ASSOCIATIONS OF MEAN AND VARIABILITY OF ACTIGRAPHIC SLEEP AND GENERAL COGNITIVE PERFORMANCE
Bibliographic record
Abstract
Abstract Sleep health is associated with cognitive function in midlife and older adults. Little research investigated the relationship between cognitive performance and variability, in addition to mean sleep measures. Using a subsample HANDLSleep study (n=81; 72% female, 52% Black adults; age range: 46-81), this study examined the cross-sectional relationship between cognitive function and both variability and mean sleep with a Bayesian variability model. Cognitive function was measured using Montreal Cognitive Assessment (MoCA). Sleep measures were estimated using one week of wrist actigraphy (mean ± SD = 6.8 ± 0.4 day), including nighttime sleep time (TST), wake after sleep onset (WASO), nighttime sleep midpoint (Timing), Sleep maintenance efficiency (SMEff), and nap minutes (NAP). All models controlled for age, race, sex, WRAT3 literacy scores, and poverty status. Results showed that more variability in sleep Timing (b = -2.75, CI [-5.18, -0.45], p = 0.02) and longer mean nap minutes (b = -3.58, CI [-6.52, -0.93], p = 0.006) were associated with worse cognitive performance (lower MoCA score). No significant associations were observed for TST, WASO, SMEff and MoCA. Findings suggest the importance of considering both variability and mean sleep measures in understanding how sleep relates to cognitive function in middle-age and 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 | 0.002 | 0.005 |
| 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 | 0.001 | 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".