Sleep quality features and their association with mood symptoms and cognitive factors in a non‐clinical sample of older Brazilian adults
Bibliographic record
Abstract
AIM: There is strong interest in sleep disorders in the elderly, but there are gaps in identifying how multiple factors affect sleep quality in this population. We aimed to assess sleep quality and its relationship to mood, general cognition, and sociodemographic factors in a sample of cognitively active older adults. METHODS: We assessed 105 non-clinical older adults (mean age ± SD: 69.64 ± 0.66 years) based on a sociodemographic profile questionnaire, the Beck Anxiety Inventory, the Beck Depression Inventory II, the Montreal Cognitive Assessment, the Pittsburgh Sleep Quality Index, and the Epworth Sleepiness Scale. Separate analyses were conducted, controlled by sleep quality and daytime sleepiness, to understand how variables were associated. RESULTS: About 46.7% of individuals had significantly poor sleep quality. Univariate analysis showed that non-workers had a lower risk of impaired sleep quality (prevalence ratio (PR) = 0.67; P = 0.044). However, there was an increased risk of poor sleep quality in those experiencing depressive symptoms (PR = 1.78; P < 0.001) and anxiety symptoms (PR = 1.98; P < 0.001). In multivariate analysis, the language component of the Montreal Cognitive Assessment (PR = 0.80; P = 0.011) was associated with a lower risk of poor sleep quality, and anxiety symptoms (PR = 1.99; P < 0.001) remained significantly associated with a higher risk of poor sleep quality. No significant difference was observed in variables related to daytime sleepiness. CONCLUSION: We found that overall quality of sleep potentially relates to mood, cognition, and sociodemographic factors. Further studies using multifactorial approaches to sleep investigation are required.
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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".