There are more than 400 fertility apps available, and over 100 million women worldwide are using them.
In years gone by, women would rely on the calendar on the wall to work out when their next menstrual cycle might occur. They would look to physical signs to tell them when they might be ovulating, and therefore when they’d be most likely to fall pregnant.
More recently, we’ve seen the proliferation of mobile phone applications helping women track their current cycle, predict their next cycle, and work out when the best time is to try for a baby.
The personalisation and convenience of apps makes them empowering and attractive. But they require some caution in their use.
While fertility apps use individualised information to estimate the most fertile period, they are not completely reliable. And even if an app indicates when a woman is most fertile, it doesn’t mean a pregnancy will follow if a couple has sex during this window.
How the apps work
When a woman logs the beginning of her menstrual cycle, fertility apps attempt to predict, using inbuilt algorithms, when ovulation might occur. The app then recommends the timing of intercourse accordingly to optimise the user’s chance of becoming pregnant.
Calendar-based apps rely entirely on menstrual cycle length and an assumption ovulation occurs 14 days before the next period.
Many of the more sophisticated apps collect data on basal body temperature, while some also call for a woman to examine her cervical mucus secretions, or include results from at-home ovulation test kits.
There may be ancillary options to track mood and feelings, diet and exercise, and sexual intercourse.
Are Fertility Apps effective?
Australian researchers recently looked at 36 fertility apps most commonly downloaded by Australian women. The research, yet to be published, indicated less than half (42.7%) of the apps predicted the correct ovulation date.
A published study looking at 12 fertility apps found the calendar-based apps did not correctly determine the ovulation date when the average length of previous cycles was different to the estimated current cycle length. The prediction of fertile days based solely on previous cycle lengths is a clear limitation of calendar-based apps.
For apps collecting temperature data, the prediction of highly fertile days was also commonly missed due to the use of data from previous rather than current cycles.
It’s likely the apps which request more information will have better accuracy. But their effectiveness also relies on the user entering information correctly and consistently.