Evidence map›Paper›PMID 35945397›Full record

SynthesisJournal of neurology2022

A systematic review of digital technology to evaluate motor function and disease progression in motor neuron disease.

Emily Beswick, Thomas Fawcett, Zack Hassan, Deborah Forbes, Rachel Dakin, Judith Newton, Sharon Abrahams, Alan Carson, Siddharthan Chandran, David Perry and 1 more

Open access · hybridAbstract readSystematic Review
In one paragraph

Synthesis in Journal of neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
3.0field-weighted citation impact, top 7% 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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. The use of digital tools in rare neurological diseases towards a new care model: a narrative review.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2024
    Review
  7. Review
  8. Review
  9. Article
  10. Article
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

11 authors at 4 institutions in 2 countries.

Emily BeswickCentre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland, UK.ORCID http://orcid.org/0000-0001-9843-0778
Thomas FawcettThe School of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh, Scotland, UK.
Zack HassanCentre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland, UK.
Deborah ForbesCentre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland, UK.
Rachel DakinCentre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland, UK.
Judith NewtonCentre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland, UK.
Sharon AbrahamsEuan MacDonald Centre for MND Research, The University of Edinburgh, Edinburgh, Scotland, UK.
Alan CarsonCentre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland, UK.
Siddharthan ChandranCentre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland, UK.
David PerryCentre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland, UK.
Suvankar PalCentre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland, UK. suvankar.pal@ed.ac.uk.ORCID http://orcid.org/0000-0003-4276-639X
University of Edinburgh · GBLanguage Science (South Korea) · KRMRC Centre for Regenerative Medicine · GBUK Dementia Research Institute · GB

Funding

Medical Research Council MC_EX_MR/N50192X/1
6 · The paper itself

Abstract

Amyotrophic lateral sclerosis (ALS) is the most common subtype of motor neuron disease (MND). The current gold-standard measure of progression is the ALS Functional Rating Scale-Revised (ALS-FRS(R)), a clinician-administered questionnaire providing a composite score on physical functioning. Technology offers a potential alternative for assessing motor progression in both a clinical and research capacity that is more sensitive to detecting smaller changes in function. We reviewed studies evaluating the utility and suitability of these devices to evaluate motor function and disease progression in people with MND (pwMND). We systematically searched Google Scholar, PubMed and EMBASE applying no language or date restrictions. We extracted information on devices used and additional assessments undertaken. Twenty studies, involving 1275 (median 28 and ranging 6-584) pwMND, were included. Sensor type included accelerometers (n = 9), activity monitors (n = 4), smartphone apps (n = 4), gait (n = 3), kinetic sensors (n = 3), electrical impedance myography (n = 1) and dynamometers (n = 2). Seventeen (85%) of studies used the ALS-FRS(R) to evaluate concurrent validity. Participant feedback on device utility was generally positive, where evaluated in 25% of studies. All studies showed initial feasibility, warranting larger longitudinal studies to compare device sensitivity and validity beyond ALS-FRS(R). Risk of bias in the included studies was high, with a large amount of information to determine study quality unclear. Measurement of motor pathology and progression using technology is an emerging, and promising, area of MND research. Further well-powered longitudinal validation studies are needed.

Indexed as

Amyotrophic Lateral SclerosisMotor Neuron DiseaseDigital TechnologyDisease ProgressionHumansAmyotrophic lateral sclerosisClinical trialsDevicesMotor neuron diseaseSensors

Identifiers

PMID35945397
PMCPMC9363141
OpenAlexW4291674791

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.