Evidence map›Paper›PMID 42286763›Full record

ArticleNeurological research and practice2026

Rethinking EDSS-based ambulation assessment in multiple sclerosis using continuous variable monitoring.

Noah M Werner, Melanie Schuette, Ramona Hagler, Jan Voth, Balázs Danajka, Patricia Kirschner, Markus Heibel, Tjalf Ziemssen, Uwe K Zettl, Sven G Meuth and 2 more

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Article in Neurological research and practice, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Noah M WernerDepartment of Neurology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine University, Düsseldorf, Germany.
Melanie SchuetteDepartment of Neurology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine University, Düsseldorf, Germany.
Ramona HaglerDepartment of Neurology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine University, Düsseldorf, Germany.
Jan VothDepartment of Neurology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine University, Düsseldorf, Germany.
Balázs DanajkaDepartment of Neurology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine University, Düsseldorf, Germany.
Patricia KirschnerDepartment of Neurology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine University, Düsseldorf, Germany.
Markus HeibelSauerlandklinik, Hachen, Germany.
Tjalf ZiemssenDepartment of Neurology, Faculty of Medicine and University Hospital Carl Gustav Carus, Center of Clinical Neuroscience, Dresden, Germany.
Uwe K ZettlDepartment of Neurology, Neuroimmunological Section, Rostock University Medical Center, Rostock, Germany.
Sven G MeuthDepartment of Neurology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine University, Düsseldorf, Germany.
Marc Pawlitzki *Department of Neurology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine University, Düsseldorf, Germany. marcguenter.pawlitzki@med.uni-duesseldorf.de.ORCID http://orcid.org/0000-0003-3080-2277
Lars Masanneck *Department of Neurology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine University, Düsseldorf, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMultiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system (CNS) characterized by relapses and progressive disability. The Expanded Disability Status Scale (EDSS), used to quantify disability, is based on single, discretely assessed and potentially inaccurate patient-estimated walking ability, whereas digital health technologies (DHTs) enable continuous activity monitoring and more objective assessment of real-world functional performance.

methodsIn this prospective observational study conducted at two German centers, patients with relapsing-remitting MS (RRMS) underwent clinical assessments at baseline (V1) and study completion (V2). Walking distance and step counts were measured using a measuring wheel and pedometer, while continuous physical activity was assessed via smartwatch-derived metrics.

resultsSixteen patients with RRMS were included (median age 57.5 years [interquartile range (IQR) 49.25-63.25]; median EDSS 4.5 [IQR 3.5-6]). Patient-estimated walking distance at V1 showed moderate correlation with clinically measured distance (Spearman's ρ = 0.60, p = 0.013), with 12 of 16 patients misjudging distances relative to EDSS thresholds. Walking distance showed intra-individual variability between V1 and V2 (median absolute difference: 113.6 m). Median daily walking distance (ρ = -0.61, p = 0.0123), step count (ρ = -0.64, p = 0.0082), and peak steps (ρ = -0.69, p = 0.0032) correlated negatively with EDSS.

conclusionPatient-estimated maximum walking distance demonstrated moderate agreement with clinical performance and frequently crossed EDSS thresholds, while clinical assessments varied substantially within individuals over the short study duration, underscoring the limitations of single evaluations. In contrast, smartwatch-derived metrics aligned with clinical measures, reflected EDSS scores, and captured real-world mobility.

Indexed as

Digital health technologiesExpanded disability status scaleMultiple sclerosisProof-of-concept studyReal-world mobility

Identifiers

PMID42286763
PMCPMC13263936

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.