Interface of Sleep Quality and Cognitive Health in Prevention of Dementia: SMRUTHI‐INDIA
Bibliographic record
Abstract
BACKGROUND: SMRUTHI-INDIA is a Cohort Multiple Randomized Controlled Trial (cmRCT) study examining risk and protective factors in preventing dementia. Globally, around 50 million adults are diagnosed with dementia, with India accounting for approximately 16% of these cases. Identifying protective factors, such as sleep quality, may help delay or prevent its onset. This study explores the potential protective role of sleep quality in maintaining cognitive health among the elderly. METHOD: This study employed a cmRCT design, allowing for repeated RCTs within an established cohort. The cohort was formed across four rural zones of India, focusing on dementia risk factors with annual neuropsychological assessments over five years. The current sample includes 2,402 individuals aged 55 and above. Sleep quality and cognition were assessed using the Pittsburgh Sleep Quality Index (PSQI) and Addenbrooke's Cognitive Examination III (ACE-III). Sex-based comparisons were analysed using the Wilcoxon Rank-Sum test. RESULT: Of 2,402 participants, 61.62% were females. Based on PSQI scores, 1,981 participants (83%) had good sleep quality (PSQI < 5), where data of 14 females and 10 males was not available due to lack of consent. Among females, 1,183 had good sleep quality; among males, 798 had good sleep quality. Cognitive performance, measured using ACE-III, was compared across sleep quality groups using Wilcoxon Rank-Sum test. In males, poorer sleep quality was significantly associated with lower cognitive scores (p = 0.01). However, no significant association was observed in females (p = 0.7), indicating a potential sex-specific difference in the relationship between sleep quality and cognitive function. CONCLUSION: The findings of current study suggest that poor sleep quality is significantly associated with reduced cognitive performance in elderly males but not in females, highlighting a possible sex-specific difference in how sleep affects cognitive health. These results underscore the importance of considering gender-specific approaches when designing interventions to preserve cognitive function and potentially prevent dementia in aging populations. Further longitudinal analysis may help clarify the underlying mechanisms and long-term impact of sleep quality on cognitive decline.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".