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

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 me...

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Autores principales:	Kathy Li, Iñigo Urteaga, Chris H. Wiggins, Anna Druet, Amanda Shea, Virginia J. Vitzthum, Noémie Elhadad
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/6dbf3df4478a4eda941ff103364754ab
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spelling	oai:doaj.org-article:6dbf3df4478a4eda941ff103364754ab2021-12-02T14:47:39ZCharacterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data10.1038/s41746-020-0269-82398-6352https://doaj.org/article/6dbf3df4478a4eda941ff103364754ab2020-05-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-0269-8https://doaj.org/toc/2398-6352Abstract 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 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.Kathy LiIñigo UrteagaChris H. WigginsAnna DruetAmanda SheaVirginia J. VitzthumNoémie ElhadadNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-13 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Kathy Li Iñigo Urteaga Chris H. Wiggins Anna Druet Amanda Shea Virginia J. Vitzthum Noémie Elhadad Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data
description	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 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.
format	article
author	Kathy Li Iñigo Urteaga Chris H. Wiggins Anna Druet Amanda Shea Virginia J. Vitzthum Noémie Elhadad
author_facet	Kathy Li Iñigo Urteaga Chris H. Wiggins Anna Druet Amanda Shea Virginia J. Vitzthum Noémie Elhadad
author_sort	Kathy Li
title	Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data
title_short	Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data
title_full	Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data
title_fullStr	Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data
title_full_unstemmed	Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data
title_sort	characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/6dbf3df4478a4eda941ff103364754ab
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Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data

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