Evidence map›Paper›PMID 40720824›Full record

Trial reportJournal of medical Internet research2025

Population-Based Digital Health Interventions to Deliver at-Home COVID-19 Testing: SCALE-UP II Randomized Clinical Trial.

Guilherme Del Fiol, Tatyana V Kuzmenko, Brian Orleans, Jonathan J Chipman, Tom Greene, Ray Meads, Kimberly A Kaphingst, Bryan Gibson, Kensaku Kawamoto, Andy J King and 13 more

Abstract readPragmatic Clinical TrialRandomized Controlled Trial
In one paragraph

Trial report in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. GARDE-Chat: a scalable, open-source platform for building and deploying health chatbots.Journal of the American Medical Informatics Association : JAMIA · 2026
    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

23 authors.

Guilherme Del FiolDepartment of Biomedical Informatics, University of Utah, 421 Wakara Way, Suite 140, Salt Lake City, UT, 84108, United States, 1 8015814080.ORCID 0000-0001-9954-6799
Tatyana V KuzmenkoDepartment of Biomedical Informatics, University of Utah, 421 Wakara Way, Suite 140, Salt Lake City, UT, 84108, United States, 1 8015814080.ORCID 0009-0003-8209-2577
Brian OrleansHuntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA.ORCID 0000-0001-9312-0674
Jonathan J ChipmanHuntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA.ORCID 0000-0002-3021-2376
Tom GreeneDepartment of Population Health Sciences, University of Utah, Salt Lake City, UT, United States.ORCID 0000-0002-3706-7570
Ray MeadsHuntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA.ORCID 0009-0004-7332-0184
Kimberly A KaphingstHuntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA.ORCID 0000-0003-2668-9080
Bryan GibsonDepartment of Biomedical Informatics, University of Utah, 421 Wakara Way, Suite 140, Salt Lake City, UT, 84108, United States, 1 8015814080.ORCID 0000-0003-4747-6383
Kensaku KawamotoDepartment of Biomedical Informatics, University of Utah, 421 Wakara Way, Suite 140, Salt Lake City, UT, 84108, United States, 1 8015814080.ORCID 0000-0003-4282-9338
Andy J KingHuntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA.ORCID 0000-0002-2789-2550
Tracey SiaperasHuntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA.ORCID 0009-0007-9689-7076
Shlisa HughesAssociation for Utah Community Health, Salt Lake City, UT, United States.ORCID 0009-0006-0082-9718
Alan PruhsAssociation for Utah Community Health, Salt Lake City, UT, United States.ORCID 0009-0006-7086-7736
Courtney Pariera DinkinsAssociation for Utah Community Health, Salt Lake City, UT, United States.ORCID 0009-0006-1834-1497
Cho Y LamHuntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA.ORCID 0000-0001-9926-4361
Joni H PierceDepartment of Biomedical Informatics, University of Utah, 421 Wakara Way, Suite 140, Salt Lake City, UT, 84108, United States, 1 8015814080.ORCID 0000-0002-8537-2211
Ryzen BensonUniversity of California, San Francisco, CA, USA.ORCID 0000-0003-0348-7849
Emerson P BorsatoDepartment of Biomedical Informatics, University of Utah, 421 Wakara Way, Suite 140, Salt Lake City, UT, 84108, United States, 1 8015814080.ORCID 0000-0002-2786-6395
Ryan C CorniaDepartment of Biomedical Informatics, University of Utah, 421 Wakara Way, Suite 140, Salt Lake City, UT, 84108, United States, 1 8015814080.ORCID 0009-0001-3850-9086
Leticia StevensDepartment of Biomedical Informatics, University of Utah, 421 Wakara Way, Suite 140, Salt Lake City, UT, 84108, United States, 1 8015814080.ORCID 0009-0000-9266-9729
Richard L BradshawDepartment of Biomedical Informatics, University of Utah, 421 Wakara Way, Suite 140, Salt Lake City, UT, 84108, United States, 1 8015814080.ORCID 0000-0001-7363-0327
Chelsey R SchlechterHuntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA.ORCID 0000-0002-8355-6316
David W WetterHuntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA.ORCID 0000-0002-4013-1932

Funding

UTAH REGIONAL CANCER CENTERP30CA042014 · NCI · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI Jared P Rutter · 1986 to 2026
$72.6M
Utah Center for Clinical and Translational ScienceUL1TR002538 · NCATS · UNIVERSITY OF UTAH · PI HESS, RACHEL, MAJERSIK, JENNIFER JUHL · 2018 to 2022
$26.0M
CTSA UM1 Program at University of UtahUM1TR004409 · NCATS · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI RACHEL HESS, Jennifer Juhl Majersik · 2023 to 2026
$21.9M
GARDE: Scalable Clinical Decision Support for Individualized Cancer Risk ManagementU24CA274582 · NCI · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI GUILHERME DEL FIOL, Kensaku Kawamoto · 2023 to 2026
$3.3M
SCALE UP Utah II: Community-Academic Partnership to Address COVID-19 Testing and Vaccination Among Utah Community Health CentersU01MD017421 · NIMHD · UNIVERSITY OF UTAH · PI DEL FIOL, GUILHERME, WETTER, DAVID W · 2022 to 2023
$2.3M
NCATS NIH HHS UL1 TR002538NCATS NIH HHS UM1 TR004409NCI NIH HHS P30 CA042014NCI NIH HHS U24 CA274582NIMHD NIH HHS U01 MD017421
6 · The paper itself

Abstract

Background: Digital health interventions could be a scalable approach to delivering at-home COVID-19 testing. Objective: SCALE-UP II aimed to investigate the effectiveness of 3 digital health interventions on the delivery of mailed at-home COVID-19 testing: SMS text messaging, automated chatbot, and patient navigation upon request. Methods: The study was a pragmatic randomized controlled trial. Participants who self-reported that they had a smartphone were randomized in a 2×2×2 factorial design (smartphone study) to receive (1) chatbot or text messaging, (2) the option to request patient navigation, and (3) intervention frequency every 10 or 30 days. All other participants were randomized in a 2×2 factorial design (nonsmartphone study) to receive the option to request patient navigation and intervention frequency every 10 or 30 days. Study settings were safety net community health centers located across the state of Utah, United States. Eligible patients were >18 years old, with a primary care visit in the last 3 years, and a valid cellphone in the community health centers electronic health record. The primary outcome was the proportion of participants requesting at-home COVID-19 tests. Results: The trial enrolled 2117 in the smartphone study and 31,439 in the nonsmartphone study. In the smartphone study, the proportion of participants who requested test kits in the Chatbot arm was lower than in SMS text messaging (174/1051, 16.6% vs 555/1066, 52.1%; adjusted risk ratio (aRR) 0.317, 98.33% CI 0.27-0.38; P<.001). In the nonsmartphone study, the proportion of participants who requested test kits was higher if they were messaged every 10 days rather than every 30 days (860/15,717, 5.5% vs 752/15,722, 4.8%; aRR 1.144, 97.5% CI 1.03-1.28; P=.005). However, participants in the 10-day versus 30-day condition were more likely to opt out of receiving study interventions (1977/15,717, 12.6% vs 1147/15,722, 7.3%; aRR 1.72, 97.5% CI 1.59-1.86; P<.001). In the nonsmartphone study, the proportion of participants who requested test kits was lower for those in the patient navigation condition compared with no patient navigation (680/15,718, 4.3% vs 932/15,721, 5.9%; aRR 0.729, 97.5% CI 0.65-0.81; P<.001). Conclusions: Simple bidirectional text messaging was more effective than an interactive web-based chatbot on the delivery of COVID-19 testing. Although messaging every 10 days was more effective than every 30 days, it also led to a larger opt-out rate. Digital health interventions based on automated bidirectional SMS text messaging are a simple, scalable, and low-cost strategy to offer access to at-home COVID-19 testing. Similar approaches may be used to support public health response and other forms of at-home testing.

Indexed as

COVID-19COVID-19 TestingTelemedicineAdultAgedDigital HealthFemaleHumansMaleMiddle AgedSARS-CoV-2SmartphoneText Messagingat-home testingchatbotsCOVID-19digital healthtext messaging

Identifiers

PMID40720824
PMCPMC12303405

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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.