Prevalence and Determinants of Periodic Limb Movement: A Systematic Review of Current Evidences
Bibliographic record
Abstract
Background: Periodic Limb Movements during Sleep (PLMS) is a prevalent polysomnographic finding with evolving clinical significance, extending beyond its association with Restless Legs Syndrome. Aim: This systematic review aimed to synthesize contemporary evidence on the epidemiology, determinants, and clinical impact of PLMS. Methods: Following PRISMA guidelines, we searched five databases for studies published between January 2020 and December 2024. Ten studies encompassing diverse populations (e.g., general community, obstructive sleep apnea, stroke, multiple sclerosis) were included and assessed for quality using the Newcastle- Ottawa Scale. Results: The findings reveal PLMS is highly prevalent, with rates ranging from 9-16% in general adult populations, increasing to 15-20% in patients with obstructive sleep apnea, and exceeding 30% in specific clinical groups such as those with stroke or multiple sclerosis. Key demographic determinants consistently identified include older age and male sex, with novel evidence pointing to ethnic disparities. Significant clinical determinants encompass the use of antidepressants, sleep-disordered breathing severity, iron deficiency, and dyslipidemia. Crucially, PLMS is independently associated with adverse health outcomes, including impaired sleep architecture, poorer functional recovery after stroke, accelerated decline in executive function among older adults, and markers of increased cardiovascular risk (e.g., subclinical atherosclerosis, established cardiovascular disease). Treatment with CPAP for comorbid sleep apnea did not ameliorate PLMS, suggesting independent pathological pathways. Conclusion: This review establishes PLMS as a common disorder with multifactorial determinants and significant multisystem morbidity, necessitating its active identification in clinical practice and positioning it as a potential modifiable target for improving sleep, cognitive, and cardiovascular outcomes.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".