Objective: Fertility tracking devices offer women direct-to-user information about their fertility. The objective of this study is to understand how a fertility tracking device algorithm adjusts to changes of the individual menstrual cycle and under different conditions.
Methods: A retrospective analysis was conducted on a cohort of women who were using the device between January 2004 and November 2014. Available temperature and menstruation inputs were processed through the Daysy 1.0.7 firmware to determine fertility outputs. Sensitivity analyses on temperature noise, skipped measurements, and various characteristics were conducted.
Results: A cohort of 5328 women from Germany and Switzerland contributed 107,020 cycles. Mean age of the sample was 30.77 [SD 5.1] years, with a BMI of 22.07 kg/m^2 [SD 2.4]. The mean cycle length reported was 29.54 [SD 3.0] days. The majority of women were using the device 80-100% of the time during the cycle (53.1%). For this subset of women, the fertility device identified on average 41.4% [SD 6.4] possibly fertile (red) days, 42.4% [SD 8.7] infertile (green) days and 15.9% [SD 7.3] yellow days. The number of infertile (green) days decreases proportionally to the number of measured days, whereas the number of undefined (yellow) days increases.
Conclusion: Overall, these results showed that the fertility tracker algorithm was able to distinguish biphasic cycles and provide personalised fertility statuses for users based on daily basal body temperature readings and menstruation data. We identified a direct linear relationship between the number of measurements and output of the fertility tracker.
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