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Using Smart Watches to Detect and Monitor COVID-19 (CovIdentify)

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ClinicalTrials.gov Identifier: NCT04623047
Recruitment Status : Not yet recruiting
First Posted : November 10, 2020
Last Update Posted : March 9, 2021
Sponsor:
Information provided by (Responsible Party):
Duke University

Brief Summary:
CovIdentify is a research initiative to promote early detection of COVID-19 infections from wearable device data. CovIdentify will primarily be a feasibility study to explore the potential of wearables to detect COVID-19 infection. The investigators will refine our previous statistical and machine learning-based anomaly detection algorithms toward COVID-19 detection using personalized models of health and detect deviations indicative of infection. The investigators will validate and test specificity and sensitivity of the models for detecting COVID-19 infection vs. non-infection against symptom surveys and COVID-19 test results and hospital admissions data.

Condition or disease
Covid19

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Study Type : Observational
Estimated Enrollment : 100000 participants
Observational Model: Other
Time Perspective: Prospective
Official Title: Using Smart Watches to Detect and Monitor COVID-19
Estimated Study Start Date : July 1, 2021
Estimated Primary Completion Date : February 1, 2023
Estimated Study Completion Date : February 1, 2023

Group/Cohort
Adults 18 years of age and up
The study will recruit any adult over the age of 18 years.



Primary Outcome Measures :
  1. Accuracy of predictive model using smart watch data to predict Covid-19 symptoms as measured by self-reports of symptom questionnaire. [ Time Frame: 12 Months ]
  2. Accuracy of predictive model using smart watch data to predict Covid-19 symptoms as measured by COVID-19 and influenza test result. [ Time Frame: 12 Months ]
  3. Accuracy of predictive model using smart watch data to predict Covid-19 symptoms as measured by hospital admission questionnaire. [ Time Frame: 12 Months ]

Secondary Outcome Measures :
  1. Accuracy comparison of symptom-only model to sensor-only model to symptom + sensor model to predict Covid-19 symptoms. [ Time Frame: 12 Months ]


Information from the National Library of Medicine

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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   Yes
Sampling Method:   Non-Probability Sample
Study Population
Adults age 18 years of age and older
Criteria

Inclusion Criteria:

  • 18 years of age and older

Exclusion Criteria:

  • Less than 18 years of age

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04623047


Contacts
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Contact: Jessilyn Dunn 919-668-9798 covidentify@duke.edu

Locations
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United States, North Carolina
Duke University
Durham, North Carolina, United States, 27705
Contact: Jessilyn Dunn       covidentify@duke.edu   
Sponsors and Collaborators
Duke University
Investigators
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Principal Investigator: Chris Woods Duke University
Principal Investigator: Jessilyn Dunn Duke University
Principal Investigator: Ryan Shaw Duke University
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Responsible Party: Duke University
ClinicalTrials.gov Identifier: NCT04623047    
Other Study ID Numbers: PRO00106404
First Posted: November 10, 2020    Key Record Dates
Last Update Posted: March 9, 2021
Last Verified: November 2020
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Duke University:
Wearable Device
Smart Watch