Cognitive behavioral therapy and mindfulness-based cognitive therapy for depressive symptoms in patients with diabetes: design of a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Depressive symptoms are a common problem in patients with diabetes, laying an additional burden on both the patients and the health care system. Patients suffering from these symptoms rarely receive adequate evidence-based psychological help as part of routine clinical care. Offering brief evidence-based treatments aimed at alleviating depressive symptoms could improve patients' medical and psychological outcomes. However, well-designed trials focusing on the effectiveness of psychological treatments for depressive symptoms in patients with diabetes are scarce. The Mood Enhancement Therapy Intervention Study (METIS) tests the effectiveness of two treatment protocols in patients with diabetes. Individually administered Cognitive Behavioral Therapy (CBT) and Mindfulness-Based Cognitive Therapy (MBCT) are compared with a waiting list control condition in terms of their effectiveness in reducing the severity of depressive symptoms. Furthermore, we explore several potential moderators and mediators of change underlying treatment effectiveness, as well as the role of common factors and treatment integrity. METHODS/DESIGN: The METIS trial has a randomized controlled design with three arms, comparing CBT and MBCT with a waiting list control condition. Intervention groups receive treatment immediately; the waiting list control group receives treatment three months later. Both treatments are individually delivered in 8 sessions of 45 to 60 minutes by trained therapists. Primary outcome is severity of depressive symptoms. Anxiety, well-being, diabetes-related distress, HbA1c levels, and intersession changes in mood are assessed as secondary outcomes. Assessments are held at pre-treatment, several time points during treatment, at post-treatment, and at 3-months and 9-months follow-up. The study has been approved by a medical ethical committee. DISCUSSION: Both CBT and MBCT are expected to help improve depressive symptoms in patients with diabetes. If MBCT is at least equally effective as CBT, MBCT can be established as an alternative approach to CBT for treating depressive symptoms in patients with diabetes. By analyzing moderators and mediators of change, more information can be gathered for whom and why CBT and MBCT are effective. TRIAL REGISTRATION: Clinical Trials NCT01630512.
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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 | 0.033 | 0.030 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.008 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".