Determinants of sleep problems in patients with spondyloarthropathy
Bibliographic record
Abstract
OBJECTIVE: To characterize sleep complaints and identify biopsychosocial factors associated with sleep problems in patients with spondyloarthropathy (SpA). METHODS: The sample comprised 125 patients with SpA. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). Participants completed standardized questionnaires assessing depressed mood, perceived stress, leisure time physical activity, functional disability and disease activity. A series of hierarchical multiple regressions were computed to examine the determinants of the following sleep parameters: quality, latency, duration and efficiency. RESULTS: The mean global PSQI score was 8.7 (SD = 5.0), with 69% of the sample classified as poor sleepers (PSQI global score >5). Worse functional status was associated with poorer sleep quality (p = 0.006), longer sleep latency (p = 0.004), shorter sleep duration (p = 0.001) and poorer sleep efficiency (p = 0.004). Higher depressed mood scores emerged in the multivariate analyses as a significant determinant of poorer sleep quality (p = 0.010), shorter sleep duration (p = 0.007) and poorer sleep efficiency (p = 0.006). Higher perceived stress was an independent contributor of poorer sleep quality (p = 0.033). The relationships between worse functional status and poorer sleep quality and shorter sleep duration were more pronounced for participants who completed the questionnaires in the English language. CONCLUSIONS: Sleep problems are prevalent among patients with SpA. Our findings suggest that multiple factors are associated with sleep complaints in persons with SpA with functional status, depressed mood and stress differentially contributing to specific sleep parameters. Multimodal interventions, which include non-pharmacological methods targeting these biopsychosocial factors, require evaluation to optimize the management of sleep disruptions in SpA.
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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 | 0.000 | 0.003 |
| 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) | 0.002 | 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".