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.

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 (ICC_3,1) 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 (ICC_3,1: 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 (ICC_3,1: 0.33 to 0.35 [poor]) and 5.5-year-old (ICC_3,1= 0.68 [moderate]) groups, and double-leg timed hop test (ICC_3,1= 0.67 [moderate]) in the 4.5-year-old group, demonstrated good to excellent relative reliability (ICC_3,1: 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.

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°).

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.

This study aimed to compare the neuromuscular activation of selected core musculature in supine and prone bridge exercises under stable versus suspended conditions. Forty-three healthy male participants were recruited to measure the electromyographic activities of the rectus abdominis (RA), lumbar multifidus (LM), thoracic erector spinae (TES), rectus femoris (RF), gluteus maximus (GM), and biceps femoris (BF) during supine and prone bridge exercises under six conditions: control, both arms and feet on the floor (Prone_con and Supine_con); arms on the floor and feet on the suspension system (Prone-Feet_suspension and Supine-Feet_suspension); and arms on the suspension system and feet on the floor (Prone-Arm_suspension and Supine-Arm_suspension). Prone-Arm_suspension yielded significantly higher activities in the RA, RF, TES, and LM than Prone-Feet_suspension (p < 0.01) and Prone_con (p < 0.001). Moreover, Supine-Feet_suspension elicited significantly higher activities in the RA, RF, TES, LM, and BF than Supine-Arm_suspension (p < 0.01) and Supine_con (p < 0.001). Furthermore, Supine-Feet_suspension elicited significantly higher activities in the RF, TES, and BF than Supine_con (p < 0.01). Therefore, if the RA and/or RF were the target training muscles, then Prone-Arm_suspension was recommended. However, if the TES, LM, and/or BF were the target training muscles, then Supine-Feet_suspension was recommended.

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.