Effectiveness of artificial intelligence vs. human coaching in diabetes prevention: a study protocol for a randomized controlled trial.
Open accessAbstract
Background: Prediabetes is a highly prevalent condition that heralds an increased risk of progression to type 2 diabetes, along with associated microvascular and macrovascular complications. The Diabetes Prevention Program (DPP) is an established effective intervention for diabetes prevention. However, participation in this 12-month lifestyle change program has historically been low. Digital DPPs have emerged as a scalable alternative, accessible asynchronously and recognized by the Centers for Disease Control and Prevention (CDC). Yet, most digital programs still incorporate human coaching, potentially limiting scalability. Furthermore, existing effectiveness results of digital DPPs are primarily derived from per protocol, longitudinal non-randomized studies, or comparisons to control groups that do not represent the standard of care DPP. The potential of an AI-powered DPP as an alternative to the DPP is yet to be investigated. We propose a randomized controlled trial (RCT) to directly compare these two approaches. Methods: This open-label, multicenter, non-inferiority RCT will compare the effectiveness of a fully automated AI-powered digital DPP (ai-DPP) with a standard of care human coach-based DPP (h-DPP). A total of 368 participants with elevated body mass index (BMI) and prediabetes will be randomized equally to the ai-DPP (smartphone app and Bluetooth-enabled body weight scale) or h-DPP (referral to a CDC recognized DPP). The primary endpoint, assessed at 12 months, is the achievement of the CDC’s benchmark for type 2 diabetes risk reduction, defined as any of the following: at least 5% weight loss, at least 4% weight loss and at least 150 min per week on average of physical activity, or at least a 0.2-point reduction in hemoglobin A1C. Physical activity will be objectively measured using serial actigraphy at baseline and at 1-month intervals throughout the trial. Secondary endpoints, evaluated at 6 and 12 months, will include changes in A1C, weight, physical activity measures, program engagement, and cost-effectiveness. Participants include adults aged 18–75 years with laboratory confirmed prediabetes, a BMI of ≥ 25 kg/m 2 (≥ 23 kg/m 2 for Asians), English proficiency, and smartphone users. This U.S. study is conducted at Johns Hopkins Medicine in Baltimore, MD, and Reading Hospital (Tower Health) in Reading, PA. Discussion: Prediabetes is a significant public health issue, necessitating scalable interventions for the millions affected. Our pragmatic clinical trial is unique in directly comparing a fully automated AI-powered approach without direct human coach interaction. If proven effective, it could be a scalable, cost-effective strategy. This trial will offer vital insights into both AI and human coach-based behavioral change strategies in real-world clinical settings. Trial registration: ClinicalTrials.gov NCT05056376. Registered on September 24, 2021, https://clinicaltrials.gov/study/NCT05056376
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Randomization used centrally administered REDCap with randomly permuted block sizes of 2, 4, or 8, stratified by baseline HbA1c and site, and concealed the sequence from research coordinators.
“Eligible participants underwent block randomization using centrally administered REDCap software.22 Randomization was stratified by baseline HbA1c level (≤6.0% or 6.1%-6.4%) and recruitment site, using randomly permuted block sizes of 2, 4, or 8. The REDCap system concealed the randomization sequence from the research coordinators who enrolled and assigned participants.”
From An AI-Powered Lifestyle Intervention vs Human Coaching in the Diabetes Prevention Program: A Randomized Clinical Trial (2025) · cites this paper as [22]
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Cites
Harris, P. A., Taylor, R., Thielke, R., Payne, J., Gonzalez, N., & Conde, J. G. (2009) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2008.08.010 · Cited by 51,384 · see citing works
The REDCap consortium: building an international community of software platform partners
Harris, P. A., Taylor, R., Minor, B. L., Elliott, V., Fernandez, M., O'Neal, L., McLeod, L., Delacqua, G., Delacqua, F., Kirby, J., et al. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103208 · Cited by 24,013 · cited 1× · see citing works
“Study data from research visits will be collected and managed using REDCap electronic data capture tools hosted at Johns Hopkins University [44, 45].”
MethodReduction 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
Michie, S., van Stralen, M. M., & West, R. (2011) · Implementation Science · doi:10.1186/1748-5908-6-42 · Cited by 13,697 · see citing works
The 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
Garnefski, N., Kraaij, V., Schroevers, M. J., & Somsen, G. A. (2008) · Journal of Clinical Psychology in Medical Settings · doi:10.1007/s10880-008-9136-5 · Cited by 2,487 · see citing works
Nahum-Shani, I., Smith, S. N., Spring, B. J., Collins, L. M., Witkiewitz, K., Tewari, A., & Murphy, S. A. (2018) · Annals of Behavioral Medicine · doi:10.1007/s12160-016-9830-8 · Cited by 2,171 · see citing works
A Review of eHealth Interventions for Physical Activity and Dietary Behavior Change
Norman, G. J., Zabinski, M. F., Adams, M. A., Rosenberg, D. E., Yaroch, A. L., & Atienza, A. A. (2007) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2007.05.007 · Cited by 605 · cited 1× · see citing works
“…(d-DPPs) incorporate multiple elements of eCoaching strategies, including goal setting, self-monitoring, feedback loops, behavioral prompts and reminders, scaffolding learning, socialization, gamification, and personalization, to engage users in making meaningful lifestyle changes [10, 11].”
BackgroundDiagnosis 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
A systematic review of just-in-time adaptive interventions (JITAIs) to promote physical activity
Hardeman, W., Houghton, J., Lane, K., Jones, A., & Naughton, F. (2019) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-019-0792-7 · Cited by 396 · cited 1× · see citing works
“Studies on JITAIs have demonstrated their effectiveness in promoting behavior change for various goals, including weight loss and physical activity, and across various patient demographics [26–36].”
BackgroundBalk, E. M., Earley, A., Raman, G., Avendano, E. A., Pittas, A. G., & Remington, P. L. (2015) · Annals of Internal Medicine · doi:10.7326/m15-0452 · Cited by 371 · see citing works
Hsu, P., Sung, S., Cheng, H., Yeh, J. S., Liu, W., Chan, W., Chen, C., Chou, P., & Chuang, S. (2012) · Diabetes Care · doi:10.2337/dc12-0916 · Cited by 247 · see citing works
Joiner, K. L., Nam, S., & Whittemore, R. (2017) · Preventive Medicine · doi:10.1016/j.ypmed.2017.04.033 · Cited by 223 · see citing works
3. Prevention or Delay of Diabetes and Associated Comorbidities: Standards of Care in Diabetes—2023
ElSayed, N. A., Aleppo, G., Aroda, V. R., Bannuru, R. R., Brown, F. M., Bruemmer, D., Collins, B. S., Gaglia, J. L., Hilliard, M. E., Isaacs, D. M., et al. (2022) · Diabetes Care · doi:10.2337/dc23-s003 · Cited by 210 · see citing works
Stein, N., & Brooks, K. (2017) · JMIR Diabetes · doi:10.2196/diabetes.8590 · Cited by 191 · see citing works
Reach and use of diabetes prevention services in the United States, 2016-2017.
Ali, M. K., McKeever Bullard, K., Imperatore, G., Benoit, S. R., Rolka, D. B., Albright, A. L., & Gregg, E. W. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.3160 · Cited by 153 · see citing works
Interventions for Reversing Prediabetes: A Systematic Review and Meta-Analysis
Galaviz, K. I., Weber, M. B., Suvada, K., Gujral, U. P., Wei, J., Merchant, R., Dharanendra, S., Haw, J. S., Narayan, K. M. V., & Ali, M. K. (2022) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2021.10.020 · Cited by 149 · cited 1× · see citing works
“Multiple meta-analyses have also demonstrated that these lifestyle interventions can cause reversion to normoglycemia (from prediabetes) [3, 4].”
BackgroundEngagement 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
Thomas, J. G., & Bond, D. S. (2015) · Health Psychology · doi:10.1037/hea0000304 · Cited by 117 · see citing works
Innovations in the Use of Interactive Technology to Support Weight Management
Spruijt‐Metz, D., Wen, C. K. F., O’Reilly, G. A., Li, M., Lee, S., Emken, B. A., Mitra, U., Annavaram, M., Ragusa, G., & Narayanan, S. (2015) · Current Obesity Reports · doi:10.1007/s13679-015-0183-6 · Cited by 108 · 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
Everett, E., Kane, B., Yoo, A., Dobs, A., & Mathioudakis, N. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.9723 · Cited by 101 · see citing works
Self-citationForman, E. M., Goldstein, S. P., Crochiere, R. J., Butryn, M. L., Juarascio, A. S., Zhang, F., & Foster, G. D. (2019) · Translational Behavioral Medicine · doi:10.1093/tbm/ibz137 · Cited by 94 · 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 · cited 1× · see citing works
“Instead, control groups often receive only single educational sessions or brief educational materials [15, 20].”
BackgroundExploring the State-of-Receptivity for mHealth Interventions
Künzler, F., Mishra, V., Kramer, J.-N., Kotz, D. F., Fleisch, E., & Kowatsch, T. (2019) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3369805 · Cited by 83 · see citing works
Prevalence and Correlates of Diabetes Prevention Program Referral and Participation
Venkataramani, M., Pollack, C. E., Yeh, H.-C., & Maruthur, N. M. (2019) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2018.10.005 · Cited by 82 · see citing works
Self-citationMair, J. L., Hayes, L. D., Campbell, A. K., Buchan, D. S., Easton, C., & Sculthorpe, N. F. (2022) · JMIR Formative Research · doi:10.2196/34662 · Cited by 75 · 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
A research tool for measuring non-participation of older people in research on digital health
Poli, A., Kelfve, S., & Motel-Klingebiel, A. (2019) · BMC Public Health · doi:10.1186/s12889-019-7830-x · Cited by 59 · see citing works
Yang, M.-J., Sutton, S. K., Hernandez, L. M., Jones, S. R., Wetter, D. W., Kumar, S., & Vinci, C. (2022) · Addictive Behaviors · doi:10.1016/j.addbeh.2022.107467 · Cited by 48 · see citing works
New Medicare Diabetes Prevention Coverage May Limit Beneficiary Access and Widen Health Disparities
Ritchie, N. D., & Gritz, R. M. (2018) · Medical Care · doi:10.1097/mlr.0000000000000981 · Cited by 35 · see citing works
Ismail, T., & Al‐Thani, D. (2021) · JMIR Formative Research · doi:10.2196/34309 · Cited by 32 · 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
Wang, S., Sporrel, K., van Hoof, H., Simons, M., Boer, R. D., Ettema, D., Nibbeling, N., Deutekom, M. D. L. F., & Kröse, B. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18116059 · Cited by 30 · 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
Oikonomidi, T., Ravaud, P., LeBeau, J., & Tran, V.-T. (2023) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2022.12.006 · Cited by 26 · see citing works
(2018) · PubMed · Cited by 24 · see citing works
Gruß, I. (2021) · The American Journal of Managed Care · doi:10.37765/ajmc.2021.88784 · Cited by 20 · see citing works
May, C. N., Cox‐Martin, M., Ho, A. S., McCallum, M., Chan, C., Blessing, K., Behr, H., Blanco, P., Mitchell, E. S., & Michaelides, A. P. (2023) · Obesity Science & Practice · doi:10.1002/osp4.666 · Cited by 20 · see citing works
Marsden, A., Hann, M., Barron, E., Ross, J., Valabhji, J., Murray, E., & Cotterill, S. (2023) · Preventive Medicine Reports · doi:10.1016/j.pmedr.2023.102161 · Cited by 19 · see citing works
Xu, Z., & Smit, E. S. (2023) · Digital Health · doi:10.1177/20552076231183543 · Cited by 18 · 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
Weight loss and modeled cost savings in a digital diabetes prevention program
Branch, O. H., Rikhy, M., Auster‐Gussman, L. A., Lockwood, K. G., & Graham, S. A. (2023) · Obesity Science & Practice · doi:10.1002/osp4.665 · Cited by 16 · see citing works
Wang, J., Wu, Z., Choi, S. W., Sen, S., Yan, X., Miner, J. A., Sander, A. M., Lyden, A. K., Troost, J. P., & Carlozzi, N. E. (2023) · JMIR Formative Research · doi:10.2196/43099 · Cited by 16 · see citing works
Graham, S. A., Auster‐Gussman, L. A., Lockwood, K. G., & Branch, O. H. (2023) · Population Health Management · doi:10.1089/pop.2022.0278 · Cited by 13 · see citing works
Michaud, T. L., Wilson, K. E., Katula, J. A., You, W., & Estabrooks, P. A. (2023) · Translational Behavioral Medicine · doi:10.1093/tbm/ibad008 · Cited by 12 · see citing works
Formagini, T. D. B., Brooks, J. V., Jacobson, L. T., & Roberts, A. W. (2021) · Kansas Journal of Medicine · doi:10.17161/kjm.vol14.15125 · Cited by 9 · see citing works
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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-citationLalani, B., Shim, J., Vadini, V., Valdez, Y., Zade, D., & Mathioudakis, N. (2025) · Frontiers in Clinical Diabetes and Healthcare · doi:10.3389/fcdhc.2025.1562108 · Cited by 7 · see citing works
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- Authors
- Abusamaan, M. S., Ballreich, J., Dobs, A., Kane, B., Maruthur, N., McGready, J., Riekert, K., Wanigatunga, A. A., Alderfer, M., Alver, D., Lalani, B., Ringham, B., Vandi, F., Zade, D., & Mathioudakis, N. N.
- Affiliations
- Johns Hopkins University (US)
- Reading Hospital (US)
- Corresponding author
- Mathioudakis, Nestoras N.
- ORCID
- Venue
- Trials
- Published
- 2024-05-16
- Volume
- 25
- Issue
- 1
- Pages
- 325-325
- ISSN
- 1745-6215 (ISSN-L)
- PMC
- PMC11100129
- OpenAlex
- W4396955285
- Cited by
- 16
- References
- 47
- Citations by year
- 2026: 6 · 2025: 10
- FWCI
- 9.33
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Trials (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK125780)
- 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
- artificial intelligence, diabetes prevention, prediabetes, type 2 diabetes prevention, digital health interventions, mobile application, actigraphy, physical activity, weight loss, HbA1c, non-inferiority, cost-effectiveness, Diabetes Prevention Program, program engagement, scaling up interventions, non-pharmacological treatment, racial and ethnic minorities
- MeSH terms
- Mentoring, Equivalence Trials as Topic, Adult, Artificial Intelligence, Diabetes Mellitus, Type 2, Female, Humans, Male, Middle Aged, Prediabetic State, Time Factors, Multicenter Studies as Topic, Randomized Controlled Trials as Topic, Treatment Outcome, Risk Reduction Behavior, Mobile Applications, Pragmatic Clinical Trials as Topic
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