Association of Poor Sleep Efficiency With Decreased Executive Function and Impaired Episodic Memory in Older Adults
Bibliographic record
Abstract
Background: Sleep characteristics influence both physical and psychological health. While previous studies have reported links between sleep quality and cognitive impairment in older adults, findings remain inconsistent, and evidence from the Indian population using culturally validated cognitive tools is scarce. Objective: This study aims to examine the relationship between subjective sleep quality and cognitive performance in Indian older adults using the Pittsburgh Sleep Quality Index (PSQI) and the Indian Council of Medical Research-Neurocognitive Toolbox (ICMR-NCTB). Methods: In this cross-sectional study, 67 individuals aged 50-80 years completed the PSQI and the Hindi version of the ICMR-NCTB. Participants were categorized as good (PSQI ≤ 5) or poor (PSQI > 5) sleepers. Cognitive domains assessed included global cognition, language, attention, visuospatial ability, executive function, and episodic memory. Group comparisons and correlation analyses were performed to explore associations between sleep quality and cognitive outcomes. Results: Sleep latency and sleep efficiency were significantly associated with cognitive performance. Longer sleep latency correlated with poorer executive function (Trail Making Test (TMT)-B: r = 0.347, p = 0.006; TMT-(B-A): r = 0.289, p = 0.023) and reduced performance in verbal learning (Verbal Learning Test (VLT) delayed recall: r = -0.183, p = 0.141; delayed recognition: r = -0.284, p = 0.02). Higher sleep efficiency was linked to better executive function and episodic memory. Poor sleepers scored significantly lower on global cognition (Montreal Cognitive Assessment (MoCA), p = 0.047), executive function (TMT-B, p = 0.029), verbal fluency (Phonemic Fluency Task (PFT), p = 0.031), and delayed recall (VLT, p = 0.045) compared to good sleepers. Education was positively associated with most cognitive scores but not with sleep quality. Conclusion: Prolonged sleep latency and poor sleep efficiency were associated with deficits in executive function and memory among older adults. These findings underscore the importance of sleep quality in cognitive aging, and the use of a culturally adapted cognitive tool enhances their applicability in low-literacy, non-Western settings.
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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 | 0.001 |
| 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".