Motivation and Problem Solving Versus Mobile 360° Videos to Promote Enrollment in the National Diabetes Prevention Program’s Lifestyle Change Program Among People With Prediabetes: Protocol for a Randomized Trial
Bibliographic record
Abstract
BACKGROUND: More than 88 million Americans are at risk of developing type 2 diabetes mellitus (T2DM). The National Diabetes Prevention Program's Lifestyle Change Program (DPP LCP) has been shown to be effective in reducing the risk of progressing from prediabetes to T2DM. However, most individuals who could benefit from the program do not enroll. OBJECTIVE: The aim of this trial is to test the real-world efficacy of 3 mobile phone-based approaches to increasing enrollment in the DPP LCP including a best-practice condition and 2 novel approaches. METHODS: We will conduct a 3-armed randomized clinical trial comparing enrollment and 1-month engagement in the DPP LCP among adults with prediabetes from 2 health care settings. Participants in the best-practice condition will receive SMS-based notifications that they have prediabetes and a link to a website that explains prediabetes, T2DM, and the DPP LCP. This will be followed by a single question survey, "Would you like the DPP LCP to call you to enroll?" Participants in the 2 intervention arms will receive the same best-practice intervention plus either 2 mobile 360° videos or up to 5 brief phone calls from a health coach trained in a motivational coaching approach known as Motivation and Problem Solving (MAPS). We will collect measures of diabetes-related knowledge, beliefs in the controllability of risk for T2DM, risk perceptions for T2DM, and self-efficacy for lifestyle change pre-intervention and 4 weeks later. The primary outcomes of the study are enrollment in the DPP LCP and 4-week engagement in the DPP LCP. In addition, data on the person-hours needed to deliver the interventions as well as participant feedback about the interventions and their acceptability will be collected. Our primary hypotheses are that the 2 novel interventions will lead to higher enrollment and engagement in the DPP LCP than the best-practice intervention. Secondary hypotheses concern the mechanisms of action of the 2 intervention arms: (1) whether changes in risk perception are associated with program enrollment among participants in the mobile 360° video group and (2) whether changes in self-efficacy for lifestyle change are associated with program enrollment among participants in the MAPS coaching group. Finally, exploratory analyses will examine the cost effectiveness and acceptability of the interventions. RESULTS: The project was funded in September 2020; enrollment began in February 2021 and is expected to continue through July 2022. CONCLUSIONS: We are conducting a test of 2 novel, scalable, mobile phone-based interventions to increase enrollment in the DPP LCP. If effective, they have tremendous potential to be scaled up to help prevent T2DM nationwide. TRIAL REGISTRATION: ClinicalTrials.gov NCT04746781; https://clinicaltrials.gov/ct2/show/NCT04746781. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/28884.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.024 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.087 | 0.013 |
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".