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#author("2024-11-12T22:03:18+09:00","","") f backs documented the greatest relative distance (73.2 ± 8.0 m·min-1; p = 0.005), with the back three reported to cover the greatest distances walking (1,169 ± 178 m; p = 0.001) and at high-speed distances (353 ± 175 m; p = 0.002). The back three also recorded the greatest mean MV (7.2 ± 0.4 m·s-1; p = 0.000). This is the first study in the Northern hemisphere to report the game movement demands of interprovincial women's RU and the information derived from this study may provide practitioners with normative data to assist coaches with preparation. Han, J, Liu, M, Shi, J, and Li, Y. Construction of a machine learning model to estimate physiological variables of speed skating athletes under hypoxic training conditions. J Strength Cond Res XX(X) 000-000, 2021-Monitoring changes in athletes' physiological variables is essential to create a safe and effective hypoxic training plan for speed skating athletes. This research aims to develop a machine learning estimation model to estimate physiological variables of athletes under hypoxic training conditions based on their physiological measurements collected at sea level. The research team recruited 64 professional speed skating athletes to participate in a 10-week training program, including 3 weeks of sea-level training, followed by 4 weeks of hypoxic training and then a 3-week sea-level recovery period. We measured several physiological variables that could reflect the athletes' oxygen transport capacity in the first 7 weeks, including red blood cell (RBC) count and hemoglobin (Hb) concentration. The physia week and then modeled as a mathematical model to estimate measurements' changes using the maximum likelihood method. The mathematical model was then used to construct a machine learning model. Furthermore, the original data (measured once per week) were used to construct a polynomial model using curve fitting. We calculated and compared the mean absolute error between estimated values of the 2 models and measured values. Our results show that the machine learning model estimated RBC count and Hb concentration accurately. The errors of the estimated values were within 5% of the measured values. Compared with the curve fitting polynomial model, the accuracy of the machine learning model in estimating hypoxic training's physiological variables is higher. This study successfully constructed a machine learning model that used physiological variables measured at the sea level to estimate the physiological variables during hypoxic training. Andersen, V, Paulsen, G, Stien, N, Baarholm, M, Seynnes, O, and Saeterbakken, AH. Resistance training with different velocity loss thresholds induce similar changes in strengh and hypertrophy. J Strength Cond Res XX(X) 000-000, 2021-The aim of this study was to compare the effects of 2 velocity-based resistance training programs when performing resistance training with matched training volume. Ten resistance-trained adults volunteered (age, 23 ± 4.3 years; body mass, 68 ± 8.9 kg; and height, 171 ± 8 cm) with a mean resistance training experience of 4.5 years. A within person, between leg design was used. For each subject, the legs were randomly assigned to either low velocity loss (LVL) threshold at 15% or high velocity loss (HVL) threshold at 30% velocity loss. Leg press and leg extension were trained unilaterally twice per week over a period of 9 weeks. Before and after the intervention, both legs were tested in 1 repetition maximum (RM) (kg), maximal voluntary contraction (MVC) (N), rate of force developvastus lateralis and rectus femoris, pennation angle (°) of the vastus lateralis, and the fascicle length (mm) of the vastus lateralis were measured using ultrasound imaging. The data were analyzed using mixed-design analysis of variance. No differences between the legs in any of the variables were found; however, both low and HVL were effective for increasing 1 RM (ES = 1.25-1.82), MVC (effect size [ES] = 0.42-0.64), power output (ES = 0.31-0.86), and muscle thickness (ES = 0.24-0.51). In conclusion, performing velocity-based resistance training with low and HVL with equal training volume resulted in similar effects in maximal and explosive strength in addition to muscular adaptations. Nakata, H, Nakanishi, Y, Otsuki, S, Mizuno, M, Connor, J, and Doma, K. Six weeks of hip joint training using a novel multihip joint board improves sprint performance in competitive collegiate male sprinters. J Strength Cond Res XX(X) 000-000, 2021-In a previous study, we identified the possibility that hip joint training using a multihip joint board (MHJB) may increase the cross-sectional area (CSA) of the psoas major (PM) muscle and improve sprint performance. However, the preliminary study reported descriptive findings because of a limited sample size. Therefore, we aimed to investigate and statistically infer the effects of the MHJB training protocol with a larger sample of male collegiate sprinters. The sprinters were randomly assigned to either the MHJB group (n = 7) or the control group (n = 7). The MHJB protocol consisted of 7 separate exercises targeting the development of the hip musculature, all using the MHJB device. The MHJB group undertook the MHJB protocol 3 times per week for 6 weeks, after tive male sprinters. Wikander, L, Kirshbaum, MN, Waheed, N, and Gahreman, DE. Urinary incontinence in competitive women weightlifters. J Strength Cond Res XX(X) 000-000, 2021-Urinary incontinence has the potential to diminish athletic performance and discourage women from participating in sport and exercise. This study determined the prevalence and possible risk factors for urinary incontinence in competitive women weightlifters. This research was a cross-sectional, survey-based study completed by 191 competitive women weightlifters. https://www.selleckchem.com/products/stf-31.html The frequency and severity of urinary incontinence was determined using the Incontinence Severity Index. Urinary incontinence was defined as an Incontinence Severity Index score >0. The survey questions focused on risk factors, the context and triggers for urinary incontinence, and self-care strategies. Approximately, 31.9% of subjects experienced urinary incontinence within 3 months of completing the survey. Incontinence Severity Index scores were significantly correlated with parity (r = 0.283, p = 0.
