Researchers at the University of Southern California, in collaboration with the RAND Corp. and Evidation Health, have launched an initiative to create what they call the first large-scale, digital health dataset of Americans that is fully representative across all socio-demographic groups.
The American Life in Real-time (ALiR) project, which is being funded by a $1.2 million, four-year grant from the National Institutes of Health, will build a subset of individuals that are nationally representative from an existing survey panel and couple it with Evidation Health’s Achievement Platform, which is designed to help people share digital data from their everyday lives with researchers built on a foundation of user privacy and user control over their health data.
The project will enroll a subset of a long-standing, nationally representative, probability-based survey panel, and provide participants with a Fitbit device to share person-generated health data including physical activity, sleep, and heart rate. Participants also will complete frequent surveys about health-related topics and outcomes. Along with developing new data science methods, this study aims to better understand real-world health and behavior of a representative sample of Americans, the influence of social determinants on digital health technology engagement, and the role of social determinants in sleep behaviors and outcomes.
Biomedical engineer Ritika Chaturvedi, Ph.D., who recently joined the USC Schaeffer Center for Health Policy & Economics, will serve as principal investigator for the project, which will use digital technologies to create precision public health interventions that focus on reducing health disparities among underrepresented populations by focusing on their unique needs and the various dynamic elements that influence health.
“Current research is limited by a lack of complete and representative data sets. Our goal is to change this and ultimately better understand how different populations have different health behaviors and experience different social determinants of health,” said Chaturvedi in a statement. “With that information we hope to create precision public health interventions that meet individual needs, rather than relying on our current one-size-fits-all approach.”
By using a representative sample and including validated digital health technologies, the project offers an opportunity to identify disparities in key health outcomes and metrics, particularly in populations that are vulnerable to negative health outcomes but are currently under-represented in this type of research, explained RAND Senior Behavioral and Social Scientist Wendy Troxel.
Most existing data sets have issues with biased data, which ends up being magnified in research when these data are used for analysis. For example, information collected by internet-enabled devices such as fitness trackers and smart watches is increasingly being used to study public health, but so far, such information has been collected primarily from people who purchase the devices on their own, the researchers note. Those people tend to be young, healthy, affluent and female. Because of this field-wide limitation, resulting interventions may systematically underrepresent the most vulnerable.
The team aims to build a suite of data science tools that overcome systemic bias in data science and artificial intelligence applications of “big data” in healthcare.
