Comprehensive pregnancy monitoring with a network of wireless, soft, and flexible sensors in high- and low-resource health settings.
Dennis RyuDong-Hyun KimJoan T PriceJong Yoon LeeHa Uk ChungEmily AllenJessica R WalterHyoyoung JeongJingyue CaoElena KulikovaHajar Abu-ZayedRachel LeeKnute L MartellMichael ZhangBrianna R KampmeierMarc HillJooHee LeeEdward KimYerim ParkHokyung JangHany M ArafaClaire LiuMaureen ChisembeleBellington VwalikaNtazana SindanoM Bridget SpelkeAmy S PallerAshish PremkumarWilliam A GrobmanJeffrey S A StringerJohn A RogersShuai XuPublished in: Proceedings of the National Academy of Sciences of the United States of America (2021)
Vital signs monitoring is a fundamental component of ensuring the health and safety of women and newborns during pregnancy, labor, and childbirth. This monitoring is often the first step in early detection of pregnancy abnormalities, providing an opportunity for prompt, effective intervention to prevent maternal and neonatal morbidity and mortality. Contemporary pregnancy monitoring systems require numerous devices wired to large base units; at least five separate devices with distinct user interfaces are commonly used to detect uterine contractility, maternal blood oxygenation, temperature, heart rate, blood pressure, and fetal heart rate. Current monitoring technologies are expensive and complex with implementation challenges in low-resource settings where maternal morbidity and mortality is the greatest. We present an integrated monitoring platform leveraging advanced flexible electronics, wireless connectivity, and compatibility with a wide range of low-cost mobile devices. Three flexible, soft, and low-profile sensors offer comprehensive vital signs monitoring for both women and fetuses with time-synchronized operation, including advanced parameters such as continuous cuffless blood pressure, electrohysterography-derived uterine monitoring, and automated body position classification. Successful field trials of pregnant women between 25 and 41 wk of gestation in both high-resource settings (n = 91) and low-resource settings (n = 485) demonstrate the system's performance, usability, and safety.
Keyphrases
- heart rate
- blood pressure
- pregnancy outcomes
- low cost
- pregnant women
- heart rate variability
- healthcare
- public health
- preterm birth
- randomized controlled trial
- type diabetes
- mental health
- primary care
- polycystic ovary syndrome
- birth weight
- deep learning
- preterm infants
- gestational age
- risk assessment
- metabolic syndrome
- adipose tissue
- white matter
- insulin resistance
- multiple sclerosis
- blood flow
- functional connectivity
- single cell
- health promotion
- low birth weight
- social media
- resting state