Wearable Sensors Offer Opportunities for Phenotyping Perinatal Mood Disorders: a Narrative Review.

Abstract

Pregnancy and the postpartum period are known to elicit changes in sleep, circadian rhythms, and hormonal dynamics. The same biological axes are implicated in anxiety and depression in the general population. Given the high burden of perinatal mental health and the dire consequences of maternal well-being on the mother and infant alike, research differentiating those most at risk is a priority. Why some pregnancies experience these changes without causing mood disorders when many do is unclear but suggests there must be multiple types of disruption, some associated with more risk than others. Current standards of care rely on periodic, and often self-reported, assessments collected around scheduled visits, which can limit opportunities for early detection and timely intervention. We review evidence that wearable technology can complement the gold standard by offering continuous, high-resolution, personalized ecological monitoring of sleep, circadian rhythms, and hormonal dynamics at the individual level. These rich data, coupled with appropriate data science and machine learning techniques enable early typing of pregnancies to support earlier diagnosis and intervention, with the potential for personalized insights, tailored treatment, and faster recovery.