Infection Watch Study

Respiratory Syncytial Virus Infections Clinical Trial

Official title:

Digital Health Technologies for Infectious Disease Monitoring

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04623047
• Other study ID #: PRO00106404
• Status: Completed
• Start date: June 28, 2023
• Est. completion date: June 4, 2024

Study information

• Verified date: June 2024
• Source: Duke University
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 10034
• Est. completion date: June 4, 2024
• Est. primary completion date: June 4, 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• 18 years of age and older

Exclusion Criteria:

• Less than 18 years of age

Related Conditions & MeSH terms

• Communicable Diseases
• Infections
• Influenza Viral Infections
• Respiratory Syncytial Virus Infections
• Respiratory Tract Infections
• Rhinoviral Infections
• Virus Diseases

Locations

Country	Name	City	State
United States	Duke University	Durham	North Carolina

Sponsors (2)

Lead Sponsor	Collaborator
Duke University	Biomedical Advanced Research and Development Authority

Country where clinical trial is conducted

United States, 

Outcome

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