Improvements in Glycemic Control and Depressive Symptoms Among Adults With Type 2 Diabetes: Retrospective Study
Bibliographic record
Abstract
BACKGROUND: The prevalence of diabetes remains high, with traditional lifestyle interventions demonstrating limited success in improving diabetes-related outcomes, particularly among individuals with diabetes-related mental health comorbidities. Digital health interventions provide the ability to ease the sustained and rigorous self-management needs associated with diabetes care and treatment. Current interventions though, are plagued by small sample sizes, underpowered pilot studies, and immense heterogeneity in program intervention, duration, and measured outcomes. OBJECTIVE: Therefore, this work aimed to evaluate the effectiveness of a mobile health diabetes management program on measures of glycemic control in a high-risk population with type 2 diabetes (hemoglobin A1c [HbA1c] ≥8.0%), utilizing a sample of 1128 participants who provided baseline and follow-up data. The sustainability of this change in glycemic control was examined in a subset of participants (n=455) at 6 months and 1 year following program enrollment. A secondary analysis examined changes in glycemic control among a subset of participants with self-reported mild-to-moderate depression at baseline. METHODS: This study utilized a single-arm, retrospective design. Participants were enrolled in the Vida Health Diabetes Management Program. This app-based intervention utilized one-on-one remote sessions with a health coach, registered dietitian nutritionist, and/or a certified diabetes care and education specialist and structured lessons and tools related to diabetes management and self-care. Participants provided baseline (-365 to 21 days of program enrollment) as well as follow-up (at least 90 days following program enrollment) HbA1c values. Paired t tests were used to evaluate changes in HbA1c between baseline and follow-up time points. The 8-item Patient Health Questionnaire and the 7-item Generalized Anxiety Disorder Scale were utilized to assess self-reported depressive and anxiety symptoms, respectively. Paired t tests and linear regression modeling accounting for pertinent covariates were used to evaluate changes in mental health symptom acuity and their relationship with changes in glycemic control. RESULTS: We observed a significant decrease in HbA1c of -1.35 points between baseline (mean 9.84, SD 1.64) and follow-up (mean 8.48, SD 1.77; t=22.56, P<.001) among this large, high-risk sample. This decrease was sustained up to 1 year following program enrollment. Additionally, a significant relationship between improvements in depressive symptom acuity and improvements in HbA1c was observed (β=-0.74, P=.03). CONCLUSIONS: This study demonstrates clinically meaningful improvements in glycemic control among participants enrolled in the Vida Health Diabetes Management Program. Additionally, this work presents one of the largest studied samples of participants enrolled in a digital health diabetes management program to date.
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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".