Between-person and within-person associations of sleep and working-memory in the everyday lives of old and very old adults: initial level, learning, and variability
Bibliographic record
Abstract
STUDY OBJECTIVES: Sleep duration affects various aspects of cognitive performance, such as working-memory and learning, among children and adults. However, it remains open, whether similar or even stronger associations exist in old and very old age when changes in sleep and cognitive decrements are common. METHODS: Using repeated daily-life assessments from a sample of 121 young-old (66-69 years old) and 39 old-old adults (84-90 years old), we assessed links between sleep duration and different aspects of working-memory (initial level, practice-related learning, and residualized variability) between and within persons. Participants reported their sleep durations every morning and performed a numerical working-memory updating task six times a day for seven consecutive days. RESULTS: Both people who slept longer and those who slept shorter than the sample average showed lower initial performance levels, but a stronger increase of WM over time (i.e. larger learning effects), relative to people with average sleep. Sleep duration did not predict performance variability. Within-person associations were found for people sleeping relatively little on average: For them, working-memory performance was lower on days with shorter than average sleep, yet higher on days with longer than average sleep. Except for lower initial levels of working-memory in old-old adults, no differences between young-old and old-old adults were observed. CONCLUSION: We conclude that sufficient sleep remains important for working-memory performance in older adults and that it is relevant to include different aspects of working-memory performance, because effects differed for initial performance and learning.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".