An international multi-cohort investigation of self-reported sleep and future depressive symptoms in older adults
Bibliographic record
Abstract
Poor subjective sleep is associated with future depression in older adults, but there is limited consensus on which sleep features have the strongest associations. Moreover, composite scores incorporating multiple features may better represent sleep burden than individual sleep items. We analyzed older adults (age ≥ 60) without clinically relevant depressive symptoms from a multi-cohort United States sample (US; N = 4826) and the Netherlands' Rotterdam Study (RS; N = 3663), with the goal of identifying individual and composite sleep features that are associated with future clinically relevant depressive symptoms 3-6 years later. Sleep-related daytime symptoms (Risk Ratio [95% CI] 2.10 [1.58, 2.80] in US; 2.10 [1.40, 3.14] in RS) and difficulty falling asleep (1.87 [1.49, 2.35] in US; RS = 1.90 [1.50, 2.43] in RS) were the strongest individual sleep features. Moreover, the combination of these features was most impactful (3.32 [2.33, 4.73] in US; 3.19 [2.64, 3.86] in RS), providing the largest effect size with the fewest number of items. Future studies should assess whether screening tools incorporating these features, paired with targeted sleep treatment, could reduce rates of incident depression in older adults. Examining mechanisms underlying these associations could improve the effectiveness of sleep-related treatments in older adults.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".