Physiological Data Collected from Wearable Devices Identify and Predict Inflammatory Bowel Disease Flares.

Background And Aims: Wearable devices capture physiological signals non-invasively and passively. Many of these parameters have been linked to inflammatory bowel disease (IBD) activity. We evaluated the associative ability of several physiological metrics with IBD flares and how they change before the development of flare.

Use of Accelerometry and Global Positioning System (GPS) to Describe Children's Park-Based Physical Activity Among Racial and Ethnic Minority Youth.

Parks are one component of the built environment to promote regular physical activity (PA) among youth. This study investigated differences in park-based PA among youth based on demographics and park features in racially or ethnically diverse communities. In 2017-2018, parks were selected in majority Asian (n = 3), Latino (n = 5), and Black (n = 4) neighborhoods in North Carolina (n = 6) and New York (n = 6).

Remote Short Sessions of Heart Rate Variability Biofeedback Monitored With Wearable Technology: Open-Label Prospective Feasibility Study.

Background: Heart rate variability (HRV) biofeedback is often performed with structured education, laboratory-based assessments, and practice sessions. It has been shown to improve psychological and physiological function across populations. However, a means to remotely use and monitor this approach would allow for wider use of this technique.

Development of the ehive Digital Health App: Protocol for a Centralized Research Platform.

Background: The increasing use of smartphones, wearables, and connected devices has enabled the increasing application of digital technologies for research. Remote digital study platforms comprise a patient-interfacing digital application that enables multimodal data collection from a mobile app and connected sources. They offer an opportunity to recruit at scale, acquire data longitudinally at a high frequency, and engage study participants at any time of the day in any place.

A machine learning approach to determine resilience utilizing wearable device data: analysis of an observational cohort.

Objective: To assess whether an individual's degree of psychological resilience can be determined from physiological metrics passively collected from a wearable device.

Materials And Methods: Data were analyzed in this secondary analysis of the Warrior Watch Study dataset, a prospective cohort of healthcare workers enrolled across 7 hospitals in New York City. Subjects wore an Apple Watch for the duration of their participation.