Evidence map›Paper›PMID 33293531›Full record

ArticleNPJ Parkinson's disease2020

The CloudUPDRS smartphone software in Parkinson's study: cross-validation against blinded human raters.

Ashwani Jha, Elisa Menozzi, Rebecca Oyekan, Anna Latorre, Eoin Mulroy, Sebastian R Schreglmann, Cosmin Stamate, Ioannis Daskalopoulos, Stefan Kueppers, Marco Luchini and 3 more

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

Article in NPJ Parkinson's disease, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
1.4field-weighted citation impact, top 14% 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

17 citing papers in PubMed, 31 citations in OpenAlex.

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  14. Remote Assessments of Hand Function in Neurological Disorders: Systematic Review.JMIR rehabilitation and assistive technologies · 2022
    Review
  15. Digital Phenotyping in Clinical Neurology.Seminars in neurology · 2022
    Article
  16. Article
  17. 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

13 authors at 6 institutions in 3 countries.

Ashwani JhaDepartment of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK. ashwani.jha@ucl.ac.uk.
Elisa MenozziDepartment of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK.
Rebecca OyekanDepartment of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK.
Anna LatorreDepartment of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK.
Eoin MulroyDepartment of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK.
Sebastian R SchreglmannDepartment of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK.
Cosmin StamateBirkbeck College, University of London, London, UK.
Ioannis DaskalopoulosBirkbeck College, University of London, London, UK.
Stefan KueppersBirkbeck College, University of London, London, UK.
Marco LuchiniBenchmark Performance Ltd, Colchester, UK.
John C RothwellDepartment of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK.
George RoussosQueen Square Movement Disorders Centre, Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK.ORCID http://orcid.org/0000-0002-7665-7303
Kailash P BhatiaDepartment of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK. k.bhatia@ucl.ac.uk.
National Hospital for Neurology and Neurosurgery · GBBirkbeck, University of London · GBBenchmark (United Kingdom) · GBInstitute of Ophthalmology · MXQueen Mary University of London · GBUniversity of Modena and Reggio Emilia · IT

Funding

Innovate UK 102160
6 · The paper itself

Abstract

Digital assessments of motor severity could improve the sensitivity of clinical trials and personalise treatment in Parkinson's disease (PD) but have yet to be widely adopted. Their ability to capture individual change across the heterogeneous motor presentations typical of PD remains inadequately tested against current clinical reference standards. We conducted a prospective, dual-site, crossover-randomised study to determine the ability of a 16-item smartphone-based assessment (the index test) to predict subitems from the Movement Disorder Society-Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III) as assessed by three blinded clinical raters (the reference-standard). We analysed data from 60 subjects (990 smartphone tests, 2628 blinded video MDS-UPDRS III subitem ratings). Subject-level predictive performance was quantified as the leave-one-subject-out cross-validation (LOSO-CV) accuracy. A pre-specified analysis classified 70.3% (SEM 5.9%) of subjects into a similar category to any of three blinded clinical raters and was better than random (36.7%; SEM 4.3%) classification. Post hoc optimisation of classifier and feature selection improved performance further (78.7%, SEM 5.1%), although individual subtests were variable (range 53.2-97.0%). Smartphone-based measures of motor severity have predictive value at the subject level. Future studies should similarly mitigate against subjective and feature selection biases and assess performance across a range of motor features as part of a broader strategy to avoid overly optimistic performance estimates.

Identifiers

PMID33293531
PMCPMC7722731
OpenAlexW3111914383

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.