Feasibility and lessons learned on remote trial implementation from TestBoston, a fully remote, longitudinal, large-scale COVID-19 surveillance study
Bibliographic record
Abstract
ABSTRACT Importance Remote clinical trials may reduce barriers to research engagement resulting in more representative samples. A critical evaluation of this approach is imperative to optimize this paradigm shift in research. Objective To assess design and implementation factors required to maximize enrollment and retention in a fully remote, longitudinal COVID-19 testing study. Design Fully remote longitudinal study launched in October 2020 and ongoing; Study data reported through July 2021. Setting Brigham and Women’s Hospital, Boston MA Participants Adults, 18 years or older, within 45 miles of Boston, MA. Intervention Monthly and “on-demand” at-home SARS-CoV-2 RT-PCR and antibody testing using nasal swab and dried blood spot self-collection kits and electronic surveys to assess symptoms and risk factors for COVID-19. Main Outcomes Enrollment, retention, and lessons learned. Results Between October 2020 and January 2021, we enrolled 10,289 participants reflective of Massachusetts census data. Mean age was 47 years (range 18-93), 5855 (56.9%) were assigned female sex at birth, 7181(69.8%) reported being White non-Hispanic, 952 (9.3%) Hispanic/Latinx, 925 (9.0%) Black, 889 (8.6%) Asian, and 342 (3.3%) other and/or more than one race. Lower initial enrollment among Black and Hispanic/Latinx individuals required an adaptive approach, leveraging connections to the medical system, coupled with community partnerships to ensure a representative cohort. Longitudinal retention was higher among participants who were White non-Hispanic, older, working remotely, and with lower socioeconomic vulnerability. Considerable infrastructure, including a dedicated participant support team and robust technology platforms was required to reduce barriers to enrollment, promote retention, ensure scientific rigor, improve data quality, and enable an adaptive study design to increase real-world accessibility. Conclusions The decentralization of clinical trials through remote models offers tremendous potential to engage representative cohorts, scale biomedical research, and promote accessibility by reducing barriers common in traditional trial design. Our model highlights the critical role that hospital-community partnerships play in remote recruitment, and the work still needed to ensure representative enrollment. Barriers and burdens within remote trials may be experienced disproportionately across demographic groups. To maximize engagement and retention, researchers should prioritize intensive participant support, investment in technologic infrastructure and an adaptive approach to maximize engagement and retention. Trial Registration N/A Key Points Question Longitudinal clinical studies typically rely on in-person interactions to support recruitment, retention, and implementation. We define factors that promote demographically representative recruitment and retention through implementation of a fully remote COVID-19 study. Findings Remote trial models can reduce barriers to research participation and engage representative cohorts. Recruitment was strengthened by leveraging the medical system. Implementation highlighted participant burdens unique to this model, underscoring the need for a significant participant support team, robust technological infrastructure, and an adaptive, iterative approach. Meaning As remote trials become more common following the COVID-19 pandemic, methodologies to ensure accessibility, representation, and efficiency are crucial.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.408 | 0.274 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".