0315 Quantifying the Temporal Relationship Between Self-report Sleep Quality and Cognition in Older Adults
Bibliographic record
Abstract
Abstract Introduction Poor sleep is a promising modifiable risk factor for impaired cognition in older adults. However, the relationship between sleep and cognition is likely bi-directional, and few studies have examined these temporal associations. We seek to investigate the temporal relationships between self-report sleep quality and global cognition. Methods Our analytic sample includes 1,610 participants from the Memory and Aging Project and Minority Aging Research Study without cognitive impairment at the initial visit (41% black, 77% female, mean[min,max] age = 77[54,100] years). Participants have cognition and sleep quality measured at an initial visit and up to 14 years of annual follow-up (median 6 years). Sleep quality was measured using a modified 10-item Pittsburgh Sleep Quality Index score (higher scores indicating worse quality) and standardized; global cognition was a composite z-score computed from an average of 19 cognitive tests. We used linear mixed effects models to quantify the concurrent and prospective (1-year) relationships of sleep quality and global cognition. Quadratic terms were also tested to allow for a potentially U-shaped relationship. Results When examining same-year associations with cognition as the outcome, sleep quality and cognition exhibit a negative quadratic association (linear term BL[p] = 0.01[0.021]; quadratic term BQ[p] = -0.01[0.051]), indicating that both better- and worse-than-average sleep quality are associated with lower cognition. Regarding 1-year associations, both better- and worse-than average sleep quality predict worse next-year global cognition (BL[p] = 0.01[0.008], BQ[p] = -0.01[0.033]). In contrast, better-than-average cognition predicts worse next-year sleep quality (B L[p] = 0.05[p=0.005]; BQ[p] = -0.01[0.650]) with a stronger association in this direction. Conclusion Understanding the temporal association between sleep and cognition has important implications for screening and development of novel treatments and interventions. The finding that both better and worse sleep quality are associated with worse cognition may reflect an underreporting of poor sleep symptoms in older adults with worsened cognition. Future work will examine these associations considering specific domains of self-report sleep (e.g., timing, efficiency, duration) and cognitive function (memory and perception), consider mechanisms relating sleep and cognition, and use objective measures of sleep (e.g., actigraphy). Support (If Any) RF1AG056331 (PI: Wallace), R01AG17917, R01AG22018
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".