The new research reveals that at least five physiological parameters — not just temperature — collected simultaneously interact, can precisely predict the fertile window in a woman’s monthly cycle.
The results improve upon other fertility tracking methods which rely on only a single parameter, such as the date of your period (calendar apps) or basal temperature, cannot accurately detect the entire fertile window.
The research looked at whether the phase-based differences detected from a wearable bracelet which were – users’ wrist skin temperature, heart rate, heart rate variability, respiratory rate, and skin perfusion – can be used to make predictions about the optimal time for a woman to try to become pregnant. To assess these factors, the researchers used artificial intelligence and machine learning, and used the data to develop an algorithm to identify the fertile window in real time.
The results showed that the Ava is capable of detecting significant, phase-based shifts in multiple physiological parameters throughout a woman. These insights were then used to establish a menstrual cycle that can pinpoint her fertile window.
The research used and analysed data from over 200 women and involved more than 1,000 menstrual cycles. The collected data showed that changes in multiple physiological parameters can accurately identify the five most fertile days of a woman’s cycle with 89 percent accuracy.
Lead researcher Dr. Brigitte Leeners, who is a professor at the University Hospital of Zurich, said: “For the first time, we are able to demonstrate that tracking several physiological parameters – not just temperature – is important for fertility tracking. Ava helps women learn about their fertility and enables individualized, real-time fertility detection that is easy to use.”
The new research has been published in the Journal of Internet Medical Research. The research is titled “Wearable Sensors Reveal Menses-Driven Changes in Physiology and Enable Prediction of the Fertile Window: Observational Study.”
At a later stage, Ava is intending to publish results comparing their method with other period tracking methods which use population statistics.
