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.

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.

Natural Cycles app: contraceptive outcomes and demographic analysis of UK users.

PURPOSE: Digital fertility awareness-based contraception offers an alternative choice for women who do not wish to use hormonal or invasive methods. The aim of this study was to investigate the key demographics of current users of the Natural Cycles app and assess the contraceptive outcomes of women preventing pregnancy in a UK cohort of women. MATERIALS AND METHODS: This was a real world observational prospective observational study. The typical-use effectiveness of the method was calculated using both 13-cycle cumulative probability of pregnancy (life table analysis) and Pearl Index for the entire study cohort. Perfect-use PI was calculated using data from cycles where sexual intercourse during the fertile window was marked as protected and no unprotected sex was recorded on fertile days. RESULTS: 12,247 women were included in the study and contributed an average of 9.9 months of data for a total of 10,066 woman years of exposure. The mean age of the cohort was 30, mean BMI 23.4, the majority were in a stable relationship (83.2%) and had a university degree or higher (83%). The one year typical use, PI was 6.1 (95% CI: 5.6, 6.6) and with perfect-use was 2.0 (95% CI: 1.3, 2.8). 13 cycle pregnancy probability was 7.1%. CONCLUSIONS: This is the first study which describes the use of a digital contraceptive by women in the UK. It describes the demographics of users and how they correlate with the apps effectiveness at preventing pregnancy.

Contraceptive Effectiveness of an FDA-Cleared Birth Control App: Results from the Natural Cycles U.S. Cohort.

Background: Digital fertility awareness-based methods of birth control are an attractive alternative to hormonal or invasive birth control for modern women. They are also popular among women who may be planning a pregnancy over the coming years and wish to learn about their individual menstrual cycle. The aim of this study was to assess the effectiveness of the Natural Cycles app at preventing pregnancy for a cohort of women from the United States and to describe the key demographics of current users of the app in such a cohort. Materials and Methods: This prospective real-world cohort study included users who purchased an annual subscription to prevent pregnancy. Demographics were assessed through answers to in-app questionnaires. Birth control effectiveness estimates for the entire cohort were calculated using 1-year pearl index (PI) and 13-cycle cumulative pregnancy probability (Kaplan-Meier life table analysis). Results: The study included 5879 women who contributed an average of 10.5 months of data for a total of 5125 woman-years of exposure. The average user was 30 years old with a body mass index of 24 and reported being in a stable relationship. With typical use, the app had a 13-cycle cumulative pregnancy probability of 7.2% and a 1-year typical use PI of 6.2. When the app was used under perfect use, the PI was 2.0. Conclusions: The data presented in this study give insights into the cohort of women using this app in the United States, and provide country-specific effectiveness estimates. The contraceptive effectiveness of the app was in line with previously published figures from Natural Cycles (PI of seven for typical use and two for perfect use).

Advantages of determining the fertile window with the individualised Natural Cycles algorithm over calendar-based methods.

Purpose: This study aims to compare the accuracy of fertile window identification with the contraceptive app Natural Cycles against the Rhythm Method and Standard Days Method (SDM).Materials and methods: Menstruation dates, basal body temperature (BBT), and luteinising hormone (LH) test results were collected anonymously from Natural Cycles app users. The fraction of green days (GDs) and wrong green days (WGDs) allocated by the various algorithms was determined over 12 cycles. For comparison of Natural Cycles and the Rhythm Method, 26,626 cycles were analysed.Results: Natural Cycles' algorithms allocated 59% GDs (LH, BBT) in cycle 12, while the fraction of WGDs averaged 0.08%. The Rhythm Method requires monitoring of six cycles, resulting in no GDs or WGDs in cycle 1-6. In cycle 7, 49% GDs and 0.26% WGDs were allocated. GDs and WGDs decreased to 43% and 0.08% in cycle 12. The probabilities of WGDs on the day before ovulation with Natural Cycles were 0.31% (BBT) and 0% (LH, BBT), and 0.80% with the Rhythm Method. The probability of WGDs on the day before ovulation was 6.90% with the SDM.Conclusions: This study highlights that individualised algorithms are advantageous for accurate determination of the fertile window and that static algorithms are more likely to fail during the most fertile days.

Real-world menstrual cycle characteristics of more than 600,000 menstrual cycles.

The use of apps that record detailed menstrual cycle data presents a new opportunity to study the menstrual cycle. The aim of this study is to describe menstrual cycle characteristics observed from a large database of cycles collected through an app and investigate associations of menstrual cycle characteristics with cycle length, age and body mass index (BMI). Menstrual cycle parameters, including menstruation, basal body temperature (BBT) and luteinising hormone (LH) tests as well as age and BMI were collected anonymously from real-world users of the Natural Cycles app. We analysed 612,613 ovulatory cycles with a mean length of 29.3 days from 124,648 users. The mean follicular phase length was 16.9 days (95% CI: 10-30) and mean luteal phase length was 12.4 days (95% CI: 7-17). Mean cycle length decreased by 0.18 days (95% CI: 0.17-0.18, R 2 = 0.99) and mean follicular phase length decreased by 0.19 days (95% CI: 0.19-0.20, R 2 = 0.99) per year of age from 25 to 45 years. Mean variation of cycle length per woman was 0.4 days or 14% higher in women with a BMI of over 35 relative to women with a BMI of 18.5-25. This analysis details variations in menstrual cycle characteristics that are not widely known yet have significant implications for health and well-being. Clinically, women who wish to plan a pregnancy need to have intercourse on their fertile days. In order to identify the fertile period it is important to track physiological parameters such as basal body temperature and not just cycle length.

Short- and long-term effect of contraceptive methods on fecundity.

Objective: The aim of the study was to compare the effect of previously used contraceptive methods on women's short- and long-term fecundity. Use of hormonal contraception (HC) was compared with the use of a contraceptive mobile application (app). Methods: This real-life prospective observational study comprised 2874 women who were attempting to become pregnant using the Natural Cycles mobile app to monitor their fertility. The women registered to use the app between August 2014 and June 2016 with the intention of planning a pregnancy and had previously either used the same app to prevent pregnancy or had recently discontinued HC use. We calculated the average time to pregnancy (TTP) for all women who became pregnant during the study and performed Kaplan-Meier life-table analysis to examine the cumulative probabilities of pregnancy for all women in the study. Results: The average TTP was 2.3 (95% confidence interval [CI] 2.1, 2.4) and 3.7 (95% CI 3.4, 3.9) cycles for women who had previously used Natural Cycles and HC, respectively. The time to reach 30% pregnancy probability for women previously on HC was 1.6 (95% CI 1.5, 1.8) times longer than for women previously using Natural Cycles. There was no significant difference in the 13 cycle cumulated pregnancy probability between the two groups. Conclusion: The results show that fertility awareness-based methods of contraception increase short-term pregnancy rates relative to HC, but have no effect on long-term pregnancy rates.

Typical use effectiveness of Natural Cycles: postmarket surveillance study investigating the impact of previous contraceptive choice on the risk of unintended pregnancy.

OBJECTIVE: To investigate the association between contraceptive effectiveness of Natural Cycles and users' previous choice of contraceptive, and to evaluate the impact of shifting from other methods to Natural Cycles on the risk of unintended pregnancy. SETTING: Natural Cycles mobile application. PARTICIPANTS: 16 331 Natural Cycles users in Sweden for the prevention of pregnancy. OUTCOME MEASURES: Risk of unintended pregnancy. STUDY DESIGN: Real world evidence was collected from Natural Cycles users regarding contraceptive use prior to using Natural Cycles and sexual activity while using Natural Cycles. We calculated the typical use 1-year Pearl Index (PI) and 13-cycle failure rate of Natural Cycles for each cohort. The PI was compared with the population PI of their stated previous methods. RESULTS: For women who had used condoms before, the PI of Natural Cycles was the lowest at 3.5±0.5. For women who had used the pill before, the PI of Natural Cycles was the highest at 8.1±0.6. The frequency of unprotected sex on fertile days partially explained some of the observed variation in PI between cohorts. 89% of users switched to Natural Cycles from methods with higher or similar reported PIs. CONCLUSION: The effectiveness of Natural Cycles is influenced by previous contraceptive choice and this should be considered when evaluating the suitability of the method for the individual. We estimate that Natural Cycles usage can reduce the overall likelihood of having an unintended pregnancy by shifting usage from less effective methods.

Ground-state oxygen holes and the metal-insulator transition in the negative charge-transfer rare-earth nickelates.

The metal-insulator transition and the intriguing physical properties of rare-earth perovskite nickelates have attracted considerable attention in recent years. Nonetheless, a complete understanding of these materials remains elusive. Here we combine X-ray absorption and resonant inelastic X-ray scattering (RIXS) spectroscopies to resolve important aspects of the complex electronic structure of rare-earth nickelates, taking NdNiO3 thin film as representative example. The unusual coexistence of bound and continuum excitations observed in the RIXS spectra provides strong evidence for abundant oxygen holes in the ground state of these materials. Using cluster calculations and Anderson impurity model interpretation, we show that distinct spectral signatures arise from a Ni 3d8 configuration along with holes in the oxygen 2p valence band, confirming suggestions that these materials do not obey a conventional positive charge-transfer picture, but instead exhibit a negative charge-transfer energy in line with recent models interpreting the metal-insulator transition in terms of bond disproportionation.

Fertility awareness-based mobile application for contraception.

OBJECTIVES: The aim of the study was to retrospectively evaluate the effectiveness of a fertility awareness-based method supported by a mobile-based application to prevent unwanted pregnancies as a method of natural birth control. METHODS: In a retrospective analysis, the application's efficiency as a contraceptive method was examined on data from 4054 women who used the application as contraception for a total of 2085 woman-years. RESULTS: The number of identified unplanned pregnancies was 143 during 2053 woman-years, giving a Pearl Index of 7.0 for typical use. Ten of the pregnancies were due to the application falsely attributing a safe day within the fertile window, producing a perfect-use Pearl Index of 0.5. Calculating the cumulative pregnancy probability by life-table analysis resulted in a pregnancy rate of 7.5% per year (95% confidence interval 5.9%, 9.1% per year). CONCLUSIONS: The application appears to improve the effectiveness of fertility awareness-based methods and can be used to prevent pregnancies if couples consistently protect themselves on fertile days.

Identification and prediction of the fertile window using NaturalCycles.

OBJECTIVES: The aim of the study was to evaluate the ability of a novel web and mobile application to identify a woman's ovulation day and fertile window, in order to use it as a method of natural birth control. METHODS: A retrospective study was performed on 1501 cycles of 317 women aged 18 to 39 years. Women entered their basal body temperatures, ovulation test results and date of menstruation into the application. RESULTS: The mean delay from the first positive ovulation test to the temperature-based estimation of the ovulation day was 1.9 days; the length of the luteal phase varied on average by 1.25 days per user. Only 0.05% of non-fertile days were falsely attributed and found within the fertile window. CONCLUSIONS: The method is effective at identifying a user's ovulation day and fertile window and can therefore be used as a natural method of birth control.

Exchange bias in LaNiO3-LaMnO3 superlattices.

The wide spectrum of exotic properties exhibited by transition-metal oxides stems from the complex competition between several quantum interactions. The capacity to select the emergence of specific phases at will is nowadays extensively recognized as key for the design of diverse new devices with tailored functionalities. In this context, interface engineering in complex oxide heterostructures has developed into a flourishing field, enabling not only further tuning of the exceptional properties of these materials, but also giving access to hidden phases and emergent physical phenomena. Here we demonstrate how interfacial interactions can induce a complex magnetic structure in a non-magnetic material. We specifically show that exchange bias can unexpectedly emerge in heterostructures consisting of paramagnetic LaNiO3 (LNO) and ferromagnetic LaMnO3 (LMO). The observation of exchange bias in (111)-oriented LNO-LMO superlattices, manifested as a shift of the magnetization-field loop, not only implies the development of interface-induced magnetism in the paramagnetic LNO layers, but also provides us with a very subtle tool for probing the interfacial coupling between the LNO and LMO layers. First-principles calculations indicate that this interfacial interaction may give rise to an unusual spin order, resembling a spin-density wave, within the LNO layers.