Building Toward a Population-Based Approach to Diabetes Screening and Prevention for US Adults

Bowen, Michael E.; Schmittdiel, Julie A.; Kullgren, Jeffrey T.; Ackermann, Ronald T.; O’Brien, Matthew J.

doi:10.1007/s11892-018-1090-5

Cited by 21 publications

(22 citation statements)

References 69 publications

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“…Although clinicians recognize diabetes prevention as an urgent public health need that can dramatically affect the well-being of their patients, a lack of funding, collaboration, and other staff support have been reported to be key obstacles for DPP implementation in clinical practice. In addition, patients have expressed low urgency in seeking further health care after a prediabetes diagnosis [ 15 , 16 ]. Furthermore, highly effective, in-person DPPs can have low participation and adherence [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

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Mobile Delivery of the Diabetes Prevention Program in People With Prediabetes: Randomized Controlled Trial

Toro‐Ramos¹,

Michaelides²,

Anton³

et al. 2020

JMIR Mhealth Uhealth

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Background The Centers for Disease Control and Prevention (CDC) diabetes prevention program (DPP) has formed the foundation for Type 2 Diabetes Mellitus (T2DM) prevention efforts and lifestyle change modifications in multiple care settings. To our knowledge, no randomized controlled trial has verified the efficacy of a fully mobile version of CDC’s diabetes prevention program (DPP). Objective This study aimed to investigate the long-term weight loss and glycemic efficacy of a mobile-delivered DPP compared with a control group receiving usual medical care. Methods Adults with prediabetes (N=202) were recruited from a clinic and randomized to either a mobile-delivered, coach-guided DPP (Noom) or a control group that received regular medical care including a paper-based DPP curriculum and no formal intervention. The intervention group learned how to use the Noom program, how to interact with their coach, and the importance of maintaining motivation. They had access to an interactive coach-to-participant interface and group messaging, daily challenges for behavior change, DPP-based education articles, food logging, and automated feedback. Primary outcomes included changes in weight and hemoglobin A1c (HbA1c) levels at 6 and 12 months, respectively. Exploratory secondary outcomes included program engagement as a predictor of changes in weight and HbA1c levels. Results A total of 202 participants were recruited and randomized into the intervention (n=101) or control group (n=99). In the intention-to-treat (ITT) analyses, changes in the participants’ weight and BMI were significantly different at 6 months between the intervention and control groups, but there was no difference in HbA1c levels (mean difference 0.004%, SE 0.05; P=.94). Weight and BMI were lower in the intervention group by −2.64 kg (SE 0.71; P<.001) and −0.99 kg/m2 (SE 0.29; P=.001), respectively. These differences persisted at 12 months. However, in the analyses that did not involve ITT, program completers achieved a significant weight loss of 5.6% (SE 0.81; P<.001) at 6 months, maintaining 4.7% (SE 0.88; P<.001) of their weight loss at 12 months. The control group lost −0.15% at 6 months (SE 0.64; P=.85) and gained 0.33% (SE 0.70; P=.63) at 12 months. Those randomized to the intervention group who did not start the program had no meaningful weight or HbA1c level change, similar to the control group. At 1 year, the intervention group showed a 0.23% reduction in HbA1c levels; those who completed the intervention showed a 0.28% reduction. Those assigned to the control group had a 0.16% reduction in HbA1c levels. Conclusions This novel mobile-delivered DPP achieved significant weight loss reductions for up to 1 year compared with usual care. This type of intervention reduces the risk of overt diabetes without the added barriers of in-person interventions. Trial Registration ClinicalTrials.gov NCT03865342; https://clinicaltrials.gov/ct2/show/NCT03865342

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Section: Introductionmentioning

confidence: 99%

“…Evidence-based, scalable interventions for preventive treatment are urgently needed [ 16 ]. Exploring novel ways to empower patients to pursue lifestyle changes to prevent or delay the onset of diabetes is critical in addressing the growing diabetes epidemic.…”

Section: Introductionmentioning

confidence: 99%

Mobile Delivery of the Diabetes Prevention Program in People With Prediabetes: Randomized Controlled Trial

Toro‐Ramos¹,

Michaelides²,

Anton³

et al. 2020

JMIR Mhealth Uhealth

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“…17 Despite the comparably low rate of engagement upon algorithmic referrals, the potential to automate this approach is important for scalability, as well as reducing provider burden to meet US Preventive Services Task Force recommendations to refer at-risk individuals to intensive lifestyle interventions. 18,19 Relatively few point-of-care referrals to the NDPP are made currently. 16 First, many providers are unaware of the NDPP as a resource for their patients.…”

Section: Increasing Provider Referrals To the Ndppmentioning

confidence: 99%

<p>Current Perspectives on the Impact of the National Diabetes Prevention Program: Building on Successes and Overcoming Challenges</p>

Ritchie¹,

Baucom²,

Sauder³

2020

DMSO

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To address the public health and economic burden of type 2 diabetes, the Centers for Disease Control and Prevention (CDC) began dissemination of the National Diabetes Prevention Program (NDPP) in the United States in 2010. Based on the intensive lifestyle intervention from a large efficacy trial, the NDPP aims to reduce incidence through lifestyle change and weight loss. This narrative review summarizes evidence on reach, effectiveness, and sustainability of the NDPP, while highlighting opportunities to overcome challenges in these areas. Major successes include reaching hundreds of thousands of at-risk individuals across the nation, with notable effectiveness upon full participation and widespread insurance coverage. Yet, more work is needed to ensure greater public health impact, particularly among priority populations at heightened risk who also experience disparities in program outcomes. Preliminary evidence suggests a number of strategies may improve reach and effectiveness of the NDPP, often with more rigorous study needed prior to widespread uptake. Updating the NDPP to better match the current evidence-base may also be important, such as directly targeting glycemia with a patient-centered approach and promoting metformin as an adjunct or second-line treatment. Finally, revisiting pay-for-performance reimbursement models may be critical to sustainability by ensuring adequate availability of suppliers and ultimately reducing diabetes prevalence.

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“…For example, type 2 diabetes reduces quality of life, increases multimorbidity risk, and accelerates time to mortality [ 1 , 2 ]. These complications also have pronounced economic implications on healthcare systems, especially as the prevalence of those with type 2 diabetes continues to grow [ 1 , 3 , 4 , 5 ]. As the American population ages and the prevalence of individuals with type 2 diabetes continues to rise, projections place the number of diagnosed type 2 diabetes cases in the United States around 39 million by the year 2050 [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

Health Insurance Is Associated with Decreased Odds for Undiagnosed Prediabetes and Type 2 Diabetes in American Adults

Mahoney

Bradley

Pitts

et al. 2020

IJERPH

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Over a third of adults in the United States have prediabetes, and many of those with prediabetes will progress to type 2 diabetes within 3–5 years. Health insurance status may factor into a proper diagnosis of prediabetes and diabetes. This study sought to determine the associations between health insurance and undiagnosed prediabetes and diabetes in a national sample of American adults. Publicly available data from 13,029 adults aged 18–64 years from the 2005–2016 waves of the National Health and Nutrition Examination Survey were analyzed. Health insurance type (Medicaid, Private, Other, None) was self-reported. Prediabetes and diabetes status were assessed with measures of self-report, glycohemoglobin, fasting plasma glucose, and two-hour glucose. Covariate-adjusted logistic models were used for the analyses. Overall, 5976 (45.8%) participants had undiagnosed prediabetes, while 897 (6.8%) had undiagnosed diabetes. Having health insurance was associated with decreased odds ratios for undiagnosed prediabetes: 0.87 (95% confidence interval (CI: 0.79, 0.95)) for private insurance, 0.84 (CI: 0.73, 0.95) for other insurance, and 0.78 (CI: 0.67, 0.90) for Medicaid. Moreover, having private health insurance was associated with 0.82 (CI: 0.67, 0.99) decreased odds for undiagnosed diabetes. Health insurance coverage and screening opportunities for uninsured individuals may reduce prediabetes and diabetes misclassifications.

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Building Toward a Population-Based Approach to Diabetes Screening and Prevention for US Adults

Cited by 21 publications

References 69 publications

Mobile Delivery of the Diabetes Prevention Program in People With Prediabetes: Randomized Controlled Trial

Mobile Delivery of the Diabetes Prevention Program in People With Prediabetes: Randomized Controlled Trial

<p>Current Perspectives on the Impact of the National Diabetes Prevention Program: Building on Successes and Overcoming Challenges</p>

Health Insurance Is Associated with Decreased Odds for Undiagnosed Prediabetes and Type 2 Diabetes in American Adults

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