ArticleBrain sciences2025
Disability and Non-Motor Symptoms in Multiple Sclerosis: Exploring Associations and Predictive Factors.
Article in Brain sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Machine Learning-Based Prediction of Sleep Quality in Patients with Multiple Sclerosis.Medical sciences (Basel, Switzerland) · 2026Observational
- Investigating the Relationship Between Visual Evoked Potentials, Neurological and Neuropsychological Status in Primary Progressive Multiple Sclerosis.Journal of clinical medicine · 2026Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND/
objectivesThe relationship between multiple sclerosis (MS) disability and co-occurring non-motor symptomatology is not well understood. This study examined the association between disability status and non-motor symptoms-sleep quality, depression, anxiety, and fatigue-in people with multiple sclerosis (MS), as well as the contribution of sleep quality to the prediction of fatigue, depression, and anxiety in MS.
methodsA cross-sectional study included 469 MS and 369 control subjects. Disability status of MS subjects was assessed with the Expanded Disability Status Scale (EDSS), while fatigue, depression, anxiety, and sleep quality were evaluated with the Fatigue Severity Scale (FSS), the Hospital Anxiety and Depression Scale (HADS), and the Pittsburgh Sleep Quality Index (PSQI), respectively. Statistical analyses encompassed group comparisons, Pearson correlations, and hierarchical regression models adjusted for age, sex, and EDSS.
resultsThe results show that MS subjects exhibited higher FSS, HADS-D, and PSQI scores than controls, with intercorrelations and only weak associations with EDSS severity (r = 0.15-0.29). Moreover, PSQI global and HADS-D scores increased with higher EDSS severity, while FSS scores peaked in the moderate EDSS range (4.5-6.5). Global PSQI score independently predicted FSS, HADS-D, and HADS-A. Daytime dysfunction, sleep disturbances, and sleep medication use significantly predicted FSS, HADS-D, and HADS-A scores.
conclusionsStudy findings highlight the role of sleep quality in exacerbating depression, anxiety, and fatigue in MS.
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