Evidence map›Paper›PMID 37528946›Full record

ArticleJournal of clinical and translational science2023

Remote and semi-automated methods to conduct a decentralized randomized clinical trial.

Teresa Cafaro, Patrick J LaRiccia, Brigid Bandomer, Helen Goldstein, Tracy L Brobyn, Krystal Hunter, Satyajeet Roy, Kevin Q Ng, Ludmil V Mitrev, Alan Tsai and 4 more

Abstract read
In one paragraph

Article in Journal of clinical and translational science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Guideline
  2. Article
  3. Progress toward realizing the promise of decentralized clinical trials.Journal of clinical and translational science · 2024
    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

14 authors.

Teresa CafaroDepartment of Anesthesiology, Cooper University Health Care, Camden, NJ, USA.ORCID https://orcid.org/0000-0002-8963-9075
Patrick J LaRicciaWon Sook Chung Foundation, Moorestown, NJ, USA.
Brigid BandomerWon Sook Chung Foundation, Moorestown, NJ, USA.
Helen GoldsteinWon Sook Chung Foundation, Moorestown, NJ, USA.
Tracy L BrobynWon Sook Chung Foundation, Moorestown, NJ, USA.
Krystal HunterCooper Research Institute, Cooper University Health Care, Camden, NJ, USA.
Satyajeet RoyCooper Medical School of Rowan University, Camden, NJ, USA.
Kevin Q NgWon Sook Chung Foundation, Moorestown, NJ, USA.
Ludmil V MitrevDepartment of Anesthesiology, Cooper University Health Care, Camden, NJ, USA.ORCID https://orcid.org/0000-0002-6261-9816
Alan TsaiCooper Medical School of Rowan University, Camden, NJ, USA.
Denise ThwingWon Sook Chung Foundation, Moorestown, NJ, USA.
Mary Ann MaagWon Sook Chung Foundation, Moorestown, NJ, USA.
Myung K ChungWon Sook Chung Foundation, Moorestown, NJ, USA.
Noud van HelmondDepartment of Anesthesiology, Cooper University Health Care, Camden, NJ, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Designing and conducting clinical trials is challenging for some institutions and researchers due to associated time and personnel requirements. We conducted recruitment, screening, informed consent, study product distribution, and data collection remotely. Our objective is to describe how to conduct a randomized clinical trial using remote and automated methods. Methods: A randomized clinical trial in healthcare workers is used as a model. A random group of workers were invited to participate in the study through email. Following an automated process, interested individuals scheduled consent/screening interviews. Enrollees received study product by mail and surveys via email. Adherence to study product and safety were monitored with survey data review and via real-time safety alerts to study staff. Results: A staff of 10 remotely screened 406 subjects and enrolled 299 over a 3-month period. Adherence to study product was 87%, and survey data completeness was 98.5% over 9 months. Participants and study staff scored the System Usability Scale 93.8% and 90%, respectively. The automated and remote methods allowed the study maintenance period to be managed by a small study team of two members, while safety monitoring was conducted by three to four team members. Conception of the trial to study completion was 21 months. Conclusions: The remote and automated methods produced efficient subject recruitment with excellent study product adherence and data completeness. These methods can improve efficiency without sacrificing safety or quality. We share our XML file for researchers to use as a template for learning purposes or designing their own clinical trials.

Indexed as

decentralizedelectronic data captureinternet methodsRandomized clinical trialRCTREDCapremote methods

Identifiers

PMID37528946
PMCPMC10388435

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.