Pregnancy health in POWERMOM participants living in rural versus urban zip codes

Abstract

Background: Pregnant women living in rural locations in the USA have higher rates of maternal and infant mortality compared to their urban counterparts. One factor contributing to this disparity may be lack of representation of rural women in traditional clinical research studies of pregnancy. Barriers to participation often include transportation to research facilities, which are typically located in urban centers, childcare, and inability to participate during nonwork hours. Methods: POWERMOM is a digital research app which allows participants to share both survey and sensor data during their pregnancy. Through non-targeted, national outreach a study population of 3612 participants (591 from rural zip codes and 3021 from urban zip codes) have been enrolled so far in the study, beginning on March 16, 2017, through September 20, 2019. Results: On average rural participants in our study were younger, had higher pre-pregnancy weights, were less racially diverse, and were more likely to plan a home birth compared to the urban participants. Both groups showed similar engagement in terms of week of pregnancy when they joined, percentage of surveys completed, and completion of the outcome survey after they delivered their baby. However, rural participants shared less HealthKit or sensor data compared to urban participants. Discussion: Our study demonstrated the feasibility and effectiveness of enrolling pregnant women living in rural zip codes using a digital research study embedded within a popular pregnancy app. Future efforts to conduct remote digital research studies could help fill representation and knowledge gaps related to pregnant women.

Passive detection of COVID-19 with wearable sensors and explainable machine learning algorithms

“…While previous studies have shown the importance of remote monitoring of individuals, extending health research beyond the limits of brick and mortar health systems 33 , 34 , additional disparities are introduced when the study relies on wearable sensors, due to reduced accuracy for certain skin tones 35 and unequal access to this digital technology 36 . The decreasing cost of wearable sensors (some now less than $35) and the inner adaptability of our detection algorithm to any sensor and any given level of engagement of the participant with the in-app system will hopefully help in decreasing the barriers for underserved and underrepresented populations.…”

Passive Detection of COVID-19 with Wearable Sensors and Explainable Machine Learning Algorithms

“…While previous studies have shown the importance of remote monitoring of individuals, extending health research beyond the limits of brick and mortar health systems, 32,33 additional disparities are introduced when the study relies on wearable sensors, due to reduced accuracy for certain skin tones 34 and unequal access to this digital technology. 35 The decreasing cost of wearable sensors (some now less than $35) and the inner adaptability of our detection algorithm to any sensor and any given level of engagement of the participant with the in-app system will hopefully help in decreasing the barriers for underserved and underrepresented populations.…”

Using New Technologies and Wearables for Characterizing Sleep in Population-based Studies

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