An interdisciplinary research team at Washington University in St. Louis has found that variability in sleep patterns in people experiencing pregnancy can effectively predict preterm birth. Research team used machine learning models to analyze sleep data from pregnant participants. While disrupted sleep is known as a predictor of preterm birth, which is delivery before 37 weeks’ gestation, the reasons behind it have been unclear because the data was self-reported by patients.
The patients wore a clinically validated wristwatch, called an actigraph, that measured body movements for roughly two-week periods. The data allows the team to extract daily patterns in the length of sleep, what time the patients went to sleep and woke up, their movement during sleep and several other variables. Research team combined the two sources of data and plugged them into machine-learning models to learn the impact of sleep patterns on preterm birth.
Research team model shows that a machine-learning model is better than a more statistical model, who also is a member of the Center for Reproductive Health Sciences. Based on that, a direction of a potential intervention is to promote a more consistent sleep schedule. Research team hoping that this will be much more helpful in getting predictive power of women who are going to be at higher risk.
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