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Completed

NCT Number: NCT04623047

Infection Watch Study

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.

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Key information

Conditions

COVID-19 Respiratory Infection Infections Influenza Viral Infections Mononegavirales Infections Paramyxoviridae Infections Pneumovirus Infections RNA Virus Infections Respiratory Syncytial Virus Infections Rhinoviral Infections Virus Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Duke University

Durham, North Carolina, 27705, United States

About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • 18 years of age and older

Exclusion criteria

  • Less than 18 years of age

Treatment and study plan

Primary outcomes

  1. Develop a forecasting model to predict infection onset prior to symptom onset using the amount of time between known symptom onset and test dates

    Time frame: 18 Months

    Known symptom onset and test dates will serve to validate the model

Secondary outcomes

  1. Determine if there are signal differences that can differentiate the type of respiratory infection (e.g., COVID-19 vs. Influenza)

    Time frame: 18 Months

  2. Percentage of missingness in the wearable device data

    Time frame: 18 Months

    Used to determine the performance of the forecasting model.

  3. Determine the performance of the forecasting model on a new viral strain through transfer learning

    Time frame: 18 Months

  4. Determine if there are physiological differences between initial infection and reinfection

    Time frame: 18 Months

  5. Determine if there are physiological differences between varying respiratory infections over time

    Time frame: 18 Months

  6. Determine the performance of the forecasting model based on the severity of symptoms

    Time frame: 18 Months

Sponsors and collaborators

Lead sponsor

Duke University

Other

Collaborators

  • Biomedical Advanced Research and Development Authority

Registry information

Official study title

Digital Health Technologies for Infectious Disease Monitoring

Important dates

Study start
2023
Primary completion
2024
Study completion
2024
First posted
Nov 10, 2020
Registry last updated
Jul 3, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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