Risk factors and mediation role of sleep quality for depression in cognitively frail older adults: a cross-sectional study
Bibliographic record
Abstract
Objective Aimed to investigate the risk factors associated with depression in community-dwelling older adults with cognitive frailty and to examine the mediating role of sleep quality in the relationship between activities of daily living (ADL) and depression. Methods A cross-sectional study was conducted using convenience sampling, enrolling older adults with cognitive frailty from six communities in Beijing from July 2023 to December 2023. Cognitive frailty was assessed using the Montreal Cognitive Assessment (MoCA) alongside with the Fried Frailty Phenotype, while depressive symptoms were measured with the Geriatric Depression Scale (GDS-15). Multivariate logistic regression analysis was used to identify risk factors influencing depression, and mediation analysis was employed to explore the mediating effect of sleep quality on the relationship between ADL and depression. Results Among the 529 elderly participants with cognitive frailty, 128 (24.2%) were found to exhibit depressive symptoms. Multivariate logistic regression identified ADL [Mild Dependence: OR = 176.729 (95% CI 32.427–963.172), p < 0.001; Moderate Dependence: OR = 51.769 (95% CI 12.541–213.697), p < 0.001], loneliness [OR = 13.821 (95% CI 6.095–31.338), p < 0.001], and sleep quality [Suspected Insomnia: OR = 7.310 (95% CI 2.316–23.074), p = 0.001] were significantly associated with depression. Sleep quality was found to mediate the relationship between ADL and depression, accounting for 2.82% of the total effect. Conclusion Dependence in ADL, loneliness, and poor sleep quality are potential risk factors of depression for cognitive frailty in aging adults. Moreover, sleep quality was found to mediate the relationship between ADL dependence and depressive symptoms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".