Unraveling the link between chronic pain and sleep quality: Insights from a national study
Bibliographic record
Abstract
To assess the relationship between chronic pain and sleep quality in U.S. men and women. Data included adults aged 25-84 from the 2013-2018 nationally representative National Health Interview Surveys (n=161,282). We examined three measures of sleep quality –self-reported days with difficulty falling asleep, difficulty staying asleep, and days not feeling rested. We analyzed multiple measures of chronic pain – any chronic pain, the location of chronic pain, and the count of chronic pain locations. Linear regression models of each sleep outcomes were estimated on the pooled sample, then by sex and age. The presence of any chronic pain, migraines/headaches, and the number of chronic pain sites were all associated with worse sleep quality across all three measures. Having migraines tended to be most strongly associated with sleep quality. Our findings also indicate sex differences in how chronic pain affects sleep, with women's sleep issues being associated with minor chronic pain while men's sleep problems are associated more with major chronic pain. Chronic pain influences the sleep of the American population, with migraines in particular having a strong relationship. Future research should consider the bi-directionality in the relationship.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".