Equity and Efficiency of Using Wearables Data for COVID-19 Monitoring (2020-2021)

The coronavirus pandemic has resulted in over 35 million infections and over 612,000 deaths in the U.S. alone as of mid-August 2021. To quickly identify and isolate new infections and clusters, public health officials are seeking new tools to target diagnostic testing for individuals who exhibit symptoms.

In April 2020, Duke launched CovIdentify to test the viability of using wearables to quickly identify individuals who may have contracted the coronavirus. The CovIdentify platform integrates information from widely used wearables with simple daily electronic self-reports on symptoms and social distancing, for up to 12 months. CovIdentify’s overarching objective is to implement existing digital biomarkers and establish new digital biomarkers to develop, validate and translate CovIdentify as a continuous screening tool. Since April, the study has collected data from over 7,500 individuals using a “bring-your-own-device” (BYOD) model.

This project team enhanced and expanded the CovIdentify study by creating a data pipeline to more easily manage and analyze collected data and modifying the iOS application to more easily collect participant information and reduce barriers to symptom reporting. Team members also increased participant engagement through targeted outreach and examined biases that may occur in the BYOD study model. Their research on biases in study design and the potential effects on downstream technology development resulted in a publication, including a set of guidelines for improving demographic imbalances.


Fall 2020 – Spring 2021

Team Outputs

CovIdentify website

The Demographic Improvement Guideline to Reduce Bias Resulting from Bring-Your-Own-Device Study Design

This Team in the News

Duke Researchers Working on Wearable Tech that could Help with Variety of Medical Issues

Duke Research Study that Tracks COVID-19 via Smartphones, Smartwatches Goes Global

Duke, Partners Expand Project Tracking COVID-19 via Smartwatches, Phones

COVID-19, and the Costs of Big Data

Early Detection of COVID-19: How Your Smartwatch Could Help

See related team, Developing Predictive Models for COVID-19 with Wearables Data (2021-2022).


Team Leaders

  • Peter Cho, Pratt–Biomedical Engineering–Ph.D. Student
  • Jessilyn Dunn, Pratt School of Engineering-Biomedical Engineering
  • Ryan Shaw, School of Nursing

/graduate Team Members

  • Oana Enache, Biostatistics - Master, Biomedical Data Sci-Master
  • Karnika Singh, Biomedical Engineering-PHD
  • Will Wang, Biomedical Engineering-PHD

/undergraduate Team Members

  • Yen Dinh, Biology (BS)
  • Ethan Ho, Biomedical Engineering (BSE)
  • Libba Lawrence, Electrical & Computer Egr(BSE), Computer Science (BSE2)
  • Aneesh Patil, Economics (BS), Political Science (AB2)
  • Rami Sbahi, Computer Science (BS), Statistical Science (BS2)
  • Amanda Stern, Computer Science (BS)
  • Bilge Tatar, Computer Science (BS), Neuroscience (BS2)
  • Jaclyn Xiao, Biomedical Engineering (BSE), Computer Science (BS2)
  • Jaehan Yi, Economics (BS)

/zcommunity Team Members

  • Duke Clinical and Translational Science Institute, Community Engaged Research Initiative