Long sleep duration, cognitive performance, and the moderating role of depression: A cross‐sectional analysis in the Framingham Heart Study
Bibliographic record
Abstract
INTRODUCTION: We investigated whether depression modified the associations between sleep duration and cognitive performance. METHODS: We examined the associations between sleep duration and cognition in 1853 dementia-and-stroke-free participants (mean age 49.8 years, [range 27-85]; 42.7% male). Participants were categorized into four groups: no depressive symptoms, no antidepressants; depressive symptoms without antidepressant use; antidepressant use without depressive symptoms; and depressive symptoms and antidepressant use. RESULTS: Long sleep was associated with reduced overall cognitive function (β ± standard error = -0.25 ± 0.07, p < 0.001), with strongest effects in those with depressive symptoms using (-0.74 ± 0.30, p = 0.017) and not using antidepressants (-0.60 ± 0.26, p = 0.024). Weaker but significant effects were observed in those without depressive symptoms (-0.18 ± 0.09, p = 0.044). No significant associations were observed in participants using antidepressants without depressive symptoms. DISCUSSION: Associations between sleep duration and cognitive performance are strongest in individuals with depressive symptoms, regardless of antidepressant use. Future research should elucidate underlying mechanisms and temporal relationships. HIGHLIGHTS: Sleeping ≥ 9 hours/night was associated with worse cognitive performance. This association was stronger among those with depression. Long sleepers were more likely to report symptoms of depression. Sleep may be a modifiable risk for cognitive decline in people with depression.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".