Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data.

NPJ Digital Medicine
Kathy LiNoémie Elhadad

Abstract

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 GmbH 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 patte...Continue Reading

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Citations

Jul 1, 2020·NPJ Digital Medicine·Iñigo UrteagaNoémie Elhadad
Oct 7, 2020·American Journal of Human Biology : the Official Journal of the Human Biology Council·Virginia J Vitzthum
Dec 18, 2020·Frontiers in Physiology·Eder ZavalaKrasimira Tsaneva-Atanasova

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Methods Mentioned

BETA
medical procedure

Software Mentioned

Clue
BioWink

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