Effects of loading positions on the activation of trunk and hip muscles during flywheel and dumbbell single-leg Romanian deadlift exercises.

Objective: The study compared the activities of the surface electromyography (sEMG) of trunk and hip muscles during single-leg Romanian deadlift (SLRDL) exercises using a flywheel and dumbbell with different loading positions (ipsilateral and contralateral). Method: Twelve active male subjects with at least 2 years of strength training experience (age: 26.7 ± 3.3 years; weight: 73.9 ± 6.2 kg) participated in this study. sEMG in the percentage of maximum voluntary isometric contraction of four SLRDL exercises (ipsilateral and contralateral loading position for dumbbell and flywheel) in a randomized order for superior gluteus maximus (SGM), inferior gluteus maximus (IGM), gluteus medius (GM), biceps femoris (BF), erector spinae (ES), external oblique (EO), and adductor longus (AL) were measured. One-way repeated measure ANOVA with Bonferroni adjustment (statistical significance at 0.05) and the non-clinical magnitude-based decision with a standardized difference were performed for statistical analysis. Results: The overall results demonstrated a very high level of SGM (105.4%-168.6%) and BF (69.6%-122.4%) muscle activities. A significant moderate increase of sEMG signals in GM, IGM, and ES (dominant side) and a large increase in SGM activity during concentric action when the loading position of flywheel SLRDL was changed from ipsilateral to the contralateral side. No significant difference was observed between flywheel and dumbbell SLRDL exercises. Conclusion: Strength coaches may adopt dumbbell or flywheel SLRDL exercises using the contralateral loading position to simultaneously strengthen the hip extensors and trunk stabilizers effectively.

A meta-analysis of the criterion-related validity of Session-RPE scales in adolescent athletes.

BACKGROUND: The objective of this study was to establish the criterion-related validity of the session-rating of perceived exertion (s-RPE) method in adolescent athletes. METHODS: According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines, a meta-analysis (PROSPERO ID: CRD42022373126) was performed using Stata 15.1 software. Eight databases using the following terms: ('s-RPE' OR 'Rating Perceived Exertion session' OR 'RPE session' OR 'RPE' OR 'Rate of Perceived Exertion' OR 'Rated of Perceived Exertion') AND ('Adolescen*' OR 'Youth*' OR 'Teen*') AND ('validity' OR 'correlation' OR 'concurrent validity') were searched up to 2022. Articles meeting the inclusion criteria were screened and adopted the "Methodological Index for Non-Randomized Studies (MINORS)" to evaluate the risk of bias. RESULTS: An initial 1798 studies using the s-RPE method were identified and finally, a total of 16 studies were included for further analysis. The relationship between assessment instruments CR-10 or CR-100 modified methods of s-RPE and the heart rate measures of these selected studies were calculated using correlation coefficient (r values) and Fisher's z-score. A strong to very strong correlation between s-RPE and HR was observed (overall: r = 0.74; CR-10: r = 0.69; CR-100: r = 0.80). CR-100 scale (Fisher's z = 1.09) was shown to have a higher criterion validity than that of the CR-10 scale (Fisher's z = 0.85). CONCLUSION: Preliminary findings showed that s-RPE using either CR-10 or CR-100 scales can be used "stand-alone" for monitoring internal training load for children and adolescent athletes. Future studies should focus on whether CR-100 could better perform than CR-10 for junior and children athletes in different age groups and sports as well as the causes leading to potential scoring biases.

Post-activation performance enhancement of flywheel training on lower limb explosive power performance.

The study aimed to investigate the post-activation performance enhancement (PAPE) of flywheel training (FT) on lower limb explosive power performance. Using a randomized crossover design, 20 trained men (age = 21.5 ± 1.4 years; training experience 5.5 ± 1.2 years) completed seven main conditions after three familiarization sessions. The first three conditions tested the PAPE of the FT on the counter movement jump (CMJ) under three different inertial loads (0.041 kg·m2 as L; 0.057 kg·m2 as ML; and 0.122 kg·m2 as P), whereas the following four conditions tested the PAPE of FT on the 30 m sprint, which consisted of three inertial loads (L, ML, and P) and a control condition. Participants were required to perform the CMJ or 30 m sprint at baseline (Tb) and immediately (T0), 4 min (T4), 8 min (T8), 12 min (T12), and 16 min (T16) after exercise, respectively. The results of the CMJ conditions showed that PAPE peaked at T4 (p < 0.01) and almost subsided at T12 (p > 0.05) in ML and P conditions. Meanwhile, PAPE appeared earlier in the P condition, and the effect was more significant (P:ES = 1.09; ML:ES = 0.79). 30 m sprint results showed significant improvement only in the ML condition. The PAPE peaked at T4 (p < 0.05, ES = -0.47) and almost subsided at T8 (p > 0.05). It was mainly due to the significant enhancement of the 10-30 m segmental timing performance at T4 (p < 0.05, ES = -0.49). This study indicates that the size of the inertial load could influence the magnitude of the PAPE produced by the explosive force of the lower limb. The PAPE of the vertical explosive force increased with increasing inertial load, but the PAPE of the horizontal explosive force did not appear at the maximum inertial load. The most effective elicitation of the PAPE was at 4-8 min after the FT.

Using Machine Learning Algorithms to Pool Data from Meta-Analysis for the Prediction of Countermovement Jump Improvement.

To solve the research-practice gap and take one step forward toward using big data with real-world evidence, the present study aims to adopt a novel method using machine learning to pool findings from meta-analyses and predict the change of countermovement jump. The data were collected through a total of 124 individual studies included in 16 recent meta-analyses. The performance of four selected machine learning algorithms including support vector machine, random forest (RF) ensemble, light gradient boosted machine, and the neural network using multi-layer perceptron was compared. The RF yielded the highest accuracy (mean absolute error: 0.071 cm; R2: 0.985). Based on the feature importance calculated by the RF regressor, the baseline CMJ ("Pre-CMJ") was the most impactful predictor, followed by age ("Age"), the total number of training sessions received ("Total number of training_session"), controlled or non-controlled conditions ("Control (no training)"), whether the training program included squat, lunge, deadlift, or hip thrust exercises ("Squat_Lunge_Deadlift_Hipthrust_True", "Squat_Lunge_Deadlift_Hipthrust_False"), or "Plyometric (mixed fast/slow SSC)", and whether the athlete was from an Asian pacific region including Australia ("Race_Asian or Australian"). By using multiple simulated virtual cases, the successful predictions of the CMJ improvement are shown, whereas the perceived benefits and limitations of using machine learning in a meta-analysis are discussed.

Effects of flywheel resistance training on the running economy of young male well-trained distance runners.

The study aimed to investigate the effect of flywheel accentuated eccentric loading (AEL) training on the running economy (RE) of young male well-trained distance runners. Twenty-two runners participated and were randomly assigned to the flywheel (FG, n = 12) and the control group (CG, n = 10). Traditional endurance training was performed in both groups three times a week for 6-week, while traditional resistance and flywheel AEL training was added to the CG and FG respectively. Subjects performed the incremental exercise test, squat jump, and countermovement jump (CMJ) before and after training. The results showed that 1) the RE at 65% of peak oxygen consumption (VO2peak), 75% VO2peak, and 85% VO2peak improved significantly after 6 weeks of training (p < 0.01, Effect size (ES) = 0.76; p < 0.01, ES = 1.04; p < 0.01, ES = 1.85) in FG, and the RE of 85% VO2peak in FG was significantly lower than CG (p < 0.05, ES = 0.30); 2) in post-training, both squat jump (p < 0.01, ES = 0.73) and CMJ (p < 0.01, ES = 1.15) performance, eccentric utilization ratio (p < 0.04, ES = 0.44), the rate of force development (RFD) of squat jump (p < 0.05, ES = 0.46), and CMJRFD (p < 0.01, ES = 0.66) were significantly improved in FG. And there are no significant differents in CG group because it was maintain training for our participants. Our findings showed that 1) flywheel AEL training improves the muscles' explosive strength and other neuromuscular functions, and improves the athlete's running economy under 65%, 75%, and 85% VO2peak, which potentially increases endurance performance. 2) Flywheel AEL training can improve the height, RFD, and the eccentric utilization ratio of squat jump and CMJ, and other lower limb elastic potential energy indicators of the young male, well-trained distance runners.

Predicting student satisfaction of emergency remote learning in higher education during COVID-19 using machine learning techniques.

Despite the wide adoption of emergency remote learning (ERL) in higher education during the COVID-19 pandemic, there is insufficient understanding of influencing factors predicting student satisfaction for this novel learning environment in crisis. The present study investigated important predictors in determining the satisfaction of undergraduate students (N = 425) from multiple departments in using ERL at a self-funded university in Hong Kong while Moodle and Microsoft Team are the key learning tools. By comparing the predictive accuracy between multiple regression and machine learning models before and after the use of random forest recursive feature elimination, all multiple regression, and machine learning models showed improved accuracy while the most accurate model was the elastic net regression with 65.2% explained variance. The results show only neutral (4.11 on a 7-point Likert scale) regarding the overall satisfaction score on ERL. Even majority of students are competent in technology and have no obvious issue in accessing learning devices or Wi-Fi, face-to-face learning is more preferable compared to ERL and this is found to be the most important predictor. Besides, the level of efforts made by instructors, the agreement on the appropriateness of the adjusted assessment methods, and the perception of online learning being well delivered are shown to be highly important in determining the satisfaction scores. The results suggest that the need of reviewing the quality and quantity of modified assessment accommodated for ERL and structured class delivery with the suitable amount of interactive learning according to the learning culture and program nature.

Intraday reliability, sensitivity, and minimum detectable change of national physical fitness measurement for preschool children in China.

China General Administration of Sport has published and adopted the National Physical Fitness Measurement (NPFM-preschool children version) since 2000. However, studies on intraday reliability, sensitivity, and minimum detectable change (MDC) are lacking. This study aimed to investigate and compare the reliability, sensitivity, and MDC values of NPFM in preschool children between the ages of 3.5 to 6 years. Six items of NPFM including 10-m shuttle run, standing long jump, balance beam walking, sit-and-reach, tennis throwing, and double-leg timed hop, were tested for 209 Chinese kindergarten children in Beijing in the morning. Intraday relative reliability was tested using intraclass correlation coefficient (ICC3,1) with a 95% confidence interval while absolute reliability was expressed in standard error of measurement (SEM) and percentage of coefficient of variation (CV%). Test sensitivity was assessed by comparing the smallest worthwhile change (SWC) with SEM, while MDC values with 95% confidence interval (MDC95) were established. Measurements in most groups, except 10-m shuttle run test (ICC3,1: 0.56 to 0.74 [moderate]) in the 3.5 to 5.5-year-old groups, balance beam test in 4- and 5-year-old (ICC3,1: 0.33 to 0.35 [poor]) and 5.5-year-old (ICC3,1 = 0.68 [moderate]) groups, and double-leg timed hop test (ICC3,1 = 0.67 [moderate]) in the 4.5-year-old group, demonstrated good to excellent relative reliability (ICC3,1: 0.77 to 0.97). The balance beam walking test showed poor absolute reliability in all the groups (SEM%: 11.76 to 22.28 and CV%: 15.40 to 24.78). Both standing long jump and sit-and-reach tests demonstrated good sensitivity (SWC > SEM) in all subjects group, boys, and girls. Pairwise comparison revealed systematic bias with significantly better performance in the second trial (p<0.01) of all the tests with moderate to large effect size.

Effects of knee flexion angles in supine bridge exercise on trunk and pelvic muscle activity.

This study investigated the activity of surface electromyography (sEMG) on trunk and pelvic muscles during supine bridge exercise (SBE) with different knee flexion angles. Twenty-five physically active males participated in this study. Subjects received maximum voluntary isometric contraction (MVIC) tests followed by four SBEs with different knee flexion angles (40°, 60°, 90° and 120°) in random. sEMG activities of rectus abdominis (RA), erector spinae (ER), gluteus medius (GMed), superior gluteus maximus (SGMax), inferior gluteus maximus (IGMax), biceps femoris (BF) long head, and the ratio of SGMax/BF and IGMax/BF on the dominant side were measured. Non-clinical magnitude-based inference was performed to compare the effect. The results indicated a substantial change of muscle activity, especially between SBE with 40° and 120° knee flexion. With respect to ER and BF, moderate effect (-0.70 ± 0.17) and extremely large effect (-4.78 ± 0.51) were recorded, whereas very large effect for SGMax/BF (2.68 ± 0.23) and IGMax/BF (2.95 ± 0.26) was observed, respectively. Both ER and BF worked better with smaller knee flexion angles (40° > 60° > 90° > 120°), while SGMax and IGMax were more favourable to SBE with large knee flexion angles (90° = 120° > 60° > 40°).