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Record W7117317186 · doi:10.1002/alz70857_106939

Trajectories of cognitive function and longitudinal trends in actigraphy derived measure of sleep health in older adults:Einstein Aging Study

2025· article· en· W7117317186 on OpenAlexaboutno aff
Ángel García de la Garza, Carol A. Derby, Qi Gao, Laura A. Rabin, Orfeu M. Buxton, Lindsay Master, Mindy J. Katz, Richard B. Lipton, Cuiling Wang

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsnot available
Fundersnot available
KeywordsActigraphyCognitionSleep (system call)Cognitive declineMeasure (data warehouse)Longitudinal studyHealthy aging

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.018
GPT teacher head0.301
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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