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CovIdentify: Using Smart Watches to Detect and Monitor COVID-19

Sponsor
Duke University (Other)
Overall Status
Not yet recruiting
CT.gov ID
NCT04623047
Collaborator
(none)
100,000
Enrollment
1
Location
19
Anticipated Duration (Months)
5275.1
Patients Per Site Per Month

Study Details

Study Description

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 Intervention/Treatment Phase

    Study Design

    Study Type:
    Observational
    Anticipated Enrollment :
    100000 participants
    Observational Model:
    Other
    Time Perspective:
    Prospective
    Official Title:
    Using Smart Watches to Detect and Monitor COVID-19
    Anticipated Study Start Date :
    Dec 1, 2022
    Anticipated Primary Completion Date :
    Dec 31, 2023
    Anticipated Study Completion Date :
    Jun 30, 2024

    Arms and Interventions

    Arm Intervention/Treatment
    Adults 18 years of age and up

    The study will recruit any adult over the age of 18 years.

    Outcome Measures

    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. [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. [12 Months]

    3. Accuracy of predictive model using smart watch data to predict Covid-19 symptoms as measured by hospital admission questionnaire. [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. [12 Months]

    Eligibility Criteria

    Criteria

    Ages Eligible for Study:
    18 Years and Older
    Sexes Eligible for Study:
    All
    Accepts Healthy Volunteers:
    Yes
    Inclusion Criteria:
    • 18 years of age and older
    Exclusion Criteria:
    • Less than 18 years of age

    Contacts and Locations

    Locations

    Site City State Country Postal Code
    1 Duke University Durham North Carolina United States 27705

    Sponsors and Collaborators

    • Duke University

    Investigators

    • Principal Investigator: Chris Woods, Duke University
    • Principal Investigator: Jessilyn Dunn, Duke University
    • Principal Investigator: Ryan Shaw, Duke University

    Study Documents (Full-Text)

    None provided.

    More Information

    Publications

    None provided.
    Responsible Party:
    Duke University
    ClinicalTrials.gov Identifier:
    NCT04623047
    Other Study ID Numbers:
    • PRO00106404
    First Posted:
    Nov 10, 2020
    Last Update Posted:
    Jul 18, 2022
    Last Verified:
    Nov 1, 2021
    Individual Participant Data (IPD) Sharing Statement:
    No
    Plan to Share IPD:
    No
    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
    Additional relevant MeSH terms:
    COVID-19

    Study Results

    No Results Posted as of Jul 18, 2022