Association Between Sleep Disturbance and Subsequent Pain Interference in Patients With Early Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: This study investigated whether sleep disturbance can predict the extent to which pain interferes with daily functioning in patients with early rheumatoid arthritis (RA). METHODS: Data were from adults with early RA (joint symptoms ≤12 months) enrolled in the Canadian Early Arthritis Cohort between 2016 and 2023. Participants underwent standardized clinical assessments and completed Patient-Reported Outcomes Measurement Information System measures at 0, 6, 12, 18, and 24 months to assess sleep disturbance (primary predictor) and pain interference (primary outcome). Linear mixed-effects models were used to estimate crude and adjusted (age, sex, body mass index, education, income, smoking status, comorbidities, disease activity, treatment, and depression) effects of sleep disturbance on pain interference over the 24-month study period. The analysis was lagged so that repeat measures of sleep disturbance at 0, 6, 12, and 18 months were evaluated as predictors of pain interference 6 months later at 6, 12, 18, and 24 months' follow-up. RESULTS: The analysis included 502 patients with early RA. At baseline, the sample was 68% female and 81% White; the mean age was 56 (SD 14) years, and the mean disease duration was 5.4 (SD 2.9) months. The unadjusted and adjusted linear mixed-effects models revealed a significant association between sleep disturbance and subsequent pain interference scores, indicating that worse sleep six months prior was associated with greater pain interference at the following six-month evaluation. CONCLUSION: These findings underscore the importance of addressing sleep disturbances as part of pain management strategies soon after RA diagnosis. Identifying and targeting problematic sleep disturbances early on may help improve long-term pain outcomes.
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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".