Application of a digital ambulatory protocol to assess the associations of level and consistency of sleep with daily cognitive performance and cognitive variability
Bibliographic record
Abstract
Abstract Background Most prior work regarding sleep and cognition in older adults has not examined the relation of sleep health to daily cognitive performance measured in real world settings. Fewer studies have examined the extent to which both level and consistency of sleep are related to variability in daily cognitive performance. We applied daily ambulatory assessment methods to examine whether associations of objective and daily subjective measures of sleep quality and duration were related to mean and day‐to‐day variability in cognitive performance. Method Adults (N = 261) age ≥ 70 years (mean 77.2 ± 4.7) in the Einstein Aging Study sleep project wore an actigraphy watch and completed smart‐phone cognitive assessments over two weeks. All participants were free of dementia (47% Non‐Hispanic White, 40% Non‐Hispanic Black, 13% Other race/ethnicity; mean years education 15 ± 3.6; 67% female). Brief (4‐5 minute) cognitive tests were completed 6 times/day to assess processing speed (Symbol Match Test‐SMT), Visual short term memory binding (Color Shape Test‐CST) and spatial working memory (Grid Memory Test‐GMT). Wake after sleep onset (WASO), night sleep duration (hours), and daytime napping (minutes) were estimated. All measures were summarized as mean over the 2‐weeks and day‐to‐day variability (SD). Linear regression was used to examine associations of mean and variability of sleep with mean and variability (SD) of cognitive performance, adjusting for age, sex, race, and education. Result Both mean and SD of sleep were associated with mean cognitive performance: Higher mean and greater SD WASO were associated with worse mean SMT and CST (all p <0.04). Greater SD duration was associated with worse mean GMT (p = 0.003) and greater SD napping was associated with worse SMT (p = 0.04). Sleep was also associated with daily variability in cognition. Greater mean napping was associated with greater SD GMT (p = 0.009), while SD WASO, SD duration and SD napping were each associated with greater variability in GMT (all p <0.01). Conclusion Both level and consistency of optimal sleep health are related to both average levels of cognitive functioning and day‐to‐day variability in cognitive function. Consistency of sleep health may be particularly important for minimizing variability in spatial working memory.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".