Mindfulness-based cognitive therapy for recurrent depression: A translational research study with 2-year follow-up
Bibliographic record
Abstract
OBJECTIVE: While mindfulness-based cognitive therapy (MBCT) has demonstrated efficacy in reducing depressive relapse/recurrence over 12-18 months, questions remain around effectiveness, longer-term outcomes, and suitability in combination with medication. The aim of this study was to investigate within a pragmatic study design the effectiveness of MBCT on depressive relapse/recurrence over 2 years of follow-up. METHOD: This was a prospective, multi-site, single-blind trial based in Melbourne and the regional city of Geelong, Australia. Non-depressed adults with a history of three or more episodes of depression were randomised to MBCT + depression relapse active monitoring (DRAM) (n=101) or control (DRAM alone) (n=102). Randomisation was stratified by medication (prescribed antidepressants and/or mood stabilisers: yes/no), site of usual care (primary or specialist), diagnosis (bipolar disorder: yes/no) and sex. Relapse/recurrence of major depression was assessed over 2 years using the Composite International Diagnostic Interview 2.1. RESULTS: The average number of days with major depression was 65 for MBCT participants and 112 for controls, significant with repeated-measures ANOVA (F(1, 164)=4.56, p=0.03). Proportionally fewer MBCT participants relapsed in both year 1 and year 2 compared to controls (odds ratio 0.45, p<0.05). Kaplan-Meier survival analysis for time to first depressive episode was non-significant, although trends favouring the MBCT group were suggested. Subgroup analyses supported the effectiveness of MBCT for people receiving usual care in a specialist setting and for people taking antidepressant/mood stabiliser medication. CONCLUSIONS: This work in a pragmatic design with an active control condition supports the effectiveness of MBCT in something closer to implementation in routine practice than has been studied hitherto. As expected in this translational research design, observed effects were less strong than in some previous efficacy studies but appreciable and significant differences in outcome were detected. MBCT is most clearly demonstrated as effective for people receiving specialist care and seems to work well combined with antidepressants.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".