Improved Glycemic Control With a Digital Health Intervention in Adults With Type 2 Diabetes: Retrospective Study
Bibliographic record
Abstract
Background Traditional lifestyle interventions have shown limited success in improving diabetes-related outcomes. Digital interventions with continuously available support and personalized educational content may offer unique advantages for self-management and glycemic control. Objective In this study, we evaluated changes in glycemic control among participants with type 2 diabetes who enrolled in a digital diabetes management program. Methods The study employed a single-arm, retrospective design. A total of 950 participants with a hemoglobin A1c (HbA1c) baseline value of at least 7.0% enrolled in the Vida Health Diabetes Management Program. The intervention included one-to-one remote sessions with a Vida provider and structured lessons and tools related to diabetes management. HbA1c was the primary outcome measure. Of the 950 participants, 258 (27.2%) had a follow-up HbA1c completed at least 90 days from program start. Paired t tests were used to evaluate changes in HbA1c between baseline and follow-up. Additionally, a cluster-robust multiple regression analysis was employed to evaluate the relationship between high and low program usage and HbA1c change. A repeated measures analysis of variance was used to evaluate the difference in HbA1c as a function of the measurement period (ie, pre-Vida enrollment, baseline, and postenrollment follow-up). Results We observed a significant reduction in HbA1c of –0.81 points between baseline (mean 8.68, SD 1.7) and follow-up (mean 7.88, SD 1.46; t257=7.71; P<.001). Among participants considered high risk (baseline HbA1c≥8), there was an average reduction of –1.44 points between baseline (mean 9.73, SD 1.68) and follow-up (mean 8.29, SD 1.64; t139=9.14; P<.001). Additionally, average follow-up HbA1c (mean 7.82, SD 1.41) was significantly lower than pre-enrollment HbA1c (mean 8.12, SD 1.46; F2, 210=22.90; P<.001) There was also significant effect of program usage on HbA1c change (β=–.60; P<.001) such that high usage was associated with a greater decrease in HbA1c (mean –1.02, SD 1.60) compared to low usage (mean –.61, SD 1.72). Conclusions The present study revealed clinically meaningful improvements in glycemic control among participants enrolled in a digital diabetes management intervention. Higher program usage was associated with greater improvements in HbA1c. The findings of the present study suggest that a digital health intervention may represent an accessible, scalable, and effective solution to diabetes management and improved HbA1c. The study was limited by a nonrandomized, observational design and limited postenrollment follow-up data.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".