Developing Predictive Models for COVID-19 with Wearables Data (2021-2022)

As COVID-19 continues to inflict suffering worldwide, data is a powerful tool to track and combat its spread. Previous research has demonstrated that wearable technologies can detect several physiologic and behavioral changes when a user becomes infected with influenza, from heightened resting heart rate to disturbed sleep. These “digital biomarkers” form a signature of infection that can help public health officials track the spread of infectious diseases such as COVID-19 and target diagnostic testing.

This team expanded on the work of the BIG IDEAs Lab CovIdentify study, which was launched in April 2020 to develop an early detection model for SARS-CoV-2 based on wearables data. They worked to house and develop actionable insights from the data as well as develop a cloud-based infrastructure on Microsoft Azure. One subteam constructed an end-to-end data pipeline to preprocess wearable data for analysis; another built data visualization dashboards that connect to real-time survey data to monitor study activity.

Learn more about this project team by viewing the team's video.

Timing

Summer 2021 – Spring 2022 

Team Outputs

Using Wearable Data to Track and Combat Infection (2022 Fortin Foundation Bass Connections Virtual Showcase)

Constructing Cloud-based Infrastructure for COVID-19 Data (poster by Qi Xuan Khoo, Yvonne Kuo, Tommy Tseng, Amrita Lakhanpal, Sean Fiscus, Peter Cho, Md Mobashir Hasan Shandhi, Ali Roghanizad and Jessilyn Dunn, presented at Fortin Foundation Bass Connections Showcase, Duke University, April 13, 2022)

Demographic Imbalances Resulting From the Bring-Your-Own-Device Study Design. Peter Jaeho Cho, Jaehan Yi, Ethan Ho, Md Mobashir Hasan Shandhi, Yen Dinh, Aneesh Patil, Leatrice Martin, Geetika Singh, Brinnae Bent, Geoffrey Ginsburg, Matthew Smuck, Christopher Woods, Ryan Shaw, Jessilyn Dunn. 2022. JMIR 10(4).

A Method for Intelligent Allocation of Diagnostic Testing by Leveraging Data from Commercial Wearable Devices: A Case Study on COVID-19. Md Mobashir Hasan Shandhi, Peter J. Cho, Ali R. Roghanizad, Karnika Singh, Will Wang, Oana M. Enache, Amanda Stern, Rami Sbahi, Bilge Tatar, Sean Fiscus, Qi Xuan Khoo, Yvonne Kuo, Xiao Lu, Joseph Hsieh, Alena Kalodzitsa, Amir Bahmani, Arash Alavi, Utsab Ray, Michael P. Snyder, Geoffrey S. Ginsburg, Dana K. Pasquale, Christopher W. Woods, Ryan J. Shaw, Jessilyn P. Dunn. 2022. NPJ Digital Medicine.

This Team in the News

How You Can Help Scientists Better Understand COVID Variants Through Wearable Devices

See related Data+ summer project, CovIdentify (2021), and earlier related team, Equity and Efficiency of Using Wearables Data for COVID-19 Monitoring (2020-2021).

 

Image: Video still from CovIdentify website

Video still from CovIdentify website.

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

  • Tommy Tseng, Interdisciplinary Data Science - Masters

/undergraduate Team Members

  • Sean Fiscus
  • Qi Khoo, Economics (BS), Computer Science (BS2)
  • Yvonne Kuo, Computer Science (BS)
  • Amrita Lakhanpal, Computer Science (BS)
  • Benjamin Simon, Biomedical Engineering (BSE)

/yfaculty/staff Team Members

  • Leonor Corsino, School of Medicine-Medicine
  • Rosa Gonzalez-Guarda, School of Nursing
  • Leatrice Martin, Ctsi-Clinical

/zcommunity Team Members

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