Evidence map›Paper›PMID 31665036›Full record

ArticleJournal of neuroengineering and rehabilitation2019

Harnessing smartphone technology and three dimensional printing to create a mobile rehabilitation system, mRehab: assessment of usability and consistency in measurement.

Sutanuka Bhattacharjya, Matthew C Stafford, Lora Anne Cavuoto, Zhuolin Yang, Chen Song, Heamchand Subryan, Wenyao Xu, Jeanne Langan

Open access · goldAbstract read
In one paragraph

Article in Journal of neuroengineering and rehabilitation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 2 pooled it
3.0field-weighted citation impact, top 9% 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

12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 27 citations in OpenAlex.

  1. Pooled it
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  11. mHealth Intervention Applications for Adults Living With the Effects of Stroke: A Scoping Review.Archives of rehabilitation research and clinical translation · 2021
    Article
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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

8 authors at 1 institution in 1 country.

Sutanuka BhattacharjyaRehabilitation Science, University at Buffalo, Buffalo, NY, USA. sutanuka@buffalo.edu.ORCID 0000-0003-1534-9961
Matthew C StaffordComputer Science and Engineering, University at Buffalo, Buffalo, NY, USA.
Lora Anne CavuotoIndustrial and Systems Engineering, University at Buffalo, Buffalo, NY, USA.
Zhuolin YangComputer Science and Engineering, University at Buffalo, Buffalo, NY, USA.
Chen SongComputer Science and Engineering, University at Buffalo, Buffalo, NY, USA.
Heamchand SubryanCenter for Inclusive Design and Environmental Access, University at Buffalo, Buffalo, NY, USA.
Wenyao XuComputer Science and Engineering, University at Buffalo, Buffalo, NY, USA.
Jeanne LanganRehabilitation Science, University at Buffalo, Buffalo, NY, USA.
University at Buffalo, State University of New York · US

Funding

A Functional Upper Limb Training and Assessment Tool to Enhance Efficacy and Scalability of Rehabilitation in Ecological EnvironmentsR21HD092243 · NICHD · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI LANGAN, JEANNE, XU, WENYAO · 2017 to 2018
$425k
NICHD NIH HHS R21 HD092243
6 · The paper itself

Abstract

backgroundResidual sensorimotor deficits are common following stroke. While it has been demonstrated that targeted practice can result in improvements in functional mobility years post stroke, there is little to support rehabilitation across the lifespan. The use of technology in home rehabilitation provides an avenue to better support self-management of recovery across the lifespan. We developed a novel mobile technology, capable of quantifying quality of movement with the purpose of providing feedback to augment rehabilitation and improve functional mobility. This mobile rehabilitation system, mRehab, consists of a smartphone embedded in three dimensional printed items representing functional objects found in the home. mRehab allows individuals with motor deficits to practice activities of daily living (ADLs) and receive feedback on their performance. The aim of this study was to assess the usability and consistency of measurement of the mRehab system.

methodsTo assess usability of the mRehab system, four older adults and four individuals with stroke were recruited to use the system, and complete surveys to discuss their opinions on the user interface of the smartphone app and the design of the 3D printed items. To assess the consistency of measurement by the mRehab system, 12 young adults were recruited and performed mRehab ADLs in three lab sessions within 1 week. Young adults were chosen for their expected high level of consistency in motor performance.

resultsUsability ratings from older adults and individuals with stroke led us to modify the design of the 3D printed items and improve the clarity of the mRehab app. The modified mRehab system was assessed for consistency of measurement and six ADLs resulted in coefficient of variation (CV) below 10%. This is a commonly used CV goal for consistency. Two ADLs ranged between 10 and 15% CV. Only two ADLs demonstrated high CV.

conclusionsmRehab is a client-centered technology designed for home rehabilitation that consistently measures performance. Development of the mRehab system provides a support for individuals working on recovering functional upper limb mobility that they can use across their lifespan.

Indexed as

Mobile ApplicationsPrinting, Three-DimensionalSmartphoneActivities of Daily LivingAgedAged, 80 and overFeedback, PsychologicalFemaleHumansMaleMiddle AgedMobile Health UnitsMobility LimitationMovement DisordersPsychomotor PerformanceRehabilitation3 dimensional printingMeasurementRehabilitationSmart technologyStroke

Identifiers

PMID31665036
PMCPMC6820925
OpenAlexW2982553039

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.