Sleep Problems in Patients With Psoriatic Arthritis: A Systematic Literature Review and Metaanalysis
Bibliographic record
Abstract
Objective The aim of this systematic review and metaanalysis is to summarize evidence regarding the relationship between psoriatic arthritis (PsA) and sleep problems. Methods We identified 36 eligible studies—26 cross-sectional, 7 cohort, and 3 interventional studies—in PubMed and Embase. Results The prevalence of self-reported sleep problems in patients with PsA ranged from 30% to 85%. A metaanalysis of 6 studies that used the Pittsburgh Sleep Quality Index revealed a prevalence of poor sleep quality for patients with PsA of 72.9% (95% CI 63-81.8; I 2 = 78%), which was statistically higher than in healthy controls (26.9%, 95% CI 11.7-45.4; I 2 = 81%) but not significantly different than in patients with psoriasis (59.8%, 95% CI 46.9-72.1; I 2 = 51%). Sleep disturbance was ranked in the top 4 health-related quality of life domains affected by PsA. One study suggested a bidirectional relationship between PsA and obstructive sleep apnea. Predictors of sleep problems included anxiety, pain, erythrocyte sedimentation rate, depression, fatigue, physical function, and tender or swollen joint count. Tumor necrosis factor inhibitors, guselkumab, and filgotinib (a Janus kinase inhibitor) were associated with improved sleep outcomes. Conclusion Poor sleep quality is prevalent in patients with PsA. Objective sleep measures (ie, actigraphy and polysomnography) have not been used in PsA studies, and evidence on the validity of patient-reported sleep measures in PsA is lacking. Future studies should validate self-reported sleep measures in PsA, explore how sleep quality relates to PsA disease activity and symptoms using both objective and subjective sleep measures, assess the efficacy of strategies to manage sleep problems, and assess the effects of such management on symptoms and disease signs in patients with PsA.
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.038 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".