Associations Between Sleep Duration and Cognitive Function Among Older Adults: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Sleep duration plays a crucial role in cognitive health and is closely linked to cognitive decline. However, the relationship between sleep duration and cognitive function in the Chinese population remains poorly understood. OBJECTIVE: This study aims to evaluate the association between sleep duration and cognitive function among middle-aged and older adults in China. METHODS: Using data from 15,526 participants in the 2020 China Health and Retirement Longitudinal Study, we used 3 composite indicators, encompassing episodic memory, mental acuity, and overall cognitive function to evaluate cognitive performance. Sleep duration per night, self-reported through face-to-face interviews, was also obtained. Adjustments were made using multiple generalized linear regression models, accounting for demographic, lifestyle, and health-related covariates. RESULTS: Among the 15,526 respondents analyzed, 53.02% (8232/15,526) were female and 46.98% (7294/15,526) were male, with an average age of 61.5 (SD 9.27) years. Those reporting sleep durations of 4 hours or less (β=-1.85, 95% CI -2.07 to -1.62), 5 hours (β=-0.55, 95% CI -0.78 to -0.33; P<.001), 9 hours (β=-1.78, 95% CI -2.17 to -1.39), and 10 hours or more (β=-3.01, 95% CI -3.39 to -2.63) per night had a significant negative relationship with cognitive function. In the adjusted model, the negative impact of long sleep (≥10 hours) on overall cognitive function became more pronounced (β=-3.01, 95% CI -3.39 to -2.63; P<.001), followed closely by extremely short sleep (≤4 hours; β=-1.85, 95% CI -2.07 to -1.62; P<.001). CONCLUSIONS: This study reveals an inverted U-shaped relationship between sleep duration and global cognitive decline, indicating that cognitive function should be closely monitored in individuals with both short and long sleep durations. Consequently, public health strategies should prioritize the promotion of moderate sleep to mitigate the cognitive risks associated with aging, particularly in culturally specific contexts.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".