The Relationship Between Daily Rapid-Eye Movement Sleep and Cognitive Functioning in Older Adults
Bibliographic record
Abstract
Abstract Previous cross-sectional research suggests that age-related decreases in Rapid-Eye Movement (REM) sleep may contribute to poorer cognitive functioning (CF); however, few studies have examined the relationship at the intraindividual level by measuring habitual sleep over multiple days. Applying a 14-day daily diary design, the current study examines the dynamic relationship between REM sleep and CF in 69 healthy older adults (M age=70.8 years, SD=3.37; 73.9% female; 66.6% completed at least an undergraduate degree). A Fitbit device provided actigraphy indices of REM sleep (minutes and percentage of total sleep time), while CF was measured four times daily on a smartphone via ambulatory cognitive tests that captured processing speed and working memory. This research addressed the following questions: At the within-person level, are fluctuations in quantity of REM sleep associated with fluctuations in next day cognitive measures across days? Do individuals who spend more time in REM sleep on average, perform better on cognitive tests than adults who spend less time in REM sleep? A series of multilevel models were fit to examine the extent to which each index of sleep accounted for daily fluctuations in performance on next day cognitive tests. Results indicated that during nights when individuals had more REM sleep minutes than was typical, they performed better on the working memory task the next morning (estimate = -.003, SE = .002, p = .02). These results highlight the impact of REM sleep on CF, and further research may allow for targeted interventions for earlier treatment of sleep-related cognitive impairment.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".