Endocrine Abstracts

JOINT516

Fertility depends on the complex interaction of various factors. The most common causes include hormonal dysregulation or sperm abnormalities. The follow-up of the Pearl Cycle App study aimed to characterize fertility influencing factors (FIF), and the number and course of pregnancies observed. Women aged between 21 and 45, trying to conceive for up to 24 months and without hormone intake, were recruited in the gynaecological outpatient clinics of the Technical University Munich and the University of Ulm. After informed consent participants downloaded the app “Pearl Fertility” and received OPKs with lateral flow immunoassay test strips (LFIA) for LH, FSH, PdG and hCG for at home measurements. The LFIA colour change was analysed via camera detection and three algorithms calculated the ovulation date. Folliculometry was performed sonographically before and after the predicted day of ovulation. Participants were observed for up to three cycles and followed-up after one year. Three questionnaires assessed baseline characteristics, user experience, and later pregnancy outcomes and FIF. Out of 89 recruited women, 22,5% (20) and 11,2% (10) of their partners, had at least one FIF which became known before, during, or after participation. In 29,2% (26) of the couples at least one FIF was identified. In participants with hormonal dysregulation and/or irregular cycles, ovulation prediction was correct in 61,5% (16) of 26 cycles analysed. 15,7% (14) became pregnant after 1,3 (SD±0,5) cycles. Of these, 21,4% (3) had at least one FIF. 71,4% (10) of these pregnancies resulted in live births. 48/89 (53,9%) women responded to the follow-up. 50% (24) had become pregnant 11,1 (SD±8,3) months after participation. 14 (48,3%) of a total of 29 pregnancies post participation resulted in live births. 33,3% (8/24) were currently pregnant at the time of the follow-up. 37,5% (9/24) had used assisted reproduction technology and 8,33% (2/24) ovulation induction. The information on pregnancies and FIF is based on the patient surveys. Therefore, the true numbers might be even higher. The high proportion of couples with FIF and/or ART use after the study combined with the short participation period may explain why a pregnancy did not occur during participation or did not result in a birth. This could also reflect a selection bias as the app was likely used by women who had struggled to conceive. This information can also provide additional insights into the effectiveness of the ovulation prediction and highlights the need for medical evaluation of personalised approaches to fertility treatment.