Rhythmicity of sleep and clinical outcomes in major depressive disorder: A CAN-BIND-1 report
Bibliographic record
Abstract
Although there is substantial research demonstrating the effects of duration and quality of sleep on outcomes in major depressive disorder (MDD), there is little research examining whether rhythmicity of sleep can also affect outcomes in MDD. The objective of our study was to investigate the relationship between rhythmicity of sleep and clinical outcomes in MDD The prospective study (N = 208) included MDD patients treated with escitalopram for 8-weeks. Rhythmicity of sleep was assessed with Biological Rhythms Interview for Assessment in Neuropsychiatry (BRIAN) at baseline and after 8-weeks. Depression was assessed with Montgomery-Asberg Depression Rating Scale (MADRS) and Quick Inventory of Depressive Symptomatology Self-Report (QIDS-SR) throughout 8-weeks. Outcomes were depression severity over 8-weeks and remission of depression after 8-weeks. Mixed effect models (MMRMs) and logistic regression models were conducted Baseline BRIAN sleep score predicted MADRS (Adjusted B = 0.34;95%CI:0.01–0.66,p = 0.04) and QIDS-SR (Adjusted B = 0.19;95%CI:0.02–0.36,p = 0.03) scores over time in MMRMs. BRIAN falling asleep (Adjusted B = 0.44;95%CI:0.01–0.86,p = 0.04) and switching off (Adjusted B = 0.50;95%CI:0.03–0.97,p = 0.04) items predicted QIDS-SR scores over time. BRIAN sleep score predicted remission with MADRS (Adjusted OR:0.87;95%CI:0.76–0.99) and QIDS-SR (Adjusted OR:0.76;95%CI:0.64–0.90) in logistic regression models. BRIAN falling asleep item predicted remission with MADRS (Adjusted OR:0.71;95%CI:0.64–0.90) and QIDS-SR (Adjusted OR:0.62;95%CI:0.42–0.92). BRIAN switching off item predicted remission with QIDS-SR (Adjusted OR:0.62;95%CI:0.40–0.96) Did not examine for circadian rhythm sleep-wake disorders or social conditions that causes circadian disturbances (e.g., shiftwork) A disturbed rhythmicity of sleep, particularly in the ability to switch off when resting and falling asleep, may increase the risk of poor clinical outcomes in MDD.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".