Correlation of Sleep Quality with Cognition, Exercise Capacity, and Fatigue in Patients with Chronic Respiratory Diseases
Bibliographic record
Abstract
Background: Sleep is an important component for person's well-being. It is a basic human need. 1 Studies have reported increased incidence of cognitive errors and increased fatigue in sleep-deprived normal individuals after 8 hours of work. 2 Sleep quality is known to be affected in COPD patients but less studied in other chronic respiratory diseases though the symptoms may be the same. This study aims to assess sleep quality in patients suffering from both COPD and non-COPD respiratory conditions and correlate sleep quality with cognition, exercise capacity, and fatigue in patients with chronic respiratory diseases. Material and methodology: An observational cross-sectional study consisting of 142 stable chronic respiratory disease patients was conducted from September 2016 to March 2017. Sleep quality was evaluated using Pittsburgh sleep quality index (PSQI), cognition using montreal cognitive assessment (MoCA), exercise capacity was measured with incremental shuttle walk test, and fatigue with fatigue severity scale (FSS). Results: Spearman's test was used to assess correlation of sleep quality with cognition, exercise capacity, and fatigue. Significant but very weak and poor inverse correlation of sleep quality was found with cognition and exercise capacity, respectively, whereas there was weak and linear correlation of sleep quality with fatigue. There was no significant difference in sleep quality of COPD and non-COPD patients as well as hypoxemic and non-hypoxemic patients. Conclusion: Though there is very weak correlation of sleep quality with cognition, sleep quality is poor in 55.63% of patients and cognition is affected in 93.6% of patients (n = 133). Clinical significance: Sleep quality should be assessed regularly as a part of primary assessment in all chronic respiratory disease patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".