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Park S, Ballreich J, Ward T, Shi L. Cost-effectiveness analysis of a digital diabetes-prevention programme versus an in-person diabetes-prevention programme in people with prediabetes in the United States. Diabetes Obes Metab 2024; 26:4522-4534. [PMID: 39056211 DOI: 10.1111/dom.15807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 07/02/2024] [Accepted: 07/02/2024] [Indexed: 07/28/2024]
Abstract
AIM To assess the cost-effectiveness of a digital diabetes prevention programme (d-DPP) compared with a diabetes prevention programme (DPP) for preventing type 2 diabetes (T2D) in individuals with prediabetes in the United States. METHODS A Markov cohort model was constructed, simulating a 10-year period starting at the age of 45 years, with a societal and healthcare sector perspective. The effectiveness of the d-DPP intervention was evaluated using a meta-analysis, with that of the DPP as the comparator. The initial cycle represented the treatment period, and transition probabilities for the post-treatment period were derived from a long-term lifestyle intervention meta-analysis. The onset of T2D complications was estimated using microsimulation. Quality-adjusted life years (QALYs) were calculated based on health utility measured by short form (SF)-12 scores, and a willingness-to-pay threshold of $100 000 per QALY gained was applied. RESULTS The d-DPP intervention resulted in cost savings of $3,672 from a societal perspective and $2,990 from a healthcare sector perspective and a gain of 0.08 QALYs compared with the DPP. The dropout rate was identified as a significant factor influencing the results. Probabilistic sensitivity analysis showed that the d-DPP intervention was preferred in 85.8% in the societal perspective and 85.2% in the healthcare sector perspective. CONCLUSIONS The d-DPP is a cost-effective alternative to in-person lifestyle interventions for preventing the development of T2D among individuals with prediabetes in the United States.
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Affiliation(s)
- Sooyeol Park
- Department of Health Policy and Management, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA
- Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Jeromie Ballreich
- Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Trevor Ward
- Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Lizheng Shi
- Department of Health Policy and Management, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA
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Michaud TL, Zagurski C, Wilson KE, Porter GC, Johnson G, Estabrooks PA. Reach and Weight Loss Among Comparison Group Participants Who Enrolled in the Active Intervention After a Diabetes Prevention Trial. Prev Chronic Dis 2024; 21:E40. [PMID: 38843118 PMCID: PMC11192497 DOI: 10.5888/pcd21.230358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/23/2024] Open
Abstract
We examined participation rates, engagement, and weight-loss outcomes of comparison group participants in a diabetes prevention trial who enrolled in a digitally delivered diabetes prevention program (ie, an active intervention) after the original trial ended. We evaluated these outcomes by using the Wilcoxon signed-rank test and 1-sample z test. We found a high participation rate (73%) among comparison group participants and comparable weight-loss outcomes at 12 months (6.8 lb) after initiating participation in the active intervention relative to intervention group participants during the original trial. Findings support providing evidence-based interventions for comparison or control group participants post-trial. Findings also support examining the cost-effectiveness of post-trial interventions, regardless of the limitations of acquiring post-trial data on weight in an uncontrolled setting.
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Affiliation(s)
- Tzeyu L Michaud
- Department of Health Promotion, College of Public Health, University of Nebraska Medical Center, Omaha
- Center for Reducing Health Disparities, College of Public Health, University of Nebraska Medical Center, Omaha
- Department of Health Promotion, 986075 Nebraska Medical Center, Omaha, NE 68198
| | - Cleo Zagurski
- Department of Health Promotion, College of Public Health, University of Nebraska Medical Center, Omaha
| | - Kathryn E Wilson
- Department of Kinesiology and Health, College of Education & Human Development, Georgia State University, Atlanta
- Center for the Study of Stress, Trauma, and Resilience, College of Education and Human Development, Georgia State University, Atlanta
| | - Gwenndolyn C Porter
- Department of Health Promotion, College of Public Health, University of Nebraska Medical Center, Omaha
| | - George Johnson
- Department of Health Promotion, College of Public Health, University of Nebraska Medical Center, Omaha
| | - Paul A Estabrooks
- Department of Health and Kinesiology, College of Health, University of Utah, Salt Lake City
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Abusamaan MS, Ballreich J, Dobs A, Kane B, Maruthur N, McGready J, Riekert K, Wanigatunga AA, Alderfer M, Alver D, Lalani B, Ringham B, Vandi F, Zade D, Mathioudakis NN. Effectiveness of artificial intelligence vs. human coaching in diabetes prevention: a study protocol for a randomized controlled trial. Trials 2024; 25:325. [PMID: 38755706 PMCID: PMC11100129 DOI: 10.1186/s13063-024-08177-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 05/14/2024] [Indexed: 05/18/2024] Open
Abstract
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/m2 (≥ 23 kg/m2 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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Affiliation(s)
- Mohammed S Abusamaan
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jeromie Ballreich
- Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Adrian Dobs
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Brian Kane
- Tower Health Medical Group Family Medicine, Reading, PA, USA
| | - Nisa Maruthur
- Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - John McGready
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Kristin Riekert
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Amal A Wanigatunga
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | - Defne Alver
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Benjamin Lalani
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Benjamin Ringham
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Fatmata Vandi
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Daniel Zade
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Nestoras N Mathioudakis
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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Clements MA, Kaufman N, Mel E. Using Digital Health Technology to Prevent and Treat Diabetes. Diabetes Technol Ther 2024; 26:S90-S107. [PMID: 38441446 DOI: 10.1089/dia.2024.2506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Affiliation(s)
- Mark A Clements
- Children's Mercy Hospital, Kansas City, MO
- University of Missouri-Kansas City, Kansas City, MO
| | - Neal Kaufman
- Fielding School of Public Health, Geffen School of Medicine, University of California, Los Angeles, CA
- Canary Health Inc., Los Angeles, CA
| | - Eran Mel
- Jesse Z. and Sara Lea Shaffer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel
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