Delivered in the form of an app, Natural Cycles is a digital fertility awareness-based contraceptive and pregnancy planner that uses a sophisticated algorithm to accurately and conveniently determine a woman’s daily fertility based on basal body temperature.
Women take their temperature with a basal thermometer first thing in the morning (when body temperature is at its lowest) and enter the information into the app. That data builds into a personalized fertility indicator that informs her when she needs to use protection, or time intercourse to minimize or maximize the chance of conception.
When used for contraception, Natural Cycles automatically stores and analyzes data inputs via a mobile device, reducing women’s need for prolonged education on method use or time spent manual charting and analyzing data, which is required with traditional FABM and NFP techniques.
In a retrospective analysis of more than 15,000 women with a total exposure time of 7,353 women years, Natural Cycles demonstrated a typical use Pearl Index of 6.5. This means that, on average, 6.5 women in a year will get pregnant when using Natural Cycles. This includes method failure as well as user error.
The effectiveness of Natural Cycles has been evaluated in clinical studies. In a large prospective, observational clinical study that involved over 22,000 women with an average age of 29.2 years, Natural Cycles has demonstrated a typical-use effectiveness rate of 93% for contraception (see our Publication and Clinical Findings).
“Natural Cycles identifies the fertile window for each individual woman based on her unique cycle pattern, changes in hormone levels and temperature fluctuations.”
How does Natural Cycles work?
Women are only at risk of pregnancy during their fertile window, which falls within six days of their cycle. This includes the day of ovulation, and the five preceding days (which covers the length of time that sperm can survive in the body).¹ Without knowing when these fertile days are, contraception is needed throughout the cycle.
Natural Cycles uses a sophisticated algorithm to analyse a woman’s basal body temperature (BBT) and, based on this information, can accurately determine her daily fertility. Green days indicate that she is not fertile and can have intercourse without protection. Red days indicate that she is very likely to be fertile and must use protection or abstain from intercourse to avoid a pregnancy.
The algorithm adapts to every woman’s unique cycle pattern by learning over time, as she adds more data (at least 5 values per week are recommended). The number of green days increases accordingly, meaning the more consistent and accurate data supplied, the fewer fertile days the algorithm is likely to allocate. Women also need to enter their menstruation dates, and can supplement the BBT data by measuring their Luteinising Hormone (LH) levels by means of a simple urine test strip and entering this data into the app. This helps the algorithm get to know her cycle faster.
The image above illustrates the increase in temperature that occurs after ovulation, and how the hormone levels influence the basal temperature.
The Natural Cycles algorithm is a bio-statistical model that calculates a woman’s fertility based on user-logged data on daily BBT and menstrual cycles. The algorithm has been intricately designed to account for sperm survival, variation in cycle length, ovulation day, temperature fluctuations and the length of the follicular and luteal phase, and is sensitive to subtle patterns in a woman’s cycle.
- Ovulation is detected by identifying the increase in BBT associated with the progesterone surge around ovulation. Logged BBT is tested against values of her past follicular or luteal phases to determine where she is in her cycle to determine whether the individual is within the follicular or luteal phase. These calculations are affected by factors such as the likelihood of ovulation at that point in the menstrual cycle and the outcome of a luteinizing hormone (LH) test.
- Ovulation prediction is based on a weighted average of when ovulation occurred in preceding cycles. The algorithm assigns the 5 days prior to the predicted ovulation day plus approximately three times the number of standard deviation days as fertile (red) days. Thus, a user who ovulated on Day 16 with a standard deviation of two days will have red days from Day 6 until ovulation has been detected.