By Neha MathurOct 2 2023Reviewed by Danielle Ellis, B.Sc. In a recent article published in npj Digital Medicine, researchers developed series2signal, a novel, state-of-the-art prediction model that used wearable data from pregnant women to predict gestational age as a proxy for the progression of pregnancy and the likelihood of poor pregnancy outcomes, especially preterm birth , based on wearable-detected changes in maternal sleep quality and physical activity levels.
Maternal physical activity levels and sleep quality are the two robust modulators of stress and physiological inflammation, which can influence the course of pregnancy and increase the likelihood of prematurity. Data on sleep quality and PA, which reflects maternal stress, is easy and inexpensive to collect via wearable devices but difficult to interpret.
About the study In the present study, researchers recruited a cohort of 1083 females from the Washington University School of Medicine and the local community who were planning pregnancy and were willing to wear an actigraphy monitor throughout their pregnancy. The team used Kruskal-Wallis or chi-squared tests, with a significance level of p<0.001, for statistical analyses.
Series2signal performed significantly better than seven other machine learning methods in predicting GA and reliably predicted the risk of PTB. This data could help identify cost-effective interventions to reduce its likelihood. Conclusions To date, the combination of LMP and ultrasound remains more accurate than wearables in determining GA. However, a predictive model combining prediction and AI-based interpretability could aid clinicians in precisely identifying when PA and sleep behavior alterations would increase the likelihood of PTB.