SAN FRANCISCO, May 2, 2017 /PRNewswire/ -- Researchers at the National Institutes of Health are collaborating with Glow to update a model for predicting a couple's chances for achieving a pregnancy. The NIH researchers originally developed a statistical model to account for both male and female attributes, as well as the couple's intercourse pattern relative to the ovulation day. The NIH researchers will work with anonymized Glow data sets to hone and improve the model.
Researchers Germaine Buck Louis and Rajeshwari Sundaram of NIH's Eunice Kennedy Shriver National Institute of Child Health and Human Development published the original study, which analyzed data from an observational study of pre-conceptionally enrolled couples to devise a model of menstrual cycle length and the chances of pregnancy. Next, the NIH researchers will collaborate with researchers at Glow to update the model, using Glow's data sets. The revised model will be submitted for publication to a peer reviewed scientific journal.
Glow is a leading fertility app that delivers accurate, data-driven cycle predictions—allowing women to better track their reproductive health. The main purpose of the app is to help women and couples get pregnant naturally. The company is happy to announce that over 8 million women are now a part of the Glow community, and that over 500,000 pregnancies have been recorded on the Glow app. Glow has agreed to provide its large database of anonymized cycle data to help NIH improve its fertility prediction algorithms.
NIH researchers will modify their algorithm to encompass the variation found in a real-world data set: the anonymized Glow data. The result will be a joint model that will be even more comprehensive and accurate, since it will have learned from anonymized Glow data in addition to study-specific data.
"From the beginning, Glow has aimed to leverage our data to advance medical research," said Ryan Ye, Glow Co-founder and Head of Data Science. "We're honored to partner with NIH in what is the first collaboration of its kind. We look forward to putting our large dataset to work to broaden academic understanding of reproductive health."
SOURCE Glow, Inc.