A randomised controlled feasibility trial of Behavioural activation as a treatment for people with diabetes and depression: (DiaDeM feasibility trial)
Bibliographic record
Abstract
BACKGROUND: There is a lack of evidence on effective treatments for depression in people with T2DM, particularly in Low and Middle-Income Countries (LMICs). This study aims to test the feasibility and acceptability of a culturally adapted Behavioural Activation (BA) intervention (DiaDeM) for people with depression and T2DM in two South Asian LMICs. METHODS: A multicountry, individually randomised-controlled feasibility trial was conducted from March 2022 to November 2022. We recruited adults from diabetes healthcare facilities in Bangladesh and Pakistan with a diagnosis of depression and T2DM. Consenting individuals were randomised to either optimised usual care or the DiaDeM intervention, which comprised six BA sessions delivered by non-mental health facilitators over six to twelve weeks. Participants were followed up at three and six months post-randomisation. The feasibility and acceptability of recruitment and retention, intervention delivery, and data collection were assessed. A mixed-methods process evaluation was also performed to inform the main trial. RESULTS: The DiaDeM feasibility trial successfully recruited 128 participants, with 85 % retention at six months follow-up. The majority of participants engaged with the intervention, demonstrating good adherence to the Behavioural Activation (BA) sessions. Data completeness for key outcomes, including depression severity and HbA1c levels, was high across all time points (>90 %). The process evaluation showed high acceptability of the intervention, with participants reporting increased motivation and improved management of both T2DM and depression. DISCUSSION: Good recruitment and retention rates, completeness of data collection, and high acceptability of the intervention showed that it would be feasible to undertake a full-scale trial.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".