"For decades, sports medicine has relied on data collected almost exclusively from men - an inequity that experts say underserves female athletes, and creates a barrier to preventing career-ending injuries. The university's "Women's Health and Performance Initiative" will collect biometric data from women student-athletes and professional players and use machine learning to create new predictive health models specifically for female physiology."
"Published research in sports and exercise focused on women is nearly obsolete; less than 10% of sports medicine and sports science research has involved women athletes exclusively. "An ACL injury today can be career-ending for a female athlete, but for their male counterparts, no longer," Napolitano said. In an email to KQED, Chang noted that the first phase of research will focus on identifying the most common injuries to establish baseline data that doesn't currently exist."
Sports medicine historically relied on data collected almost exclusively from men, leaving female athletes underserved and increasing risks of preventable, career-ending injuries. UC Berkeley launched the Women's Health and Performance Initiative to gather biometric data from collegiate and professional women athletes and apply machine learning to create predictive health models tailored to female physiology. Published sports and exercise research focused solely on women comprises less than ten percent of the literature. Initial research will identify common injuries, establish baseline data, analyze return-to-play protocols, and assess mental and physical variables affecting recovery, with plans to expand beyond collegiate athletes.
Read at Kqed
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