Evidence map›Paper›PMID 29449825›Full record

ReviewFrontiers in neurology2017

Technologies for Advanced Gait and Balance Assessments in People with Multiple Sclerosis.

Camille J Shanahan, Frederique M C Boonstra, L Eduardo Cofré Lizama, Myrte Strik, Bradford A Moffat, Fary Khan, Trevor J Kilpatrick, Anneke van der Walt, Mary P Galea, Scott C Kolbe

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in neurology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed, 4 pooled it
4.8field-weighted citation impact, top 4% 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.

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

41 citing papers in PubMed, 4 syntheses or guidelines pooled it, 79 citations in OpenAlex.

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  10. Sensors in Multiple Sclerosis.Current neurology and neuroscience reports · 2025
    Review
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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

10 authors at 3 institutions in 1 country.

Camille J ShanahanDepartment of Anatomy and Neuroscience, University of Melbourne, Parkville, VIC, Australia.
Frederique M C BoonstraDepartment of Anatomy and Neuroscience, University of Melbourne, Parkville, VIC, Australia.
L Eduardo Cofré LizamaDepartment of Medicine, University of Melbourne, Parkville, VIC, Australia.
Myrte StrikDepartment of Anatomy and Neuroscience, University of Melbourne, Parkville, VIC, Australia.
Bradford A MoffatDepartment of Anatomy and Neuroscience, University of Melbourne, Parkville, VIC, Australia.
Fary KhanDepartment of Medicine, University of Melbourne, Parkville, VIC, Australia.
Trevor J KilpatrickDepartment of Anatomy and Neuroscience, University of Melbourne, Parkville, VIC, Australia.
Anneke van der WaltDepartment of Medicine, University of Melbourne, Parkville, VIC, Australia.
Mary P GaleaDepartment of Medicine, University of Melbourne, Parkville, VIC, Australia.
Scott C KolbeDepartment of Anatomy and Neuroscience, University of Melbourne, Parkville, VIC, Australia.
University of Melbourne · AUFlorey Institute of Neuroscience and Mental Health · AUThe Royal Melbourne Hospital · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Subtle gait and balance dysfunction is a precursor to loss of mobility in multiple sclerosis (MS). Biomechanical assessments using advanced gait and balance analysis technologies can identify these subtle changes and could be used to predict mobility loss early in the disease. This update critically evaluates advanced gait and balance analysis technologies and their applicability to identifying early lower limb dysfunction in people with MS. Non-wearable (motion capture systems, force platforms, and sensor-embedded walkways) and wearable (pressure and inertial sensors) biomechanical analysis systems have been developed to provide quantitative gait and balance assessments. Non-wearable systems are highly accurate, reliable and provide detailed outcomes, but require cumbersome and expensive equipment. Wearable systems provide less detail but can be used in community settings and can provide real-time feedback to patients and clinicians. Biomechanical analysis using advanced gait and balance analysis technologies can identify changes in gait and balance in early MS and consequently have the potential to significantly improve monitoring of mobility changes in MS.

Indexed as

balancebiomechanicsgaitmobility lossmultiple sclerosis

Identifiers

PMID29449825
PMCPMC5799707
OpenAlexW2789259524

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.