@article{AraziAsadiKhalkhalietal.2020, author = {Arazi, Hamid and Asadi, Abbas and Khalkhali, Farhood and Boullosa, Daniel and Hackney, Anthony C. and Granacher, Urs and Zouhal, Hassane}, title = {Association Between the Acute to Chronic Workload Ratio and Injury Occurrence in Young Male Team Soccer Players}, series = {Frontiers in Physiology}, volume = {11}, journal = {Frontiers in Physiology}, publisher = {Frontiers Research Foundation}, address = {Lausanne}, issn = {1664-042X}, doi = {10.3389/fphys.2020.00995}, pages = {7}, year = {2020}, abstract = {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.}, language = {en} } @article{ThielePrieskeLesinskietal.2020, author = {Thiele, Dirk and Prieske, Olaf and Lesinski, Melanie and Granacher, Urs}, title = {Effects of Equal Volume Heavy-Resistance Strength Training Versus Strength Endurance Training on Physical Fitness and Sport-Specific Performance in Young Elite Female Rowers}, series = {Frontiers in Physiology}, volume = {11}, journal = {Frontiers in Physiology}, publisher = {Frontiers Research Foundation}, address = {Lausanne}, issn = {1664-042X}, doi = {10.3389/fphys.2020.00888}, pages = {12}, year = {2020}, abstract = {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.}, language = {en} } @article{ZhouFischerTuncaetal.2020, author = {Zhou, Lin and Fischer, Eric and Tunca, Can and Brahms, Clemens Markus and Ersoy, Cem and Granacher, Urs and Arnrich, Bert}, title = {How We Found Our IMU}, series = {Sensors}, volume = {20}, journal = {Sensors}, number = {15}, publisher = {MDPI}, address = {Basel}, issn = {1424-8220}, doi = {10.3390/s20154090}, pages = {29}, year = {2020}, abstract = {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.}, language = {en} } @article{RaveGranacherBoullosaetal.2020, author = {Rav{\´e}, Guillaume and Granacher, Urs and Boullosa, Daniel and Hackney, Anthony C. and Zouhal, Hassane}, title = {How to Use Global Positioning Systems (GPS) Data to Monitor Training Load in the "Real World" of Elite Soccer}, series = {Frontiers in Physiology}, volume = {11}, journal = {Frontiers in Physiology}, publisher = {Frontiers Research Foundation}, address = {Lausanne}, issn = {1664-042X}, doi = {10.3389/fphys.2020.00944}, pages = {11}, year = {2020}, language = {en} } @article{LesinskiSchmelcherHerzetal.2020, author = {Lesinski, Melanie and Schmelcher, Alina and Herz, Michael and Puta, Christian and Gabriel, Holger and Arampatzis, Adamantios and Laube, Gunnar and B{\"u}sch, Dirk and Granacher, Urs}, title = {Maturation-, age-, and sex-specific anthropometric and physical fitness percentiles of German elite young athletes}, series = {Plos One}, volume = {15}, journal = {Plos One}, number = {8}, publisher = {Plos One}, address = {San Francisco, California}, issn = {1932-6203}, doi = {10.1371/journal.pone.0237423}, pages = {19}, year = {2020}, abstract = {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.}, language = {en} } @article{SaidiBenAbderrahmanBoullosaetal.2020, author = {Saidi, Karim and Ben Abderrahman, Abderraouf and Boullosa, Daniel and Dupont, Gr{\´e}gory and Hackney, Anthony C. and Bideau, Benoit and Pavillon, Thomas and Granacher, Urs and Zouhal, Hassane}, title = {The Interplay Between Plasma Hormonal Concentrations, Physical Fitness, Workload and Mood State Changes to Periods of Congested Match Play in Professional Soccer Players}, series = {Frontiers in Physiology}, volume = {11}, journal = {Frontiers in Physiology}, publisher = {Frontiers Research Foundation}, address = {Lausanne}, issn = {1664-042X}, doi = {10.3389/fphys.2020.00835}, pages = {14}, year = {2020}, abstract = {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.}, language = {en} } @article{AraziAsadiKhalkhalietal.2020, author = {Arazi, Hamid and Asadi, Abbas and Khalkhali, Farhood and Boullosa, Daniel and Hackney, Anthony C. and Granacher, Urs and Zouhal, Hassane}, title = {Association Between the Acute to Chronic Workload Ratio and Injury Occurrence in Young Male Team Soccer Players}, volume = {11}, publisher = {Frontiers Research Foundation}, address = {Lausanne}, issn = {1664-042X}, doi = {10.3389/fphys.2020.00608}, pages = {7}, year = {2020}, abstract = {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 (r²) 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.}, language = {en} }