Ignite Creation Date:
2024-05-06 @ 3:24 PM
Last Modification Date:
2024-10-26 @ 1:49 PM
Study NCT ID:
NCT04623047
Status:
COMPLETED
Last Update Posted:
2024-07-03
First Post:
2020-11-09
Brief Title:
Infection Watch Study
Sponsor:
Duke University
Organization:
Duke University
Study Overview
Official Title:
Digital Health Technologies for Infectious Disease Monitoring
Status:
COMPLETED
Status Verified Date:
2024-06
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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 ie 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
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 ie 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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None