Researchers at SRI International have introduced WAVES (Women’s Health Assessment through Variability in Endocrine-Related Signals), an open-source algorithm designed to analyze daily physiological signals linked to the menstrual cycle. The goal is to move beyond a narrow focus on fertility and use the vast amount of data from non-conceptive cycles—about 99% of all cycles—as a tool for monitoring overall health.
In a study published in Science Advances, the team applied WAVES to data from 5,674 cycles collected from 753 participants aged 18 to 42. The algorithm breaks down each cycle into 32 specific metrics, such as cycle length, timing of ovulation, temperature rise speed, and day-to-day temperature fluctuations.
Comparing younger women (aged 18–35) with those aged 35–42, the researchers found age-related changes: older women had shorter cycles, slightly higher resting body temperatures, and less regular patterns. Importantly, the algorithm also showed that each individual maintains a stable personal "health footprint" over multiple cycles, with changes occurring only gradually with age. This consistency suggests that WAVES could be used to track long-term wellness and develop personalized digital biomarkers for women’s health.