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Scott McLean, Hugo A. Kerhervé, Nicholas Stevens, and Paul M. Salmon

In recent years, scrutiny on sport-science research has intensified from both internal and external sources. 1 , 2 Several debates have arisen concerning methodological and theoretical issues, such as magnitude-based inferences (MBI) 3 and the acute chronic workload ratio (ACWR). 4 For example

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Israel Halperin, Andrew D. Vigotsky, Carl Foster, and David B. Pyne

Over the passing years, exercise and sport sciences have developed into a large field of study consisting of several disciplines including physiology, biomechanics, psychology, nutrition, performance analysis, motor learning and control, strength and conditioning, and sports medicine. Much like

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Iñigo Mujika and Ritva S. Taipale

performed on female athletes: 2 studies were conducted on synchronized swimmers (now called artistic swimmers), 1 on handball players, and 1 on soccer players. By contrast, one of us (R.S.T.) has made a career in sport science by mainly studying women and sex differences in responses and adaptations to

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Shona L. Halson and David T. Martin

“gold-medal-winning factory.” In an attempt to increase international competitiveness, many countries built their own centralized elite sport centers. 2 East Germany learned from the Soviet Union, and with heavy state funding, exceptional facilities, committed coaching, and sport science support, the

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Patrick Ward, Johann Windt, and Thomas Kempton

Sport science, the application of scientific principles to inform practice, 1 has become increasingly common as professional sporting organizations seek to gain a performance advantage. These organizations increasingly employ sport scientists from varying backgrounds including physiology, strength

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Tim Newans, Phillip Bellinger, Christopher Drovandi, Simon Buxton, and Clare Minahan

pre-average data before running analyses. 18 Therefore, it is reasonable to suggest that mixed models are the most appropriate statistical methodology to analyze longitudinal data sets often acquired by sports scientists. This aligns with previous guidance by Hopkins et al 19 in encouraging sport-science

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Alan D. Ruddock, Craig Boyd, Edward M. Winter, and Mayur Ranchordas

from the goal of becoming an Olympic champion or an Olympian might be 8 hours away. • Why do they seek scientific support? • What are their expectations of support? • What support have they had previously? To identify what the athlete thinks of sport sciences and to understand their expectations of

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Iñigo Mujika

nature of sport science both in the field with coaches and athletes and in academic circles. These metrics are easily generated, but the challenge is to identify and articulate the impact of sport science. For instance, these numbers were achieved while simultaneously helping individual athletes and

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Carl Foster

Sport science can mean a lot of different things. At one level, it can be the collation and transmission of scientific findings to coaches and athletes. At another, it can be the evaluation of athletes in the laboratory, intended to give the coach a venue free view of the current status and

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Lieselot Decroix, Kevin De Pauw, Carl Foster, and Romain Meeusen

Aim:

To review current cycling-related sport-science literature to formulate guidelines to classify female subject groups and to compare this classification system for female subject groups with the classification system for male subject groups.

Methods:

A database of 82 papers that described female subject groups containing information on preexperimental maximal cycle-protocol designs, terminology, biometrical and physiological parameters, and cycling experience was analyzed. Subject groups were divided into performance levels (PLs), according to the nomenclature. Body mass, body-mass index, maximal oxygen consumption (VO2max), peak power output (PPO), and training status were compared between PLs and between female and male PLs.

Results:

Five female PLs were defined, representing untrained, active, trained, well-trained, and professional female subjects. VO2max and PPO significantly increased with PL, except for PL3 and PL4 (P < .01). For each PL, significant differences were observed in absolute and relative VO2max and PPO between male and female subject groups. Relative VO2max is the most cited parameter for female subject groups and is proposed as the principal parameter to classify the groups.

Conclusion:

This systematic review shows the large variety in the description of female subject groups in the existing literature. The authors propose a standardized preexperimental testing protocol and guidelines to classify female subject groups into 5 PLs based on relative VO2max, relative PPO, training status, absolute VO2max, and absolute PPO.