92Poor Subjective Sleep Quality Associates Variably With Different Frailty Measures in Cross-Sectional Study of Community Dwelling Older People
Bibliographic record
Abstract
Poor subjective sleep quality has been associated with phenotypic frailty and is a potential target for frailty prevention or treatment. Exhaustion and reduced day-time activity: two of the five phenotype model variables: have been associated with sleep disorder. We investigated the association between poor sleep quality and frailty using three frailty scores that varied in the weighting each gave to sleep related symptoms. Cross-sectional study design using data from participants in the Yorkshire and Humber Community Ageing Research 75+ cohort study. Self-reported subjective sleep quality was the main exposure variable. Frailty was the outcome of interest, as defined using the phenotype model, cumulative deficit frailty index, and the Edmonton Frailty Scale. We ran 3 logistic regression models, one for each frailty measure, to adjust for pertinent confounders (age, depression, and cognitive function). Data from 173 patients is included. Median age was 80 years, 97 (56%) of whom were female. Frailty prevalence was 20% using the cumulative deficit frailty index with frailty defined at a value of 0.3. There was no clear association between sleep disturbance and frailty measured using the phenotypic model (OR 0.9, 95% CI 0.4 to 2.1), or the Edmonton Frailty Scale (OR 1.6, 95% CI 0.4 to 5.9). Poor sleep quality was associated with frailty as determined by the cumulative deficit frailty index (OR 3.2, 95% CI 1.2 to 8.7). Poor sleep quality is associated with increased frailty, measured using the cumulative deficit frailty index. There was no association between poor sleep quality and frailty using two other frailty measures. The direction and magnitude of association between poor sleep quality and frailty may be influenced by choice of frailty model, and thereby the population identified as frail by each model.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".