GENDER DIFFERENCES IN ASSOCIATIONS OF SLEEP INTRAINDIVIDUAL VARIABILITY AND MILD COGNITIVE IMPAIRMENT
Bibliographic record
Abstract
Abstract Previous studies suggest that cognitive function in older adults is related to sleep, with females more vulnerable to poor sleep. Little is known about the relationship between cognition and intraindividual variability of sleep (IIV). We examined the cross-sectional relationship between sleep IIV and mild cognitive impairment (MCI) by gender using data from the Einstein Aging Study. We hypothesize that individuals with MCI have a greater IIV in daily sleep characteristics, and these associations will be stronger for females. Eighty-nine men (age = 76.81±4.73, 61% White, 28% Black, 27% MCI) and 197 women (age = 76.60 ± 4.93, 40% White, 46% Black, 29% MCI) were included in the analysis. Individuals completed 2 weeks of actigraphy (mean ± SD = 15.7 ± 1.1 days), and the Montreal Cognitive Assessment, and MCI was based on the Jack/Bondi criteria. Sleep IIV was estimated as within person standard deviation of nighttime sleep duration, 24-hour sleep duration, wake after sleep onset, and nighttime sleep midpoint. Linear regression models evaluated associations between sleep IIV and MCI status separately by gender, controlling for BMI, age, ethnicity/race, nocturnal hypoxemia, Oxygen Desaturation Index ≥15. Among women, having MCI was associated with greater IIV in nighttime sleep midpoint (b=0.19, p=.002), and in 24-hour total sleep duration (b=13.10, p = .047) with a similar trend in nighttime sleep duration (p = .050). No significant associations were found among men. Findings suggest the importance to keep considering gender in work to understand how sleep variability relates to MCI status 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".