ECSS Paris 2023: OP-AP01
INTRODUCTION: Sprint running requires rapid leg swings. The angular acceleration of the leg is determined by the torque exerted by the hip muscles and the moment of inertia (MoI) about the hip joint. From the perspective of torque exertion, many studies have examined the characteristics of muscle volume in sprinters [1, 2]. Meanwhile, greater muscle volume entails an increase in MoI. At the segment level, male sprint runners have larger muscle mass than untrained controls without a proportional increase in MoI [3], implying a specialized muscle mass distribution. However, less information is available regarding the muscle MoI and its effect on the relationship between muscle volume and sprint running performance. Thus, we examined the association between the muscle volume relative to MoI of 31 lower limb muscles and sprint running performance, including kinematic variables. METHODS: The axial MR images of the whole limb were acquired in the prone position in 18 male sprint runners. The volume of 31 lower limb muscles was determined from the MR images with semi-automatic segmentation using a deep learning method. The MoI of individual muscles about the hip joint was calculated from muscle volume and its radius of gyration in the sagittal plane [4]. Participants performed a 100 m maximal sprint run. The running velocity and swing velocity of the leg (line connecting the greater trochanter and the lateral malleolus) were determined from recorded video (240 fps) at the 50–60 m interval. Pearson’s correlation analysis was used to examine the relationships between measured variables. RESULTS: The running velocity was correlated with the maximum velocity of forward leg swing (r = 0.584, p = 0.011). This swing velocity was positively correlated with hip flexor muscle volume (r = 0.482, p = 0.045), suggesting its contribution to forward leg swing. However, the maximum velocity of forward leg swing did not correlate with the MoI of the whole leg, thigh, or shank muscles. When the hip flexor muscle volume was normalized to the MoI of the triceps surae or the medial gastrocnemius, its relation to the maximum velocity of forward leg swing became stronger (r = 0.536, p = 0.024 and r = 0.548, p = 0.020, respectively) than in the case of muscle volume alone. CONCLUSION: The present results suggest that larger hip flexor volume relative to MoI of the distal muscles, such as the triceps surae, enables rapid forward leg swing, thereby achieving a high running velocity. Training programs designed to attenuate hypertrophy of the triceps surae, while strengthening the hip flexors, may be advantageous for enhancing sprint running performance. REFERENCES: 1) Handsfield et al. Scand J Med Sci Sports (2017) 2) Miller et al. Med Sci Sports Exerc (2021) 3) Sado et al. Med Sci Sports Exerc (2023) 4) Takahashi et al. J Biomech (2024)
Read CV Norifumi FujiiECSS Paris 2023: OP-AP01
INTRODUCTION: In Europe, the majority of sport participation occurs in informal, self-organised contexts (Bergsgard, 2025), often lacking professional guidance. However, little is known about the quality of this training or the competence of individuals to train themselves in a goal-oriented and sustainable manner. The aim of this study was therefore to develop and validate an instrument for assessing perceived training competence in strength and endurance. METHODS: Based on conceptual models of the training process (Impellizzeri et al., 2019) and expert surveys, an item pool was generated, which was tested on an initial subsample of young adults (n = 692; M_age = 18.39 ± 2.9) and optimized based on item analyses and exploratory factor analysis. The resulting factor structure was subsequently tested in a second subsample (n = 347; M_age = 18.28 ± 2.6) using confirmatory factor analysis. Measurement invariance was tested across sex, sport activity and sports club membership. Criterion validity was examined using the physical self-concept (endurance, strength) as external criteria, controlling for physical activity (Hair et al., 2019). RESULTS: The exploratory factor analysis revealed a simple structure with the factors training planning, implementation and control for endurance and strength respectively. In the subsequent confirmatory test, this model was confirmed with a good fit (CFI = .912, TLI = .908, RMSEA = .054, SRMR = .051) and adequate factor and indicator reliability (composite reliability ω ≥ 0.91, squared multiple correlations ≥ .43). Measurement invariance across sex, sport activity and sports club membership was supported at the configural, metric, and scalar level (ΔCFI ≤ .01; ΔRMSEA ≤ .015). In addition, significant correlations were found between the scales and the corresponding part of the physical self-concept. CONCLUSION: The newly developed scales can be used both for individual reflection and identification of development potential and in fitness and health contexts to determine the support needs of trainees. In addition, they serve to evaluate interventions for teaching training competence. The scales thus contribute to the optimisation of individual training processes and to the quality assurance of training-related educational programmes. References Bergsgard, N. A. (2025). Factors that determine the level of participation in sport and exercise—An analyze of public policy for sport and exercise in European countries. Frontiers in Sports and Active Living, 7, 1633869. https://doi.org/10.3389/fspor.2025.1633869 Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (Eighth edition). Cengage. Impellizzeri, F. M., Marcora, S. M., & Coutts, A. J. (2019). Internal and External Training Load: 15 Years On. International Journal of Sports Physiology and Performance, 14(2), 270–273. https://doi.org/10.1123/ijspp.2018-0935
Read CV Johannes große SiemerECSS Paris 2023: OP-AP01
INTRODUCTION: Body composition (BC) assessment is critical in sports science and health promotion for monitoring nutritional status and adapting diet or training [1;2;3]. Gold standards like dual-energy X-ray absorptiometry (DXA), air displacement plethysmography (ADP), and bioimpedance analysis (BIA) are often limited by costs and device variability. Recently, AI-driven body scans offer accessible alternatives, with 2D mobile scans being more practical than 3D ones [4;5;6]. While DXA is the benchmark for tissue differentiation, ADP serves as a suitable reference for validating volumetric algorithms. Accordingly, this study aims to investigate accuracy and scientific validity, as well as precision (intra-operator/device), and reproducibility (inter-operator/device) of a mobile AI-2D scan for measuring multi-compartment BC data in comparison with ADP and BIA, as no prior research exists on this topic. METHODS: In this cross-sectional comparative study, 100 adults (age 18-70 y.o., BMI 18.5-29.9 kg/m²) were recruited. Assessment included height, weight, Body Volume (BV) via ADP (BODPOD), Total Body Water (TBW) via BIA (Tanita BC-418), and AI-2D scan (SizeYou®) on two devices (iPad, iPhone) by two operators (three consecutive measurements each). The app uses AI pose detection to extract body contours from two photos (posterior, lateral). Statistical analyses evaluated: accuracy using Bland-Altman and Pearson’s r; precision via ICC, SEM, CV, and MDC; reproducibility with paired t-tests, Cohen’s d, Pearson’s r, and MAPE. RESULTS: Preliminary analyses included 43 participants (age 26.2±7.0 y.o; 42% males, BMI 21.0±2.24 kg/m²; 58% females, BMI 18.0±2.62 kg/m²). Accuracy analyses showed BV was slightly underestimated (Bias≈ -1.1 L), while TBW was marginally overestimated (Bias=0.03–0.45 L). BV had wider Limits of Agreement(-18.3–16.1 L) than TBW (-3.4–3.9 L), but both showed strong correlations with gold standards (r≥0.78). Precision was nearly perfect for BV (ICC=1, SEM=0, CV≤0.1%, MDC=0) and TBW (ICC≥0.999, SEM≤0.26, CV≤0.6%, MDC≤0.72 L). Despite significant t-test differences between devices-operators (p<0.001, d>1), high correlations (r≥0.999), low mean differences (0.1L for BV; -0.4L for TBW), and MAPE (0,22% for BV; ≤0.4% for BW) support clinical interchangeability. CONCLUSION: SizeYou® provides validity and precision for evaluating BV and TBW. The app performs superiorly in BV as it relies on direct geometric reconstruction. While TBW correlates strongly with BIA (r≥0.97), both methods rely on indirect predictive modelling. Thus, discrepancies may result from BIA variability rather than app error. Repeatability enables device and operator interchangeability, ideal for longitudinal monitoring in sports and health promotion settings. In conclusion, this AI-driven 2D technology seems to offer a cost-effective alternative to BC assessment methods. References: 1. Castro et al.,2020 2. Dehghani et al.,2025 3. Duren et al.,2008 4. Ferreira et al.,2025 5. Yamamoto et al.,2025 6. Graybeal et al.,2022
Read CV Francesca Strassoldo di VillanovaECSS Paris 2023: OP-AP01