Using big data to debunk common fertility myths
Findings from a mobile application–based cohort are consistent with established knowledge of the menstrual cycle, fertile window, and conception
Published: Journal of Fertility & Sterility
Authors: Dr. Adam Wolfberg
Clinical Pathways is a new series on the Ovia Health blog highlighting the research conducted by our clinical team.
Led by Dr. Adam Wolfberg — practicing OB/GYN, Maternal Fetal Medicine specialist, and Chief Medical Officer of Ovia Health — the Ovia Health clinical team has made important contributions to further the fields of women’s health and reproductive science. They have published manuscripts in the leading fertility and maternal health academic journals and presented their research at 22 conferences, including the American College of Obstetricians and Gynecologists (ACOG) and the American Society for Reproductive Medicine (ASRM).
Recently, the Ovia Health clinical team replicated a landmark fertility study by investigating components of the menstrual cycle and fertile window. Notably, our team conducted the study by analyzing digitally collected, patient-reported data. The results were published in the September 2019 volume of Fertility and Sterility, one of the field’s leading journals.
The study
The original study was published in the New England Journal of Medicine (NEJM) in 1995 by Wilcox et al. and included 221 women (contributing 625 cycles). The result of the study was the identification of the fertile window and the day of the cycle with the highest probability of conception.
Ovia Health replicated the study by analyzing five years of data: 225,596 menstrual cycles from 98,903 women — the largest sample size ever published on the topic. The original NEJM paper relied on traditional methods of data collection, asking participants to provide urine samples and keep a written log throughout their cycle. Ovia was able to collect these same data, without depending on the same onerous data collection processes. Instead, Ovia users simply used the Ovia Fertility app to report details about their cycle, leading to a reinforcement and expansion of the NEJM paper’s original and important findings.
We learned that:
- The greatest likelihood of conceiving occurs the day before ovulation, not the day of ovulation — something that researchers continue to debate.
- The “typical” 28-day menstrual cycle is not so typical after all; many women have longer, shorter, or more irregular cycles than traditionally thought.
- Conditions such as PCOS, endometriosis, and uterine fibroids make it more difficult to conceive, but they’re not definitive impediments. For women with and without these conditions, the ability to track menstrual cycle phases and associated symptoms is an important step to recognize signs of ovulation.
These findings demonstrate that to accurately predict ovulation, those with irregular cycles or cycles longer or shorter than 28 days require a more nuanced approach than traditional calendar methods. Ovia Health’s fertility program is an effective cycle monitoring tool for women trying to conceive — accommodating unique cycles, identifying menstrual phases, and tailoring fertility projections to each user.
Beyond the important synergies between our findings and the landmark NEJM publication, Ovia Health’s conclusions and methods are shifting the conversation around data collection, advancing the field of women’s health by proving the power of digitally-collected, patient-reported data.
Ovia Health’s clinical team is the backbone of our maternity & family benefits solution. The work of our expert clinicians, analysts, and researchers dictates the design of our clinical pathways, helping change behavior and improve outcomes for our members. The team is continuously looking for new ways to make an impact on the health of women and families around the world. Ovia Health’s findings in fertility science, C-sections, mental health, preterm delivery — among many other areas of research — are validating the efficacy of digital health solutions and redefining the maternal and reproductive health landscape.