Assessing depression symptoms in those with insomnia: An examination of the Beck Depression Inventory Second Edition (BDI-II)
Bibliographic record
Abstract
Background: Due to concerns about overlapping symptomatology between medical conditions and depression, the validity of the Beck Depression Inventory (BDI-II) has been assessed in various medical populations. Although Major Depressive Disorder (MDD) and Primary Insomnia (PI) share some daytime symptoms, the BDI-II has not been evaluated for use with insomnia patients. Method Participants (N = 140) were screened for the presence of insomnia using the Duke Structured Clinical Interview for Sleep Disorders (DSISD), and evaluated for diagnosis of MDD using the Structured Clinical Interview for DSM-IV-TR (SCID). Participants’ mean BDI-II item responses were compared across two groups [insomnia with or without MDD) using multivariate analysis of variance (MANOVA), and the accuracy rates of suggested clinical cutoffs for the BDI-II were evaluated using a Receiver Operating Characteristic (ROC) curve analysis. Results The insomnia with depression group had significantly higher scores on several items; however, the groups did not differ on insomnia, fatigue, concentration problems, irritability, libido, increased appetite, and thoughts relating to suicide, self-criticism and punishment items. The ROC curve analysis revealed moderate accuracy for the BDI-II’s identification of depression in those with insomnia. The suggested BDI cutoff of ≥ 17 had 81% sensitivity and 79% specificity. Use of the mild cutoff for depression (≥14) had high sensitivity (91%) but poor specificity (66%). Conclusion Several items on the BDI-II might reflect sleep disturbance symptoms rather than depression per se. The recommended BDI-II cutoffs in this population have some support but a lower cutoff could result in an overclassification of depression in insomnia patients, a documented problem in the clinical literature. Understanding which items discriminate insomnia patients without depression may help address this nosological issue.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".