dbGaP Study Accession: phs002523

NIH Institute/Center: NINR

RADx Data Program: RADx-rad

DOI: 10.60773/dwzp-am60

Release Date: 11/07/2022

Updated Date: 01/17/2024

Study Description: The data collection system was extended and improved in using wearables for the early detection of COVID-19 infection. With over 5,000 patients recruited in the original Phase 1 IRB-approved study, the existing algorithms were further optimized, especially on its application in individuals with diverse ethnic backgrounds (Aim 1). This study further integrated and optimized the Personal Health Dashboard (MyPHD) to enable real-time online detection of COVID-19 (Aim 2). MyPHD was deployed on the different concentrated populations including pediatric kidney transplant patients, Pac-12 student athletes, and senior living residents (Aim 3).

Principal Investigator: Snyder, Michael P

Has Data Files: Yes

Study Domain: Biosensor Technology; Disease Surveillance; Multimodal Surveillance; Digital Health Applications

Data Collection Method: Wearable

Keywords: Predictive Markers; Transplant Patients; Surveillance Data

Study Design: Longitudinal Cohort

Multi-Center Study: FALSE

Data Types: Physical Activity; Questionnaires/Surveys; Other

Data Types, Other: Biosensor Samples (heartrate, steps)

Study Start Date: 12/21/2020

Study End Date: 11/30/2022

Species: Human Data

Estimated Cohort Size: 3700

Study Population Focus: Dialysis Patients; Intellectual and Developmental Disabilities; Lower Socioeconomic Status (SES) Population; Incarcerated/Institutionalized (or Criminal Legal System Involvement); School Community Members; Underserved/Vulnerable Population; Older Adults or Elderly

Publication URL: https://pubmed.ncbi.nlm.nih.gov/34845389/; https://pubmed.ncbi.nlm.nih.gov/34189532/

Acknowledgement Statement: This study was supported through funding, 4R01NR020105-02, for the National Institute of Nursing Research (NINR) as part of the RADx-rad program. This work was also supported by gifts from the Flu Lab, as well as departmental funding from the Stanford Genetics department. This study was supported by the Amazon Web Services Diagnostic Development Initiative. The Google Cloud Platform costs were covered by Google for Education academic research and COVID-19 grant awards. The team acknowledges the Stanford Genetics Bioinformatics Service Center (GBSC) for providing the gateway to the SCG cluster, Google Cloud Platform and Amazon Web Services for this research. The Stanford REDCap platform (http://redcap.stanford.edu) was developed and operated by the Stanford Medicine Research IT team. The REDCap platform services at Stanford are subsidized by a) Stanford School of Medicine Research Office, and b) the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through grant UL1TR001085. Approved users should acknowledge the provision of data access by dbGaP for accession phs002523.v1.p1, and the NIH RADx Data Hub. Approved users should also acknowledge the specific version(s) of the dataset(s) obtained from the NIH RADx Data Hub.

Funding Opportunity Announcement (FOA) Number: RFA-OD-20-016

NIH Grant or Contract Number(s): 4R01NR020105-02

Consent/Data Use Limitations: General Research Use