Cloud-Integrated Smart Nanomembrane Wearables for Remote Wireless Continuous Health Monitoring of Postpartum Women.
Jared MatthewsIra SoltisMichelle Villegas-DownsTara A PetersAnne M FinkJihoon KimLauren ZhouLissette RomeroBarbara L McFarlinWoon-Hong YeoPublished in: Advanced science (Weinheim, Baden-Wurttemberg, Germany) (2024)
Noncommunicable diseases (NCD), such as obesity, diabetes, and cardiovascular disease, are defining healthcare challenges of the 21st century. Medical infrastructure, which for decades sought to reduce the incidence and severity of communicable diseases, has proven insufficient in meeting the intensive, long-term monitoring needs of many NCD disease patient groups. In addition, existing portable devices with rigid electronics are still limited in clinical use due to unreliable data, limited functionality, and lack of continuous measurement ability. Here, a wearable system for at-home cardiovascular monitoring of postpartum women-a group with urgently unmet NCD needs in the United States-using a cloud-integrated soft sternal device with conformal nanomembrane sensors is introduced. A supporting mobile application provides device data to a custom cloud architecture for real-time waveform analytics, including medical device-grade blood pressure prediction via deep learning, and shares the results with both patient and clinician to complete a robust and highly scalable remote monitoring ecosystem. Validated in a month-long clinical study with 20 postpartum Black women, the system demonstrates its ability to remotely monitor existing disease progression, stratify patient risk, and augment clinical decision-making by informing interventions for groups whose healthcare needs otherwise remain unmet in standard clinical practice.
Keyphrases
- healthcare
- cardiovascular disease
- polycystic ovary syndrome
- blood pressure
- case report
- type diabetes
- deep learning
- big data
- clinical practice
- pregnancy outcomes
- decision making
- insulin resistance
- public health
- electronic health record
- heart rate
- cervical cancer screening
- health information
- weight loss
- breast cancer risk
- physical activity
- machine learning
- risk factors
- coronary artery disease
- pregnant women
- clinical trial
- data analysis