Researchers have developed an algorithm to analyze health data that may be hidden within the menstrual cycle
A team in the United States has developed an open-source tool that enables the analysis of health data related to the menstrual cycle. So far, after analyzing 5,674 non-reproductive cycles recorded by 753 participants, they have found correlations between cycle variability, basal body temperature, and aging. According to the authors, who published the results in Science Advances, the algorithm could help advance the discovery of digital biomarkers, and they note that “most attention in menstrual health focuses exclusively on the reproductive aspect and fails to leverage these non-reproductive menstrual cycles (99%) as health indicators.”