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

For women of reproductive age, the most common way of assessing their menstrual health means regular visits to a gynecologist. When it comes to evaluating the changes in fertility and menstrual health, these visits typically rely on the patient memory, which can lead to significant inaccuracies.

Go to the profile of Laura Symul
Jul 16, 2019
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Nowadays, many women today are turning to so-called “fertility awareness” (FAM) smartphone apps to support them in tracking their menstrual cycles. There is a multitude of such apps available, which only demonstrates how popular they’ve become in recent years.

But how accurate are FAM apps? What do users track? Can they help them and their gynecologists to make better clinical decisions?

To bridge the gap between the medical references that practitioners read and the tools that women use, an extensive description and a deeper understanding of the digital records voluntary and autonomously tracked by the app users had become necessary.  

In this study, records from 200,000 users of two FAM apps, Sympto and Kindara were used. Both apps support the “Sympto-Thermal Method” and facilitate the identification of the fertile and infertile times of a woman’s menstrual cycle by taking into account recordings of cervical fluid, body temperature at wake-up, and other biological signs. 30 million days of observations from over 2.7 million menstrual cycles had been logged in the acquired datasets. The overall study had two main aims: First, to see how and what users voluntarily track on FAM apps. Second, to find out if these records allow an accurate estimation of ovulation timing.  

In terms of user demographics and behavior, the study found that the typical FAM app user is around 30 years old, lives in a western country (in Europe or Northern America) and has a healthy BMI. App users log their observations more frequently when they also log sexual intercourse, and at the population level, reported fertility awareness body signs exhibit temporal patterns that follow closely those that have been found in small-scale clinical studies.  

While missing data is a frequent concern when dealing with digital self-reports, specific subsets of the users’ populations were found to be very dedicated: women who were seeking pregnancy recorded Sympto-Thermal measurements every single day for up to 40% of their menstrual cycles.  

To address the second aim of the study, i.e. provide an estimate of ovulation time from fertility awareness records, a hidden Markov model was defined. This statistical framework presents several advantages as it is adequate to describe biologically meaningful sequence of events, such as the succession of menstrual phases (menses, follicular phase, ovulation, luteal phase), performs well even when data is missing and provides a measure of uncertainty and confidence of the estimated time of ovulation. This model can thus be applied to automatically label time-series collected from menstrual-cycle tracking apps.  

To summarize, this study provides a common ground for users and their practitioners to incorporate digital records in their visits, evaluate their own menstrual patterns and compare them with the statistics we report. ­­

We also believe that it shows convincing evidence that menstrual cycle tracking apps, in particular those offering support for fertility-awareness tracking, are useful and affordable tools that can easily be incorporated in clinical studies to evaluate quantitatively menstrual health and fertility changes and thus provide opportunities for studying the interactions between the menstrual cycle and other physiological systems on a large scale.    

Laura Symul, Nikolaos Papageorgiou


Reference:
Laura Symul, Katarzyna Wac, Paula Hillard, Marcel Salathé.
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


Go to the profile of Laura Symul

Laura Symul

Post-doc, EPFL

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