Clinical trial NCT04623047: Infection Watch Study
Official title: Digital Health Technologies for Infectious Disease Monitoring
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: COVID-19 Respiratory Infection
Respiratory Syncytial Virus Infections
Rhinoviral Infections
Influenza Viral Infections
Keywords: Wearable Device
Smart Watch
Lead sponsor: Duke University
Sponsor class: OTHER
Collaborators: name: Biomedical Advanced Research and Development Authority
First posted: 2020-11-10
Enrollment: 10034
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: OTHER
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Adults age 18 years of age and older

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.

Eligibility criteria: Inclusion Criteria:

* 18 years of age and older

Exclusion Criteria:

* Less than 18 years of age

Primary outcomes:
measure: Develop a forecasting model to predict infection onset prior to symptom onset using the amount of time between known symptom onset and test dates
timeFrame: 18 Months
Secondary outcomes:
measure: Determine if there are signal differences that can differentiate the type of respiratory infection (e.g., COVID-19 vs. Influenza)
timeFrame: 18 Months
measure: Percentage of missingness in the wearable device data
timeFrame: 18 Months
measure: Determine the performance of the forecasting model on a new viral strain through transfer learning
timeFrame: 18 Months
measure: Determine if there are physiological differences between initial infection and reinfection
timeFrame: 18 Months
measure: Determine if there are physiological differences between varying respiratory infections over time
timeFrame: 18 Months
measure: Determine the performance of the forecasting model based on the severity of symptoms
timeFrame: 18 Months
Other outcomes:
Locations:
city: Durham
country: United States
facility: Duke University
state: North Carolina
Study officials:
affiliation: Duke University
name: Chris Woods
role: PRINCIPAL_INVESTIGATOR
affiliation: Duke University
name: Jessilyn Dunn
role: PRINCIPAL_INVESTIGATOR
affiliation: Duke University
name: Ryan Shaw
role: PRINCIPAL_INVESTIGATOR
References: