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A research team from &Eacute;cole Polytechnique F&eacute;d&eacute;rale de Lausanne (EPFL) and Stanford University recently conducted a large-scale analysis of <a href="https://www.docwirenews.com/abstracts/assessment-of-menstrual-health-status-and-evolution-through-mobile-apps-for-fertility-awareness/">fertility awareness apps</a> to better understand their effects. This data came from 200,000 users of the menstrual tracking <a href="https://www.docwirenews.com/docwire-pick/home-page-picks/can-smartphone-app-help/">smartphone apps</a> Sympto and Kindara, and provides information regarding demographics, behavior patterns, and accuracy in measuring menstrual health. Their work was recently published in&nbsp;<a href="https://www.nature.com/articles/s41746-019-0139-4#Sec8">npj Digital Medicine</a>.&nbsp;Historically, women assess their menstrual health and fertility through physician evaluation. This method is effective; however, a shortcoming is that it relies heavily on the patient remembering their menstrual cycle. With many menstrual tracking apps available now, women are beginning to use these to more accurately document their menstruation.Though many of these apps are made by experts in the field, there is little research regarding their impact at the population level. As for each app&rsquo;s accuracy, how they&rsquo;re used by consumers, and how they help them and their gynecologists, there is currently a lack of relevant data.<h2>Background of the Fertility App Study</h2>To address this gap in knowledge, Laura Symul and EPFL&rsquo;s Digital Epidemiology Lab worked with Stanford to gather this large-scale data. 200,000 users of Sympto and Kindara were included in the study. Both apps use the sympto-thermal method to track the user&rsquo;s fertility through evaluation of cervical fluid, body temperature, and other physiological data.In this population level study, over 30 million days of observations were gathered from more than 2.7 million menstruation cycles. This study had two main goals: to describe the typical app user in terms of behavior and to evaluate how accurate the apps are in detecting ovulation timing.Symul and colleagues found that the typical menstrual app user is roughly 30 years of age, lives in North America or Europe, and is at a healthy weight (measured via BMI). Users who also log in their sexual intercourse were found to enter their observations more frequently. In addition, those who reported fertility awareness body signs showed timing patterns similar to those seen in small-scale clinical research.The researchers noted that the women aiming to get pregnant entered Sympto-Thermal readings every day for up to 40% of their menstrual cycles. It was also found that the average range and duration of the follicular phase (time from cycle start to end of ovulation) were both larger than previous data showed, with only 24% of ovulations were found to occur at days 14-15 in the menstrual cycle. As for the latter portion of the cycle, the luteal phase, the researchers found that the duration and range was consistent with other studies.&ldquo;Our study provides a common ground for users and their doctors to incorporate digital records in their visits, evaluate their own menstrual patterns and compare them with the statistics we report,&rdquo; said Symul. &ldquo;New technologies, and in particular self-tracking, are changing the way we perceive our bodies and health. Both users and doctors wonder about the opportunities and the usefulness of digital self-tracking. Our study shows that users voluntarily track their menstrual cycle and fertility-related body signs very frequently, and what they track is aligned with what is expected in the vast majority of cases.&rdquo;Symul concluded that though the measurements are not always &ldquo;perfectly regular, they provide valuable information for inferring the underlying hormonal changes and timing of ovulation in a way that is scalable both in time and in number of participants.&rdquo;<blockquote class="twitter-tweet">
Are fertility apps useful? Researchers have carried out an analysis of the largest datasets from fertility awareness apps &ndash; via <a href="https://twitter.com/ScienceDaily?ref_src=twsrc%5Etfw">@ScienceDaily</a> <a href="https://t.co/S3EY8JKxzA">https://t.co/S3EY8JKxzA</a>
&mdash; EPFL (@EPFL_en) <a href="https://twitter.com/EPFL_en/status/1152215520604803072?ref_src=twsrc%5Etfw">July 19, 2019</a>
</blockquote><script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>

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A research team from École Polytechnique Fédérale de Lausanne (EPFL) and Stanford University recently conducted a large-scale analysis of fertility awareness apps to better ...

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