The menstrual cycle is a key indicator of overall health for women of reproductive age. Previously, the study of women's menstruation was done primarily through survey results; however, as mobile apps for menstrual tracking become more widely adopted, they provide an increasingly large, content-rich source of menstrual health experiences and behaviors over time. In this paper, we show that self-reported data from menstrual trackers can reveal statistically significant relationships between per-person variability of cycle length and self-reported qualitative symptoms. Specifically, we explore a database collected using the Clue app by Biowink GmbH of over 378,000 users and 4.9 million natural cycles of user-tracked observations across a wide range of categories to better understand variation of menstrual experience within and across individuals. A concern for self-tracked data is that these reflect not only physiological behaviors, but also the engagement dynamics of app users. We mitigate such potential artifacts by developing a procedure to exclude cycles lacking user engagement, thereby allowing us to better distinguish true menstrual patterns from tracking anomalies. We find that women located at different ends of the spectrum of menstrual patterns , based on the consistency of their cycle length statistics, exhibit statistically significant differences in  cycle characteristics and symptom tracking . Our findings showcase the potential of longitudinal, high-resolution self-tracked data for an improved understanding of menstruation and women's health as a whole.
The menstrual cycle is a key indicator of overall health for women of reproductive age. Previously, menstruation was primarily studied through survey results; however, as menstrual tracking mobile apps become more widely adopted, they provide an increasingly large, content-rich source of menstrual health experiences and behaviors over time. By exploring a database of user-tracked observations from the Clue app by BioWink of over 378,000 users and 4.9 million natural cycles, we show that self-reported menstrual tracker data can reveal statistically significant relationships between per-person cycle length variability and self-reported qualitative symptoms. A concern for self-tracked data is that they reflect not only physiological behaviors, but also the engagement dynamics of app users. To mitigate such potential artifacts , we develop a procedure to exclude cycles lacking user engagement, thereby allowing us to better distinguish true menstrual patterns from tracking anomalies. We uncover that women located at different ends of the menstrual variability spectrum , based on the consistency of their cycle length statistics, exhibit statistically significant differences in their cycle characteristics and symptom tracking patterns. We also find that cycle and period length statistics are stationary over the app usage timeline across the variability spectrum. The symptoms that we identify as showing statistically significant association with timing data can be useful to clinicians and users for predicting cycle variability from symptoms or as potential health indicators for conditions like endometriosis. Our findings showcase the potential of longitudinal, high-resolution self-tracked data to improve understanding of menstruation and women's health as a whole.
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