Evidence map›Paper›PMID 40308291›Full record

ReviewFrontiers in clinical diabetes and healthcare2025

Consumer-oriented review of digital diabetes prevention programs: insights from the CDC's diabetes prevention recognition program.

Benjamin Lalani, Jalene Shim, Vidhu Vadini, Yllka Valdez, Daniel Zade, Nestoras Mathioudakis

Abstract readReview
In one paragraph

Review in Frontiers in clinical diabetes and healthcare, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–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

7 citing papers in PubMed.

  1. Trial
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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

6 authors.

Benjamin LalaniDivision of Endocrinology, Diabetes & Metabolism Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Jalene ShimDivision of Endocrinology, Diabetes & Metabolism Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Vidhu VadiniDivision of Endocrinology, Diabetes & Metabolism Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Yllka ValdezDivision of Endocrinology, Diabetes & Metabolism Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Daniel ZadeDivision of Endocrinology, Diabetes & Metabolism Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Nestoras MathioudakisDivision of Endocrinology, Diabetes & Metabolism Johns Hopkins University School of Medicine, Baltimore, MD, United States.

Funding

Supplement to Effectiveness of Digital Versus In-Person Diabetes Prevention ProgramsR01DK125780 · NIDDK · JOHNS HOPKINS UNIVERSITY · PI MATHIOUDAKIS, NESTORAS N · 2020 to 2023
$3.0M
NIDDK NIH HHS R01 DK125780
6 · The paper itself

Abstract

Background: Prediabetes is highly prevalent and significantly increases the risk of type 2 diabetes. While access to proven interventions like the Diabetes Prevention Program (DPP) has historically been limited, digital DPPs (dDPPs) present a promising and scalable option. With the recent growth of dDPP offerings and potential variability across platforms, access to accurate and clear information is crucial for individuals seeking diabetes prevention options. This review provides an overview of the dDPP landscape and characterizes the "direct-to-consumer" information available-or lacking-for patients choosing a dDPP. Methods: We identified dDPPs through the CDC Diabetes Prevention Recognition Program (DPRP) Registry. Data were extracted from three sources available to consumers: the CDC DPRP Registry, the CDC "Find a Lifestyle Program" Website, and program-specific websites. Extracted data included CDC recognition status, intended audience, available languages, program features (e.g., artificial intelligence, integration with smart devices), website availability and functionality, demonstrations of credibility (e.g., ADA endorsement), clinical performance metrics (e.g., average weight loss), and user experience factors (e.g., satisfaction). Descriptive statistics were used to summarize extracted data. Results: A total of 97 dDPPs were included in the review, with most in the early stages of CDC recognition. Only 35% of dDPPs listed in the CDC registry had functional websites, though additional websites were identified through manual searches. Program-specific features included AI-driven health recommendations, device integration (e.g., digital scales and activity trackers), nutrition tracking tools, and telehealth platforms. Nearly half of the dDPPs reported clinical performance metrics such as weight loss and A1C outcomes. User experience details were often presented through patient testimonials and satisfaction scores. Notably, many programs required users to provide personal information to access additional information. Conclusion: We found that available dDPPs vary significantly in their features, designs, and structures, reflecting a diverse and evolving landscape of diabetes prevention options. Concurrently, many dDPPs lack accessible information due to missing or incomplete websites. Centralized sources of information provided by the CDC are also insufficient, with discrepancies and gaps that hinder transparency and consumer decision-making. Addressing these issues through enhanced program visibility and improved centralized databases will be critical to optimizing the reach and impact of dDPPs.

Indexed as

artificial intelligencecdcdiabetesdiabetes preventiondigital healthlifestyle interventionmHealthprediabetes

Identifiers

PMID40308291
PMCPMC12040819

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.