Evidence map›Paper›PMID 32880724›Full record

ArticleJournal of neurology2021

Feasibility and initial validation of 'HD-Mobile', a smartphone application for remote self-administration of performance-based cognitive measures in Huntington's disease.

Brendan McLaren, Sophie C Andrews, Yifat Glikmann-Johnston, Emily-Clare Mercieca, Nicholas W G Murray, Clement Loy, Mark A Bellgrove, Julie C Stout

Abstract read
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In one paragraph

Article in Journal of neurology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.0field-weighted citation impact, top 28% 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

8 citing papers in PubMed, 14 citations in OpenAlex.

  1. A smartphone sensor-based digital outcome assessment of multiple sclerosis.Multiple sclerosis (Houndmills, Basingstoke, England) · 2022
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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 4 institutions in 1 country.

Brendan McLarenTurner Institute for Brain and Mental Health. School of Psychological Sciences, Monash University, 18 Innovation Walk, Clayton, VIC, 3800, Australia.ORCID http://orcid.org/0000-0002-8769-8629
Sophie C AndrewsTurner Institute for Brain and Mental Health. School of Psychological Sciences, Monash University, 18 Innovation Walk, Clayton, VIC, 3800, Australia.ORCID http://orcid.org/0000-0002-2103-7748
Yifat Glikmann-JohnstonTurner Institute for Brain and Mental Health. School of Psychological Sciences, Monash University, 18 Innovation Walk, Clayton, VIC, 3800, Australia.ORCID http://orcid.org/0000-0002-6128-7768
Emily-Clare MerciecaTurner Institute for Brain and Mental Health. School of Psychological Sciences, Monash University, 18 Innovation Walk, Clayton, VIC, 3800, Australia.ORCID http://orcid.org/0000-0002-8162-0679
Nicholas W G MurrayHuntington Disease Service, Westmead Hospital, Sydney, NSW, Australia.
Clement LoyBrain and Mind Centre, The University of Sydney, Sydney, NSW, Australia.
Mark A BellgroveTurner Institute for Brain and Mental Health. School of Psychological Sciences, Monash University, 18 Innovation Walk, Clayton, VIC, 3800, Australia.ORCID http://orcid.org/0000-0003-0186-8349
Julie C StoutTurner Institute for Brain and Mental Health. School of Psychological Sciences, Monash University, 18 Innovation Walk, Clayton, VIC, 3800, Australia. Julie.stout@monash.edu.ORCID http://orcid.org/0000-0001-9637-8590
Monash University · AUThe University of Sydney · AUUNSW Sydney · AUWestmead Hospital · AU

Funding

Dementia Collaborative Research Centres, Australia S1.16.05
6 · The paper itself

Abstract

objectiveSmartphone-based cognitive assessment measures allow efficient, rapid, and convenient collection of cognitive datasets. Establishment of feasibility and validity is essential for the widespread use of this approach. We describe a novel smartphone application (HD-Mobile) that includes three performance-based cognitive tasks with four key outcome measures, for use with Huntington's disease (HD) samples. We describe known groups and concurrent validity, test-retest reliability, sensitivity, and feasibility properties of the tasks.

methodsForty-two HD CAG-expanded participants (20 manifest, 22 premanifest) and 28 healthy controls completed HD-Mobile cognitive tasks three times across an 8-day period, on days 1, 4, and 8. A subsample of participants had pen-and-paper cognitive task data available from their most recent assessment from their participation in a separate observational longitudinal study, Enroll-HD.

resultsManifest-HD participants performed worse than healthy controls for three of four HD-Mobile cognitive measures, and worse than premanifest-HD participants for two of four measures. We found robust test-retest reliability for manifest-HD participants (ICC = 0.71-0.96) and with some exceptions, in premanifest-HD (ICC = 0.52-0.96) and healthy controls (0.54-0.96). Correlations between HD-Mobile and selected Enroll-HD cognitive tasks were mostly medium to strong (r = 0.36-0.68) as were correlations between HD-Mobile cognitive tasks and measures of expected disease progression and motor symptoms for the HD CAG-expanded participants (r = - 0.34 to - 0.54).

conclusionsResults indicated robust known-groups, test-retest, concurrent validity, and sensitivity of HD-Mobile cognitive tasks. The study demonstrates the feasibility and utility of HD-Mobile for conducting convenient, frequent, and potentially ongoing assessment of HD samples without the need for in-person assessment.

Indexed as

Huntington DiseaseCognitionFeasibility StudiesHumansLongitudinal StudiesNeuropsychological TestsReproducibility of ResultsSmartphoneCognitionHuntington’s diseaseNeuropsychologyRemote assessment

Identifiers

PMID32880724
OpenAlexW3083766368

What OpenQuestion holds

Textmetadata
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.