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

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.