For all their potential to drive changes in health, wearables have struggled to gain a foothold in medicine. The dramatic changes during pregnancy are a fertile ground to test their potential, though — and new research shows how applying machine learning methods to streams of data from wearable devices could be used to understand the mystery of premature birth.
Machine learning researchers at Stanford University used a deep learning model to analyze wearable activity and sleep data from pregnant participants. No surprise: Their sleep typically got worse and their activity slowed down over the course of pregnancy. But some participants had data profiles that didn’t match their pregnancy stage — and it was those pregnancies, the researchers found, that were more likely to result in a preterm birth.
Preterm birth is the leading cause of death in children under five around the world, and in the United States, about 11% of all live births occur before 37 weeks of gestation — a number that’s been steadily increasing over the last decade. Black women are particularly at risk, being about 1.5 times as likely as white women to deliver prematurely.
This article is exclusive to STAT+ subscribers
Unlock this article — and get additional analysis of the technologies disrupting health care — by subscribing to STAT+.
Already have an account? Log in
Already have an account? Log in
About the Author Reprints
To submit a correction request, please visit our Contact Us page.
STAT encourages you to share your voice. We welcome your commentary, criticism, and expertise on our subscriber-only platform, STAT+ Connect