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This exploratory study aimed to monitor long-term seasonal developments in measures of anthropometry, body composition, and physical fitness in young judo athletes, and to compute associations between these measures and sporting success. Forty-four young judoka (20 females, 24 males) volunteered to participate. Tests for the assessment of anthropometry (e.g., body height/mass), body-composition (e.g., lean body mass), muscle strength (isometric handgrip strength), vertical jumping (e.g., countermovement-jump (CMJ) height), and dynamic balance (Y-balance test) were conducted at the beginning and end of a 10-month training season. Additionally, sporting success at the end of the season was recorded for each athlete. Analyses revealed significant time x sex interaction effects for lean-body-mass, isometric handgrip strength, and CMJ height (0.7 <= d <= 1.6). Post-hoc analyses showed larger gains for all measures in young males (1.9 <= d <= 6.0) compared with females (d = 2.4) across the season. Additionally, significant increases in body height and mass as well as Y-balance test scores were found from pre-to-post-test (1.2 <= d <= 4.3), irrespective of sex. Further, non-significant small-to-moderate-sized correlations were identified between changes in anthropometry/body composition/physical fitness and sporting success (p > 0.05; -0.34 <= rho <= 0.32). Regression analysis confirmed that no model significantly predicted sporting success. Ten months of judo training and/or growth/maturation contributed to significant changes in anthropometry, body composition, and physical fitness, particularly in young male judo athletes.
The load-depended loss of vertical barbell velocity at the end of the acceleration phase limits the maximum weight that can be lifted. Thus, the purpose of this study was to analyze how increased barbell loads affect the vertical barbell velocity in the sub-phases of the acceleration phase during the snatch. It was hypothesized that the load-dependent velocity loss at the end of the acceleration phase is primarily associated with a velocity loss during the 1st pull. For this purpose, 14 male elite weightlifters lifted seven load-stages from 70-100% of their personal best in the snatch. The load-velocity relationship was calculated using linear regression analysis to determine the velocity loss at 1st pull, transition, and 2nd pull. A group mean data contrast analysis revealed the highest load-dependent velocity loss for the 1st pull (t = 1.85, p = 0.044, g = 0.49 [-0.05, 1.04]) which confirmed our study hypothesis. In contrast to the group mean data, the individual athlete showed a unique response to increased loads during the acceleration sub-phases of the snatch. With the proposed method, individualized training recommendations on exercise selection and loading schemes can be derived to specifically improve the sub-phases of the snatch acceleration phase. Furthermore, the results highlight the importance of single-subject assessment when working with elite athletes in Olympic weightlifting.
This study sought to analyze the relationship between in-season training workload with changes in aerobic power (VO2max), maximum and resting heart rate (HRmax and HRrest), linear sprint medium (LSM), and short test (LSS), in soccer players younger than 16 years (under-16 soccer players). We additionally aimed to explain changes in fitness levels during the in-season through regression models, considering accumulated load, baseline levels, and peak height velocity (PHV) as predictors. Twenty-three male sub-elite soccer players aged 15.5 ± 0.2 years (PHV: 13.6 ± 0.4 years; body height: 172.7 ± 4.2 cm; body mass: 61.3 ± 5.6 kg; body fat: 13.7% ± 3.9%; VO2max: 48.4 ± 2.6 mL⋅kg–1⋅min–1), were tested three times across the season (i.e., early-season (EaS), mid-season (MiS), and end-season (EnS) for VO2max, HRmax, LSM, and LSS. Aerobic and speed variables gradually improved over the season and had a strong association with PHV. Moreover, the HRmax demonstrated improvements from EaS to EnS; however, this was more evident in the intermediate period (from EaS to MiS) and had a strong association with VO2max. Regression analysis showed significant predictions for VO2max [F(2, 20) = 8.18, p ≤ 0.001] with an R2 of 0.45. In conclusion, the meaningful variation of youth players’ fitness levels can be observed across the season, and such changes can be partially explained by the load imposed.
Performance- and healthrelated benefits of yoThere is ample evidence that youth resistance training (RT) is safe, joyful, and effective for different markers of performance (e.g., muscle strength, power, linear sprint speed) and health (e.g., injury prevention). Accordingly, the first aim of this narrative review is to present and discuss the relevance of muscle strength for youth physical development. The second purpose is to report evidence on the effectiveness of RT on muscular fitness (muscle strength, power, muscle endurance), on movement skill performance and injury prevention in youth. There is evidence that RT is effective in enhancing measures of muscle fitness in children and adolescents, irrespective of sex. Additionally, numerous studies indicate that RT has positive effects on fundamental movement skills (e.g., jumping, running, throwing) in youth regardless of age, maturity, training status, and sex. Further, irrespective of age, sex, and training status, regular exposure to RT (e.g., plyometric training) decreases the risk of sustaining injuries in youth. This implies that RT should be a meaningful element of youths’ exercise programming. This has been acknowledged by global (e.g., World Health Organization) and national (e.g., National Strength and Conditioning Association) health- and performance-related organizations which is why they recommended to perform RT as an integral part of weekly exercise programs to promote muscular strength, fundamental movement skills, and to resist injuries in youth.uth resistance training
Objective: We investigated the effects of combined balance and strength training on measures of balance and muscle strength in older women with a history of falls.
Methods: Twenty-seven older women aged 70.4 ± 4.1 years (age range: 65 to 75 years) were randomly allocated to either an intervention (IG, n = 12) or an active control (CG, n = 15) group. The IG completed 8 weeks combined balance and strength training program with three sessions per week including visual biofeedback using force plates. The CG received physical therapy and gait training at a rehabilitation center. Training volumes were similar between the groups. Pre and post training, tests were applied for the assessment of muscle strength (weight-bearing squat [WBS] by measuring the percentage of body mass borne by each leg at different knee flexions [0°, 30°, 60°, and 90°], sit-to-stand test [STS]), and balance. Balance tests used the modified clinical test of sensory interaction (mCTSIB) with eyes closed (EC) and opened (EO), on stable (firm) and unstable (foam) surfaces as well as spatial parameters of gait such as step width and length (cm) and walking speed (cm/s).
Results: Significant group × time interactions were found for different degrees of knee flexion during WBS (0.0001 < p < 0.013, 0.441 < d < 0.762). Post hoc tests revealed significant pre-to-post improvements for both legs and for all degrees of flexion (0.0001 < p < 0.002, 0.697 < d < 1.875) for IG compared to CG. Significant group × time interactions were found for firm EO, foam EO, firm EC, and foam EC (0.006 < p < 0.029; 0.302 < d < 0.518). Post hoc tests showed significant pre-to-post improvements for both legs and for all degrees of oscillations (0.0001 < p < 0.004, 0.753 < d < 2.097) for IG compared to CG. This study indicates that combined balance and strength training improved percentage distribution of body weight between legs at different conditions of knee flexion (0°, 30°, 60°, and 90°) and also decreased the sway oscillation on a firm surface with eyes closed, and on foam surface (with eyes opened or closed) in the IG.
Conclusion: The higher positive effects of training seen in standing balance tests, compared with dynamic tests, suggests that balance training exercises including lateral, forward, and backward exercises improved static balance to a greater extent in older women.
Sprint and jump performances in highly trained young soccer players of different chronological age
(2020)
Objective
The aim of this study was to examine the effects of two different sprint-training regimes on sprint and jump performances according to age in elite young male soccer players over the course of one soccer season.
Methods
Players were randomly assigned to two training groups. Group 1 performed systematic change-of-direction sprints (CODST, U19 [n = 9], U17 [n = 9], U15 [n = 10]) while group 2 conducted systematic linear sprints (LST, U19 [n = 9], U17 [n = 9], U15 [n = 9]). Training volumes were similar between groups (40 sprints per week x 30 weeks = 1200 sprints per season). Pre and post training, all players performed tests for the assessment of linear and slalom sprint speed (5-m and 10-m), countermovement jump, and maximal aerobic speed performance.
Results
For all physical fitness measures, the baseline-adjusted means data (ANCOVA) across the age groups showed no significant differences between LST and CODST at post (0.061 < p < 0.995; 0.0017 < d < 1.01). The analyses of baseline-adjusted means for all physical fitness measures for U15, U17, and U19 (LST vs. CODST) revealed no significant differences between LST and CODST for U15 (0.213 < p < 0.917; 0.001 < d < 0.087), U17 (0.132 < p < 0.976; 0.001 < d < 0.310), and U19 (0.300 < p < 0.999; 0.001 < d < 0.049) at post.
Conclusions
The results from this study showed that both, LST and CODST induced significant changes in the sprint, lower limbs power, and aerobic performances in young elite soccer players. Since no significant differences were observed between LST and CODST, the observed changes are most likely due to training and/or maturation. Therefore, more research is needed to elucidate whether CODST, LST or a combination of both is beneficial for youth soccer athletes’ performance development.
Gait analysis is an important tool for the early detection of neurological diseases and for the assessment of risk of falling in elderly people. The availability of low-cost camera hardware on the market today and recent advances in Machine Learning enable a wide range of clinical and health-related applications, such as patient monitoring or exercise recognition at home. In this study, we evaluated the motion tracking performance of the latest generation of the Microsoft Kinect camera, Azure Kinect, compared to its predecessor Kinect v2 in terms of treadmill walking using a gold standard Vicon multi-camera motion capturing system and the 39 marker Plug-in Gait model. Five young and healthy subjects walked on a treadmill at three different velocities while data were recorded simultaneously with all three camera systems. An easy-to-administer camera calibration method developed here was used to spatially align the 3D skeleton data from both Kinect cameras and the Vicon system. With this calibration, the spatial agreement of joint positions between the two Kinect cameras and the reference system was evaluated. In addition, we compared the accuracy of certain spatio-temporal gait parameters, i.e., step length, step time, step width, and stride time calculated from the Kinect data, with the gold standard system. Our results showed that the improved hardware and the motion tracking algorithm of the Azure Kinect camera led to a significantly higher accuracy of the spatial gait parameters than the predecessor Kinect v2, while no significant differences were found between the temporal parameters. Furthermore, we explain in detail how this experimental setup could be used to continuously monitor the progress during gait rehabilitation in older people.
This study aimed to investigate the relationship between the acute to chronic workload ratio (ACWR), based upon participant session rating of perceived exertion (sRPE), using two models [(1) rolling averages (ACWRRA); and (2) exponentially weighted moving averages (ACWREWMA)] and the injury rate in young male team soccer players aged 17.1 ± 0.7 years during a competitive mesocycle. Twenty-two players were enrolled in this study and performed four training sessions per week with 2 days of recovery and 1 match day per week. During each training session and each weekly match, training time and sRPE were recorded. In addition, training impulse (TRIMP), monotony, and strain were subsequently calculated. The rate of injury was recorded for each soccer player over a period of 4 weeks (i.e., 28 days) using a daily questionnaire. The results showed that over the course of the study, the number of non-contact injuries was significantly higher than that for contact injuries (2.5 vs. 0.5, p = 0.01). There were also significant positive correlations between sRPE and training time (r = 0.411, p = 0.039), ACWRRA (r = 0.47, p = 0.049), and ACWREWMA (r = 0.51, p = 0.038). In addition, small-to-medium correlations were detected between ACWR and non-contact injury occurrence (ACWRRA, r = 0.31, p = 0.05; ACWREWMA, r = 0.53, p = 0.03). Explained variance (r 2) for non-contact injury was significantly greater using the ACWREWMA model (ranging between 21 and 52%) compared with ACWRRA (ranging between 17 and 39%). In conclusion, the results of this study showed that the ACWREWMA model is more sensitive than ACWRRA to identify non-contact injury occurrence in male team soccer players during a short period in the competitive season.
Strength training is an important means for performance development in young rowers. The purpose of this study was to examine the effects of a 9-week equal volume heavy-resistance strength training (HRST) versus strength endurance training (SET) in addition to regular rowing training on primary (e.g., maximal strength/power) and secondary outcomes (e.g., balance) in young rowers. Twenty-six female elite adolescent rowers were assigned to an HRST (n = 12; age: 13.2 ± 0.5 yrs; maturity-offset: +2.0 ± 0.5 yrs) or a SET group (n = 14; age: 13.1 ± 0.5 yrs; maturity-offset: +2.1 ± 0.5 yrs). HRST and SET comprised lower- (i.e., leg press/knee flexion/extension), upper-limbs (i.e., bench press/pull; lat-pull down), and complex exercises (i.e., rowing ergometer). HRST performed four sets with 12 repetitions per set at an intensity of 75–95% of the one-repetition maximum (1-RM). SET conducted four sets with 30 repetitions per set at 50–60% of the 1-RM. Training volume was matched for overall repetitions × intensity × training per week. Pre-post training, tests were performed for the assessment of primary [i.e., maximal strength (e.g., bench pull/knee flexion/extension 1-RM/isometric handgrip test), muscle power (e.g., medicine-ball push test, triple hop, drop jump, and countermovement jump), anaerobic endurance (400-m run), sport-specific performance (700-m rowing ergometer trial)] and secondary outcomes [dynamic balance (Y-balance test), change-of-direction (CoD) speed (multistage shuttle-run test)]. Adherence rate was >87% and one athlete of each group dropped out. Overall, 24 athletes completed the study and no test or training-related injuries occurred. Significant group × time interactions were observed for maximal strength, muscle power, anaerobic endurance, CoD speed, and sport-specific performance (p ≤ 0.05; 0.45 ≤ d ≤ 1.11). Post hoc analyses indicated larger gains in maximal strength and muscle power following HRST (p ≤ 0.05; 1.81 ≤ d ≤ 3.58) compared with SET (p ≤ 0.05; 1.04 ≤ d ≤ 2.30). Furthermore, SET (p ≤ 0.01; d = 2.08) resulted in larger gains in sport-specific performance compared with HRST (p < 0.05; d = 1.3). Only HRST produced significant pre-post improvements for anaerobic endurance and CoD speed (p ≤ 0.05; 1.84 ≤ d ≤ 4.76). In conclusion, HRST in addition to regular rowing training was more effective than SET to improve selected measures of physical fitness (i.e., maximal strength, muscle power, anaerobic endurance, and CoD speed) and SET was more effective than HRST to enhance sport-specific performance gains in female elite young rowers.
How We Found Our IMU
(2020)
Inertial measurement units (IMUs) are commonly used for localization or movement tracking in pervasive healthcare-related studies, and gait analysis is one of the most often studied topics using IMUs. The increasing variety of commercially available IMU devices offers convenience by combining the sensor modalities and simplifies the data collection procedures. However, selecting the most suitable IMU device for a certain use case is increasingly challenging. In this study, guidelines for IMU selection are proposed. In particular, seven IMUs were compared in terms of their specifications, data collection procedures, and raw data quality. Data collected from the IMUs were then analyzed by a gait analysis algorithm. The difference in accuracy of the calculated gait parameters between the IMUs could be used to retrace the issues in raw data, such as acceleration range or sensor calibration. Based on our algorithm, we were able to identify the best-suited IMUs for our needs. This study provides an overview of how to select the IMUs based on the area of study with concrete examples, and gives insights into the features of seven commercial IMUs using real data.
The aim of this study was to establish maturation-, age-, and sex-specific anthropometric and physical fitness percentile reference values of young elite athletes from various sports. Anthropometric (i.e., standing and sitting body height, body mass, body mass index) and physical fitness (i.e., countermovement jump, drop jump, change-of-direction speed [i.e., T-test], trunk muscle endurance [i.e., ventral Bourban test], dynamic lower limbs balance [i.e., Y-balance test], hand grip strength) of 703 male and female elite young athletes aged 8–18 years were collected to aggregate reference values according to maturation, age, and sex. Findings indicate that body height and mass were significantly higher (p<0.001; 0.95≤d≤1.74) in more compared to less mature young athletes as well as with increasing chronological age (p<0.05; 0.66≤d≤3.13). Furthermore, male young athletes were significantly taller and heavier compared to their female counterparts (p<0.001; 0.34≤d≤0.50). In terms of physical fitness, post-pubertal athletes showed better countermovement jump, drop jump, change-of-direction, and handgrip strength performances (p<0.001; 1.57≤d≤8.72) compared to pubertal athletes. Further, countermovement jump, drop jump, change-of-direction, and handgrip strength performances increased with increasing chronological age (p<0.05; 0.29≤d≤4.13). In addition, male athletes outperformed their female counterpart in the countermovement jump, drop jump, change-of-direction, and handgrip strength (p<0.05; 0.17≤d≤0.76). Significant age by sex interactions indicate that sex-specific differences were even more pronounced with increasing age. Conclusively, body height, body mass, and physical fitness increased with increasing maturational status and chronological age. Sex-specific differences appear to be larger as youth grow older. Practitioners can use the percentile values as approximate benchmarks for talent identification and development.
Background: The regular assessment of hormonal and mood state parameters in professional soccer are proposed as good indicators during periods of intense training and/or competition to avoid overtraining.
Objective: The aim of this study was to analyze hormonal, psychological, workload and physical fitness parameters in elite soccer players in relation to changes in training and match exposure during a congested period of match play.
Methods: Sixteen elite soccer players from a team playing in the first Tunisian soccer league were evaluated three times (T1, T2, and T3) over 12 weeks. The non-congested period of match play was from T1 to T2, when the players played 6 games over 6 weeks. The congested period was from T2 to T3, when the players played 10 games over 6 weeks. From T1 to T3, players performed the Yo-Yo intermittent recovery test level 1 (YYIR1), the repeated shuttle sprint ability test (RSSA), the countermovement jump test (CMJ), and the squat jump test (SJ). Plasma Cortisol (C), Testosterone (T), and the T/C ratio were analyzed at T1, T2, and T3. Players had their mood dimensions (tension, depression, anger, vigor, fatigue, confusion, and a Total Mood Disturbance) assessed through the Profile of Mood State questionnaire (POMS). Training session rating of perceived exertion (sRPE) was also recorded on a daily basis in order to quantify internal training load and elements of monotony and strain.
Results: Significant performance declines (T1 < T2 < T3) were found for SJ performance (p = 0.04, effect size [ES] ES₁₋₂ = 0.15−0.06, ES₂₋₃ = 0.24) from T1 to T3. YYIR1 performance improved significantly from T1 to T2 and declined significantly from T2 to T3 (p = 0.001, ES₁₋₂ = 0.24, ES₂₋₃ = −2.54). Mean RSSA performance was significantly higher (p = 0.019, ES₁₋₂ = −0.47, ES₂₋₃ = 1.15) in T3 compared with T2 and T1. Best RSSA performance was significantly higher in T3 when compared with T2 and T1 (p = 0.006, ES₂₋₃ = 0.47, ES₁₋₂ = −0.56), but significantly lower in T2 when compared with to T1. T and T/C were significantly lower in T3 when compared with T2 and T1 (T: p = 0.03, ES₃₋₂ = −0.51, ES₃₋₁ = −0.51, T/C: p = 0.017, ES₃₋₂ = −1.1, ES₃₋₁ = −1.07). Significant decreases were found for the vigor scores in T3 when compared to T2 and T1 (p = 0.002, ES₁₋₂ = 0.31, ES₃₋₂ = −1.25). A significant increase was found in fatigue scores in T3 as compared to T1 and T2 (p = 0.002, ES₁₋₂ = 0.43, ES₂₋₃ = 0.81). A significant increase was found from T1 < T2 < T3 intension score (p = 0.002, ES₁₋₂ = 1.1, ES₂₋₃ = 0.2) and anger score (p = 0.03, ES₁₋₂ = 0.47, ES₂₋₃ = 0.33) over the study period. Total mood disturbance increased significantly (p = 0.02, ES₁₋₂ = 0.91, ES₂₋₃ = 1.1) from T1 to T3. Between T1-T2, significant relationships were observed between workload and changes in T (r = 0.66, p = 0.003), and T/C ratio (r = 0.62, p = 0.01). There were significant relationships between performance in RSSAbest and training load parameters (workload: r = 0.52, p = 0.03; monotony: r = 0.62, p = 0.01; strain: r = 0.62, p = 0.009). Between T2-T3, there was a significant relationship between Δ% of total mood disturbance and Δ% of YYIR1 (r = −0.54; p = 0.04), RSSAbest (r = 0.58, p = 0.01), SJ (r = −0,55, p = 0.01), T (r = 0.53; p = 0.03), and T/C (r = 0.5; p = 0.04).
Conclusion: An intensive period of congested match play significantly compromised elite soccer players’ physical and mental fitness. These changes were related to psychological but not hormonal parameters; even though significant alterations were detected for selected measures. Mood monitoring could be a simple and useful tool to determine the degree of preparedness for match play during a congested period in professional soccer.