Infection Watch Study

Completed

• Conditions: Influenza Viral Infections; COVID-19 Respiratory Infection; Respiratory Syncytial Virus Infections; Rhinoviral Infections

• Registration Number: NCT04623047
• Lead Sponsor: Duke University

Brief Summary

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.

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.

Basic Information
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Recruitment & Eligibility
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Study & Design
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Trial Locations

Study Timeline

• Study First Submitted: 2020-11-09
• Study First Submitted QC: 2020-11-09
• Study First Posted: 2020-11-10
• Study Start: 2023-06-28
• Status Verified: 2024-06
• Primary Completion: 2024-06-15
• Study Completion: 2024-06-15
• Last Update Submitted: 2024-06-28
• Last Update Posted: 2024-07-03

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 10034

Inclusion Criteria

• 18 years of age and older

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Exclusion Criteria

• Less than 18 years of age

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Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

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

Secondary Outcome Measures

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

Trial Locations

Locations (1)

Duke University; 🇺🇸 Durham, North Carolina, United States