Factors associated with poor sleep quality in patients with pre‐dialysis chronic kidney disease: A systematic review
Bibliographic record
Abstract
AIMS: To determine factors associated with poor sleep quality among patients with pre-dialysis chronic kidney disease. DESIGN: This is a systematic review study guided by Preferred Reporting Items for Systematic Reviews and Meta-Analyses statements and checklist. DATA SOURCES: Empirical evidence was sought in major electronic databases, including Embase, MEDLINE, CINAHL and Airiti library from March to September 2022. REVIEW METHODS: Studies investigating factors associated with poor sleep quality among adult patients with chronic kidney disease were included. Study participants who received renal replacement therapy and had sleep disorders and serious illnesses such as cancer were excluded. Of the 526 studies that resulted from the search after removing duplicate articles, 20 studies were assessed for quality by using Joanna Briggs Institute and Newcastle-Ottawa Scale by two doctorial prepared nursing scientists. RESULTS: A total of 20 studies were included in this review. The prevalence of poor sleep quality in these studies ranged from 11% to 97.5%. Demographics (older age and female), physiological conditions (higher body mass index, higher hip circumferences, higher systolic blood pressure, poor cardiovascular function, dyspnoea, pain, cramps, itchiness or moderate to extreme pruritus, lower fasting plasma glucose, electrolyte imbalance, higher total cholesterol and gastrointestinal symptoms), depression, smoking, arousal-related and cognitive arousal behaviours were associated with poor sleep quality. CONCLUSION: Poor sleep quality was prevalent and influenced kidney function, increased mortality as well as decreased quality of life in patients with pre-dialysis chronic kidney disease. This review synthesizes factors associated with poor sleep quality. Managing these factors can mitigate and prevent poor sleep quality. IMPACT: Healthcare providers, especially nurses, can assess the risk factors of poor sleep quality and reinforce patients' self-management. Future research should elucidate the assessment and management of risk factors and transfer these into widespread use in the routine care of patients with chronic kidney disease.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".