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.