Background Heart rate is routinely measured as part of the clinical examination but is rarely acted upon unless it is well outside a population-based normal range. With wearable sensor technologies, heart rate can now be continuously measured, making it possible to accurately identify an individual's "normal" heart rate and potentially important variations in it over time. Our objective is to describe inter-and intra-individual variability in resting heart rate (RHR) collected over the course of two years using a wearable device, studying the variations of resting heart rate as a function of time of year, as well as individuals characteristics like age, sex, average sleep duration, and body mass index (BMI).

<b><i>Background:</i></b> The availability of a wide range of innovative wearable sensor technologies today allows for the ability to capture and collect potentially important health-related data in ways not previously possible. These sensors can be adopted in digitalized clinical trials, i.e., clinical trials conducted outside the clinic to capture data about study participants in their day-to-day life. However, having participants activate, charge, and wear the digital sensors for long hours may prove to be a significant obstacle to the success of these trials. <b><i>Objective:</i></b> This study explores a broad question of wrist-wearable sensor effectiveness in terms of data collection as well as data that are analyzable per individual. The individuals who had already consented to be part of an asymptomatic atrial fibrillation screening trial were directly sent a wrist-wearable activity and heart rate tracker device to be activated and used in a home-based setting. <b><i>Methods:</i></b> A total of 230 participants with a median age of 71 years were asked to wear the wristband as frequently as possible, night and day, for at least a 4-month monitoring period, especially to track heart rhythm during sleep. <b><i>Results:</i></b> Of the individuals who received the device, 43% never transmitted any data. Those who used the device wore it a median of ∼15 weeks (IQR 2–24) and for 5.3 days (IQR 3.2–6.5) per week. For rhythm detection purposes, only 5.6% of all recorded data from individuals were analyzable (with beat-to-beat intervals reported). <b><i>Conclusions:</i></b> This study provides some important learnings. It showed that in an older population, despite initial enthusiasm to receive a consumer-quality wrist-based fitness device, a large proportion of individuals never activated the device. However, it also found that for a majority of participants it was possible to successfully collect wearable sensor data without clinical oversight inside a home environment, and that once used, ongoing wear time was high. This suggests that a critical barrier to overcome when incorporating a wearable device into clinical research is making its initiation of use as easy as possible for the participant.

Drs Jaiswal and Quer had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs Jaiswal and Quer equally contributed to the analysis of the data, the discussion, and the preparation of the manuscript and are co-first authors.

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.