Sarah Johnson1, Lorrae Marriott2, Michael Zinaman3. 1. a SPD Development Company Ltd , Clinical and Regulatory Affairs , Bedford , UK. 2. b SPD Development Company Ltd , Statistics and Data Management , Bedford , UK. 3. c Department of Obstetrics & Gynecology and Women's Health, Albert Einstein College of Medicine , New York , NY , USA.
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 .
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 .
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Authors: Sarah Johnson; Joseph B Stanford; Graham Warren; Sharon Bond; Sharon Bench-Capon; Michael J Zinaman Journal: J Womens Health (Larchmt) Date: 2019-09-04 Impact factor: 2.681