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
Authors
Herbert, CarlyBroach, John
Gerber, Ben S
Fahey, Nisha
Orvek, Elizabeth Aaker
Lazar, Peter
Ferranto, Julia M.
Noorishirazi, Kamran
Valpady, Shivakumar
Shi, Qiming
Lin, Honghuang
Marvel, Kathryn
Gibson, Laura L
Barton, Bruce A
Lemon, Stephenie C
Hafer, Nathaniel
McManus, David D.
Soni, Apurv
UMass Chan Affiliations
Prevention Research CenterGraduate School of Biomedical Sciences
Division of Cardiovascular Medicine, Department of Medicine
Division of Infectious Diseases and Immunology, Department of Medicine
Division of Clinical Informatics, Department of Medicine
UMass Center for Clinical and Translational Science
Department of Pediatrics
Department of Population and Quantitative Health Sciences
Department of Emergency Medicine
Program in Digital Medicine, Department of Medicine
Document Type
Journal ArticlePublication Date
2022-06-16Keywords
COVID-19COVID-19 testing
SARS-CoV-2
adherence
coronavirus
digital health
digital surveillance
disease spread
health application
health care
infectious disease
mHealth
mobile health
prevention
public health
rapid antigen tests
reporting
smartphone app
surveillance
vaccination
UMCCTS funding
Behavioral Medicine
Community Health and Preventive Medicine
Health Information Technology
Health Services Administration
Health Services Research
Infectious Disease
Preventive Medicine
Virus Diseases
Metadata
Show full item recordAbstract
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.Source
Herbert C, Kheterpal V, Suvarna T, Broach J, Marquez JL, Gerber B, Schrader S, Nowak C, Harman E, Heetderks W, Fahey N, Orvek E, Lazar P, Ferranto J, Noorishirazi K, Valpady S, Shi Q, Lin H, Marvel K, Gibson L, Barton B, Lemon S, Hafer N, McManus D, Soni A. 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. JMIR Form Res. 2022 Jun 16;6(6):e38113. doi: 10.2196/38113. PMID: 35649180; PMCID: PMC9205422. Link to article on publisher's site
DOI
10.2196/38113Permanent Link to this Item
http://hdl.handle.net/20.500.14038/44673PubMed ID
35649180Notes
Full author list omitted for brevity. For the full list of authors, see article.
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Rights
Copyright © 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. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.Distribution License
http://creativecommons.org/licenses/by/4.0/ae974a485f413a2113503eed53cd6c53
10.2196/38113
Scopus Count
Except where otherwise noted, this item's license is described as Copyright © 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. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.
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