JMIR Publications

ABSTRACT

Background:

Wearable biometric monitoring technologies (BioMeTs) have become increasingly popular in recent years, especially as BioMeTs have been improving, offering more capabilities and greater functionality to assess behaviors and physiology in free-living conditions. The All of Us Research Program (AoURP) is an initiative that is seeking to collect health-related information, such as BioMeT data, from a diverse cohort of over 1 million participants in the United States. AoURP participant BioMeT data is collected along with their electronic health record (EHR), biospecimens, surveys, and standardized physical measurements. The goal is for this data to be accessible to both researchers and participants to advance precision diagnosis, prevention, and treatment [1]. AoURP offers enhanced data access and analysis tools on its Researcher Workbench. The workbench includes access to BioMeT data, such as Fitbits [2]. With the increased use of such wearable devices in AoURP and other research and clinical settings, researchers might ask “How reliable are these devices?”, “What are the sources of biases one needs to account for when using this data for research?”, and “How does one account for said biases?”. In this article, we examine the use of Fitbit data in the context of data originating from the AoURP. We focus on Fitbit devices given their wide market share [3, 4], the ongoing data-collection from Fitbit users in the All Of Us Research Program (e.g., Bring-Your-Own-Device Program) [5, 6], and this data’s availability to registered Researcher Workbench users [7]. Specifically, this article focuses on physical activity (steps and intensities) and heart rate data generated by Fitbits on a per day and per minute basis. Objectives: The aim of this tutorial is to (1) provide a background on the reliability and potential sources of bias associated with Fitbit data and (2) outline error mitigation strategies that can be considered to alleviate these biases.