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.
Highlights
A broad diversity of fertility awareness methods (FAMs) has been developed in the past century,[1,2] primarily designed to help couples manage fertility and family planning
This study’s goal was to describe and explore the suitability of datasets collected through two mobile applications (Kindara and Sympto) supporting Fertility Awareness Method (FAM) tracking for the assessment of menstrual health in general, both at the individual level and at the population level
The primary aim was to provide health practitioners with an overview of how and what FAM app users voluntarily track on these apps
Summary
A broad diversity of fertility awareness methods (FAMs) has been developed in the past century,[1,2] primarily designed to help couples manage fertility and family planning. Modern methods developed in the last quarter of the twentieth century take advantage of the precise description of menstrual variation of the basal body temperature (BBT) or waking temperature, taken with a thermometer with a 0.01 °C or 0.5 °F precision, cervical mucus quality and quantity, vaginal sensation, and cervical position.[3,4,5,6] These methods have defined a set of rules that allows the identification of the fertile window around ovulation, so that couples can adapt their sexual behavior according to their reproductive objectives.[7,8,9] The sympto-thermal method, which combines BBT and cervical mucus observations, is arguably amongst the most reliable FAM for family planning.[1,2,4,10] Recently, a number of mobile apps have been developed by private organizations to facilitate FAM tracking. Over the past few years, an increasing number of women, estimated at over 200 million in 2016,12 have started using these apps, contributing to the accumulation of menstrual-related data (Fig. 1) from a diverse population of users at different stage of life (Fig. 2a; Table 1, see Methods)
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