Associations between sleep and cognitive performance in a racially/ethnically diverse cohort: the Study of Women’s Health Across the Nation
Bibliographic record
Abstract
STUDY OBJECTIVES: To determine whether actigraphy-assessed indices of sleep are associated with cognitive performance in women, and explore whether these associations vary by race/ethnicity. METHODS: Participants were 1,126 postmenopausal community-dwelling females (mean age 65 years) from the observational Study of Women's Health Across the Nation (SWAN); 25% were black, 46% white, 13% Chinese, 11% Japanese, and 5% Hispanic. Actigraphy-assessed sleep measures included total sleep time, wake after sleep onset (WASO), and fragmentation. Cognitive measures included immediate and delayed verbal memory, working memory, and information processing speed. All measures were assessed in conjunction with SWAN annual visit 15. RESULTS: Across the sample, after covariate adjustment, greater WASO and fragmentation were concurrently associated with slower information processing speed. Black participants had significantly worse sleep relative to other race/ethnic groups. Significant race/sleep interactions were observed; in black, but not white, participants, greater fragmentation was concurrently associated with worse verbal memory and slower information processing speed, and greater WASO was concurrently associated with slower information processing speed. Sleep-cognitive performance associations were not different in Chinese and Japanese participants relative to white participants. CONCLUSIONS: Greater wakefulness and fragmentation during sleep are concurrently associated with slower information processing. Sleep continuity impacted concurrent cognitive performance in black, but not white, women. This effect may not have been detected in white women because their sleep was largely within the normal range. Future longitudinal studies in diverse samples are critical to further understand whether race/ethnicity moderates the influence of sleep on cognitive performance.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".