Outcomes of a Digital Health Program With Human Coaching for Diabetes Risk Reduction in a Medicare Population
Open accessAbstract
OBJECTIVE: To examine the outcomes of a Medicare population who participated in a program combining digital health with human coaching for diabetes risk reduction. METHOD: People at risk for diabetes enrolled in a program combining digital health with human coaching. Participation and health outcomes were examined at 16 weeks and 6 and 12 months. RESULTS: A total of 501 participants enrolled; 92% completed at least nine of 16 core lessons. Participants averaged 19 of 31 possible opportunities for weekly program engagement. At 12 months, participants lost 7.5% ( SD = 7.8%) of initial body weight; among participants with clinical data, glucose control improved (glycosylated hemoglobin [HbA1c] change = -0.14%, p = .001) and total cholesterol decreased (-7.08 mg/dL, p = .008). Self-reported well-being, depression, and self-care improved ( p < .0001). DISCUSSION: This Medicare population demonstrated sustained program engagement and improved weight, health, and well-being. The findings support digital programs with human coaching for reducing chronic disease risk among older adults.
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?
Castro Sweet et al used PPC, TCP, and PHT; after the interventions, the mean HbA1c of participants reduced by 0.1% (P=.001).
“For example, Castro Sweet et al [10] used PPC, TCP, and PHT in their DHIs; after the interventions, the change in the mean HbA1c of participants reduced by 0.1% (P=.001).”
From Digital Health Interventions to Prevent Type 2 Diabetes Mellitus: Systematic Review (2025) · cites this paper as [10]
The Method > Intervention section describes personalized health coaching, phone contact, and digital tracking tools, broadly consistent with the intervention description, though it does not use the acronyms PPC, TCP, or PHT. Results > Biometrics reports a mean HbA1c decrease of 0.14% in the 69 participants with available results, but gives P=.0001, not P=.001; the HbA1c result does not cover all participants. (LLM, conf=0.90)
Experimental — accuracy not yet measured
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 · cited 2× · see citing works
“Furthermore, the weight loss achieved by this sample is consistent with the magnitude of weight loss that was associated with improved glucose control and reduced diabetes rates in the original DPP study (Knowler et al., 2002).”
BackgroundResultThe WHO-5 well-being index: A systematic review of the literature
Topp, C. W., Østergaard, S. D., Søndergaard, S., & Bech, P. (2015) · Psychotherapy and Psychosomatics · doi:10.1159/000376585 · Cited by 4,708 · see citing works
An Ultra-Brief Screening Scale for Anxiety and Depression: The PHQ–4
Kroenke, K., Spitzer, R. L., Williams, J. B. W., & Löwe, B. (2009) · Psychosomatics · doi:10.1016/s0033-3182(09)70864-3 · Cited by 4,261 · see citing works
Di Angelantonio, E., Bhupathiraju, S. N., Wormser, D., Gao, P., Kaptoge, S., de Gonzalez, A. B., Cairns, B. J., Huxley, R., Jackson, C. L., Joshy, G., et al. (2016) · The Lancet · doi:10.1016/s0140-6736(16)30175-1 · Cited by 2,821 · see citing works
The summary of diabetes self-care activities measure: results from 7 studies and a revised scale.
Toobert, D. J., Hampson, S. E., & Glasgow, R. E. (2000) · Diabetes Care · doi:10.2337/diacare.23.7.943 · Cited by 2,561 · see citing works
Tate, D. F., Jackvony, E. H., & Wing, R. R. (2003) · JAMA · doi:10.1001/jama.289.14.1833 · Cited by 666 · see citing works
Siu, A. L., & on behalf of the U.S. Preventive Services Task Force* (2015) · Annals of Internal Medicine · doi:10.7326/m15-2345 · Cited by 319 · see citing works
The Diabetes Prevention Program Research Group [Prepared by Jill Crandall, D. S. (2006) · The Journals of Gerontology Series A: Biological Sciences and Medical Sciences · doi:10.1093/gerona/61.10.1075 · Cited by 254 · see citing works
Sepah, S. C., Jiang, L., & Peters, A. L. (2014) · The Diabetes Educator · doi:10.1177/0145721714531339 · Cited by 139 · see citing works
Flagged first, then by citations
Research works printed but not linked: 17
- Albright A. L. Gregg E. W. (2013). Preventing type 2 diabetes in communities across the U.S.: The National Diabetes Prevention Program. American Journal of Preventive Medicine, 44, (Suppl 4), S346- S351. doi: 10.1016/j.amepre.2012.12.009 23498297 PMC4539613 · DOI
- Azar K. M. Koliwad S. Poon T. Xiao L. Lv N. Griggs R. Ma J. (2016). The electronic CardioMetabolic Program (eCMP) for patients with cardiometabolic risk: A randomized controlled trial. Journal of Medical Internet Research, 18(5), Article e134. doi: 10.2196/jmir.5143 PMC4902854 27234480 · DOI
- Block G. Azar K. M. Romanelli R. J. Block T. J. Hopkins D. Carpenter H. A.... Block C. H. (2015). Diabetes prevention and weight loss with a fully automated behavioral intervention by email, web, and mobile phone: A randomized controlled trial among persons with prediabetes. Journal of Medical Internet Research, 17(10), Article e240. doi: 10.2196/jmir.4897 PMC4642405 26499966 · DOI · Paper page
- Brokaw S. M. Carpenedo D. Campbell P. Butcher M. K. Furshong G. Helgerson S. D. Harwell T. S. (2015). Effectiveness of an adapted diabetes prevention program lifestyle intervention in older and younger adults. Journal of the American Geriatric Society, 63(6), 1067- 1074. doi: 10.1111/jgs.13428 26031428 · DOI
- Delahanty L. M. Peyrot M. Shrader P. J. Williamson D. A. Meigs J. B. Nathan D. M.; DPP Research Group. (2013). Pretreatment, psychological, and behavioral predictors of weight outcomes among lifestyle intervention participants in the Diabetes Prevention Program (DPP). Diabetes Care, 36(1), 34- 40. doi: 10.2337/dc12-0733 23129133 PMC3526204 · DOI
- Finkelstein E. A. Trogdon J. G. Cohen J. W. Dietz W. (2009). Annual medical spending attributable to obesity: Payer-and service-specific estimates. Health Affairs, 28(5), w822- w831. doi: 10.1377/hlthaff.28.5.w822 19635784 · DOI
- Flegal K. M. Kruszon-Moran D. Carroll M. D. Fryar C. D. Ogden C. L. (2016). Trends in obesity among adults in the United States, 2005 to 2014. Journal of the American Medical Association, 315(21), 2284- 2291. doi: 10.1001/jama.2016.6458 27272580 PMC11197437 · DOI · Paper page
- Garvey W. T. Mechanick J. I. Brett E. M. Garber A. J. Hurley D. L. Jastreboff A. M.... Plodkowski R. (2016). American Association of Clinical Endocrinologists and American College of Endocrinology Comprehensive Clinical Practice Guidelines for medical care of patients with obesity executive summary. Endocrine Practice, 22(7), 842- 884. doi: 10.4158/ep161356.esgl 27219496 · DOI
- LeBlanc E. S. O’Connor E. Whitlock E. P. Patnode C. D. Kapka T. (2011). Effectiveness of primary care—Relevant treatments for obesity in adults: A systematic evidence review for the U.S. Preventive Services Task Force. Annals of Internal Medicine, 155(7), 434- 447. doi: 10.7326/0003-4819-155-7-201110040-00006 21969342 · DOI
- LeFevre M. L. Siu A. L. Peters J. J. Bibbins-Domingo K. Ciofu Baumann L. Curry S. J.... Pignone M. P. (2014). Behavioral counseling to promote a healthful diet and physical activity for cardiovascular disease prevention in adults with cardiovascular risk factors: U.S. Preventive Services Task Force recommendation statement. Annals of Internal Medicine, 161(8), 587- 593. doi: 10.7326/M14-1796 25155419 · DOI · Paper page
- McAdam-Marx C. Bellows B. K. Unni S. Wygant G. Mukherjee J. Brixner D. I. (2014). Impact of adherence and weight loss on glycemic control in patients with type 2 diabetes: Cohort analyses of integrated medical record, pharmacy claims, and patient-reported data. Journal of Managed Care & Specialty Pharmacy, 20(7), 691- 700. doi: 10.18553/jmcp.2014.20.7.691 24967522 PMC10437951 · DOI
- McTigue K. M. Conroy M. B. Hess R. Bryce C. L. Fiorillo A. B. Fischer G. S.... Simkin-Silverman L. R. (2009). Using the internet to translate an evidence-based lifestyle intervention into practice. Telemedicine and e-Health, 15(9), 851- 858. doi: 10.1089/tmj.2009.0036 19919191 · DOI
- Sepah S. C. Jiang L. Peters A. L. (2015). Long-term outcomes of a web-based diabetes prevention program: 2-year results of a single-arm longitudinal study. Journal of Medical Internet Research, 17(4), Article e92. doi: 10.2196/jmir.4052 PMC4409647 25863515 · DOI · Paper page
- Smith K. J. Kuo S. Zgibor J. C. McTigue K. M. Hess R. Bhargava T. Bryce C. L. (2016). Cost effectiveness of an internet-delivered lifestyle intervention in primary care patients with high cardiovascular risk. Preventive Medicine, 87, 103- 109. doi: 10.1016/j.ypmed.2016.02.036 26921656 · DOI
- Steptoe A. Deaton A. Stone A. A. (2015). Psychological wellbeing, health and ageing. The Lancet, 385(9968), 640- 648. doi: 10.1016/S0140-6736(13)61489-0 PMC4339610 25468152 · DOI
- Thorpe K. E. Yang Z. (2011). Enrolling people with prediabetes ages 60-64 in a proven weight loss program could save Medicare $7 billion or more. Health Affairs, 30(9), 1673- 1679. doi: 10.1377/hlthaff.2010.0944 21900657 · DOI
- Tice J. Chapman R. Shore K. Seidner M. Ollendorf D. Weissberg J. Pearson S. (2016). Diabetes Prevention Programs: Effectiveness and value—Final evidence report and meeting summary. Boston, MA Retrieved from https://icer-review.org/material/final-report-dpp/
Cited by
Sorted by citations
Van Rhoon, L., Byrne, M., Morrissey, E., Murphy, J., & McSharry, J. (2020) · DIGITAL HEALTH · doi:10.1177/2055207620914427 · Cited by 182 · see citing works
McGowan, B. M., Bruun, J. M., Capehorn, M., Pedersen, S. D., Pietiläinen, K. H., Muniraju, H. A. K., Quiroga, M., Varbo, A., & Lau, D. C. W. (2024) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(24)00182-7 · Cited by 176 · 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
Toro-Ramos, T., Michaelides, A., Anton, M., Karim, Z., Kang-Oh, L., Argyrou, C., Loukaidou, E., Charitou, M. M., Sze, W., & Miller, J. D. (2020) · JMIR mHealth and uHealth · doi:10.2196/17842 · Cited by 107 · see citing works
Digital coaching strategies to facilitate behavioral change in type 2 diabetes: a systematic review
Gershkowitz, B. D., Hillert, C. J., & Crotty, B. H. (2021) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgaa850 · Cited by 67 · see citing works
Relationship Between Age and Weight Loss in Noom: Quasi-Experimental Study
DeLuca, L., Toro-Ramos, T., Michaelides, A., Seng, E., & Swencionis, C. (2020) · JMIR Diabetes · doi:10.2196/18363 · Cited by 36 · see citing works
Digital Health–Supported Lifestyle Change Programs to Prevent Type 2 Diabetes
Kirley, K., & Sachdev, N. (2018) · Diabetes Spectrum · doi:10.2337/ds18-0019 · Cited by 29 · see citing works
Commercial weight loss programs in the management of obesity: an update.
Laudenslager, M., Chaudhry, Z. W., Rajagopal, S., Clynes, S., & Gudzune, K. A. (2021) · Current Obesity Reports · doi:10.1007/s13679-021-00428-y · Cited by 25 · see citing works
Kim, D. (2023) · Frontiers in Digital Health · doi:10.3389/fdgth.2023.1117280 · Cited by 21 · see citing works
Digital Health Interventions to Prevent Type 2 Diabetes Mellitus: Systematic Review
Duong, T., Olsen, Q., Menon, A., Woods, L., Wang, W., Varnfield, M., Jiang, L., & Sullivan, C. (2025) · Journal of Medical Internet Research · doi:10.2196/67507 · Cited by 14 · see citing works
Equity and AI Governance at Academic Medical Centers
(2024) · The American Journal of Managed Care · doi:10.37765/ajmc.2024.89555 · Cited by 8 · see citing works
Fitzpatrick, S. L., Mayhew, M., Rawlings, A. M., Smith, N., Nyongesa, D. B., Vollmer, W. M., Stevens, V. J., Grall, S. K., & Fortmann, S. P. (2022) · Clinical Diabetes · doi:10.2337/cd21-0114 · Cited by 5 · see citing works
- Authors
- Castro Sweet, C. M., Chiguluri, V., Gumpina, R., Abbott, P., Madero, E. N., Payne, M., Happe, L., Matanich, R., Renda, A., & Prewitt, T.
- Affiliations
- Omada Health, San Francisco, CA, USA
- Humana (United States) (US)
- University of California, Berkeley (US)
- Venue
- Journal of Aging and Health
- Publisher
- SAGE Publications
- Published
- 2017-01-24
- Volume
- 30
- Issue
- 5
- Pages
- 692-710
- ISSN
- 0898-2643 (ISSN-L)
- 1552-6887
- PMC
- PMC5944079
- OpenAlex
- W2582153527
- Cited by
- 130
- References
- 9
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 19 · 2023: 14 · 2022: 16 · 2021: 29 · 2020: 17 · 2019: 11 · 2018: 11 · 2017: 4
- FWCI
- 19.06
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Aging and Health (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- 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 › Demography › Technology Use by Older Adults
- Keywords
- diabetes prevention, digital health, Medicare beneficiaries, older adults, weight loss, HbA1c, cholesterol, self-care, depression, well-being, program engagement, chronic disease prevention
- MeSH terms
- Aged, Diabetes Mellitus, Type 2, Female, Medicare, Health Promotion, Glycated Hemoglobin, Humans, Male, Middle Aged, Preventive Health Services, Self Care, United States, Program Evaluation, Telemedicine, Risk Reduction Behavior
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
