Individual Mindfulness-Based Cognitive Therapy and Cognitive Behavior Therapy for Treating Depressive Symptoms in Patients With Diabetes: Results of a Randomized Controlled Trial
Bibliographic record
Abstract
OBJECTIVE: Depression is a common comorbidity of diabetes, undesirably affecting patients' physical and mental functioning. Psychological interventions are effective treatments for depression in the general population as well as in patients with a chronic disease. The aim of this study was to assess the efficacy of individual mindfulness-based cognitive therapy (MBCT) and individual cognitive behavior therapy (CBT) in comparison with a waiting-list control condition for treating depressive symptoms in adults with type 1 or type 2 diabetes. RESEARCH DESIGN AND METHODS: In this randomized controlled trial, 94 outpatients with diabetes and comorbid depressive symptoms (i.e., Beck Depression Inventory-II [BDI-II] ≥14) were randomized to MBCT (n = 31), CBT (n = 32), or waiting list (n = 31). All participants completed written questionnaires and interviews at pre- and postmeasurement (3 months later). Primary outcome measure was severity of depressive symptoms (BDI-II and Toronto Hamilton Depression Rating Scale). Anxiety (Generalized Anxiety Disorder 7), well-being (Well-Being Index), diabetes-related distress (Problem Areas In Diabetes), and HbA1c levels were assessed as secondary outcomes. RESULTS: Results showed that participants receiving MBCT and CBT reported significantly greater reductions in depressive symptoms compared with patients in the waiting-list control condition (respectively, P = 0.004 and P < 0.001; d = 0.80 and 1.00; clinically relevant improvement 26% and 29% vs. 4%). Both interventions also had significant positive effects on anxiety, well-being, and diabetes-related distress. No significant effect was found on HbA1c values. CONCLUSIONS: Both individual MBCT and CBT are effective in improving a range of psychological symptoms in individuals with type 1 and type 2 diabetes.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".