Time to pregnancy recognition among users of an FDA-cleared fertility application.

Background: Previous investigations of time-to-pregnancy recognition have analysed data from national surveys and clinics, but this has not been investigated in the context of digital fertility applications. Timely pregnancy recognition can help individuals in health and pregnancy management, reducing maternal and foetal risk and costs, whilst increasing treatment options, availability, and cost. Methods: This dataset contained 23,728 pregnancies (conceived between June 2018 and December 2022) from 20,429 participants using a Food and Drug Administration (FDA) cleared fertility app in the United States. Most participants (with non-missing information) identified as Non-Hispanic White, and one-third reported obtaining a university degree. We used two-tailed Welch's t-test, Mann-Whitney U-test, and two-tailed Z-tests to compare time to pregnancy recognition between those using the app to conceive or contracept. Results: Participants using an app to conceive recognised pregnancy on average at 31.3 days from last menstrual period (LMP) compared to 35.9 days among those using the app to prevent pregnancy. Conclusion: Generalisability is limited, as all participants were using a fertility app and had relatively homogenous sociodemographic characteristics.

People who recognise pregnancy early may benefit, as earlier recognition can reduce costs and risks, and make more treatment options available. In the past, researchers have studied the time it takes for an individual to recognise that they are pregnant by asking them in national surveys or when they attend a clinic. However, with the advent of digital fertility tracking apps, we investigated the time it takes to recognise pregnancy when using such an app. We analysed data from 23,728 pregnancies from 20,429 users of the Natural Cycles app between June 2018 and December 2022. We found that participants using the app to try to get pregnant recognised pregnancy an average of 4.6 days earlier than those using the app to prevent pregnancy.

Association Between Menstrual Cycle Length and Coronavirus Disease 2019 (COVID-19) Vaccination: A U.S. Cohort.

OBJECTIVE: To assess whether coronavirus disease 2019 (COVID-19) vaccination is associated with changes in cycle or menses length in those receiving vaccination as compared with an unvaccinated cohort. METHODS: We analyzed prospectively tracked menstrual cycle data using the application "Natural Cycles." We included U.S. residents aged 18-45 years with normal cycle lengths (24-38 days) for three consecutive cycles before the first vaccine dose followed by vaccine-dose cycles (cycles 4-6) or, if unvaccinated, six cycles over a similar time period. We calculated the mean within-individual change in cycle and menses length (three prevaccine cycles vs first- and second-dose cycles in the vaccinated cohort, and the first three cycles vs cycles four and five in the unvaccinated cohort). We used mixed-effects models to estimate the adjusted difference in change in cycle and menses length between the vaccinated and unvaccinated cohorts. RESULTS: We included 3,959 individuals (vaccinated 2,403; unvaccinated 1,556). Most of the vaccinated cohort received the Pfizer-BioNTech vaccine (55%) (Moderna 35%, Johnson & Johnson/Janssen 7%). Overall, COVID-19 vaccine was associated with a less than 1-day change in cycle length for both vaccine-dose cycles compared with prevaccine cycles (first dose 0.71 day-increase, 98.75% CI 0.47-0.94; second dose 0.91, 98.75% CI 0.63-1.19); unvaccinated individuals saw no significant change compared with three baseline cycles (cycle four 0.07, 98.75% CI -0.22 to 0.35; cycle five 0.12, 98.75% CI -0.15 to 0.39). In adjusted models, the difference in change in cycle length between the vaccinated and unvaccinated cohorts was less than 1 day for both doses (difference in change: first dose 0.64 days, 98.75% CI 0.27-1.01; second dose 0.79 days, 98.75% CI 0.40-1.18). Change in menses length was not associated with vaccination. CONCLUSION: Coronavirus disease 2019 (COVID-19) vaccination is associated with a small change in cycle length but not menses length.

Time to Pregnancy for Women Using a Fertility Awareness Based Mobile Application to Plan a Pregnancy.

Background: Time to pregnancy (TTP) is a biomarker of fecundability and has been associated with behavioral and environmental characteristics; however, these associations have not been examined in a large population-based sample of application (app) users. Materials and Methods: This observational study followed 5,376 women with an age range of 18 to 45 years who used an app to identify their fertile window. We included women who started trying to conceive between September 30, 2017 and August 31, 2018. TTP was calculated as the number of menstrual cycles from when the user switched to "Plan" mode up to and including the cycle in which they logged a positive pregnancy test. We examined associations with several characteristics, including age, gravidity, body mass index, cycle length and cycle length variation, frequency of sexual intercourse, and temperature measuring frequency. Discrete time fecundability models were used to estimate fecundability odds ratios. Results: For the complete cohort the 6-cycle and 12-cycle cumulative pregnancy probabilities were found to be 61% (95% confidence interval [CI]: 59-62) and 74% (95% CI: 73-76), respectively. The median TTP was four cycles. The highest fecundability was associated with an age of less than 35 years, with cycle length variation <5 days and logging sexual intercourse on at least 20% of days added (the proportion of days in which intercourse was logged) (11.5% [n = 613] of entire sample). This group achieved a 6- and 12-cycle cumulative pregnancy probability of 88% (95% CI: 85-91) and 95% (95% CI: 94-97), respectively, and a TTP of 2 cycles. Conclusions: Natural Cycles was an effective method of identifying the fertile window and a noninvasive educational option for women planning a pregnancy. Women under age 35 with regular cycles showed a high pregnancy rate.