Description

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.