Enhanced Enrollment in the National Diabetes Prevention Program to Increase Engagement and Weight Loss for the Underserved: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Type 2 diabetes affects 9.4% of US adults with higher rates among racial and ethnic minorities and individuals of low socioeconomic status. The National Diabetes Prevention Program (NDPP) is an evidence-based and widely disseminated behavioral intervention to reduce diabetes incidence through modest weight loss. However, retention in the yearlong NDPP is problematic and leads to suboptimal weight loss, especially among diverse, underserved populations. Strategies to improve NDPP engagement and weight loss are needed urgently. Pilot results of the pre-NDPP, a novel enhancement to enrollment in the NDPP based on the Health Belief Model, were highly successful in a nonrandomized cohort study among 1140 racially diverse, predominately low-income participants. A total of 75 presession participants had doubled attendance and weight loss as compared with earlier participants who did not receive presessions. On the basis of these promising results, we are conducting a randomized controlled trial (RCT) to determine whether pre-NDPP reliably improves NDPP outcomes, as reported on ClinicalTrials.gov. OBJECTIVE: This study aims to (1) conduct an RCT comparing NDPP attendance and weight loss outcomes between participants who receive pre-NDPP versus direct enrollment into the NDPP (usual care), (2) examine potential effect mediators (perceived risk for developing diabetes and self-efficacy and readiness for weight control) and moderators (race and ethnicity; income level), and (3) evaluate implementation factors, including cost and projected return on investment. METHODS: This two-arm RCT will compare outcomes among diverse, predominately low-income participants who receive pre-NDPP versus direct enrollment into the NDPP (usual care). This is a type 1 hybrid effectiveness-implementation design to determine clinical effectiveness through an RCT, while assessing factors that may impact future pre-NDPP dissemination and implementation, including cost. Our primary research question is whether pre-NDPP improves NDPP attendance and weight loss compared with standard NDPP delivery. RESULTS: This project was funded in April 2019. Recruitment is underway as of July 2019. Initial participants began the intervention in October 2019. Data analysis and results reporting are expected to be completed in 2024. CONCLUSIONS: This RCT of pre-NDPP may lead to future dissemination of a scalable, evidence-based strategy to improve success of the NDPP, reduce disparities in NDPP effectiveness, and help prevent type 2 diabetes across the country. TRIAL REGISTRATION: ClinicalTrials.gov NCT04022499; https://clinicaltrials.gov/ct2/show/NCT04022499. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/15499.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.055 | 0.051 |
| Meta-epidemiology (narrow) | 0.009 | 0.005 |
| Meta-epidemiology (broad) | 0.016 | 0.008 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.106 | 0.018 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".