Trajectories of cognitive function and longitudinal trends in actigraphy derived measure of sleep health in older adults:Einstein Aging Study
Bibliographic record
Abstract
BACKGROUND: Sleep problems are common in older adults and have been linked with risk for cognitive impairment. Data regarding associations between longitudinal changes in sleep and changes in cognitive performance among older adults are limited. Our goal was to examine whether trajectories of actigraphically defined sleep parameters differed for individuals with different patterns of change in global cognition. METHODS: Analyses included 219 Einstein Aging Study participants (mean age = 77.50, SD = 5.01; 69.86% female; 47.94% Non-Hispanic White, 42.01% Non-Hispanic, 10.05% Hispanic; 23.74% MCI; median follow-up = 4 years, dementia-free). Participants wore an actigraphy watch 24 hours/day for 16 days annually (2017-2022). Standard algorithms extracted sleep duration, wake after sleep onset (WASO), sleep efficiency, sleep midpoint, and napping. Cognition was assessed via the validated 22-item telephone Montreal Cognitive Assessment (T-MoCA; normal cognition > 18). Latent class mixed-effects models identified cognitive trajectories, accounting for learning effects. Generalized additive mixed-effects models characterized sleep patterns across cognitive groups, adjusting for age, gender, and race/ethnicity. RESULTS: We identified three T-MoCA cognitive trajectory groups: (1) Consistently High-performance (N = 126) scoring across follow-up (mean above 18), (2) Medium-performance (N = 82) over time (mean score 15.7), and (3) Declining performance (N = 11) with an initial mean score of 15.2. These cognitive trajectory groups exhibited distinct longitudinal patterns of night-time sleep duration (p = 0.001), WASO (p < 0.001), and sleep efficiency (p < 0.001). Night-time sleep duration started higher and decreased more steeply in the decliner group, decreased more gradually in the medium group, and remained consistent in the high T-MoCA group. WASO and efficiency appeared to improve over time, with the greatest improvement in Low-performers and only gradual change in the medium and high groups. We found no significant interactions for sleep midpoint, and duration of napping. CONCLUSIONS: Actigraphy-based sleep changes over five years differ by cognitive trajectories. Larger sleep changes in the declining group in the group with declining T-MoCA scores may suggest that underying brain changes in those with more rapid cognitive decline impact sleep, although this should be confirmed in a larger sample.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".