Consumer-oriented review of digital diabetes prevention programs: insights from the CDC’s diabetes prevention recognition program
Open accessAbstract
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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
This is the first RCT to compare a fully automated DPP to standard care.
“To the authors’ knowledge, this is the first RCT to compare a fully automated DPP to standard care and adds to the limited evidence evaluating AI interventions against established standards in medicine.38,39”
From An AI-Powered Lifestyle Intervention vs Human Coaching in the Diabetes Prevention Program: A Randomized Clinical Trial (2025) · cites this paper as [38]
The Discussion (page unavailable) says prior dDPP studies were observational or used RCT control arms that did not represent the standard-of-care landmark DPP, and that a direct comparison trial was ongoing. This supports the claim that such comparisons were limited, but the review does not establish that the citing authors’ trial was the first RCT of a fully automated DPP against standard care. (LLM, conf=0.83)
Experimental — accuracy not yet measuredThis trial adds to the limited evidence evaluating AI interventions against established standards in medicine.
“To the authors’ knowledge, this is the first RCT to compare a fully automated DPP to standard care and adds to the limited evidence evaluating AI interventions against established standards in medicine.38,39”
From An AI-Powered Lifestyle Intervention vs Human Coaching in the Diabetes Prevention Program: A Randomized Clinical Trial (2025) · cites this paper as [38]
The Discussion says robust RCT evidence for digital diabetes prevention programs is lacking and that research has not established which program features, including AI, are most effective. That supports the claim of limited evidence in diabetes prevention, but the review does not assess AI interventions against established standards across medicine more broadly. The citing authors’ description of their own trial is not attributed to this source. (LLM, conf=0.85)
Experimental — accuracy not yet measuredBecause this intervention involved no human in the loop, findings from this trial could support revisiting current CDC standards for DPP recognition, which do not allow digital programs to use AI in place of human lifestyle coaching.
“Because this intervention involved no human in the loop, findings from this trial could support revisiting current CDC standards for DPP recognition, which do not allow digital programs to use AI in place of human lifestyle coaching.43”
From An AI-Powered Lifestyle Intervention vs Human Coaching in the Diabetes Prevention Program: A Randomized Clinical Trial (2025) · cites this paper as [43]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin
(2002) · New England Journal of Medicine · doi:10.1056/nejmoa012512 · Cited by 19,011 · see citing works
2. Diagnosis and classification of diabetes: standards of care in diabetes-2025
American Diabetes Association Professional Practice Committee, ElSayed, N. A., McCoy, R. G., Aleppo, G., Balapattabi, K., Beverly, E. A., Briggs Early, K., Bruemmer, D., Ebekozien, O., Echouffo-Tcheugui, J. B., et al. (2025) · Diabetes Care · doi:10.2337/dc25-s002 · Cited by 1,393 · see citing works
(2015) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(15)00291-0 · Cited by 1,029 · see citing works
Global Prevalence of Prediabetes
Rooney, M. R., Fang, M., Ogurtsova, K., Özkan, B., Echouffo-Tcheugui, J. B., Boyko, E. J., Magliano, D. J., & Selvin, E. (2023) · Diabetes Care · doi:10.2337/dc22-2376 · Cited by 545 · see citing works
Trust and Credibility in Web-Based Health Information: A Review and Agenda for Future Research
Sbaffi, L., & Rowley, J. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7579 · Cited by 537 · see citing works
Diagnosis and Management of Prediabetes: A Review
Echouffo-Tcheugui, J. B., Perreault, L., Ji, L., & Dagogo-Jack, S. (2023) · JAMA · doi:10.1001/jama.2023.4063 · Cited by 473 · see citing works
Whitehead, L., Talevski, J., Fatehi, F., & Beauchamp, A. (2023) · Journal of Medical Internet Research · doi:10.2196/42719 · Cited by 274 · see citing works
Elements of Trust in Digital Health Systems: Scoping Review
Adjekum, A., Blasimme, A., & Vayena, E. (2018) · Journal of Medical Internet Research · doi:10.2196/11254 · Cited by 231 · see citing works
Stein, N., & Brooks, K. (2017) · JMIR Diabetes · doi:10.2196/diabetes.8590 · Cited by 191 · see citing works
3. Prevention or Delay of Diabetes and Associated Comorbidities: Standards of Care in Diabetes—2025
ElSayed, N. A., McCoy, R. G., Aleppo, G., Balapattabi, K., Beverly, E. A., Early, K. B., Bruemmer, D., Ebekozien, O. A., Echouffo-Tcheugui, J. B., Ekhlaspour, L., et al. (2024) · Diabetes Care · doi:10.2337/dc25-s003 · Cited by 161 · see citing works
Engagement and outcomes in a digital Diabetes Prevention Program: 3-year update
Sepah, S. C., Jiang, L., Ellis, R. J., McDermott, K., & Peters, A. L. (2017) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2017-000422 · Cited by 122 · see citing works
Results From a Trial of an Online Diabetes Prevention Program Intervention
Moin, T., Damschroder, L. J., AuYoung, M., Maciejewski, M. L., Havens, K. L., Ertl, K., Vasti, E. C., Weinreb, J. E., Steinle, N., Billington, C. J., et al. (2018) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2018.06.028 · Cited by 108 · see citing works
Effects of a Digital Diabetes Prevention Program: An RCT
Katula, J. A., Dressler, E. V., Kittel, C. A., Harvin, L. N., Almeida, F. A., Wilson, K. E., Michaud, T. L., Porter, G. C., Brito, F. A., Goessl, C. L., et al. (2022) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2021.10.023 · Cited by 86 · see citing works
Rural/urban disparities in access to the National Diabetes Prevention Program
Ariel-Donges, A. H., Gordon, E. L., Dixon, B. N., Eastman, A., Bauman, V., Ross, K. M., & Perri, M. G. (2019) · Translational Behavioral Medicine · doi:10.1093/tbm/ibz098 · Cited by 60 · see citing works
Cannon, M. J., Ng, B. P., LLOYD, K. R. K., Reynolds, J., & ELY, E. (2022) · Journal of Diabetes Research · doi:10.1155/2022/2942918 · Cited by 60 · see citing works
Barron, E., Bradley, D., Safazadeh, S., McGough, B., Bakhai, C., Young, B., Khunti, K., Murray, E., Wareham, N., Jebb, S., et al. (2023) · Diabetic Medicine · doi:10.1111/dme.15028 · Cited by 32 · see citing works
Auster‐Gussman, L. A., Lockwood, K. G., Graham, S. A., Pitter, V., & Branch, O. H. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.886783 · Cited by 30 · see citing works
Batten, R., Alwashmi, M. F., Mugford, G., Nuccio, M., Besner, A., & Gao, Z. (2022) · JMIR Diabetes · doi:10.2196/23243 · Cited by 16 · see citing works
Abusamaan, M. S., Ballreich, J., Dobs, A., Kane, B., Maruthur, N., McGready, J., Riekert, K., Wanigatunga, A. A., Alderfer, M., Alver, D., et al. (2024) · Trials · doi:10.1186/s13063-024-08177-8 · Cited by 16 · see citing works
Self-citationBeuthin, O., Bhui, K. S., Yu, L., Shahid, S., Almidani, L., Bilalaga, M. M., Hussein, R., Harba, A., & Nasser, Y. (2023) · JMIR Research Protocols · doi:10.2196/47627 · Cited by 9 · see citing works
Marsden, A., Hann, M., Barron, E., McGough, B., Murray, E., Valabhji, J., & Cotterill, S. (2023) · BMC Health Services Research · doi:10.1186/s12913-023-10365-2 · Cited by 9 · see citing works
Flagged first, then by citations
1 of the 22 references couldn’t be matched to a known paper.
Research works printed but not linked: 5
- 2024, National Diabetes Statistics Report
- Diabetes Prevention Recognition Program Registry
- Find a Lifestyle Change Program
- May, 2023, Weight loss maintenance after a digital commercial behavior change program (Noom Weight): Observational cross-sectional survey study, Obes. Sci. Pract, 9, 10.1002/osp4.v9.5 · DOI
- 2024, Centers for Disease Control and Prevention Diabetes Prevention Recognition Program Standards and Operating Procedures
Cited by
Sorted by citations
Mathioudakis, N., Lalani, B., Abusamaan, M. S., Alderfer, M., Alver, D., Dobs, A., Kane, B., McGready, J., Riekert, K., Ringham, B., et al. (2025) · JAMA · doi:10.1001/jama.2025.19563 · Cited by 41 · see citing works
Self-citationApplications of artificial intelligence and machine learning in prediabetes: a scoping review.
Lalani, B., Herur, R., Zade, D., Collins, G., Dishong, D. M., Mehta, S., Shim, J., Valdez, Y., & Mathioudakis, N. (2025) · Journal of Diabetes Science and Technology · doi:10.1177/19322968251351995 · Cited by 11 · see citing works
Self-citationMaruthur, N. M., Chan, S., Arnold, E. A., Brown, M., NASS, C. M., Tseng, E., Berkowitz, S. A., Wang, N., Yeh, H., Han, D., et al. (2025) · Clinical Diabetes · doi:10.2337/cd25-0043 · Cited by 4 · see citing works
Self-citationАвдеева, М. В., Gogoleva, M. N., Ризаханова, О. А., Shatalova, M. V., Nosacheva, V. S., Sandgarten, L. M., Signaevskaya, A. R., & Chestnyi, I. S. (2025) · Russian Journal of Preventive Medicine · doi:10.17116/profmed20252810133
AI-Driven Diabetes Prevention Program—Reply
Lalani, B., & Mathioudakis, N. N. (2026) · JAMA · doi:10.1001/jama.2025.26494
Self-citationDishong, D. M., Lalani, B., Kim, J. S., ZADE, D., SHEHADEH, A., Abusamaan, M. S., Mathioudakis, N. N., AI-DPP Study Group*, Mathioudakis, N. N., Lalani, B., et al. (2026) · Diabetes Obesity and Cardiometabolic CARE · doi:10.2337/doc25-0091
Self-citationLalani, B., SIEW, G. M., Valdez, Y., SHEHADEH, A., ZADE, D., Riekert, K. A., Mathioudakis, N. N., & AI-DPP Study Group (2026) · JMIR mhealth and uhealth · doi:10.2196/92981
Self-citationThe U.S. National Diabetes Prevention Program Demonstrates Effectiveness With Insufficient Reach
Lalani, B., & Mathioudakis, N. N. (2026) · Diabetes Care · doi:10.2337/dci26-0077
Self-citation
- Authors
- Lalani, B., Shim, J., Vadini, V., Valdez, Y., Zade, D., & Mathioudakis, N.
- Affiliations
- Johns Hopkins University (US)
- Corresponding author
- Mathioudakis, Nestoras
- Venue
- Frontiers in Clinical Diabetes and Healthcare
- Publisher
- Frontiers Media SA
- Published
- 2025-04-16
- Volume
- 6
- Pages
- 1562108-1562108
- ISSN
- 2673-6616 (ISSN-L)
- PMC
- PMC12040819
- OpenAlex
- W4409516777
- Semantic Scholar
- 0ec747ad5834c3bf4c1c1984b9b71ca90c57e18c
- Cited by
- 7
- References
- 22
- Citations by year
- 2026: 4 · 2025: 3
- FWCI
- 8.77
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Clinical Diabetes and Healthcare (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute of Diabetes and Digestive and Kidney Diseases (5R01DK125780)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Keywords
- prediabetes, type 2 diabetes prevention, consumer information access, program websites, artificial intelligence in healthcare, telemedicine, weight loss, user experience, program credibility, purchase decision, digital health platforms
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
