Sleep disturbance and fatigue in multiple sclerosis: A systematic review and meta-analysis
Bibliographic record
Abstract
Sleep disturbance is common in people with multiple sclerosis and may worsen fatigue; however, the assessment of sleep-fatigue relationships varies across studies. To better understand sleep-fatigue relationships in this population, we conducted a systematic review and random effects meta-analyses for the associations between fatigue and 10 sleep variables: Sleep-disordered breathing, daytime sleepiness, sleep quality, insomnia, restless legs, number of awakenings, sleep efficiency, sleep latency, sleep duration, and wake after sleep onset. Of the 1062 studies screened, 46 met inclusion criteria and provided sufficient data for calculating Hedges’ g. Study quality was assessed using the Newcastle-Ottawa Scale. Sample characteristics did not differ between the 10 analyses. Results indicated that sleep quality and insomnia (assessed via self-report or diagnostic criteria) were strongly associated with fatigue (all gs ≥ 0.80 and all ps < .001). In contrast, the number of awakenings and sleep duration (assessed objectively) were not significantly associated with fatigue. Remaining sleep variables yielded moderate, significant effects. Most effects did not vary based on study quality or sample demographics. Results highlight that insomnia and perceptions of poor sleep have a stronger link than objective sleep duration to fatigue in multiple sclerosis and may represent a more effective target for intervention.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.023 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".