This study investigated the test-retest reliability and discriminant validity of the Kickboxing Anaerobic Speed Test (KAST) and established a comparison with the maximal cycling sprint test (MCST). Forty-two male kickboxing athletes (18 elite and 24 sub-elite) participated in this study. Kickboxers performed the tests in both single and multiple forms. The KAST was composed of kicks and punches, performed as quickly as possible until 5 reps of a 4 techniques' combination (i.e., straight left punch, right-roundhouse kick, straight right punch, and left-roundhouse kick) were completed. The MCST was composed by a single 6-s maximal effort. The KAST was performed using 5 sequential sets of the single form following a 10s rest interval between sets. The MCST was performed using 5 × 6 s repeated cycling efforts with 10s rest intervals. The tests and retests were carried out on separate occasions. To establish the test's discriminatory capability, elite and sub-elite athletes were compared. All tests' performance scores showed excellent relative and absolute reliability (ICC > 0.900, SEM ≤ 0.98 s for KASTs and ≤ 0.74 W•kg for MCST tests). Significant correlations between the identical versions were "large" (r > 0.70). Receiving operating characteristic analyses indicated that the KASTs and cycling tests were able to effectively discriminate between elite and sub-elite kickboxers. Additionally, the findings showed that KAST, MCST, KAST, and MCST correctly classified the groups by 78.6%, 73.6%, 88.1%, and 78.6%, respectively. This study supported the test-retest reliability and the discriminant validity of the KAST and KAST to evaluate kickboxing athletes.
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http://dx.doi.org/10.1080/02701367.2022.2048783 | DOI Listing |
Brain Behav
January 2025
School of Physical Education, Shanghai University of Sport, Shanghai, China.
Objective: Whether athletes possess superior executive functions still needs further examination. Therefore, the aim of this study is to explore the executive function advantages of athletes and the differences in these advantages between open- and closed-skill sports through systematic review and meta-analysis.
Methods: Computer searches of CNKI, Web of Science, PubMed, ScienceDirect, and SPORTDiscus databases were conducted.
S Afr J Sports Med
January 2024
Division of Physiotherapy, School of Health and Rehabilitation Sciences, University of Cape Town, Cape Town, South Africa.
Background: Rugby has not grown extensively in Africa compared to other continents, necessitating talent identification (TID) programs to recruit junior talent. However, it is unclear which physical characteristics and rugby-specific game skills to base the objective recruitment of potentially talented young players.
Objectives: This study profiled the physical fitness characteristics and rugby-specific game skills of schoolboy rugby players by playing standards to identify variables differentiating elite from sub-elite players from Under 16 (U16) to U19 age categories.
Psychol Sport Exerc
December 2024
School of Life and Medical Sciences, University of Hertfordshire, UK.
Athlete body dissatisfaction is prevalent across sports and can lead to disordered eating, negative affect, and poor mental health. Whether body image concerns persist into athletic retirement is a focus of research, predominately involving survey-based data in usually sub-elite athletic samples. This study is the first to focus on the meanings elite athletes ascribe to their bodies in retirement.
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November 2024
Sports Engineering Laboratory, Department of Industrial Engineering, University of Rome Tor Vergata, Rome, Italy.
K2 performance depends on different kinematic and kinetic variables. Due to the lack of related studies in this area, we have tried to explain these features to better understand the best positioning of paddlers and how their synchronization affects performance. This study uses the DAQ system comprising two instrumented paddles-an IMU and a GPS ("E-kayak" system)-to investigate paddle synchronization and the specific positioning of paddlers' in preferred and inverted configurations.
View Article and Find Full Text PDFFront Psychol
October 2024
LiveWell-Research Centre for Active Living and Wellbeing, Polytechnic Institute of Bragança, Bragança, Portugal.
Introduction: A promising approach to optimizing recovery in youth football has been the use of machine learning (ML) models to predict recovery states and prevent mental fatigue. This research investigates the application of ML models in classifying male young football players aged under (U)15, U17, and U19 according to their recovery state. Weekly training load data were systematically monitored across three age groups throughout the initial month of the 2019-2020 competitive season, covering 18 training sessions and 120 observation instances.
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