App

Fertility awareness method and app can help women better understand their bodies, are relatively cost-effective, and have no side-effects. But the growth of the “femtech” industry, alongside a seemingly growing wave of younger women; looking to move away from hormonal methods of contraception, has led to a renewed interest.

Yet at the same time, the apps rely on dedicated daily monitoring; and data entry, and strictly abstaining from unprotected sex for several days each month. They also leave significant room for user error. Most importantly, their effectiveness remains to be properly proven with research evidence.

Women are using app

They did not know exactly how many Australian women use these apps; but a 2016 survey found 2.8% of contraceptive users aged 18 to 51; were using a fertility awareness method of contraception. Anecdotal clinical experience points to a small but increasing number of women who may at least be trying a fertility awareness app.

Importantly, not all fertility apps are equal. The earliest apps were designed to predict fertility to optimise a woman’s chance of becoming pregnant. Some apps available today can function either for this purpose; or to prevent pregnancy. Tracking apps such as Period Tracker and Clue simply monitor the menstrual cycle and associated symptoms.

Contraceptive apps go a step further; using an algorithm to compute the fertile and non-fertile days in a woman’s cycle. The app alerts the user to abstain from sex (or use a condom) on fertile days, and indicates on which days unprotected sex is less likely to lead to pregnancy. Most of these apps also collect data on basal body temperature; while some call for a woman to examine her cervical mucus secretions both additional indicators of fertility.

Work of fertility

While the eggs released from the ovary each month survive for around 24 hours; sperm can survive for five days or more in the uterus and fallopian tubes. This means a woman has roughly six fertile days each month. This “fertile window” extends from the five days before ovulation to one day after ovulation. The challenge lies in predicting exactly when ovulation the egg release from the ovary occurs.

Calendar-based methods rely entirely on menstrual cycle length; and an assumption that ovulation occurs 14 days before the next period. But both menstrual cycle length; and ovulation timing are highly variable, making these methods less effective than fertility awareness methods of contraception; which include additional fertility indicators.

A daily check for a sensation of wetness at the entrance to the vagina can also be added. Cervical mucus becomes stretchy and viscous during fertile days to help sperm swim up into the uterus. The number of directed abstinence days varies between apps.

The Natural Cycles app plays it safe with approximately ten “red for no sex” days per cycle; and sends motivational messages to help support adherence. An app called Dot advises 11–13 high risk days, depending on the user’s cycle lengths and other factors. This longer window is likely because this app relies on menstrual cycle length alone, and so may be less precise in predicting ovulation.

Contraceptive effectiveness

Contraceptive effectiveness is based on the number of pregnancies occurring in the first year of use, both in perfect research conditions and in typical everyday use. The oral contraceptive pill is more than 99% effective in perfect use, but up to seven in every 100 women become pregnant in the first year of typical use, due to forgetting pills or running out.

Implants and intrauterine devices (IUDs) are more than 99% effective in each case. High-quality studies for fertility awareness methods of contraception are lacking, but rates are quoted at between 75% for typical use, and up to more than 99% in perfect use. Studies of the Natural Cycles app report a typical use failure rate of 7%, similar to the contraceptive pill.

But these studies have been controversial and their authors have acknowledged limitations in capturing accurate data on unplanned pregnancies. For example, in one study, the pregnancy rate relied on users logging a positive pregnancy test result on the app; but also on assumptions made about possible pregnancies among women who discontinued the app and therefore not followed for the duration of the research.