Personalized Maternal Sleep Quality Assessment: An Objective IoT-based Longitudinal Study - Iman Azimi; Olugbenga Oti; Sina Labbaf; Hannakaisa Niela-Vilén; Anna Axelin; Nikil Dutt; Pasi Liljeberg; Amir M. Rahmani (IEEE, 2022)
Sleep is a composite of physiological and behavioral processes that undergo substantial changes during and after pregnancy. These changes might lead to sleep disorders and adverse pregnancy outcomes. Several studies have investigated this issue; however, they were restricted to subjective measurements or short-term actigraphy methods. This is insufficient for a longitudinal maternal sleep quality evaluation. A longitudinal study a) requires a long-term data collection approach to acquire data from everyday routines of mothers and b) demands a sleep quality assessment method exploiting a large volume of multivariate data to assess sleep adaptations and overall sleep quality. In this paper, we present an Internet-of-Things based long-term monitoring system to perform an objective sleep quality assessment. We conduct longitudinal monitoring where 20 pregnant mothers are remotely monitored for six months of pregnancy and one month postpartum. To evaluate sleep quality adaptations, we a) extract several sleep attributes and study their variations during the monitoring and b) propose a semi-supervised machine learning approach to create a personalized sleep model for each subject. The model provides an abnormality score which allows an explicit representation of the sleep quality in a clinical routine, reflecting possible sleep quality degradation with respect to her own data. Sleep data of 13 participants (out of 20) are included in our analysis, as their data are adequate for the study, including 172.15 ± 33.29 days of sleep data per person. Our fine-grained objective measurements indicate the sleep duration and sleep efficiency are deteriorated in pregnancy and notably in postpartum. In comparison to the mid of the second trimester, the sleep model indicates the increase of sleep abnormality at the end of pregnancy (2.87 times) and postpartum (5.62 times). We also show the model enables individualized and effective care for sleep disturbances during pregnancy, as compared to a baseline method.