R3 Seminar Recap: Advancing Long COVID Research by Fostering Collaboration between RECOVER and the All of Us Research Program
Researchers across NIH are sharing expertise, data, and innovative research tools to make discoveries that could help improve the lives of people with Long COVID.
During the October 8th RECOVER Research Review (R3) Seminar, researchers described how RECOVER and the All of Us Research Program collaborate to advance our understanding of Long COVID. These different National Institutes of Health (NIH) initiatives are sharing health data and developing innovative tools to gain important insights from that data.
Watch the R3 recording below or on YouTube
Chris Lunt (NIH) began the discussion by reviewing what the collaboration hoped to achieve - a way to determine if someone might have Long COVID based on their medical history. He then highlighted several key aspects of All of Us, including:
- The number of Americans (more than 830,000) from diverse backgrounds currently participating in the program and sharing health information
- The wide variety of health data All of Us participants are sharing
- How studies using this data can lead to breakthroughs in precision medicine, or care and treatment customized to a patient’s individual needs
Learn more about All of Us, including how you can get involved.
Dr. Emily Pfaff (University of North Carolina at Chapel Hill) then explained how RECOVER researchers used an advanced computer program called a machine learning (ML) model during the first phase of this collaboration. By examining millions of pieces of health data from the National COVID Cohort Collaborative (N3C), this model (or computer program) was able to detect symptom patterns that could identify people with Long COVID even if they had never been diagnosed with the condition.
Dr. Hiral Master (Vanderbilt University) described how researchers using All of Us data attempted to recreate the findings of (or validate) the RECOVER study. The researchers adapted the model Dr. Pfaff described to analyze All of Us data and found it produced similar results. Dr. Master also shared that the adaptations All of Us performed to make the model work across the different research platforms are now available to any other researcher seeking answers to important questions about Long COVID.
During the Q&A portion of the webinar, the panelists discussed the importance of being able to learn quickly from very large sets of data. They focused specifically on how these learnings might create more positive impacts for people with Long COVID, such as helping future researchers plan for and include people in clinical trials.
To find recordings and transcripts of more R3 seminars, visit the RECOVER YouTube channel and the R3 webpage.