852-P: Reduction in Emotional Burden in Type 2 Diabetes Is Associated with Improved Medication Adherence: Results from COMRADE
Bibliographic record
Abstract
The presence of distress symptoms in patients with uncontrolled type 2 diabetes (T2D) negatively influences diabetes management behaviors (DMB) and increases the risk of co-morbid complications. Cognitive behavioral interventions (CBI) are successful at reducing these symptoms, but the role of reducing Emotional Burden (EB) on diabetes behaviors is poorly understood. A prospective randomized trial involving 139 patients (mean age = 52.6 +/- 9.5 years; 27% black; 78% female; BMI = 37.0 +/- 9.0) with uncontrolled T2D (mean A1c = 9.6 +/-2) compared the effectiveness of a 16-week severity-tailored CBI plus lifestyle coaching (n=67; IG=intervention group) to usual care (n=72; CG=control group) on reducing EB and its impact on DMB such as self-care behaviors and medication adherence. Trained staff at a rural primary care clinic measured EB (subscore of Diabetes Distress Scale-17), self-care behaviors (Summary of Diabetes Self-Care Activities, SDSCA), and medication adherence (ModMAS) at baseline and 12-months follow-up using validated instruments. There were no differences between groups at baseline in mean age, race, or gender. The average reduction in EB, average improvement in ModMAS, and average improvement in self-care (SDSCA) were all significantly greater in the IG (-1.00 +/- 1.17; +1.0 ± 2.0; +1.1 ± 1.3) than in the CG (-.06 +/- 1.38; + 0.17 ± 1.9; +0.58 ± 1.4) (p=0.0001; p = 0.02; p = 0.027, respectively). Mean improvement in ModMAS was significantly and progressively related to improvements in EB in the IG (worse/same EB = -0.25 ±0.96; moderately improved EB = +0.79 ± 1.9; markedly improved EB = +1.6 ± 2.2; p = 0.045). A linear regression model showed that EB remained significantly associated with ModMAS even when controlling for age, race, and treatment group (β = -0.55; 95% CI: -0.8 to -0.3; p = 0.0001). A severity-tailored CBI plus lifestyle coaching significantly improves EB in T2D patients which is associated with significantly improved medication adherence. Disclosure D.M. Cummings: None. M. Brown: None. L. Lutes: None. B. Hambidge: None. M.A. Carraway: None. S.P. Patil: Research Support; Self; Novo Nordisk Inc. A. Adams: None. K. Littlewood: None. S.B. Edwards: None. P. Gatlin: None. Funding Bristol-Myers Squibb Foundation
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".