Can apps and calendar methods predict ovulation with accuracy?

Abstract

Objective: The accuracy of prediction of ovulation by cycle apps and published calendar methods was determined by comparing to true probability of ovulation.

Methods: A total of 949 volunteers collected urine samples for one entire menstrual cycle. Luteinizing hormone was measured to assign surge day, enabling probability of ovulation to be determined across different cycle lengths. Cycle-tracking apps were downloaded. As none provided their methodology, four published calendar-based methods were also examined: standard days, rhythm, alternative rhythm and simple calendar method. The volunteer ovulation data was applied to the app/calendar methods to determine their accuracy.

Results: Mean cycle length was 28 days (range: 23–35); 34% of women believed they had a 28-day cycle, but only 15% did. No LH surge was seen for 99 women. Most likely day of ovulation for a 28-day cycle was day 16 (21%). Accuracy of ovulation prediction was no better than 21% by the apps. The standard days and rhythm methods were most likely to predict ovulation (70% and 89%, respectively) but had very low accuracy.

Conclusions: Ovulation day varies considerably for any given menstrual cycle length, thus it is not possible for calendar/app methods that use cycle-length information alone to accurately predict the day of ovulation.

National Clinical Trial Code: NCT01577147. Registry website: www.clinicaltrials.gov.

Keywords:

• Ovulation prediction
• apps
• menstrual cycle
• fertility

Transparency

Declaration of funding

The study was funded by SPD Development Company Limited, a fully owned subsidiary of SPD Swiss Precision Diagnostics GmbH, the manufacturers of Clearblue pregnancy and fertility tests.

Author contributions: SJ study design, planning, conduct, manuscript writing, final approval of manuscript version to be published. LM study design, data analysis. MZ study design, manuscript writing. All authors agree to be accountable for all aspects of their work.

Declaration of financial/other relationships

S.J. and L.M. have disclosed that they are employees of SPD Development Company Ltd. M.Z. has disclosed that he is an advisory board member for SPD Development Company Ltd.

A peer reviewer on this manuscript was involved with the development and testing of the Standard Days Method for pregnancy prevention, which is one of the calendar-based methods included in this study. They are also currently studying the efficacy of the Dynamic Optimal Timing (Dot) app for pregnancy prevention. CMRO peer reviewers on this manuscript have received an honorarium from CMRO for their review work, and have no other relevant financial relationships to disclose.

Acknowledgements

Dionysia Lymperatou (an employee of SPD Development Company Ltd.) downloaded all the cycle apps and inputted the cycle data to obtain the app predictions. Dr Kathryn Charlwood, integrated medhealth communications (funded by SPD Development Company Ltd.), provided medical writing assistance in the form of manuscript journal styling.