Sleep quality, mental health, and cognitive function among older adults in Chinese communities: a cross-sectional study
Bibliographic record
Abstract
Background Adequate and good sleep is essential for improving mental health and cognitive function in older adults. However, there is a lack of research on the relationship between sleep and psychiatric symptoms and cognitive function in the Chinese older adult population. Methods A total of 621 community older adults aged 60 and older were included in the current study. The Pittsburgh Sleep Quality Index (PSQI), Geriatric Depression Scale (GDS), Self-rating anxiety scale (SAS), and Montreal Cognitive Assessment Scale (MoCA) were administered to all the participants. Based on PSQI, the research subjects were divided into the good sleep quality group and the bad sleep quality group. Moreover, 48 healthy individuals without mild cognitive impairment and dementia also accepted brain MR imaging. Results The prevalence of poor sleep was 49.9%, and age, physical exercise, traumatic brain injury as well as family history of sleep disorders were associated with poor sleep (p < 0.05). Poor sleepers demonstrated higher prevalence of anxiety and depressive symptoms than good sleeper (p < 0.05). In a subgroup analysis of magnetic resonance, individuals with poor sleep quality had higher left and right transverse temporal cortex thickness and higher scores on the GDS and SAS scales. The results of linear regression analysis showed that the total score of SAS was correlated with the thickness of the left transverse temporal gyrus (T = 2.115, p = 0.042). Conclusion About half of the older adults in the community have sleep problems. Poor sleep quality was associated with symptoms of anxiety and depression. Moreover, the cortical thickness of transverse temporal gyrus may be related to anxiety symptoms in older adults with poor sleep quality. This study indicates that a decline in sleep quality may increase the risk of anxiety and depression in patients, and transverse temporal gyrus may play an important regulatory role in the above process.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".