Evidence map›Paper›PMID 40334212›Full record

Observational studyJMIR formative research2025

Enrollment and Retention Outcomes from the Veterans Health Administration for a Remote Digital Health Study: Multisite Observational Study.

Jaclyn A Pagliaro, Lauren K Wash, Ka Ly, Jenny Mathew, Alison Leibowitz, Ryan Cabrera, Jolie B Wormwood, Varsha G Vimalananda

Registry-linked trialAbstract readObservational StudyMulticenter Study
In one paragraph

Observational study in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05713266 (AIDI - Research and Development of a Multisensor-Based Technology for Real-time Automated Detection of Post-acute COVID-19 Sequelae), which is not on this map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing 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.

NCT05713266 completednot on this map

AIDI - Research and Development of a Multisensor-Based Technology for Real-time Automated Detection of Post-acute COVID-19 Sequelae

TypeobservationalSponsorEdith Nourse Rogers Memorial Veterans HospitalRan2022 to 2024Enrolled204ConditionsCOVID-19, Long COVIDArmsMouthLabTM
3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Observational
  4. 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

8 authors.

Jaclyn A PagliaroCenter for Health Optimization and Implementation Research, VA Bedford Medical Center, Bedford, MA, United States.ORCID 0000-0003-4289-3073
Lauren K WashCenter for Health Optimization and Implementation Research, VA Bedford Medical Center, Bedford, MA, United States.ORCID 0000-0002-9279-6602
Ka LyClinical Informatics, Providence VA Medical Center, Providence, RI, United States.ORCID 0009-0002-0948-4403
Jenny MathewAidar Health, Inc, Columbia, MD, United States.ORCID 0009-0007-2769-1576
Alison LeibowitzCenter for Health Optimization and Implementation Research, VA Bedford Medical Center, Bedford, MA, United States.ORCID 0009-0008-8842-2764
Ryan CabreraCenter for Health Optimization and Implementation Research, VA Bedford Medical Center, Bedford, MA, United States.ORCID 0009-0001-4854-5689
Jolie B WormwoodCenter for Health Optimization and Implementation Research, VA Bedford Medical Center, Bedford, MA, United States.ORCID 0000-0001-7542-8755
Varsha G VimalanandaCenter for Health Optimization and Implementation Research, VA Bedford Medical Center, Bedford, MA, United States.ORCID 0000-0003-0738-672X

Funding

Department of Veterans Affairs (VA)HHS 75A50121C00062
6 · The paper itself

Abstract

backgroundClinical trials of remote patient monitoring (RPM) technology are well-suited to remote studies, for which patients complete key procedures online. However, remote digital health studies often suffer from low enrollment and retention, threatening the successful achievement of study outcomes and wasting resources and time. Recruiting patients from a large integrated health system offers a greater potential pool of participants for enrollment, which can increase the likelihood of successful study completion.

objectiveThis study describes enrollment and retention outcomes for a remote digital health study of an RPM device conducted in collaboration with researchers from the Veterans Health Administration (VA). The VA is the largest integrated health system in the United States, with 9 million enrollees who are, as a group, older and with more medical and mental health comorbidities than the civilian population.

methodsWe aimed to enroll 200 VA patients for a clinical study of a cellular-enabled, handheld, multisensor device that captures multiple health parameters and transmits data to a cloud-based dashboard for viewing by clinicians. Eligible patients were hospitalized with COVID-19 within 3-6 months before enrollment and had one of 6 pre-existing medical comorbidities. Potentially eligible patients were identified using the VA Corporate Data Warehouse. Every 3 weeks, up to 1000 potentially eligible patients were mailed a recruitment letter. All study tasks, including obtaining informed consent, device training and troubleshooting, and handling study-related questions, were completed online and by telephone. Device and survey data were combined with VA clinical and utilization data to develop a predictive algorithm for clinical decompensation. The geographic distribution of enrolled patients was mapped by county. Demographic and health characteristics of nonenrolled versus enrolled, and of completers versus noncompleters were compared using t tests and chi-square tests as appropriate. Reasons for noncompletion were summed. Multivariate logistic regression was used to evaluate variables associated with enrolling versus nonenrolling, and completing versus noncompleting.

resultsOf the 7714 who were mailed a study invitation, 560 were screened. Of the screened patients, 203 were enrolled (2.9% enrollment yield) and 166 completed the study (82% retention rate). Enrolled patients were broadly distributed across the United States. Among those enrolled, completers and noncompleters were similar except for a slightly higher proportion of patients with hypertension among completers. The most common reason for noncompletion of the study was that participants were unable to be contacted for study tasks.

conclusionsRemote digital health studies are increasingly common, but inadequate enrollment often results in failed studies. Recruiting patients through the VA enables access to a very large population of potentially eligible patients and can help ensure that clinical trials reach targets for enrollment and completion.

trial registrationClinicalTrials.gov NCT05713266; https://clinicaltrials.gov/study/NCT05713266.

Indexed as

COVID-19Patient SelectionTelemedicineAgedDigital HealthFemaleHumansMaleMiddle AgedSARS-CoV-2United StatesUnited States Department of Veterans Affairsclinical trialCOVID-19enrollmentremote digital health studyretentionveterans

Identifiers

PMID40334212
PMCPMC12397753

What OpenQuestion holds

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Registered trials

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.