Correlation among sleep quality, physical frailty and cognitive function of the older adults in China: the mediating role
Bibliographic record
Abstract
Objective To explore the correlation among sleep quality, physical frailty, and cognitive function in the older adults in community, and to explore the mediating role of sleep quality. Methods A total of 1,182 community-based older adults were investigated with frailty phenotype (FP), Pittsburgh sleep quality index (PISQI), Montreal cognitive assessment (MoCA) and self-made general information questionnaire. Results The incidence of physical frailty among the older adults in the community was 25.8% and the incidence of cognitive decline was 19.5%. Cognitive function was negatively correlated with physical frailty ( r = −0.236, p < 0.01) and sleep quality ( r = −0.558, p < 0.01). Sleep quality was positively correlated with physical frailty ( r = 0.337, p < 0.01). Conclusion The physical frailty of the older adults has a direct prediction effect on cognitive function, and is regulated by the mediating role of sleep quality. Sleep quality partially mediates the relationship between cognitive dysfunction and physical frailty, which is a new insight into the study of cognition and physical frailty in the older adults. In the future, we can take measures to improve the sleep quality of the older adults, so as to reduce the occurrence of cognitive dysfunction and physical frailty of the older adults.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".