Breaking Boundaries: A Chronology with Future Directions of Women in Exercise Physiology Research, Centred on Pregnancy
Historically, females were excluded from clinical research due to their reproductive roles, hindering medical understanding and healthcare quality. Despite guidelines promoting equal participation, females are underrepresented in exercise science, perpetuating misconceptions about female physiology. Even less attention has been given to exercise in the pregnant population. Research on pregnancy and exercise has evolved considerably from the initial bedrest prescriptions but concerns about exercise risks during pregnancy persisted for many decades. Recent guidelines endorse moderate-intensity physical activity during pregnancy, supported by considerable evidence of its safety and benefits. Mental health during pregnancy, often overlooked, is gaining traction, with exercise showing promise in reducing depression and anxiety. While pregnancy guidelines recommend moderate-intensity physical activity, there remains limited understanding of optimal frequency, intensity, type and time (duration) for extremes like elite athletes or those with complications. Female participation in elite sport and physically demanding jobs is rising, but research on their specific needs is lacking. Traditional practices like bed rest for high-risk pregnancies are being questioned, as evidence suggests it may not improve outcomes. Historical neglect of gestational parents in research perpetuated stereotypes of female frailty, but recent years have seen a shift towards recognizing the benefits of an active pregnancy. Closing knowledge gaps and inclusivity in research are crucial for ensuring guidelines reflect the diverse needs of gestational parents. Therefore, the purpose of this review is to summarize the evolution of exercise physiology and pregnancy research along with future directions for this novel field.
Code (0)
등록된 구현이 없습니다.
Tasks
MisconceptionsSimilar Papers 제목 키워드 기반
JailbreakZoo: Survey, Landscapes, and Horizons in Jailbreaking Large Language and Vision-Language Models
The rapid evolution of artificial intelligence (AI) through developments in Large Language Models (LLMs) and Vision-Language Models (VLMs) has brought significant advancements across various technological domains. While …
LLM JailbreakSurveyExploring the Synergies of Hybrid CNNs and ViTs Architectures for Computer Vision: A survey
The hybrid of Convolutional Neural Network (CNN) and Vision Transformers (ViT) architectures has emerged as a groundbreaking approach, pushing the boundaries of computer vision (CV). This comprehensive review provides a …
SurveyFrom LLMs to MLLMs: Exploring the Landscape of Multimodal Jailbreaking
The rapid development of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) has exposed vulnerabilities to various adversarial attacks. This paper provides a comprehensive overview of jailbreaking …
Entitled to Property: How Breaking the Gender Barrier Improves Child Health in India
Non-unitary household models posit that increasing women's bargaining power can affect child health, a significant predictor of human capital and economic status throughout adulthood. We study how a policy change, the Hi…
Designing Safe and Accountable GenAI as a Learning Companion with Women Banned from Formal Education
In gender-restrictive and surveilled contexts, where access to formal education may be restricted for women, pursuing education involves safety and privacy risks. When women are excluded from schools and universities, th…