Evidence map›Paper›PMID 33882948›Full record

SynthesisJournal of neuroengineering and rehabilitation2021

Utilization of wearable technology to assess gait and mobility post-stroke: a systematic review.

Denise M Peters, Emma S O'Brien, Kira E Kamrud, Shawn M Roberts, Talia A Rooney, Kristen P Thibodeau, Swapna Balakrishnan, Nancy Gell, Sambit Mohapatra

Registry-linked trialOpen access · goldAbstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Journal of neuroengineering and rehabilitation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06255145 (Assessment of Body Composition, Fatigue, Mobility and Functional Status in Post-Stroke Individuals), which is not on this map. Cited by 48 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed, 5 pooled it
6.5field-weighted citation impact, top 3% 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.

NCT06255145 nacompletednot on this map

Assessment of Body Composition, Fatigue, Mobility and Functional Status in Post-Stroke Individuals

TypeinterventionalSponsorPamukkale UniversityRan2021 to 2022Enrolled42ConditionsCerebrovascular Stroke, Body CompositionArmsBioelectrical Impedance Analysis
3 · Its place in the literature

Who cites it

48 citing papers in PubMed, 5 syntheses or guidelines pooled it, 93 citations in OpenAlex.

  1. Pooled it
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  6. [Personalized lower-limb gait assessment method based on musculoskeletal modeling and machine learning].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026
    Article
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  9. Walking as a Window to the Brain: Redefining Gait in Neurology.Medical sciences (Basel, Switzerland) · 2026
    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

9 authors at 1 institution in 1 country.

Denise M PetersDepartment of Rehabilitation and Movement Science, University of Vermont, 106 Carrigan Dr., Rowell 310, Burlington, VT, USA. denise.peters@med.uvm.edu.ORCID 0000-0003-2066-4624
Emma S O'BrienDepartment of Rehabilitation and Movement Science, University of Vermont, 106 Carrigan Dr., Rowell 310, Burlington, VT, USA.
Kira E KamrudDepartment of Rehabilitation and Movement Science, University of Vermont, 106 Carrigan Dr., Rowell 310, Burlington, VT, USA.
Shawn M RobertsDepartment of Rehabilitation and Movement Science, University of Vermont, 106 Carrigan Dr., Rowell 310, Burlington, VT, USA.
Talia A RooneyDepartment of Rehabilitation and Movement Science, University of Vermont, 106 Carrigan Dr., Rowell 310, Burlington, VT, USA.
Kristen P ThibodeauDepartment of Rehabilitation and Movement Science, University of Vermont, 106 Carrigan Dr., Rowell 310, Burlington, VT, USA.
Swapna BalakrishnanDepartment of Rehabilitation and Movement Science, University of Vermont, 106 Carrigan Dr., Rowell 310, Burlington, VT, USA.
Nancy GellDepartment of Rehabilitation and Movement Science, University of Vermont, 106 Carrigan Dr., Rowell 310, Burlington, VT, USA.
Sambit MohapatraDepartment of Rehabilitation and Movement Science, University of Vermont, 106 Carrigan Dr., Rowell 310, Burlington, VT, USA.
University of Vermont · US

Funding

Vermont Center for Cardiovascular and Brain HealthP20GM135007 · NIGMS · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI Diego Adrianzen Herrera · 2020 to 2026
$17.4M
National Institute of General Medical Sciences (US) P20GM135007NIGMS NIH HHS P20 GM135007
6 · The paper itself

Abstract

backgroundExtremity weakness, fatigue, and postural instability often contribute to mobility deficits in persons after stroke. Wearable technologies are increasingly being utilized to track many health-related parameters across different patient populations. The purpose of this systematic review was to identify how wearable technologies have been used over the past decade to assess gait and mobility in persons with stroke.

methodsWe performed a systematic search of Ovid MEDLINE, CINAHL, and Cochrane databases using select keywords. We identified a total of 354 articles, and 13 met inclusion/exclusion criteria. Included studies were quality assessed and data extracted included participant demographics, type of wearable technology utilized, gait parameters assessed, and reliability and validity metrics.

resultsThe majority of studies were performed in either hospital-based or inpatient settings. Accelerometers, activity monitors, and pressure sensors were the most commonly used wearable technologies to assess gait and mobility post-stroke. Among these devices, spatiotemporal parameters of gait that were most widely assessed were gait speed and cadence, and the most common mobility measures included step count and duration of activity. Only 4 studies reported on wearable technology validity and reliability metrics, with mixed results.

conclusionThe use of various wearable technologies has enabled researchers and clinicians to monitor patients' activity in a multitude of settings post-stroke. Using data from wearables may provide clinicians with insights into their patients' lived-experiences and enrich their evaluations and plans of care. However, more studies are needed to examine the impact of stroke on community mobility and to improve the accuracy of these devices for gait and mobility assessments amongst persons with altered gait post-stroke.

Indexed as

Wearable Electronic DevicesGaitHumansReproducibility of ResultsStrokeGaitMobilityRehabilitationSensorsStrokeWearable

Identifiers

PMID33882948
PMCPMC8059183
OpenAlexW3154305891

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.