Evidence map›Paper›PMID 29715279›Full record

ArticlePloS one2018

Free-living and laboratory gait characteristics in patients with multiple sclerosis.

Fabio A Storm, K P S Nair, Alison J Clarke, Jill M Van der Meulen, Claudia Mazzà

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06666816 (Observational Study for the Identification of Fatigue and Gait Biomarkers in Populations With Neuromuscular Pathologies in the Clinical Context and Daily Life), which is not on this map. Cited by 62 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
62citing papers in PubMed, 4 pooled it
10.0field-weighted citation impact, top 1% of its field
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.

NCT06666816 recruitingnot on this map

Observational Study for the Identification of Fatigue and Gait Biomarkers in Populations With Neuromuscular Pathologies in the Clinical Context and Daily Life

TypeobservationalSponsorIRCCS Eugenio MedeaRan2017 to 2026Enrolled120ConditionsMuscular Dystrophies, Myopathies, Spinal Muscular Atrophy (SMA), Charcot Marie Tooth DiseaseArmsinapplicable
3 · Its place in the literature

Who cites it

62 citing papers in PubMed, 4 syntheses or guidelines pooled it, 118 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. A smartphone sensor-based digital outcome assessment of multiple sclerosis.Multiple sclerosis (Houndmills, Basingstoke, England) · 2022
    Trial
  6. Article
  7. Article
  8. Laboratory Environments Mask Gait Differences Between Healthy Participants and Patients With Anterior Cruciate Ligament Reconstruction.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2026
    Article
  9. Digital assessment of real-world walking in people with impaired mobility: How many hours and days are needed?The international journal of behavioral nutrition and physical activity · 2025
    Article
  10. Capture of Longitudinal Change in Real-Life Walking in Cerebellar Ataxia Increases Patient Relevance and Effect Size.Movement disorders : official journal of the Movement Disorder Society · 2025
    Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Assessing Free-Living Postural Sway in Persons With Multiple Sclerosis.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2024
    Article
  17. Article
  18. Article
  19. Article
  20. Article

2 more citing papers are in PubMed but not listed here.

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

5 authors at 2 institutions in 1 country.

Fabio A StormDepartment of Mechanical Engineering, The University of Sheffield, Sheffield, United Kingdom.ORCID 0000-0002-5977-8090
K P S NairINSIGNEO Institute for in Silico Medicine, The University of Sheffield, Sheffield, United Kingdom.
Alison J ClarkeThe Gait Laboratory, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom.
Jill M Van der MeulenThe Gait Laboratory, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom.
Claudia MazzàDepartment of Mechanical Engineering, The University of Sheffield, Sheffield, United Kingdom.
Sheffield Teaching Hospitals NHS Foundation Trust · GBInsigneo · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWearable sensors offer the potential to bring new knowledge to inform interventions in patients affected by multiple sclerosis (MS) by thoroughly quantifying gait characteristics and gait deficits from prolonged daily living measurements. The aim of this study was to characterise gait in both laboratory and daily life conditions for a group of patients with moderate to severe ambulatory impairment due to MS. To this purpose, algorithms to detect and characterise gait from wearable inertial sensors data were also validated.

methodsFourteen patients with MS were divided into two groups according to their disability level (EDSS 6.5-6.0 and EDSS 5.5-5.0, respectively). They performed both intermittent and continuous walking bouts (WBs) in a gait laboratory wearing waist and shank mounted inertial sensors. An algorithm (W-CWT) to estimate gait events and temporal parameters (mean and variability values) using data recorded from the waist mounted sensor (Dynaport, Mc Roberts) was tested against a reference algorithm (S-REF) based on the shank-worn sensors (OPAL, APDM). Subsequently, the accuracy of another algorithm (W-PAM) to detect and classify WBs was also tested. The validated algorithms were then used to quantify gait characteristics during short (sWB, 5-50 steps), intermediate (iWB, 51-100 steps) and long (lWB, >100 steps) daily living WBs and laboratory walking. Group means were compared using a two-way ANOVA.

resultsW-CWT compared to S-REF showed good gait event accuracy (0.05-0.10 s absolute error) and was not influenced by disability level. It slightly overestimated stride time in intermittent walking (0.012 s) and overestimated highly variability of temporal parameters in both intermittent (17.5%-58.2%) and continuous walking (11.2%-76.7%). The accuracy of W-PAM was speed-dependent and decreased with increasing disability. The ANOVA analysis showed that patients walked at a slower pace in daily living than in the laboratory. In daily living gait, all mean temporal parameters decreased as the WB duration increased. In the sWB, the patients with a lower disability score showed, on average, lower values of the temporal parameters. Variability decreased as the WB duration increased.

conclusionsThis study validated a method to quantify walking in real life in people with MS and showed how gait characteristics estimated from short walking bouts during daily living may be the most informative to quantify level of disability and effects of interventions in patients moderately affected by MS. The study provides a robust approach for the quantification of recognised clinically relevant outcomes and an innovative perspective in the study of real life walking.

Indexed as

Activities of Daily LivingGaitLaboratoriesFemaleHumansMaleMiddle AgedMultiple Sclerosis

Identifiers

PMID29715279
PMCPMC5929566
OpenAlexW2801415007

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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.