Evidence map›Paper›PMID 35649180›Full record

ArticleJMIR formative research2022

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.

Carly Herbert, Vik Kheterpal, Thejas Suvarna, John Broach, Juan Luis Marquez, Ben Gerber, Summer Schrader, Christopher Nowak, Emma Harman, William Heetderks and 15 more

Open access · goldAbstract read
In one paragraph

Article in JMIR formative research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.8field-weighted citation impact, top 28% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Article
  3. Persistent False Positive Covid-19 Rapid Antigen Tests.The New England journal of medicine · 2024
    Article
  4. Article
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

25 authors at 3 institutions in 1 country.

Carly HerbertProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID https://orcid.org/0000-0001-8972-0474
Vik KheterpalCareEvolution, Inc, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0002-4752-4229
Thejas SuvarnaCareEvolution, Inc, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0002-4811-0298
John BroachDepartment of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID https://orcid.org/0000-0002-6581-2232
Juan Luis MarquezWashtenaw County Health Department, Washtenaw, MI, United States.ORCID https://orcid.org/0000-0002-9854-5628
Ben GerberDepartment of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID https://orcid.org/0000-0003-4367-6396
Summer SchraderCareEvolution, Inc, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0002-5678-8613
Christopher NowakCareEvolution, Inc, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0003-1796-9558
Emma HarmanCareEvolution, Inc, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0002-8006-560X
William HeetderksNational Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Kelly Services, Bethesda, MD, United States.ORCID https://orcid.org/0000-0001-7714-107X
Nisha FaheyDepartment of Pediatrics, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID https://orcid.org/0000-0001-9059-1470
Elizabeth OrvekDepartment of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID https://orcid.org/0000-0001-6974-6263
Peter LazarDepartment of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID https://orcid.org/0000-0002-0744-1461
Julia FerrantoProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID https://orcid.org/0000-0001-9993-5655
Kamran NoorishiraziProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID https://orcid.org/0000-0002-4059-2771
Shivakumar ValpadyProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID https://orcid.org/0000-0002-8862-2925
Qiming ShiProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID https://orcid.org/0000-0001-5829-4345
Honghuang LinProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID https://orcid.org/0000-0003-3043-3942
Kathryn MarvelProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID https://orcid.org/0000-0002-7791-683X
Laura GibsonDivision of Infectious Disease, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID https://orcid.org/0000-0003-0224-305X
Bruce BartonDepartment of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID https://orcid.org/0000-0001-7878-8895
Stephenie LemonDepartment of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID https://orcid.org/0000-0003-3321-6070
Nathaniel HaferUniversity of Massachusetts Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID https://orcid.org/0000-0002-0164-5092
David McManusProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID https://orcid.org/0000-0002-9343-6203
Apurv SoniProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID https://orcid.org/0000-0001-5049-3657
University of Massachusetts Chan Medical School · USCareEvolution (United States) · USNational Institutes of Health · US

Funding

The Center for Advancing Point of Care in Heart, Lung, Blood and Sleep DiseasesU54HL143541 · NHLBI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI Nathaniel Scott Hafer · 2018 to 2026
$141.0M
University of Massachusetts Center for Clinical Science and Translational SupplementUL1TR001453 · NCATS · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI LUZURIAGA, KATHERINE F · 2015 to 2024
$39.0M
University of Massachusetts Center for Clinical and Translational ScienceKL2TR001455 · NCATS · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI KIEFE, CATARINA I. · 2015 to 2024
$5.0M
NCATS NIH HHS KL2 TR001455NCATS NIH HHS UL1 TR001453NHLBI NIH HHS U54 HL143541
6 · The paper itself

Abstract

backgroundSerial 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.

objectiveThe 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.

methodsSTOP 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.

resultsAs 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%).

conclusionsPreliminary 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.

Indexed as

adherencecoronavirusCOVID-19COVID-19 testingdigital healthdigital surveillancedisease spreadhealth applicationhealth careinfectious diseasemHealthmobile healthpreventionpublic healthrapid antigen testsreportingsmartphone appsurveillancevaccination

Identifiers

PMID35649180
PMCPMC9205422
OpenAlexW4282052694

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.