Sleep as a prognostic factor in low back pain: a systematic review of prospective cohort studies and secondary analyses of randomized controlled trials
Bibliographic record
Abstract
Sleep problems are common in individuals with low back pain (LBP) and sleep restriction seems to be associated with impaired pain processing. Our objective was to investigate whether sleep is associated with future LBP outcomes (i.e. pain intensity, disability, and recovery) in adults. We conducted a systematic review of prospective cohort studies and secondary analyses of randomized controlled trials (registration-PROSPERO CRD42022370781). In December 2022, we searched the MEDLINE, Embase, CINAHL, and PsycINFO databases. Fourteen studies, totaling 19 170 participants were included. Thirteen studies were rated as having high risk of bias (QUIPS tool). We used vote-counting and meta-analysis approaches to synthesize the data. We found associations between baseline sleep with future pain intensity, recovery, and between changes in sleep with changes in pain intensity, changes in disability, and recovery. We further synthesized outcomes as "overall LBP improvement" outcomes. Baseline poor sleep was moderately associated with non-improvement in LBP in the long-very long term (OR 1.55, 95% CI: 1.39 to 1.73; three studies providing unadjusted effect sizes), and non-improvement in sleep was largely associated with non-improvement in LBP in the short-moderate term (OR 3.45, 95% CI: 2.54 to 4.69; four studies providing unadjusted effect sizes). We found no association between baseline sleep with future disability and overall LBP improvement in the short-moderate term. Therefore, sleep may be a prognostic factor for pain intensity and recovery from LBP. All findings were supported by low to very low-quality evidence. Better-conducted studies are needed to strengthen our certainty about the evidence.
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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 | 0.049 | 0.140 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.028 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".