Changes in Glycemic Control Following Use of a Spanish-Language, Culturally Adapted Diabetes Program: Retrospective Study
Bibliographic record
Abstract
Background Several barriers to diabetes treatment and care exist, particularly in underserved medical communities. Objective This study aimed to evaluate a novel, culturally adapted, Spanish-language mHealth diabetes program for glycemic control. Methods Professional Spanish translators, linguists, and providers localized the entirety of the Vida Health Diabetes Management Program into a culturally relevant Spanish-language version. The Spanish-language Vida Health Diabetes Management Program was used by 182 (n=119 women) Spanish-speaking adults with diabetes. This app-based program provided access to culturally adapted educational content on diabetes self-management, one-on-one remote counseling and coaching sessions, and on-demand in-app messaging with bilingual (Spanish and English) certified health coaches, registered dietitian nutritionists, and certified diabetes care and education specialists. Hemoglobin A1c (HbA1c) was the primary outcome measure, and a 2-tailed, paired t test was used to evaluate changes in HbA1c before and after program use. To determine the relationship between program engagement and changes in glycemic control, a cluster-robust multiple regression analysis was employed. Results We observed a significant decrease in HbA1c of –1.23 points between baseline (mean 9.65%, SD 1.56%) and follow-up (mean 8.42%, SD 1.44%; P<.001). Additionally, we observed a greater decrease in HbA1c among participants with high program engagement (high engagement: –1.59%, SD 1.97%; low engagement: –0.84%, SD 1.64%; P<.001). Conclusions This work highlights improvements in glycemic control that were clinically as well as statistically significant among Spanish-preferring adults enrolled in the Vida Health Spanish Diabetes Management Program. Greater improvements in glycemic control were observed among participants with higher program engagement. These results provide needed support for the use of digital health interventions to promote meaningful improvements in glycemic control in a medically underserved community.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".