0058 Consumer digital health and person generated health data – opportunities and challenges for sleep disparities research and clinical practice
Authors: Chaturvedi Ritika , PhD Wendy 
Published: May 2022

Abstract

Introduction
Social and structural determinants of health—including economic/educational inequalities, healthcare access, systemic racism, and lifetime stress—account for 60-80% of modifiable risk factors that contribute to sleep health disparities. Many sleep interventions use population averages to create “one-size-fits-all” approaches, but are limited by individual heterogeneity in number, magnitude, interplay, and amplification of social determinants. Person-generated health data (PGHD) from widely available consumer wearable and mobile technologies are emerging tools for developing personalized digital interventions targeting unique, multi-level needs of individuals or populations. PGHD can objectively measure individual lived experiences and biobehavioral health including sleep in an objective, low-cost, accessible, and continuous manner outside of intermittent clinical care. However, PGHD are a form of real-world data and are not captured in controlled research settings, impeding their acceptance and use across the healthcare ecosystem. Most studies involving PGHD use “bring-your-own-device” designs which have systematically underrepresented populations experiencing health disparities, including Black or American Indian/ Alaska Native individuals, and low-income populations, limiting their potential to address health inequities. Further, systemic barriers in the healthcare enterprise, including logistical, implementation, validation, interpretation, reimbursement, privacy, and data security challenges could handicap the entire field. To address these gaps, the American Life in Realtime (ALiR) was developed as a generalizable research infrastructure involving a holistic and sociodemographically representative registry of continuously-collected Fitbit and health data. The current study reviews the current challenges associated with the use of consumer PGHD to measure and improve population-specific sleep health and describes how the ALiR advances a critical community resource to mitigate methodological gaps and fully realize the immense potential of consumer PGHD in an equitable manner.

Methods
Leveraging a multidisciplinary perspective, including biomedical engineering, behavioral psychology, clinical medicine, and health policy and economics, we discuss the state-of-the-science regarding sleep PGHD producing technologies, from basic science to clinical application. We explore the strengths and weaknesses of current and emerging initiatives such as All of Us at the National Institutes of Health. Finally, we introduce ALiR and describe how it’s sample, recruitment methods, and data elements can be used to mitigate field-wide methodological gaps to improve health equity in sleep research.

Results
To date, 1007 individuals consented to participate in ALiR. Racial/ethnic distributions include 65% White, 13% Black, 4% American Indian / Alaska Native, 9% Asian, 1% Hawaiian / Pacific Islander, 8% Mixed, and 26% Hispanic/Latino, with relatively even gender and age distributions. Seventy percent of individuals are without a bachelor’s degree, and 20% have at least one chronic condition (e.g., obesity, cardiovascular disease). Overall response rates exceed 87%, averaging 90% for surveys and 82% for Fitbits. Planned analyses will include a framework for leveraging ALiR to mitigate methodological gaps associated with use of PGHD for sleep health from basic science to clinical application.

Conclusion
ALiR establishes a generalizable research infrastructure to use PGHD to explore the influence of population-specific lived-experiences on sleep and other health outcomes in virtually any population. This novel and ongoing research infrastructure which will ultimately be publically available, providing an invaluable resource to better understand and intervene on sleep health disparities.

Support (If Any)
R01LM013237