.002 Average Swing Velocity (mm/s) 4.96±1.68 6.35±3.08 0.034 Right Swing Phase (ms) 1461.81±150.64 1496.76±176.88 0.405 Left Swing Phase (ms) 1453.53±159.85 1494.83±173.13 0.333 Right Weight Transfer (%) 50.66±4.00 50.20±4.00 0.656 Left Weight Transfer (%) 49.34±4.00 49.80±4.00 0.632 SD: Standard Deviation; %: percent; mm/s: millimeter per second; g/cm 2 : gram per square centimeter; ms: millisecond; p: p-value . Data are presented as mean ± standard deviation (SD). Independent samples t-test was used to compare groups. Statistical significance was set at p < 0.05. Significance levels: p < 0.05 (significant), p < 0.01 (highly significant), p < 0.001 (extremely significant). Table 3. Comparison of Spatiotemporal Parameters of Walking Between Athletes and Sedentary Individuals. Parameter Athletes (Mean ± SD) Sedentary (Mean ± SD) p Right Step Duration (ms) 605.78±62.86 692.47±131.16 0.002 Left Step Duration (ms) 614.41±73.86 689.60±184.48 0.044 Right Step Length (mm) 544.09±52.01 498.47±84.65 0.015 Left Step Length (mm) 540.34±53.10 479.13±74.05 <0.001 Right Foot Angle (°) 4.04±3.20 7.12±6.21 0.022 Left Foot Angle (°) 6.36±3.47 9.34±5.84 0.023 Cadence (spm) 96.30±10.49 88.29±9.03 0.002 Gait Cycle Length (mm) 1089.28±51.98 1043.07±89.12 0.017 SD: Standard Deviation; ms: millisecond; mm: millimeter; °: degree; spm: steps per minute; p: p-value . Normality of the data was assessed using the Shapiro–Wilk test. Variables with normal distribution were analyzed using independent samples t-tests, while non-normally distributed variables were compared using Mann–Whitney U tests. Statistical significance was set at p < 0.05. Significance levels: p < 0.05 (significant), p < 0.01 (highly significant), p < 0.001 (extremely significant). Table 4. Comparison of Athletes Body Composition Characteristics by Branch. Parameter Football Basketball Volleyball Boxing p Age(years) 20.27±1.35 22.40±3.98 23.11±1.05 20.57±3.10 0.040 Height (cm) 178.45±5.50 179.60±6.84 182.89±7.64 176.86±4.14 0.250 Weight (kg) 69.82±4.60 80.80±16.63 80.67±16.5 74.00±6.93 0.160 Trunk SLM (kg) 30.61±2.31 30.10±5.27 31.57±2.87 30.21±4.27 0.084 Rt. Leg MBF (kg) 1.78±0.77 2.49±0.88 3.55±1.78 2.22±0.86 0.019 Rt. Leg SLM (kg) 11.02±1.64 10.65±1.28 12.27±0.80 11.48±2.13 0.211 Lt. Leg MBF (kg) 1.78±0.76 2.89±1.47 3.00±1.71 2.24±0.87 0.146 Lt. Leg SLM (kg) 10.86±1.38 11.83±2.07 11.60±1.28 11.98±1.72 0.436 Trunk MBF (kg) 5.88±1.90 6.81±1.86 5.81±2.61 6.38±2.15 0.819 PBF (%) 13.97±5.18 18.64±6.59 14.79±6.17 15.24±4.17 0.480 Total MBF (kg) 9.83±4.21 15.84±7.81 13.18±7.42 10.89±3.68 0.248 TBW (kg) 44.55±3.06 47.30±8.83 50.68±3.99 45.09±6.91 0.087 BMI (kg/m²) 21.53±1.78 25.28±4.83 25.10±4.64 23.16±4.41 0.160 SLM: Soft Lean Mass; MBF: Mass of Body Fat; PBF: Percent Body Fat; TBW: Total Body Water; BMI: Body Mass Index; Rt.: Right; Lt.: Left; m: meter; cm: centimeter; kg: kilogram; %: percent; p: p-value . Data are presented as mean ± SD. Differences among the four sports branches (football, basketball, volleyball, and boxing) were analyzed using one-way ANOVA. When significant differences were found, Bonferroni post-hoc tests were applied. Statistical significance was set at p < 0.05. Significance levels: p < 0.05 (significant), p < 0.01 (highly significant), p < 0.001 (extremely significant). Table 5. Comparison of Pressure & Balance Parameters in Athletes by Sport Branch. Parameter Football (n=11) Basketball (n=5) Volleyball (n=9) Boxing (n=7) p Right Weight Transfer (%) 51.00±3.69 51.60±3.05 50.89±5.71 49.14±2.61 0.728 Left Weight Transfer (%) 49.00±3.69 48.40±3.05 49.11±5.71 50.86±2.61 0.738 Right Swing Phase (ms) 1409.09±116.17 1555.00±120.91 1511.33±184.95 1414.43±144.11 0.177 Left Swing Phase (ms) 1430.73±113.08 1577.60±134.84 1473.56±201.61 1375.00±151.06 0.166 Right Max. Plantar Pressure (g/cm 2 ) 394.09±24.37 411.80±7.01 381.22±45.05 391.00±48.06 0.512 Left Max. Plantar Pressure (g/cm 2 ) 388.73±47.63 405.60±23.12 400.56±39.29 382.00±40.35 0.707 Average Swing Velocity (mm/s) 4.75±1.69 4.44±1.02 5.19±2.03 5.36±1.76 0.768 %: percent; mm/s: millimeter per second; g/cm 2 : gram per square centimeter; ms: millisecond; p: p-value . Data are shown as mean ± SD. One-way ANOVA was used to compare the four sports branches (football, basketball, volleyball, and boxing). Bonferroni post-hoc tests were conducted when significant differences were observed. Statistical significance was accepted at p < 0.05. Significance levels: p < 0.05 (significant), p < 0.01 (highly significant), p < 0.001 (extremely significant). Table 6. Comparison of Spatiotemporal Parameters of Walking in Athletes According to Sport Branch. Parameter Football (n=11) Basketball (n=5) Volleyball (n=9) Boxing (n=7) p Right step duration (ms) 591.82±47.77 625.00±80.12 595.78±64.56 626.86±73.76 0.558 Left step duration (ms) 646.64±70.76 632.80±79.46 583.11±77.51 590.86±57.59 0.194 Right step length (mm) 550.27±52.29 543.80±28.49 558.00±64.74 516.71±45.35 0.451 Left step length (mm) 521.00±64.54 560.80±37.25 551.89±59.41 541.29±27.57 0.471 Right foot angle (°) 4.90±3.95 3.04±1.12 2.93±2.05 4.80±3.99 0.478 Left foot angle (°) 6.41±3.26 4.09±1.22 6.72±3.93 8.04±4.47 0.350 Cadence (spm) 98.04±7.38 93.20±6.54 91.74±12.84 101.69±12.28 0.237 Gait Cycle Length (mm) 1077.91±61.56 1062.20±20.79 1124.67±54.87 1081.00±23.11 0.094 ms: millisecond; mm: millimeter; °: degree; spm: steps per minute; p: p-value . Data are presented as mean ± SD. One-way ANOVA was used to compare spatio-temporal gait parameters among the four sports branches (football, basketball, volleyball, and boxing). Bonferroni post-hoc tests were used for multiple comparisons. Statistical significance was set at p < 0.05. Significance levels: p < 0.05 (significant), p < 0.01 (highly significant), p < 0.001 (extremely significant). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8844166","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":604044213,"identity":"bf373189-00e6-468a-8710-96735501b8c0","order_by":0,"name":"Ilknur AKKUS","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIie3PPQrCMBTA8YSCLn6sloJeIR3FoziJoFNw7aASl7joriB6BbsUx4ZAXKLO4mJvUTdfXRShtm6C+UMeGfKDPIRMpl8sxBPBkkupAMODY1ksi7AXohOCswhCT4J5cssglYtkYrmTg+pM9ezbetSuToHEXpBK7GOHCV/L5uLAlVMO9nQhMcMzfUklRMMuEZcEnYrcwYGiDIiFeQ7SAGLfVopuchEfCIGP1cpsSLdZxE7IkveJq1W3VVIh9YGIT7tUdDGK5rxF6rrnnuPhmK5PUlxjL528Jx8zzP0eGn/z2GQymf6kOyu/ZndF5ZttAAAAAElFTkSuQmCC","orcid":"","institution":"Elazıg Fethi Sekin City Hospital","correspondingAuthor":true,"prefix":"","firstName":"Ilknur","middleName":"","lastName":"AKKUS","suffix":""},{"id":604044214,"identity":"58a74133-4e26-4c8f-b219-2da4c48a51a8","order_by":1,"name":"Ercan GUR","email":"","orcid":"","institution":"Fırat University","correspondingAuthor":false,"prefix":"","firstName":"Ercan","middleName":"","lastName":"GUR","suffix":""},{"id":604044215,"identity":"ee0e3f30-2a05-48d6-9f7d-2dd4525a1ecd","order_by":2,"name":"Cengiz ARSLAN","email":"","orcid":"","institution":"Fırat University","correspondingAuthor":false,"prefix":"","firstName":"Cengiz","middleName":"","lastName":"ARSLAN","suffix":""},{"id":604044216,"identity":"486241ef-752a-4d9c-8234-c804409190c5","order_by":3,"name":"Mustafa GUR","email":"","orcid":"","institution":"Fırat University","correspondingAuthor":false,"prefix":"","firstName":"Mustafa","middleName":"","lastName":"GUR","suffix":""}],"badges":[],"createdAt":"2026-02-10 17:54:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8844166/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8844166/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104470633,"identity":"00be333f-79a2-41de-9e6b-5f3e921d9705","added_by":"auto","created_at":"2026-03-12 07:22:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":131513,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative plantar pressure distribution and center of pressure trajectory during barefoot walking.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8844166/v1/614197113c37285897e786ca.png"},{"id":104780779,"identity":"591c27a9-fa25-4aae-bedb-ed8d119fb97b","added_by":"auto","created_at":"2026-03-17 07:53:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1115565,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8844166/v1/f8b74be1-f6aa-4583-9050-bd79dbba67c8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Body Composition and Gait Parameters in Young Male Athletes: A Cross-Sectional Observational Study Compared with Sedentary Controls","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe use of body composition parameters to assess an individual's overall fitness, nutritional quality, and health has become widespread in recent times (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The ratio of body composition components may vary depending on an individual's age, gender, ethnicity, and physical activity level. BC (body composition) can be measured as a whole body (total values) or segmentally (by dividing into various anatomical regions such as arms, legs, and torso). Segmental BC provides detailed data on how body mass is distributed across different regions and helps identify potential asymmetries (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The assessment and regular monitoring of body composition is directly related to athletic performance. This process contributes to setting goals, determining the athlete's level of development, and creating subsequent training or work plans (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Athletes and coaches know that body composition plays a critical role in both performance and injury risk management. Specifically, while muscle mass is positively associated with strength and agility, a high fat percentage can negatively impact athletic performance (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Gait analysis is one of the fundamental methods used to examine and evaluate individuals' walking patterns. Instrumented gait analyses (e.g., Win-Track) enable detailed evaluation of walking patterns by measuring kinetic, kinematic, and spatio-temporal variables (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Monitoring the spatio-temporal parameters of walking is used both to track performance and to detect gait abnormalities, and may help predict the risk of overuse injuries in athletes (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTherefore, comparing spatio-temporal parameters and body composition is important for understanding both performance potential and injury risks. In the literature, there are very few studies comparing gait analysis and segmental body composition between young athletes at the amateur level and sedentary individuals with similar sociodemographic characteristics. This study aims to examine the differences in plantar pressure, spatio-temporal walking data, and segmental body composition between male athletes aged 16\u0026ndash;25 (playing sports that require jumping power, such as volleyball, basketball, soccer, and boxing) and a sedentary group. We hypothesized that athletes would demonstrate lower maximum plantar pressure and more efficient spatiotemporal gait patterns compared to sedentary individuals, and that sport-specific differences in gait parameters and segmental body composition would be observed among athletes engaged in jump-intensive disciplines.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eStudy Design And Ethics\u003c/p\u003e\n\u003cp\u003eThis study is a cross-sectional observational study and has been reported in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines (7). This study was conducted in accordance with the Helsinki Declaration. Local ethics committee approval was obtained from the Fırat University Faculty of Medicine Non-Interventional Clinical Research Ethics Committee (Ethics Committee Approval No: 14.05.2025-34456). Written informed consent forms were obtained from all participants.\u003c/p\u003e\n\u003cp\u003eParticipants\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The study included participants with a sociodemographically homogeneous distribution (Group 1 age: 21.47 \u0026plusmn; 2.53 years old, Group 2 age: 20.87 \u0026plusmn; 2.35 years old; Group 1 height: 179. 53 \u0026plusmn; 6.28, Group 2 height: 178.87 \u0026plusmn; 4.87; Group 1 body mass: 75.50 \u0026plusmn; 12.10 kg, Group 2 body mass: 76.73 \u0026plusmn; 10.98 kg; mean and standard deviation, both groups of participants were male). Male young athletes at the amateur level aged 16-25 (n=32) were designated as Group 1, and sedentary male participants (n=30) were designated as Group 2. The plantar pressures, spatiotemporal gait parameters, and static balance parameters of all participants included in the study were recorded using the Win-Track platform. Segmental body composition analyses were performed using the Jawon Segmental Body Composition Analysers model AVIS 333 Plus (AVIS) device. In this analysis, muscle mass and fat percentages were evaluated for the torso, arms, and legs. Along with all measurement results, participants\u0026apos; age, gender, height, weight, body mass index (BMI) (kg/m\u0026sup2;), educational status, tobacco and alcohol consumption, sports disciplines and sports ages for athlete participants were recorded in participant forms.\u003c/p\u003e\n\u003cp\u003eYoung athletes at the amateur level group (Group 1)\u003c/p\u003e\n\u003cp\u003eThis group included athletes aged 16-25 who had been competing at an young athletes at the amateur level in volleyball, soccer, basketball, or boxing for at least 3 years. These athletes also practiced sport-specific exercises at least five days a week and participated in national leagues. Exclusion criteria were defined as follows: being an young athletes at the amateur level for less than 3 years, having a neuromuscular or orthopedic disease, having a cardiopulmonary disease, having developmental hip dysplasia, scoliosis, or foot deformity, having a BMI \u0026gt;35 kg/m2, having undergone trauma or surgery within the last year, or being unwilling to participate in the study.\u003c/p\u003e\n\u003cp\u003eSedentary group (Group 2)\u003c/p\u003e\n\u003cp\u003eFor the study, male participants who had not been members of any sports club in the past 6 months, had no previous professional or amateur sports history, had a weekly level of regular physical activity of less than 1 day per week, were desk-bound workers or students, were defined as sedentary group participants. Exclusion criteria were defined as having a neuromuscular or orthopedic disease, having a cardiopulmonary disease, having developmental hip dysplasia, scoliosis, or foot deformity, having a BMI \u0026gt;35 kg/m2, having undergone trauma or surgery within the last year, or being unwilling to participate in the study.\u003c/p\u003e\n\u003cp\u003eData Collection\u003c/p\u003e\n\u003cp\u003eSpatio-Temporal Parameters:\u003c/p\u003e\n\u003cp\u003eThe Win-Track platform (MEDICAPTEURS Technology, France) is a tool used to measure plantar pressures and spatio-temporal parameters during barefoot walking (8).\u0026nbsp;The platform\u0026apos;s length, width, and height measurements are 1610 mm x 652 mm x 30 mm, respectively. The platform is 9 mm thick and consists of 12,288 resistive sensors (8). The Win-Track platform is used to measure the patient\u0026apos;s static posture and dynamic gait parameters. In this system, data is loaded onto a computer that performs automatic footprint recognition and parameter calculations. In this study, participants\u0026apos; spatio-temporal gait parameters, plantar pressure distribution, and static balance measurements were evaluated using the Win-Track pressure-sensitive gait platform (Figure 1). Since the 3-step protocol has been shown to provide better test-retest reliability compared to the 1-step protocol, the 3-step protocol was applied barefoot in this study (9).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo ensure the 3-step protocol was correctly understood and implemented, participants were briefed on the platform before all measurements and performed three trial runs with at least three steps correctly placed on the platform. Participants were asked to look ahead and walk at a comfortable pace on the platform to ensure the most accurate measurement was recorded.\u003c/p\u003e\n\u003cp\u003eThe analyzed spatiotemporal parameters are as follows: (9)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026bull; Cadence (spm-steps/minute): Cadence refers to the number of steps taken per minute and is one of the parameters that indicates walking speed.\u003c/p\u003e\n\u003cp\u003e\u0026bull; Step duration (ms): This indicates the total time from when one foot first makes contact with the ground to when the opposite foot first makes contact with the ground.\u003c/p\u003e\n\u003cp\u003e\u0026bull; Swing phase (ms): Refers to the time the foot is off the ground and in the air.\u003c/p\u003e\n\u003cp\u003e\u0026bull; Step length (mm): The distance between the moment one foot makes contact with the ground and the moment the other foot makes contact with the ground (5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026bull; Gait Cycle Length (mm): The distance between two consecutive heel contact points of the same foot.\u003c/p\u003e\n\u003cp\u003e\u0026bull; Foot angle (degrees): The angle between the direction of foot progression and the longitudinal axis of the foot during walking.\u003c/p\u003e\n\u003cp\u003eMaxımum Plantar Pressure (g/cm\u0026sup2;)\u003c/p\u003e\n\u003cp\u003eThis term refers to the maximum amount of pressure generated at a point on the plantar region during walking (9). In addition, the weight transfer (%) to the right and left feet of all participants was recorded.\u003c/p\u003e\n\u003cp\u003eStatıc Balance Measurement\u003c/p\u003e\n\u003cp\u003eA static balance measurement, namely the average oscillation speed (mm/s), was performed by having participants remain motionless for 30 seconds while looking fixedly at the opposite wall on the platform.\u003c/p\u003e\n\u003cp\u003eBody Composition Analysis\u003c/p\u003e\n\u003cp\u003eBody segmental analyses were performed according to a standard protocol using the Jawon Segmental Body Composition Analysers model AVIS 333 Plus (AVIS, Korea) device, which has segmental analysis capabilities based on the multi-electrode bioimpedance (BIA) principle. Before starting the data collection process, the measuring instruments were checked and calibrated. Participants\u0026apos; measurements were taken between 8 and 12 in the morning, without fluid or food intake, wearing similar clothing, and with jewelry and metals removed. All participants were asked to stand upright on the device\u0026apos;s platform with bare feet and grasp the hand electrodes. Segmental fat and muscle masses (trunk, leg, arm); Body mass index (BMI) (kg/m2), body fat percentage (PBF) (%), body fat weight (MBF) (kg), and total body water (TBW) (kg) were recorded using a computer connected to a bioelectrical impedance analysis device (Jawon Segmental Body Composition Analysers model AVIS 333 Plus).\u003c/p\u003e\n\u003cp\u003eStatistical Analysis\u003c/p\u003e\n\u003cp\u003eStatistical analysis of the data obtained from the study was performed using the IBM SPSS 26.0 Statistics software package. Descriptive statistics for continuous variables are presented as mean \u0026plusmn; standard deviation (Mean \u0026plusmn; SD). Before proceeding with the analyses, outliers in the dataset were checked, the normality assumption of continuous variables was evaluated with the Shapiro\u0026ndash;Wilk test and histograms, q\u0026ndash;q plots; and homogeneity of variance was evaluated with the Levene test. In cases where the assumptions of normality and homogeneity of variance were met, the independent samples t-test was used to compare two independent groups (athletes and sedentary). One-way analysis of variance (ANOVA) was applied to compare body composition, lower extremity, balance, plantar pressure, and gait parameters according to sports branches (football, basketball, volleyball, boxing, and sedentary). For variables where a significant difference was found as a result of ANOVA, post-hoc multiple comparison tests with Bonferroni correction were performed to determine between which groups the difference occurred. Categorical variables (if any) were summarized as numbers and percentages [n (%)], and the chi-square (\u0026chi;\u0026sup2;) test was used for intergroup comparisons. A significance level of p \u0026lt; 0.05 was accepted for all statistical tests.\u003c/p\u003e\n\u003cp\u003eAn a priori power analysis was conducted using G*Power software (version 3.1.9.7; Heinrich Heine University, D\u0026uuml;sseldorf, Germany). A total sample size of 63 participants (33 athletes and 30 sedentary individuals) was sufficient to achieve approximately 80% statistical power to detect a moderate effect size (Cohen\u0026rsquo;s d = 0.5) at an alpha level of 0.05 for between-group comparisons.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eIn our study, when comparing the body composition and basic anthropometric characteristics of athletes and sedentary individuals, significant differences were observed in many variables between the groups. No significant difference was found between the two groups in terms of age, height, and weight (p \u0026gt; 0.05). However, significant differences were observed in muscle and fat distribution variables. The athlete group was found to have significantly higher trunk and leg muscle mass compared to the sedentary group (p \u0026lt; 0.05). In contrast, the PBF, MBF, and trunk and leg fat masses of sedentary individuals were found to be significantly higher than in the athlete group (p \u0026lt; 0.05). In addition, the TBW amount was found to be higher in athletes (46.82\u0026plusmn;5.77, p = 0.028) (see Table 1).\u003c/p\u003e\n\u003cp\u003eWhen lower extremity pressure and balance parameters were examined, it was found that individuals with a sedentary lifestyle had significantly higher plantar pressure and postural sway velocity compared to athletes. In sedentary individuals, the maximum plantar pressure values were higher on the right (478.53\u0026plusmn;97.47, p\u0026lt;0.001) and left (431.30\u0026plusmn;52.88, p=0.002). Similarly, the mean swing velocity was also significantly higher in sedentary individuals compared to athletes (6.35\u0026plusmn;3.08, p=0.034) (see Table 2).\u003c/p\u003e\n\u003cp\u003eAccording to the results of the spatio-temporal parameters of the gait analysis, the walking performance of the athlete group was found to be significantly better compared to sedentary individuals. The athletes had shorter right and left step duration (605.78\u0026plusmn;62.86, p=0.002 and 614.41\u0026plusmn;73.86, p=0.044), while their step lengths were significantly longer (544.09\u0026plusmn;52.01, p=0.015 and 540.34\u0026plusmn;53.10, p\u0026lt;0.001). Additionally, the athletes had higher cadence values (96.30\u0026plusmn;10.49, p=0.002) and greater gait cycle length (1089.28\u0026plusmn;51.98, p=0.017). On the other hand, sedentary individuals were found to have wider right (7.12\u0026plusmn;6.21, p=0.022) and left (9.34\u0026plusmn;5.84, p=0.023) foot angles (see Table 3).\u003c/p\u003e\n\u003cp\u003eOne-way analysis of variance results showed significant differences in age and right leg fat mass among sports branches. The effect size for age was significant (p=0.040). According to multiple comparisons with Bonferroni correction, the average age of volleyball players was found to be significantly higher than that of football players. The difference between branches was also significant for right leg fat mass (p=0.019); Bonferroni results showed that volleyball players had significantly higher right leg fat mass values than football players. In all other variables (height, weight, trunk muscle mass, trunk fat mass, PBF, MBF, TBW, and BMI), the differences between the means were not statistically significant. (See Table 4)\u003c/p\u003e\n\u003cp\u003eTable 5 examines the weight transfer, swing phase, maximum plantar pressure, and mean swing velocity values of athletes according to their sports branches. As a result of the analyses, no statistically significant difference was found between branches in all variables (p\u0026gt;0.05). The weight transfer values to the right and left feet of football and basketball players were quite similar, and it was observed that participants generally distributed their body weight equally to both feet (p=0.728 for right weight transfer; p=0.738 for left weight transfer). Similarly, there was no significant difference between the groups in the swing phase values (p=0.177 for right swing phase; p=0.166 for left swing phase). When maximum plantar pressure values were examined, it was seen that the average pressure values for both the right and left feet of basketball players were relatively higher compared to other branches, but this difference was not statistically significant (right foot p=0.512; left foot p=0.707). No significant difference was observed between branches in the average swing speed variable (p=0.768).\u003c/p\u003e\n\u003cp\u003eTable 6 presents the findings regarding walking parameters of athletes according to their sport. According to the results of the one-way analysis of variance, no statistically significant difference was found between branches in the variables of right and left step duration, step length, foot angle, and gait cycle length. (p\u0026gt;0.05).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study demonstrated that regular participation in sports involving frequent dynamic and jump-related activities is associated with favorable adaptations in both body composition and gait characteristics. Compared with sedentary individuals of similar anthropometric profiles, athletes exhibited lower fat mass and body fat percentage, higher lean muscle mass, and more efficient spatiotemporal gait parameters. In contrast, no significant differences were observed among athletes from different sports branches, suggesting that participation in jump-intensive sports may induce comparable neuromuscular and biomechanical adaptations during walking. A review of the literature reveals that studies comparing gait analysis and segmental body composition using the Win-Track platform are quite limited between athletes and sedentary individuals. The findings of this study suggest that in sports branches (such as football, volleyball, basketball, and boxing) where there is intense competition, usually culminating in a vertical jump after the walking and running phases, gaining new knowledge about the correct biomechanics of movement, developing positive attitudes, contributing to sports training, optimizing training and competition activities, and reducing susceptibility to injuries can be beneficial. Looking at the literature on this subject, it shows that observing an athlete\u0026apos;s gait style, continuously training for improvement, and identifying any gait deformities will increase performance effectiveness and reduce the risk of injury (10,11). To avoid bias in the evaluation of gait, balance parameters, and weight transfer, the study was conducted in a population where the physical characteristics of athletes and sedentary individuals were similar in terms of age, height, weight, and BMI; therefore, no significant difference was found between these physical characteristics (p\u0026gt;0.05, Table 1). Regarding body composition characteristics (PBF, MBF, TBW, TrunkMBF, TrunkSLM, Rt.LegMBF and SLM, Lt.LegMBF and SLM), the results favoring the athlete group were found to support high physical activity (p\u0026lt;0.05). These results support the effect of regular exercise on reducing body fat percentage by increasing muscle hypertrophy. Similarly to our study, in 2024, Toskic et al. reported that muscle mass was significantly higher in athletes with different training profiles, while total fat mass was significantly increased in sedentary individuals (12). Furthermore, the findings of our study are consistent with studies in the literature that show significant differences in body composition parameters between active and inactive individuals (13,14). The more unfavorable segmental profile in our sedentary group is therefore not surprising and confirms once again that regular training improves not only cardiorespiratory but also morphological adaptation (15). In our study, the maximum plantar pressure (g/cm2) and mean oscillation velocity (mm/s) in both the right and left extremities, recorded using the Win Track walking platform, were higher in sedentary groups compared to athletes (p\u0026lt;0.05), indicating that regular physical activity has positive effects on balance control and postural stability (See Table 2). Similarly, Paillard (2017) showed that balance performance is better in individuals who exercise regularly (16). This situation can be explained by the fact that athletes have better developed neuromuscular adaptations. In a study comparing the gait kinetics, postural balance, and quality of life of CrossFit practitioners and those with sedentary lifestyles in 2025, the group that exercised reported better postural control and stronger gait kinetics (17). In our group of athletes, a similar mechanism may have been at play, with sufficient lean mass, particularly in the lower extremity segments, mechanically facilitating the stride cycle. Furthermore, improved balance and higher muscle mass can enhance athletic performance in athletes, while also reducing sports injuries, especially those originating in the lower extremities (18). It has been previously reported in various sports that physically active individuals outperform their sedentary peers in key spatiotemporal parameters such as stride length; a study comparing netball players with inactive youth showed that those who were physically active had increased stride length and more controlled internal hip rotation (19). This finding supports our view that individuals who train regularly exhibit better coordination, speed, and control in their walking patterns. On the other hand, the longer gait distance observed in the athlete group likely reflects the combined effect of muscle strength, range of motion, and neuromuscular coordination gained through regular exercise. The more stable walking kinetics and higher cadence values identified in individuals who engage in CrossFit and resistance-based exercises support this trend (17). The combination of a shorter stride duration and an extended stride distance indicates that energy transfer occurs more efficiently, thus making walking more mechanically economical (20,21). The wider foot angles observed in sedentary individuals may indicate a tendency to create a wider base during walking to maintain stability. This compensatory mechanism can be associated with low muscle strength and weak proprioceptive sensation.\u003c/p\u003e\n\u003cp\u003eIn this study, comparisons made according to sports branches revealed differences between branches in body composition and walking parameters. This shows that the physical performance components required by each sport (jumping, agility, endurance, strength) shape muscle and soft tissue adaptations in different ways.\u003c/p\u003e\n\u003cp\u003eSince volleyball heavily relies on jumping and landing movements, it increases lower extremity muscle hypertrophy and optimizes plantar pressure distribution. In our findings, the right lower extremity leg muscle mass in volleyball players is higher than in other branches with an average of 12.27\u0026plusmn;0.80. In addition, the right leg max. plantar pressure is lower than in other branches with a value of 381.22\u0026plusmn;45.05. The fact that our findings did not show a statistically significant difference may be due to our sample size. Indeed, proprioceptive training has been reported to make plantar pressure distribution more homogeneous and reduce loading on the heel area in volleyball players (22).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSimilarly, significant relationships have been found between foot posture and functional jumping performance in volleyball players, showing that asymmetry in foot posture can negatively affect jumping and single-leg balance performance (23).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur study showed that the football player group had the lowest levels of fat mass in both the right and left lower extremities compared to other groups. This supports our hypothesis that the anthropometric characteristics of football players have led to sport-specific adaptations. Because football players are constantly exposed to asymmetrical movements such as changing direction, accelerating, and kicking the ball, it has been reported that plantar pressure distribution, particularly the loading on the dominant foot, is greater (24).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur study found no statistically significant difference in maximum plantar pressure and spatiotemporal parameters among athletes of different sports branches. This can be explained by several factors, including the athletes\u0026apos; relatively short sporting years, their lack of professionalism to participate in advanced competitions, and the small number of participants. Our findings show that basketball players have higher maximum plantar pressures on both the right and left feet compared to other groups. The tall stature, high body weight, and the combination of jumping and turning in basketball players\u0026apos; playing style may cause the plantar pressure center to shift to the forefoot region, leading to a different loading pattern in balance control. Indeed, it has been shown that taller handball players have higher forefoot pressure and lower heel loading (25). Boxers, due to their training which typically involves rhythmic step movements and agility, are expected to have higher cadence values compared to other sports. This result can be explained by their ability to maintain stability by constantly and dynamically shifting their center of gravity.\u003c/p\u003e\n\u003cp\u003eWhen these findings are considered generally, it can be said that each sport creates specific adaptations in lower extremity muscle strength, plantar load distribution, and gait dynamics depending on its own motoric and biomechanical demands. In particular, plantar pressure is more evenly distributed in jump-based sports (volleyball, basketball), while more asymmetrical load distributions are observed in change-of-direction and contact-focused sports (football, boxing). These differences are important for understanding the effects of sport-specific training on postural control and gait biomechanics. Therefore, when segmental body composition and spatiotemporal gait parameters are examined together, sport-specific neuromuscular adaptation patterns can be distinguished.\u003c/p\u003e\n\u003cp\u003eThe scarcity of studies in this field focusing on the football, basketball, volleyball, and boxing athletes included in our research makes direct comparisons between athletes difficult. While this limits our study to some extent, it creates scientific potential and helps open new horizons for researchers.\u003c/p\u003e\n\u003cp\u003eLimitations\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. Firstly, the number of participants included in the study is relatively limited. Specifically, separating the athlete group into different branches may have reduced the sample size for each branch and limited the statistical power in inter-branch comparisons. This may have contributed to the fact that some observed differences in certain parameters did not reach statistical significance. The cross-sectional design of the study does not allow for a cause-and-effect interpretation of the findings. Therefore, the results only reflect the existing differences between athletes and sedentary individuals and do not reveal the changes caused by physical activity over time. Furthermore, the study was conducted only with young adult males. This limits the generalization of the results to female athletes or different age groups. Similarly, while the devices used during the assessments (Win-Track and segmental BIA analyzer) provide reliable and valid measurements, they do not provide more detailed biomechanical data such as three-dimensional motion analysis or electromyographic measurements. Another important limitation is that although segmental BIA is a practical and non-invasive method, it is less sensitive than dual-energy X-ray absorptiometry (DXA), particularly in athletic populations. Future studies using larger sample sizes, including different age and gender groups, preferably with longitudinal designs, and evaluating additional biomechanical and neuromuscular variables associated with performance and injury risk, will contribute to a more comprehensive interpretation of the findings from clinical and sporting perspectives.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study demonstrated significant differences in body composition and gait parameters between young men who regularly engage in sports and sedentary individuals. The athlete group exhibited a lower body fat percentage and higher muscle mass, along with more balanced plantar pressure distribution and spatiotemporal gait parameters. In contrast, higher maximum plantar pressure values ​​and increased swing velocity in sedentary individuals suggest that postural control may be more limited. These findings suggest that regular physical activity not only creates morphological changes in the musculoskeletal system but also has positive effects on gait biomechanics and load distribution. In particular, sufficient muscle mass in the lower extremity segments may contribute to a more homogeneous distribution of load during walking and a reduction in mechanical stress. From a clinical perspective, the more balanced plantar pressure distribution and gait patterns observed in the athlete group can be interpreted as a protective mechanism against overuse injuries originating from the lower extremities. Therefore, gait analysis and segmental body composition assessments should be used not only for performance monitoring but also for other purposes. It can also be said that it is valuable in terms of risk assessment and preventive approaches in healthy individuals. In conclusion, regular physical activity in young adult males is associated with a more favorable body composition and more efficient gait biomechanics. These findings reveal that maintaining a physically active lifestyle is important for both preserving functional movement quality and supporting musculoskeletal health.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAVIS: Advanced Visceral Impedance System\u003c/p\u003e\n\u003cp\u003eBIA: Bioelectrical Impedance Analysis\u003c/p\u003e\n\u003cp\u003eBMI: Body Mass Index\u003c/p\u003e\n\u003cp\u003eMBF: Mass of Body Fat\u003c/p\u003e\n\u003cp\u003ePBF: Percent Body Fat\u003c/p\u003e\n\u003cp\u003eSLM: Soft Lean Mass\u003c/p\u003e\n\u003cp\u003eTBW: Total Body Water\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate: This study was conducted local ethics committee approval was obtained from the Fırat University Faculty of Medicine Non-Interventional Clinical Research Ethics Committee (Ethics Committee Approval No: 14.05.2025-34456).\u0026nbsp;Written informed consent forms were obtained from all participants.\u003c/p\u003e\n\u003cp\u003eConsent for publication: Not applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests: The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding: No financial support was received for the conceptual framework development, design, data collection, analysis, publication decision, or preparation of the article.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions: I.A. contributed to the study conception and design, data collection, data analysis, and drafting of the manuscript. C.A. contributed to the study design and critically revised the manuscript. E.G. contributed to data collection and manuscript revision. M.G. contributed to manuscript revision and final approval of the submitted version. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements: Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eKut\u0026aacute;č P, Bunc V, Sigmund M. Whole-body dual-energy X-ray absorptiometry demonstrates better reliability than segmental body composition analysis in college-aged students. PLoS One. 2019;14(4):e0215599. doi:10.1371/journal.pone.0215599\u003c/li\u003e\n \u003cli\u003ePonce-Garc\u0026iacute;a T, Garc\u0026iacute;a-Romero J, Carrasco-Fern\u0026aacute;ndez L, Castillo-Dom\u0026iacute;nguez A, Ben\u0026iacute;tez-Porres J. 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What gait features influence the amount and intensity of physical activity in people with multiple sclerosis? Medicine (Baltimore). 2021;100(9):e24931. doi:10.1097/MD.0000000000024931\u003c/li\u003e\n \u003cli\u003eHuang CH, Aydemir B, Foucher KC. Split-belt treadmill training improves mechanical energetics and metabolic cost in women with unilateral hip osteoarthritis: a proof-of-concept study. Biomechanics. 2023;3:220\u0026ndash;230. doi:10.3390/biomechanics3020019\u003c/li\u003e\n \u003cli\u003eMarotta N, Moggio L, Calafiore D, Prestifilippo E, Span\u0026oacute; R, Tasselli A, et al. Efficacy of proprioceptive training on plantar pressure and jump performance in volleyball players: a proof-of-principle study. Sensors (Basel). 2023;23(4):1906. doi:10.3390/s23041906\u003c/li\u003e\n \u003cli\u003eKahraman Y. Is there any weakness in sports performance in volleyball athletes regarding the correlation between foot posture index and lower limb functional hopping performance? J Foot Ankle. 2023;17(1):8\u0026ndash;15. doi:10.30795/jfootankle.2023.v17.1678\u003c/li\u003e\n \u003cli\u003eHawrylak A, Brzeźna A, Chromik K. Distribution of plantar pressure in soccer players. Int J Environ Res Public Health. 2021;18(8):4173. doi:10.3390/ijerph18084173\u003c/li\u003e\n \u003cli\u003eAbdel-aziem AA, Ameer MA. The relationship between body height and plantar pressure distribution in adult handball players: a cross-sectional study. J Orthop Trauma Rehabil. 2024;31(2):160\u0026ndash;166. doi:10.1177/22104917231208214\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Comparison of Participants\u0026apos; Anthropometric Characteristics by Group.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.6531%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAthletes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Mean \u0026plusmn; SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSedentary\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Mean \u0026plusmn; SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6531%;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e21.47 \u0026plusmn; 2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5918%;\"\u003e\n \u003cp\u003e20.87 \u0026plusmn; 2.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6531%;\"\u003e\n \u003cp\u003eHeight (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e179.53 \u0026plusmn; 6.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5918%;\"\u003e\n \u003cp\u003e178.87 \u0026plusmn; 4.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.642\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6531%;\"\u003e\n \u003cp\u003eWeight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e75.50 \u0026plusmn; 12.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5918%;\"\u003e\n \u003cp\u003e76.73 \u0026plusmn; 10.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.675\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.6531%;\"\u003e\n \u003cp\u003eTrunk SLM\u0026nbsp;(kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e29.25\u0026plusmn;3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003e27.03\u0026plusmn;3.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.6531%;\"\u003e\n \u003cp\u003eRt.Leg MBF\u0026nbsp;(kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e2.48\u0026plusmn;1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003e3.18\u0026plusmn;1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.033\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.6531%;\"\u003e\n \u003cp\u003eRt.Leg SLM\u0026nbsp;(kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e11.42\u0026plusmn;1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003e10.20\u0026plusmn;1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.6531%;\"\u003e\n \u003cp\u003eLt.Leg MBF\u0026nbsp;(kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e2.39\u0026plusmn;1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003e3.47\u0026plusmn;1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.6531%;\"\u003e\n \u003cp\u003eLt.Leg SLM\u0026nbsp;(kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e11.46\u0026plusmn;1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003e10.45\u0026plusmn;1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.6531%;\"\u003e\n \u003cp\u003eTrunk MBF\u0026nbsp;(kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e6.11\u0026plusmn;2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003e10.16\u0026plusmn;3.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.6531%;\"\u003e\n \u003cp\u003ePBF (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e15.21\u0026plusmn;5.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003e19.84\u0026plusmn;5.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.6531%;\"\u003e\n \u003cp\u003eTotal MBF (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e11.94\u0026plusmn;5.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003e15.62\u0026plusmn;6.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.6531%;\"\u003e\n \u003cp\u003eTBW (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e46.82\u0026plusmn;5.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003e43.36\u0026plusmn;6.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.028\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.6531%;\"\u003e\n \u003cp\u003eBMI\u0026nbsp;(kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e23.48\u0026plusmn;4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003e24.45\u0026plusmn;3.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.340\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSD: Standard Deviation; SLM: Soft Lean Mass; MBF: Mass of Body Fat; PBF: Percent Body Fat; TBW: Total Body Water; BMI: Body Mass Index; Rt.: Right; Lt.: Left; m: meter; cm: centimeter; kg: kilogram; %: percent; p: \u003cem\u003ep-value\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData are presented as mean \u0026plusmn; standard deviation (SD). Independent samples t-test was used to compare groups. Statistical significance was set at p \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003eSignificance levels: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 (significant), \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01 (highly significant), \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 (extremely significant).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Comparison of Lower Extremity Pressure \u0026amp; Balance Parameters Between Athletes and Sedentary Groups.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.7143%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5102%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAthletes\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(Mean \u0026plusmn; SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSedentary\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(Mean \u0026plusmn; SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.7143%;\"\u003e\n \u003cp\u003eRight Max. Plantar Pressure (g/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5102%;\"\u003e\n \u003cp\u003e392.56\u0026plusmn;35.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e478.53\u0026plusmn;97.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.7143%;\"\u003e\n \u003cp\u003eLeft Max. Plantar Pressure (g/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5102%;\"\u003e\n \u003cp\u003e393.22\u0026plusmn;39.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e431.30\u0026plusmn;52.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.7143%;\"\u003e\n \u003cp\u003eAverage Swing Velocity (mm/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5102%;\"\u003e\n \u003cp\u003e4.96\u0026plusmn;1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e6.35\u0026plusmn;3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.034\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.7143%;\"\u003e\n \u003cp\u003eRight Swing Phase (ms)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5102%;\"\u003e\n \u003cp\u003e1461.81\u0026plusmn;150.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e1496.76\u0026plusmn;176.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.7143%;\"\u003e\n \u003cp\u003eLeft Swing Phase (ms)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5102%;\"\u003e\n \u003cp\u003e1453.53\u0026plusmn;159.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e1494.83\u0026plusmn;173.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.7143%;\"\u003e\n \u003cp\u003eRight Weight Transfer (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5102%;\"\u003e\n \u003cp\u003e50.66\u0026plusmn;4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e50.20\u0026plusmn;4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.656\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.7143%;\"\u003e\n \u003cp\u003eLeft Weight Transfer (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5102%;\"\u003e\n \u003cp\u003e49.34\u0026plusmn;4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e49.80\u0026plusmn;4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.632\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSD: Standard Deviation; %: percent; mm/s: millimeter per second; g/cm\u003csup\u003e2\u003c/sup\u003e: gram per square centimeter; ms: millisecond; p: \u003cem\u003ep-value\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData are presented as mean \u0026plusmn; standard deviation (SD). Independent samples t-test was used to compare groups. Statistical significance was set at p \u0026lt; 0.05. Significance levels: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 (significant), \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01 (highly significant), \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 (extremely significant).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Comparison of Spatiotemporal Parameters of Walking Between Athletes and Sedentary Individuals.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAthletes\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(Mean \u0026plusmn; SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSedentary\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(Mean \u0026plusmn; SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6122%;\"\u003e\n \u003cp\u003eRight Step Duration (ms)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e605.78\u0026plusmn;62.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e692.47\u0026plusmn;131.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6122%;\"\u003e\n \u003cp\u003eLeft Step Duration (ms)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e614.41\u0026plusmn;73.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e689.60\u0026plusmn;184.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.044\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6122%;\"\u003e\n \u003cp\u003eRight Step Length (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e544.09\u0026plusmn;52.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e498.47\u0026plusmn;84.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6122%;\"\u003e\n \u003cp\u003eLeft Step Length (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e540.34\u0026plusmn;53.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e479.13\u0026plusmn;74.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6122%;\"\u003e\n \u003cp\u003eRight Foot Angle (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e4.04\u0026plusmn;3.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e7.12\u0026plusmn;6.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6122%;\"\u003e\n \u003cp\u003eLeft Foot Angle (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e6.36\u0026plusmn;3.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e9.34\u0026plusmn;5.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6122%;\"\u003e\n \u003cp\u003eCadence (spm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e96.30\u0026plusmn;10.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e88.29\u0026plusmn;9.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6122%;\"\u003e\n \u003cp\u003eGait Cycle Length (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e1089.28\u0026plusmn;51.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e1043.07\u0026plusmn;89.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSD: Standard Deviation; ms: millisecond; mm: millimeter; \u0026deg;: degree; spm: steps per minute; p: \u003cem\u003ep-value\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNormality of the data was assessed using the Shapiro\u0026ndash;Wilk test. Variables with normal distribution were analyzed using independent samples t-tests, while non-normally distributed variables were compared using Mann\u0026ndash;Whitney U tests. Statistical significance was set at p \u0026lt; 0.05. Significance levels: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 (significant), \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01 (highly significant), \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 (extremely significant).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Comparison of Athletes Body Composition Characteristics by Branch.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.625%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFootball\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBasketball\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVolleyball\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBoxing\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.375%;\"\u003e\n \u003cp\u003eAge(years)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e20.27\u0026plusmn;1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e22.40\u0026plusmn;3.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e23.11\u0026plusmn;1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e20.57\u0026plusmn;3.10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.040\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.375%;\"\u003e\n \u003cp\u003eHeight (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e178.45\u0026plusmn;5.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e179.60\u0026plusmn;6.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e182.89\u0026plusmn;7.64\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e176.86\u0026plusmn;4.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.375%;\"\u003e\n \u003cp\u003eWeight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e69.82\u0026plusmn;4.60\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e80.80\u0026plusmn;16.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e80.67\u0026plusmn;16.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e74.00\u0026plusmn;6.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.375%;\"\u003e\n \u003cp\u003eTrunk SLM (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.625%;\"\u003e\n \u003cp\u003e30.61\u0026plusmn;2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e30.10\u0026plusmn;5.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e31.57\u0026plusmn;2.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e30.21\u0026plusmn;4.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.375%;\"\u003e\n \u003cp\u003eRt. Leg MBF (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.625%;\"\u003e\n \u003cp\u003e1.78\u0026plusmn;0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e2.49\u0026plusmn;0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e3.55\u0026plusmn;1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e2.22\u0026plusmn;0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.375%;\"\u003e\n \u003cp\u003eRt. Leg SLM (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.625%;\"\u003e\n \u003cp\u003e11.02\u0026plusmn;1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e10.65\u0026plusmn;1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e12.27\u0026plusmn;0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e11.48\u0026plusmn;2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.375%;\"\u003e\n \u003cp\u003eLt. Leg MBF (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.625%;\"\u003e\n \u003cp\u003e1.78\u0026plusmn;0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e2.89\u0026plusmn;1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e3.00\u0026plusmn;1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e2.24\u0026plusmn;0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.375%;\"\u003e\n \u003cp\u003eLt. Leg SLM (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.625%;\"\u003e\n \u003cp\u003e10.86\u0026plusmn;1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e11.83\u0026plusmn;2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e11.60\u0026plusmn;1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e11.98\u0026plusmn;1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.436\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.375%;\"\u003e\n \u003cp\u003eTrunk MBF (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.625%;\"\u003e\n \u003cp\u003e5.88\u0026plusmn;1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e6.81\u0026plusmn;1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e5.81\u0026plusmn;2.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e6.38\u0026plusmn;2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.375%;\"\u003e\n \u003cp\u003ePBF (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.625%;\"\u003e\n \u003cp\u003e13.97\u0026plusmn;5.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e18.64\u0026plusmn;6.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e14.79\u0026plusmn;6.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e15.24\u0026plusmn;4.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.480\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.375%;\"\u003e\n \u003cp\u003eTotal MBF (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.625%;\"\u003e\n \u003cp\u003e9.83\u0026plusmn;4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e15.84\u0026plusmn;7.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e13.18\u0026plusmn;7.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e10.89\u0026plusmn;3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.375%;\"\u003e\n \u003cp\u003eTBW (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.625%;\"\u003e\n \u003cp\u003e44.55\u0026plusmn;3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e47.30\u0026plusmn;8.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e50.68\u0026plusmn;3.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e45.09\u0026plusmn;6.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.375%;\"\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.625%;\"\u003e\n \u003cp\u003e21.53\u0026plusmn;1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e25.28\u0026plusmn;4.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e25.10\u0026plusmn;4.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e23.16\u0026plusmn;4.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSLM: Soft Lean Mass; MBF: Mass of Body Fat; PBF: Percent Body Fat; TBW: Total Body Water; BMI: Body Mass Index; Rt.: Right; Lt.: Left; m: meter; cm: centimeter; kg: kilogram; %: percent; p: \u003cem\u003ep-value\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData are presented as mean \u0026plusmn; SD. Differences among the four sports branches (football, basketball, volleyball, and boxing) were analyzed using one-way ANOVA. When significant differences were found, Bonferroni post-hoc tests were applied. Statistical significance was set at p \u0026lt; 0.05. Significance levels: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 (significant), \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01 (highly significant), \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 (extremely significant).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Comparison of Pressure \u0026amp; Balance Parameters in Athletes by Sport Branch.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.9278%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFootball (n=11)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBasketball (n=5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVolleyball (n=9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBoxing (n=7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.15464%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9278%;\"\u003e\n \u003cp\u003eRight Weight Transfer (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e51.00\u0026plusmn;3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e51.60\u0026plusmn;3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e50.89\u0026plusmn;5.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e49.14\u0026plusmn;2.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.15464%;\"\u003e\n \u003cp\u003e0.728\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9278%;\"\u003e\n \u003cp\u003eLeft Weight Transfer (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e49.00\u0026plusmn;3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e48.40\u0026plusmn;3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e49.11\u0026plusmn;5.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e50.86\u0026plusmn;2.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.15464%;\"\u003e\n \u003cp\u003e0.738\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9278%;\"\u003e\n \u003cp\u003eRight Swing Phase (ms)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e1409.09\u0026plusmn;116.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e1555.00\u0026plusmn;120.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e1511.33\u0026plusmn;184.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e1414.43\u0026plusmn;144.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.15464%;\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9278%;\"\u003e\n \u003cp\u003eLeft Swing Phase (ms)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e1430.73\u0026plusmn;113.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e1577.60\u0026plusmn;134.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e1473.56\u0026plusmn;201.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e1375.00\u0026plusmn;151.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.15464%;\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9278%;\"\u003e\n \u003cp\u003eRight Max. Plantar Pressure (g/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e394.09\u0026plusmn;24.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e411.80\u0026plusmn;7.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e381.22\u0026plusmn;45.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e391.00\u0026plusmn;48.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.15464%;\"\u003e\n \u003cp\u003e0.512\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9278%;\"\u003e\n \u003cp\u003eLeft Max. Plantar Pressure (g/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e388.73\u0026plusmn;47.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e405.60\u0026plusmn;23.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e400.56\u0026plusmn;39.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e382.00\u0026plusmn;40.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.15464%;\"\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9278%;\"\u003e\n \u003cp\u003eAverage Swing Velocity (mm/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e4.75\u0026plusmn;1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e4.44\u0026plusmn;1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e5.19\u0026plusmn;2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e5.36\u0026plusmn;1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.15464%;\"\u003e\n \u003cp\u003e0.768\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e%: percent; mm/s: millimeter per second; g/cm\u003csup\u003e2\u003c/sup\u003e: gram per square centimeter; ms: millisecond; p: \u003cem\u003ep-value\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eData are shown as mean \u0026plusmn; SD. One-way ANOVA was used to compare the four sports branches (football, basketball, volleyball, and boxing). Bonferroni post-hoc tests were conducted when significant differences were observed. Statistical significance was accepted at p \u0026lt; 0.05. \u0026nbsp; Significance levels: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 (significant), \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01 (highly significant), \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 (extremely significant).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6. Comparison of Spatiotemporal Parameters of Walking in Athletes According to Sport Branch.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFootball (n=11)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBasketball (n=5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVolleyball (n=9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBoxing (n=7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.12245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.5714%;\"\u003e\n \u003cp\u003eRight step duration (ms)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e591.82\u0026plusmn;47.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e625.00\u0026plusmn;80.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e595.78\u0026plusmn;64.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e626.86\u0026plusmn;73.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.12245%;\"\u003e\n \u003cp\u003e0.558\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.5714%;\"\u003e\n \u003cp\u003eLeft step duration (ms)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e646.64\u0026plusmn;70.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e632.80\u0026plusmn;79.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e583.11\u0026plusmn;77.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e590.86\u0026plusmn;57.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.12245%;\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.5714%;\"\u003e\n \u003cp\u003eRight step length (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e550.27\u0026plusmn;52.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e543.80\u0026plusmn;28.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e558.00\u0026plusmn;64.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e516.71\u0026plusmn;45.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.12245%;\"\u003e\n \u003cp\u003e0.451\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.5714%;\"\u003e\n \u003cp\u003eLeft step length (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e521.00\u0026plusmn;64.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e560.80\u0026plusmn;37.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e551.89\u0026plusmn;59.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e541.29\u0026plusmn;27.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.12245%;\"\u003e\n \u003cp\u003e0.471\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.5714%;\"\u003e\n \u003cp\u003eRight foot angle (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e4.90\u0026plusmn;3.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e3.04\u0026plusmn;1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e2.93\u0026plusmn;2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e4.80\u0026plusmn;3.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.12245%;\"\u003e\n \u003cp\u003e0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.5714%;\"\u003e\n \u003cp\u003eLeft foot angle (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e6.41\u0026plusmn;3.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e4.09\u0026plusmn;1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e6.72\u0026plusmn;3.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e8.04\u0026plusmn;4.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.12245%;\"\u003e\n \u003cp\u003e0.350\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.5714%;\"\u003e\n \u003cp\u003eCadence (spm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e98.04\u0026plusmn;7.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e93.20\u0026plusmn;6.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e91.74\u0026plusmn;12.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e101.69\u0026plusmn;12.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.12245%;\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.5714%;\"\u003e\n \u003cp\u003eGait Cycle Length (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1077.91\u0026plusmn;61.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e1062.20\u0026plusmn;20.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e1124.67\u0026plusmn;54.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e1081.00\u0026plusmn;23.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.12245%;\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ems: millisecond; mm: millimeter; \u0026deg;: degree; spm: steps per minute; p: \u003cem\u003ep-value\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eData are presented as mean \u0026plusmn; SD. One-way ANOVA was used to compare spatio-temporal gait parameters among the four sports branches (football, basketball, volleyball, and boxing). Bonferroni post-hoc tests were used for multiple comparisons. Statistical significance was set at p \u0026lt; 0.05. \u0026nbsp;Significance levels: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 (significant), \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01 (highly significant), \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 (extremely significant).\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-sports-science-medicine-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssmr","sideBox":"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ssmr/default.aspx","title":"BMC Sports Science, Medicine and Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"gait analysis, spatiotemporal parameters, plantar pressure, segmental body composition, athletes, sedentary individuals","lastPublishedDoi":"10.21203/rs.3.rs-8844166/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8844166/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study aimed to investigate and compare differences in segmental body composition and gait characteristics (including plantar pressure distribution and spatio-temporal parameters) between athletes engaged in sports with a predominance of vertical jump (football, basketball, volleyball, and boxing) and sedentary young men using the gait analysis platform and body composition analyzer.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cross-sectional observational study included 63 healthy young men aged 16\u0026ndash;25 years (33 athletes and 30 individuals with sedentary lifestyles). Segmental body composition was assessed using a Tanita Jawon AVIS 333 Plus analyzer, and gait parameters, including spatiotemporal and plantar pressure data, were recorded using the Win-Track platform. T-tests and ANOVA were used to determine intergroup differences, with a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAthletes exhibited significantly lower body fat percentage (PBF) and higher skeletal muscle mass (SLM) in both lower extremities and trunk compared to sedentary individuals (PBF: 15.21\u0026thinsp;\u0026plusmn;\u0026thinsp;5.47, p\u0026thinsp;=\u0026thinsp;0.001; Trunk SLM: 29.25\u0026thinsp;\u0026plusmn;\u0026thinsp;3.72, p\u0026thinsp;=\u0026thinsp;0.014; Rt.Leg SLM: 11.42\u0026thinsp;\u0026plusmn;\u0026thinsp;1.57, p\u0026thinsp;=\u0026thinsp;0.002; Lt.Leg SLM: 11.46\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54, p\u0026thinsp;=\u0026thinsp;0.007, respectively). Sedentary participants showed higher mean swing velocity and maximum plantar pressure (Right and Left Max. Plantar Pressure: 478.53\u0026thinsp;\u0026plusmn;\u0026thinsp;97.47, p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and 431.30\u0026thinsp;\u0026plusmn;\u0026thinsp;52.88, p\u0026thinsp;=\u0026thinsp;0.002, respectively). However, no statistically significant difference was found between sports in spatio-temporal parameters (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eRegular participation in sports involving frequent dynamic movements can have positive effects on body composition and gait biomechanics. Compared to sedentary individuals, athletes exhibit better muscle distribution, postural control, and gait efficiency; this underscores the importance of staying physically active to support healthy muscle function and movement control.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e \u003cp\u003enot applicable.\u003c/p\u003e","manuscriptTitle":"Body Composition and Gait Parameters in Young Male Athletes: A Cross-Sectional Observational Study Compared with Sedentary Controls","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-12 07:22:21","doi":"10.21203/rs.3.rs-8844166/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-27T06:25:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-26T22:34:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-25T07:26:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-20T10:48:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-17T11:00:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109925359738748530426326030072125909360","date":"2026-03-11T06:46:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"120278061262930445999281676700494531311","date":"2026-03-09T19:54:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"279732910356555761922579261256879279537","date":"2026-03-07T14:25:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"204935315904086635840733281199921909197","date":"2026-03-06T05:17:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"106046584290670302936904735093483286680","date":"2026-03-05T21:25:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"229568515724136182192621942312752471217","date":"2026-03-05T16:49:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-05T16:34:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-25T16:19:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-16T13:14:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-14T08:23:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Sports Science, Medicine and Rehabilitation","date":"2026-02-14T08:18:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-sports-science-medicine-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssmr","sideBox":"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ssmr/default.aspx","title":"BMC Sports Science, Medicine and Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b52b5aba-9be6-4535-a9b5-ee2e8cd591ee","owner":[],"postedDate":"March 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-17T11:24:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-12 07:22:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8844166","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8844166","identity":"rs-8844166","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}