Methodological Considerations for Studies in Sport and Exercise Science with Women as Participants: A Working Guide for Standards of Practice for Research on Women.
Kirsty Jayne Elliott-SaleClare L MinahanXanne A K Janse de JongeKathryn E AckermanSarianna SipiläNaama W ConstantiniConstance M LebrunAnthony C HackneyPublished in: Sports medicine (Auckland, N.Z.) (2021)
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.
Keyphrases
- high intensity
- polycystic ovary syndrome
- public health
- pregnancy outcomes
- healthcare
- physical activity
- electronic health record
- high school
- resistance training
- big data
- anterior cruciate ligament
- case control
- pregnant women
- mental health
- type diabetes
- primary care
- palliative care
- insulin resistance
- breast cancer risk
- data analysis
- adipose tissue
- machine learning
- deep learning
- postmenopausal women
- middle aged
- silver nanoparticles