Design and Preliminary Findings of Adherence to the Self-Testing for Our Protection From COVID-19 (STOP COVID-19) Risk-Based Testing Protocol: Prospective Digital Study.

Autor:	Herbert C; Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States., Kheterpal V; CareEvolution, Inc, Ann Arbor, MI, United States., Suvarna T; CareEvolution, Inc, Ann Arbor, MI, United States., Broach J; Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States., Marquez JL; Washtenaw County Health Department, Washtenaw, MI, United States., Gerber B; Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States., Schrader S; CareEvolution, Inc, Ann Arbor, MI, United States., Nowak C; CareEvolution, Inc, Ann Arbor, MI, United States., Harman E; CareEvolution, Inc, Ann Arbor, MI, United States., Heetderks W; National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Kelly Services, Bethesda, MD, United States., Fahey N; Department of Pediatrics, University of Massachusetts Chan Medical School, Worcester, MA, United States., Orvek E; Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States., Lazar P; Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States., Ferranto J; Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States., Noorishirazi K; Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States., Valpady S; Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States., Shi Q; Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.; Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States.; University of Massachusetts Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, MA, United States., Lin H; Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.; Division of Clinical Informatics, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States., Marvel K; Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States., Gibson L; Division of Infectious Disease, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States., Barton B; Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States., Lemon S; Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States., Hafer N; University of Massachusetts Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, MA, United States., McManus D; Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.; Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States.; Division of Cardiology, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States., Soni A; Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.; Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States.; Division of Clinical Informatics, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.
Jazyk:	angličtina
Zdroj:	JMIR formative research [JMIR Form Res] 2022 Jun 16; Vol. 6 (6), pp. e38113. Date of Electronic Publication: 2022 Jun 16.
DOI:	10.2196/38113
Abstrakt:	Background: Serial testing for SARS-CoV-2 is recommended to reduce spread of the virus; however, little is known about adherence to recommended testing schedules and reporting practices to health departments. Objective: The Self-Testing for Our Protection from COVID-19 (STOP COVID-19) study aims to examine adherence to a risk-based COVID-19 testing strategy using rapid antigen tests and reporting of test results to health departments. Methods: STOP COVID-19 is a 12-week digital study, facilitated using a smartphone app for testing assistance and reporting. We are recruiting 20,000 participants throughout the United States. Participants are stratified into high- and low-risk groups based on history of COVID-19 infection and vaccination status. High-risk participants are instructed to perform twice-weekly testing for COVID-19 using rapid antigen tests, while low-risk participants test only in the case of symptoms or exposure to COVID-19. All participants complete COVID-19 surveillance surveys, and rapid antigen results are recorded within the smartphone app. Primary outcomes include participant adherence to a risk-based serial testing protocol and percentage of rapid tests reported to health departments. Results: As of February 2022, 3496 participants have enrolled, including 1083 high-risk participants. Out of 13,730 tests completed, participants have reported 13,480 (98.18%, 95% CI 97.9%-98.4%) results to state public health departments with full personal identifying information or anonymously. Among 622 high-risk participants who finished the study period, 35.9% showed high adherence to the study testing protocol. Participants with high adherence reported a higher percentage of test results to the state health department with full identifying information than those in the moderate- or low-adherence groups (high: 71.7%, 95% CI 70.3%-73.1%; moderate: 68.3%, 95% CI 66.0%-70.5%; low: 63.1%, 59.5%-66.6%). Conclusions: Preliminary results from the STOP COVID-19 study provide important insights into rapid antigen test reporting and usage, and can thus inform the use of rapid testing interventions for COVID-19 surveillance. (©Carly Herbert, Vik Kheterpal, Thejas Suvarna, John Broach, Juan Luis Marquez, Ben Gerber, Summer Schrader, Christopher Nowak, Emma Harman, William Heetderks, Nisha Fahey, Elizabeth Orvek, Peter Lazar, Julia Ferranto, Kamran Noorishirazi, Shivakumar Valpady, Qiming Shi, Honghuang Lin, Kathryn Marvel, Laura Gibson, Bruce Barton, Stephenie Lemon, Nathaniel Hafer, David McManus, Apurv Soni. Originally published in JMIR Formative Research (https://formative.jmir.org), 16.06.2022.)
Databáze:	MEDLINE
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