Real-life insights on menstrual cycles and ovulation using big data

Area of Science:

• Reproductive endocrinology
• Fertility tracking technology
• Data science in healthcare

Background:

• Timing intercourse with the fertile window is crucial for maximizing conception chances.
• Ovulation day exhibits significant inter- and intra-individual variability.

Purpose of the Study:

• To investigate variations in menstrual cycle length and ovulation day among women attempting to conceive.
• To leverage big data from a connected ovulation test system for fertility insights.

Main Methods:

• Analysis of 75,981 menstrual cycles from 32,595 women using a connected ovulation test system.
• Determination of ovulation day from fertility test results and comparison with perceived cycle length.
• Utilized an anonymized cloud database for storing hormone level data and user-inputted cycle information.

Main Results:

• Only 12.4% of users had a 28-day cycle, contrasting with 25.3% who perceived it as such.
• 87% of women had cycle lengths between 23 and 35 days, with over half experiencing variations of 5 days or more.
• A 10-day spread in ovulation days was observed even for a 28-day cycle, with Day 15 being most common.

Conclusions:

• Big data analysis from connected ovulation tests provides personalized fertility insights.
• These insights can empower women trying to conceive to better time intercourse.
• The findings highlight the extensive variability in ovulation timing, crucial for conception planning.