术语
运动医学
体育科学
运动员
多样性(政治)
心理学
包裹体(矿物)
医学
应用心理学
医学教育
物理疗法
社会心理学
生理学
政治学
哲学
法学
语言学
作者
Kirsty J. Elliott‐Sale,Clare Minahan,X. Janse de Jonge,Kathryn E. Ackerman,Sarianna Sipilä,Naama Constantini,Constance Lebrun,Anthony C. Hackney
出处
期刊:Sports Medicine
[Springer Science+Business Media]
日期:2021-03-16
卷期号:51 (5): 843-861
被引量:462
标识
DOI:10.1007/s40279-021-01435-8
摘要
Abstract Until recently, there has been less demand for and interest in female-specific sport and exercise science data. As a result, the vast majority of high-quality sport and exercise science data have been derived from studies with men as participants, which reduces the application of these data due to the known physiological differences between the sexes, specifically with regard to reproductive endocrinology. Furthermore, a shortage of specialist knowledge on female physiology in the sport science community, coupled with a reluctance to effectively adapt experimental designs to incorporate female-specific considerations, such as the menstrual cycle, hormonal contraceptive use, pregnancy and the menopause, has slowed the pursuit of knowledge in this field of research. In addition, a lack of agreement on the terminology and methodological approaches (i.e., gold-standard techniques) used within this research area has further hindered the ability of researchers to adequately develop evidenced-based guidelines for female exercisers. The purpose of this paper was to highlight the specific considerations needed when employing women (i.e., from athletes to non-athletes) as participants in sport and exercise science-based research. These considerations relate to participant selection criteria and adaptations for experimental design and address the diversity and complexities associated with female reproductive endocrinology across the lifespan. This statement intends to promote an increase in the inclusion of women as participants in studies related to sport and exercise science and an enhanced execution of these studies resulting in more high-quality female-specific data.
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