Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data.
Data mining
Data processing
Journal
NPJ digital medicine
ISSN: 2398-6352
Titre abrégé: NPJ Digit Med
Pays: England
ID NLM: 101731738
Informations de publication
Date de publication:
2020
2020
Historique:
received:
11
10
2019
accepted:
23
03
2020
entrez:
9
6
2020
pubmed:
9
6
2020
medline:
9
6
2020
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 32509976
doi: 10.1038/s41746-020-0269-8
pii: 269
pmc: PMC7250828
doi:
Types de publication
Journal Article
Langues
eng
Pagination
79Subventions
Organisme : NLM NIH HHS
ID : R01 LM013043
Pays : United States
Informations de copyright
© The Author(s) 2020.
Déclaration de conflit d'intérêts
Competing interestsK.L. is supported by NSF’s Graduate Research Fellowship Program Award #1644869. I.U., C.H.W., and N.E. are supported by NSF Award #1344668. K.L., I.U., C.W., and N.E. declare that they have no competing interests. A.D., A.S., and V.J.V. were employed by Clue by BioWink GmbH at the time of this research project.
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