ArticleBMC health services research2026
Community-engaged design of a virtual diabetes prevention program intervention with care coordination.
Article in BMC health services research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04195477 (Improving Health in Low Income Communities), which is not on this map. Not yet cited in PubMed.
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.
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.
Improving Health in Low Income Communities: Virtual Delivery of a Diabetes Prevention Program Facilitated With Community Care Coordination
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
backgroundThe Diabetes Prevention Program (DPP) is an evidence-based approach to prevent Type 2 Diabetes (T2D), the leading cause of morbidity and mortality in the United States. Despite demonstrated benefits, acceptability and uptake of DPPs remain low, especially among communities with low incomes at increased risk for diabetes. The Yale-Griffin Prevention Research Center led the design of a community-engaged virtual DPP (vDPP) intervention and assessed its feasibility of implementation in two contrasting settings.
methodsThe vDPP design followed an iterative, multi-partner mixed methods approach guided by a community-engaged research framework. The steps involved were: (1) application of the socio-ecological model theoretical framework to vDPP design (2) interviews and a technology survey with community residents and providers to assess feasibility (3) development of a program impact pathway (PIP) for ongoing process evaluation (4) five-week pilot study followed by participant focus groups, and (5) triage of findings to make necessary adaptations.
resultsThis process led to a vDPP intervention that included an online platform with asynchronous programming and a health coach as well as an added care coordination component provided by local community health workers and hospital-based community nurses. Thematic analysis showed that community members and providers valued the concept of a structured program with personalized guidance and emphasized the importance of addressing social needs and considering broad health goals. The pilot study identified program facilitators and barriers around engagement, communication, and feelings of personal accountability that informed program adaptations made prior to full implementation testing, including clarification of support roles and addition of detailed orientation materials.
conclusionsThis formative research demonstrated initial program feasibility and identified ways to improve the quality of program delivery. Pairing the PIP with a community-engaged approach led to the development of a novel health promotion intervention that is now being tested through a subsequent implementation science study.
trial registrationNCT04195477, clinicaltrials.gov, registered 7 October, 2020; https://www. CLINICALTRIALS: gov/study/NCT04195477?term=NCT04195477&rank=1 .
Indexed as
Identifiers
What OpenQuestion holds
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.