Assessment of menstrual health status and evolution through mobile apps for fertility awareness

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Assessment of menstrual health status and evolution through mobile apps for fertility awareness. / Symul, Laura; Wac, Katarzyna; Hillard, Paula; Salathé, Marcel.

In: npj Digital Medicine, Vol. 2, 64, 2019.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Symul, L, Wac, K, Hillard, P & Salathé, M 2019, 'Assessment of menstrual health status and evolution through mobile apps for fertility awareness', npj Digital Medicine, vol. 2, 64. https://doi.org/10.1038/s41746-019-0139-4

APA

Symul, L., Wac, K., Hillard, P., & Salathé, M. (2019). Assessment of menstrual health status and evolution through mobile apps for fertility awareness. npj Digital Medicine, 2, [64]. https://doi.org/10.1038/s41746-019-0139-4

Vancouver

Symul L, Wac K, Hillard P, Salathé M. Assessment of menstrual health status and evolution through mobile apps for fertility awareness. npj Digital Medicine. 2019;2. 64. https://doi.org/10.1038/s41746-019-0139-4

Author

Symul, Laura ; Wac, Katarzyna ; Hillard, Paula ; Salathé, Marcel. / Assessment of menstrual health status and evolution through mobile apps for fertility awareness. In: npj Digital Medicine. 2019 ; Vol. 2.

Bibtex

@article{5e880652b0a648e2b871a4216c8bb8b7,
title = "Assessment of menstrual health status and evolution through mobile apps for fertility awareness",
abstract = "For most women of reproductive age, assessing menstrual health and fertility typically involves regular visits to a gynecologist or another clinician. While these evaluations provide critical information on an individual's reproductive health status, they typically rely on memory-based self-reports, and the results are rarely, if ever, assessed at the population level. In recent years, mobile apps for menstrual tracking have become very popular, allowing us to evaluate the reliability and tracking frequency of millions of self-observations, thereby providing an unparalleled view, both in detail and scale, on menstrual health and its evolution for large populations. In particular, the primary aim of this study was to describe the tracking behavior of the app users and their overall observation patterns in an effort to understand if they were consistent with previous small-scale medical studies. The secondary aim was to investigate whether their precision allowed the detection and estimation of ovulation timing, which is critical for reproductive and menstrual health. Retrospective self-observation data were acquired from two mobile apps dedicated to the application of the sympto-thermal fertility awareness method, resulting in a dataset of more than 30 million days of observations from over 2.7 million cycles for two hundred thousand users. The analysis of the data showed that up to 40% of the cycles in which users were seeking pregnancy had recordings every single day. With a modeling approach using Hidden Markov Models to describe the collected data and estimate ovulation timing, it was found that follicular phases average duration and range were larger than previously reported, with only 24% of ovulations occurring at cycle days 14 to 15, while the luteal phase duration and range were in line with previous reports, although short luteal phases (10 days or less) were more frequently observed (in up to 20% of cycles). The digital epidemiology approach presented here can help to lead to a better understanding of menstrual health and its connection to women's health overall, which has historically been severely understudied.",
author = "Laura Symul and Katarzyna Wac and Paula Hillard and Marcel Salath{\'e}",
year = "2019",
doi = "10.1038/s41746-019-0139-4",
language = "English",
volume = "2",
journal = "npj Digital Medicine",
issn = "2398-6352",
publisher = "Nature Publishing Group",

}

RIS

TY - JOUR

T1 - Assessment of menstrual health status and evolution through mobile apps for fertility awareness

AU - Symul, Laura

AU - Wac, Katarzyna

AU - Hillard, Paula

AU - Salathé, Marcel

PY - 2019

Y1 - 2019

N2 - For most women of reproductive age, assessing menstrual health and fertility typically involves regular visits to a gynecologist or another clinician. While these evaluations provide critical information on an individual's reproductive health status, they typically rely on memory-based self-reports, and the results are rarely, if ever, assessed at the population level. In recent years, mobile apps for menstrual tracking have become very popular, allowing us to evaluate the reliability and tracking frequency of millions of self-observations, thereby providing an unparalleled view, both in detail and scale, on menstrual health and its evolution for large populations. In particular, the primary aim of this study was to describe the tracking behavior of the app users and their overall observation patterns in an effort to understand if they were consistent with previous small-scale medical studies. The secondary aim was to investigate whether their precision allowed the detection and estimation of ovulation timing, which is critical for reproductive and menstrual health. Retrospective self-observation data were acquired from two mobile apps dedicated to the application of the sympto-thermal fertility awareness method, resulting in a dataset of more than 30 million days of observations from over 2.7 million cycles for two hundred thousand users. The analysis of the data showed that up to 40% of the cycles in which users were seeking pregnancy had recordings every single day. With a modeling approach using Hidden Markov Models to describe the collected data and estimate ovulation timing, it was found that follicular phases average duration and range were larger than previously reported, with only 24% of ovulations occurring at cycle days 14 to 15, while the luteal phase duration and range were in line with previous reports, although short luteal phases (10 days or less) were more frequently observed (in up to 20% of cycles). The digital epidemiology approach presented here can help to lead to a better understanding of menstrual health and its connection to women's health overall, which has historically been severely understudied.

AB - For most women of reproductive age, assessing menstrual health and fertility typically involves regular visits to a gynecologist or another clinician. While these evaluations provide critical information on an individual's reproductive health status, they typically rely on memory-based self-reports, and the results are rarely, if ever, assessed at the population level. In recent years, mobile apps for menstrual tracking have become very popular, allowing us to evaluate the reliability and tracking frequency of millions of self-observations, thereby providing an unparalleled view, both in detail and scale, on menstrual health and its evolution for large populations. In particular, the primary aim of this study was to describe the tracking behavior of the app users and their overall observation patterns in an effort to understand if they were consistent with previous small-scale medical studies. The secondary aim was to investigate whether their precision allowed the detection and estimation of ovulation timing, which is critical for reproductive and menstrual health. Retrospective self-observation data were acquired from two mobile apps dedicated to the application of the sympto-thermal fertility awareness method, resulting in a dataset of more than 30 million days of observations from over 2.7 million cycles for two hundred thousand users. The analysis of the data showed that up to 40% of the cycles in which users were seeking pregnancy had recordings every single day. With a modeling approach using Hidden Markov Models to describe the collected data and estimate ovulation timing, it was found that follicular phases average duration and range were larger than previously reported, with only 24% of ovulations occurring at cycle days 14 to 15, while the luteal phase duration and range were in line with previous reports, although short luteal phases (10 days or less) were more frequently observed (in up to 20% of cycles). The digital epidemiology approach presented here can help to lead to a better understanding of menstrual health and its connection to women's health overall, which has historically been severely understudied.

U2 - 10.1038/s41746-019-0139-4

DO - 10.1038/s41746-019-0139-4

M3 - Journal article

C2 - 31341953

VL - 2

JO - npj Digital Medicine

JF - npj Digital Medicine

SN - 2398-6352

M1 - 64

ER -

ID: 225418944