Evidence map›Paper›PMID 41080649›Full record

ArticleMayo Clinic proceedings. Digital health2025

Increasing Retention in a Large-Scale Decentralized Clinical Trial: Learnings From the COVID-RED Trial.

Laura C Zwiers, Duco Veen, Marianna Mitratza, Timo B Brakenhoff, Brianna M Goodale, Paul Klaver, Kay Y Hage, Marcel van Willigen, George S Downward, Peter Lugtig and 6 more

Abstract read
In one paragraph

Article in Mayo Clinic proceedings. Digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Trial
  2. Review
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

16 authors.

Laura C ZwiersDepartment of Global Health and Bioethics, Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, The Netherlands.
Duco VeenJulius Clinical, Zeist, The Netherlands.
Marianna MitratzaDepartment of Global Health and Bioethics, Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, The Netherlands.
Timo B BrakenhoffJulius Clinical, Zeist, The Netherlands.
Brianna M GoodaleAva Femtec, Zürich, Switzerland.
Paul KlaverJulius Clinical, Zeist, The Netherlands.
Kay Y HageJulius Clinical, Zeist, The Netherlands.
Marcel van WilligenJulius Clinical, Zeist, The Netherlands.
George S DownwardDepartment of Global Health and Bioethics, Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, The Netherlands.
Peter LugtigDepartment of Methodology and Statistics, Utrecht University, The Netherlands.
Leendert van MaanenHelmholtz Institute, Experimental Psychology, Utrecht University, The Netherlands.
Stefan Van der StigchelHelmholtz Institute, Experimental Psychology, Utrecht University, The Netherlands.
Peter van der HeijdenDepartment of Methodology and Statistics, Utrecht University, The Netherlands.
Maureen CroninAva Femtec, Zürich, Switzerland.
Diederick E GrobbeeDepartment of Global Health and Bioethics, Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, The Netherlands.
COVID-RED Consortium

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To present retention strategies implemented in the coronavirus disease 2019 (COVID-19) rapid early detection trial, a decentralized trial investigating the use of a wearable device for severe acute respiratory syndrome coronavirus 2 detection, and to provide insights into study retention and investigate determinants of discontinuation. Patients and Methods: The COVID-2019 rapid early detection trial collected data from 17,825 participants from February 22, 2021 to November 18, 2021. Participants wore a wearable device overnight and synchronized it with a mobile application on waking. Retention strategies included common and personalized activities. Multivariable logistic regression was used to identify participants at high risk of discontinuation after 6 months in the trial. Results were combined with insights from behavioral theory to target participants with additional telephone calls. Results: Total of 14,326 (80.4%) participants remained in the trial after 6 months and 12,208 (68.5%) until the end of the trial. Multivariable logistic regression identified age, employment situation, living situation, and COVID-19 vaccination status as predictors of discontinuation. Subgroups at high risk of discontinuation were identified, and behavioral assessments indicated that the subgroup of vaccinated pensioners would receive additional telephone calls. Their dropout rate was 11.4% after telephone calls. Conclusion: This study describes how innovative and targeted data-driven retention strategies can be applied in a large decentralized clinical trial and presents the implemented retention strategies and discontinuation rates. Results can serve as a starting point for designing retention strategies in future decentralized trials.

Identifiers

PMID41080649
PMCPMC12514562

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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.