Adapting the Diabetes Prevention Program for Older Adults: Descriptive Study
Bibliographic record
Abstract
BACKGROUND: Prediabetes affects 26.4 million people aged 65 years or older (48.8%) in the United States. Although older adults respond well to the evidence-based Diabetes Prevention Program, they are a heterogeneous group with differing physiological, biomedical, and psychosocial needs who can benefit from additional support to accommodate age-related changes in sensory and motor function. OBJECTIVE: The purpose of this paper is to describe adaptations of the Centers for Disease Control and Prevention's Diabetes Prevention Program aimed at preventing diabetes among older adults (ages ≥65 years) and findings from a pilot of 2 virtual sessions of the adapted program that evaluated the acceptability of the content. METHODS: The research team adapted the program by incorporating additional resources necessary for older adults. A certified lifestyle coach delivered 2 sessions of the adapted content via videoconference to 189 older adults. RESULTS: The first session had a 34.9% (38/109) response rate to the survey, and the second had a 34% (30/88) response rate. Over three-quarters (50/59, 85%) of respondents agreed that they liked the virtual program, with 82% (45/55) agreeing that they would recommend it to a family member or a friend. CONCLUSIONS: This data will be used to inform intervention delivery in a randomized controlled trial comparing in-person versus virtual delivery of the adapted program.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".