Read more: Evidation, USC and RAND awarded NIH R01 Research Project Grant to develop a representative digital health dataset and novel data science methods

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Guided by our mission to enable and empower everyone to participate in better health outcomes, Evidation, along with the University of Southern California Schaeffer Center for Health Policy & Economics and the RAND Corporation are partnering to create the American Life in Real-time (ALiR) dataset, a large-scale, nationally representative digital health dataset. The project is supported by a $1.2 million, four-year R01 research project grant from the National Institutes of Health.  

Person-generated health data (PGHD), including data created, recorded, or gathered by individuals to measure health, function, perception, and feeling, has unprecedented potential to add rich insights into our understanding of health. Yet PGHD may be vulnerable to biases and data quality and validity issues, which can exacerbate existing health disparities and inequities in research outcomes and interventions. The objective of this visionary, public-private partnership is to utilize the novel ALiR dataset, alongside Evidation’s large scale connected cohort, Achievement, in order to develop generalizable data science methodologies that address these issues and account for all socio-demographic groups, including the historically underserved.

Led by Ritika Chaturvedi, PhD, Research Scientist at the Leonard D. Schaeffer Center for Health Policy & Economics at USC, and supported by co-investigators Luca Foschini, PhD, Chief Data Scientist at Evidation, and Wendy Troxel, PhD, Senior Behavioral and Social Scientist at RAND Corporation, 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.  

This project is supported by the National Library Of Medicine of the National Institutes of Health under Award Number R01LM013237.  The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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