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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2020-05-01
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doaj-6dbf3df4478a4eda941ff103364754ab2021-05-30T11:43:22ZengNature Publishing Groupnpj Digital Medicine2398-63522020-05-013111310.1038/s41746-020-0269-8Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health dataKathy Li0Iñigo Urteaga1Chris H. Wiggins2Anna Druet3Amanda Shea4Virginia J. Vitzthum5Noémie Elhadad6Department of Applied Physics and Applied Mathematics, Columbia UniversityDepartment of Applied Physics and Applied Mathematics, Columbia UniversityDepartment of Applied Physics and Applied Mathematics, Columbia UniversityClue by BioWink GmbHClue by BioWink GmbHClue by BioWink GmbHData Science Institute, Columbia UniversityAbstract 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.https://doi.org/10.1038/s41746-020-0269-8 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kathy Li Iñigo Urteaga Chris H. Wiggins Anna Druet Amanda Shea Virginia J. Vitzthum Noémie Elhadad |
spellingShingle |
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 npj Digital Medicine |
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 Publishing Group |
series |
npj Digital Medicine |
issn |
2398-6352 |
publishDate |
2020-05-01 |
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. |
url |
https://doi.org/10.1038/s41746-020-0269-8 |
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