Digital Solutions to Reduce Maternal Morbidity and Mortality in Pregnant Refugee Women

The goal of this observational study is to use digital health tracking to improve how high blood pressure and other health issues are detected in pregnant refugee women. The main questions this study aims to answer are are: * Can a digital monitoring system that checks for high blood pressure in these women be tested and refined, using clinical training and validation? * Can this digital monitoring system accurately track any related pregnancy health issues and be used to refer participants to care providers? * Can this system be used to accurately identify risks associated with the contraction of pregnancy-related conditions, such as preeclampsia and high blood pressure? Participants will: * Complete baseline and follow-up in-person appointments; * Complete surveys at these appointment that track their health, stress levels, and comorbidities/risks associated with pregnancy; * Be given a smartwatch fitness tracker and electronic blood pressure cuffs for at-home measurements.

The objective of this application, funded by the National Academy of Medicine Catalyst Award, is to maximize the performance of a digital cardiovascular monitoring system to detect gestational hypertension in pregnant refugee women. The central hypothesis is that the application of these digital health technologies will be able to diagnose gestational hypertension in refugee mothers with 85% sensitivity as compared to the gold standard (clinical diagnosis). The rationale for this investigation is to improve the diagnosis of hypertension in refugee mothers, leading to targeted treatment. This study's primary objective is to maximize the performance of digital health monitoring systems to detect gestational hypertension through system training and validation. The investigators aim to do this by utilizing technologies such as smartwatches and electronic blood pressure monitors to track health data throughout enrollment. Tracking this health data will also allow study investigators to track several other comorbid conditions in refugee mothers. This collection will take place over 24 months to train and validate this digital system. The performance of the prediction models will be assessed using two measures: Receiver Operating Characteristic (ROC) curve analysis, which evaluates the model's discrimination ability between disease states; and the Integrated Brier Score (cumulative mean squared error over time) will provide data on the model's predictive accuracy and reliability. The secondary objectives of this study are to document comorbid illness and stress, assess the program's ability to make successful referrals, and connect the study population with healthcare providers for primary care visits. This will be completed by digital health tracking and the retention of contact with the participants throughout the duration of their enrollment.

Inclusion Criteria: * Pregnant * Refugee, asylum seeking, or asylee as designated by the U.S. Government * Greater than or equal to 18 years of age * Has a personal smartphone Exclusion Criteria: * Unable to provide informed consent * Determined by the PI to be in an extremely vulnerable position and therefore not suited for research participation * Planned move from the New York City (NYC) area within the next 24 months