Evidence map›Paper›PMID 28659858›Full record

ArticleFrontiers in neurology2017

Biometric Digital Health Technology for Measuring Motor Function in Parkinson's Disease: Results from a Feasibility and Patient Satisfaction Study.

Georgia Mitsi, Enrique Urrea Mendoza, Benjamin D Wissel, Elena Barbopoulou, Alok K Dwivedi, Ioannis Tsoulos, Athanassios Stavrakoudis, Alberto J Espay, Spyros Papapetropoulos

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article 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 20 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 2 pooled it
2.1field-weighted citation impact, top 13% 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

20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 31 citations in OpenAlex.

  1. Pooled it
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  13. Remote Assessments of Hand Function in Neurological Disorders: Systematic Review.JMIR rehabilitation and assistive technologies · 2022
    Review
  14. Article
  15. Article
  16. Review
  17. Article
  18. Technology-Enabled Assessment of Functional Health.IEEE reviews in biomedical engineering · 2019
    Review
  19. 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

9 authors at 8 institutions in 3 countries.

Georgia MitsiApptomics Inc., Wellesley, MA, United States.
Enrique Urrea MendozaNeuroscience Associates, Greenville Health System, Greenville, SC, United States.
Benjamin D WisselGardner Family Center for Parkinson's Disease and Movement Disorders, Department of Neurology, University of Cincinnati, Cincinnati, OH, United States.
Elena BarbopoulouDepartment of Mathematics, City University London, London, United Kingdom.
Alok K DwivediDivision of Biostatistics and Epidemiology, Department of Biomedical Sciences, Texas Tech University Health Sciences Center, El Paso, TX, United States.
Ioannis TsoulosDepartment of Informatics and Telecommunications, Technological Educational Institute of Epirus, Epirus, Greece.
Athanassios StavrakoudisDepartment of Economics, University of Ioannina, Ioannina, Greece.
Alberto J EspayGardner Family Center for Parkinson's Disease and Movement Disorders, Department of Neurology, University of Cincinnati, Cincinnati, OH, United States.
Spyros PapapetropoulosDepartment of Neurology, Massachusetts General Hospital, Boston, MA, United States.
University of Cincinnati · USCity, University of London · GBMassachusetts General Hospital · USPrisma Health · USTechnological Educational Institute of Epirus · GRTexas Tech University · USUniversity of Ioannina · GRWellesley College · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo assess the feasibility, predictive value, and user satisfaction of objectively quantifying motor function in Parkinson's disease (PD) through a tablet-based application (iMotor) using self-administered tests.

methodsPD and healthy controls (HCs) performed finger tapping, hand pronation-supination and reaction time tasks using the iMotor application.

resultsThirty-eight participants (19 with PD and 17 HCs) were recruited in the study. PD subjects were 53% male, with a mean age of 67.8 years (±8.8), mean disease duration of 6.5 years (±4.6), Movement Disorders Society version of the Unified Parkinson Disease Rating Scale III score 26.3 (±6.7), and Hoehn & Yahr stage 2. In the univariate analysis, most tapping variables were significantly different in PD compared to HC. Tap interval provided the highest predictive ability (90%). In the multivariable logistic regression model reaction time (reaction time test) (

conclusioniMotor differentiated PD subjects from HCs using simple alternating tasks of motor function. Results of this feasibility study should be replicated in larger, longitudinal, appropriately designed, controlled studies. The impact on patient care of at-home iMotor-assisted remote monitoring also deserves further evaluation.

Indexed as

digital biomarkerdigital healthmotor symptomsobjective measuresParkinson’s diseasesmart tablet

Identifiers

PMID28659858
PMCPMC5468407
OpenAlexW2626686966

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.