Association between objectively measured, multidimensional sleep health and cognitive function in older adults: cross-sectional wearable tracker study
Bibliographic record
Abstract
Both sleep and cognition are multidimensional constructs. Using univariate methods to examine associations between sleep and cognition may inadequately characterize the association between these arrays of variables. The current study used a multivariate approach to identify key sleep metrics and cognitive domains contributing to the maximum sleep-cognition covariance in healthy older adults. In 773 community-dwelling older adults of ages 65-80 years, sleep was assessed using the Oura Ring worn for 15-28 days. Cognition performance in seven domains was assessed using standardized tests. The overall covariance between sleep and cognition was examined by a partial least square correlation (PLSC) analysis. Sleep metrics and cognitive domains contributing to significant PLSC components were identified by bootstrapping. PLSC analysis identified a component that explained 82 % of covariance between sleep and cognition matrices (r = 0.2, p < 0.001). Bootstrapping tests further identified 11 sleep continuity and regularity metrics and 3 corresponding cognitive domains that contributed significantly to the observed covariance. Post-hoc univariate analyses showed that sleep continuity metrics correlated with speed of processing, while sleep regularity metrics correlated with verbal memory, executive functions, and speed of processing. Our results suggest that sleep continuity and regularity may be more sensitive markers of impairments across multiple cognitive domains in healthy aging compared to sleep duration and timing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".