Infection Watch Study (CovIdentify)

Digital Health Technologies for Infectious Disease Monitoring

This study will reach out to patients who have undergone diagnostic testing for the following respiratory illnesses from January 1st, 2018 to July 9th, 2023: COVID-19, Influenza, Rhinovirus, and Respiratory Syncytial Virus. This study aims to develop a forecasting model to predict infection onset prior to symptom onset using wearable device data and known symptom onset and test dates.

Study Overview

• Status: Enrolling by invitation
• Conditions: Respiratory Syncytial Virus Infections; COVID-19 Respiratory Infection; Influenza Viral Infections; Rhinoviral Infections

Detailed Description

DUHS patients who have diagnostic testing for Influenza, COVID-19, Respiratory syncytial virus, and Rhinovirus testing within the past 5 years will be initially screened for an email address. Participants will learn about this study via email with a link to complete the survey. A Study ID will be generated for all individuals with an email.

Participants will be asked to complete an e-consent via a REDCap survey. If participants have questions, they are provided with study contact information via e-mail. Participants will complete the survey which will have questions on prior symptoms and device ownership (anticipated time to complete: 5 minutes). If the participant owns one of the following wearable devices (Fitbit, Garmin, or Apple Watch), they will be sent to a redirect URL to login into their device account (for Fitbit or Garmin) or be provided with instructions to export their Healthkit data and dump their data into a unique Strongbox link (for Apple Watch). If participants choose to contribute their wearable device data to the study and the data obtained pass through data quality thresholds, they will receive compensation. There is no compensation for survey completion. The investigators will ask participants if they wish to be re-contacted for future studies related to this project.

The investigators will collect endpoint data values from the wearable. These data will be used to estimate daily activity amounts and intensity (i.e., exercise and walking), standing, sleep amounts, sleep quality, heart rate variability, SpO2, respiratory rate, and heart rate. All of the wearable device data will be identified using a Study ID.

The investigators will use statistical and machine learning models to develop personalized "baseline" models of health and detect anomalies that can help in identifying COVID-19 infection. The investigators will validate and test the sensitivity and specificity of our mode for detecting respiratory infection vs. no infection against symptom surveys and diagnostic testing as ground truth. The model testing and validation will be done separately for each brand of device and will be further modified according to the type of respiratory infection.

• Study Type: Observational
• Enrollment (Estimated): 380000

Contacts and Locations

• Study Contact: Name: Jessilyn Dunn; Phone Number: 919-668-9798; Email: covidentify@duke.edu

Study Locations

• United States
  • North Carolina
    • Durham, North Carolina, United States, 27705
      • Duke University
      • Contact: Jessilyn Dunn; Email: covidentify@duke.edu

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

Adults age 18 years of age and older

Description

Inclusion Criteria:

• 18 years of age and older

Exclusion Criteria:

• Less than 18 years of age

Study Plan

How is the study designed?

Design Details

• Observational Models: Other
• Time Perspectives: Prospective

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort
Adults 18 years of age and up; The study will recruit any adult over the age of 18 years.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Develop a forecasting model to predict infection onset prior to symptom onset using the amount of time between known symptom onset and test dates	Known symptom onset and test dates will serve to validate the model	18 Months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Determine if there are signal differences that can differentiate the type of respiratory infection (e.g., COVID-19 vs. Influenza)		18 Months
Percentage of missingness in the wearable device data	Used to determine the performance of the forecasting model.	18 Months
Determine the performance of the forecasting model on a new viral strain through transfer learning		18 Months
Determine if there are physiological differences between initial infection and reinfection		18 Months
Determine if there are physiological differences between varying respiratory infections over time		18 Months
Determine the performance of the forecasting model based on the severity of symptoms		18 Months

Collaborators and Investigators

• Sponsor: Duke University
• Collaborators: Biomedical Advanced Research and Development Authority
• Investigators: Principal Investigator: Chris Woods, Duke University; Principal Investigator: Jessilyn Dunn, Duke University; Principal Investigator: Ryan Shaw, Duke University

Study record dates

Study Major Dates

• Study Start (Actual): June 28, 2023
• Primary Completion (Estimated): December 31, 2024
• Study Completion (Estimated): February 10, 2025

Study Registration Dates

• First Submitted: November 9, 2020
• First Submitted That Met QC Criteria: November 9, 2020
• First Posted (Actual): November 10, 2020

Study Record Updates

• Last Update Posted (Actual): October 13, 2023
• Last Update Submitted That Met QC Criteria: October 11, 2023
• Last Verified: October 1, 2023

More Information

Terms related to this study

• Keywords: Wearable Device; Smart Watch
• Additional Relevant MeSH Terms: Pathologic Processes; RNA Virus Infections; Respiratory Tract Diseases; Disease Attributes; Paramyxoviridae Infections; Mononegavirales Infections; Pneumovirus Infections; Infections; Communicable Diseases; Virus Diseases; Respiratory Tract Infections; Respiratory Syncytial Virus Infections
• Other Study ID Numbers: PRO00106404

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No