Evidence map›Paper›PMID 31951218›Full record

ArticleJournal of medical Internet research2020

Tools for App- and Web-Based Self-Testing of Cognitive Impairment: Systematic Search and Evaluation.

Anna Pavlina Charalambous, Annie Pye, Wai Kent Yeung, Iracema Leroi, Malcolm Neil, Chryssoula Thodi, Piers Dawes

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

31 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Cognitive Training with Neurofeedback Using fNIRS Improves Cognitive Function in Older Adults.International journal of environmental research and public health · 2022
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  18. Feasibility of Remote Unsupervised Cognitive Screening With SATURN in Older Adults.Journal of applied gerontology : the official journal of the Southern Gerontological Society · 2023
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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

7 authors.

Anna Pavlina CharalambousSchool of Sciences, European University Cyprus, Nicosia, Cyprus.ORCID 0000-0002-1500-1052
Annie PyeDivision of Neuroscience and Experimental Psychology, University of Manchester, Manchester, United Kingdom.ORCID 0000-0002-4152-4260
Wai Kent YeungDivision of Neuroscience and Experimental Psychology, University of Manchester, Manchester, United Kingdom.ORCID 0000-0001-8456-0635
Iracema LeroiDivision of Neuroscience and Experimental Psychology, University of Manchester, Manchester, United Kingdom.ORCID 0000-0003-1822-3643
Malcolm NeilUniversity of Dundee, Dundee, United Kingdom.ORCID 0000-0001-7872-3303
Chryssoula ThodiSchool of Sciences, European University Cyprus, Nicosia, Cyprus.ORCID 0000-0002-2388-9370
Piers DawesManchester Centre for Audiology and Deafness, University of Manchester and the Manchester Academic Health Sciences Centre, Manchester, United Kingdom.ORCID 0000-0003-3180-9884

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTools for app- and Web-based self-testing for identification of cognitive impairment are widely available but are of uncertain quality.

objectiveThe objective of this study was to undertake a scoping review of app- and Web-based self-tests for cognitive impairment and determine the validity of these tests.

methodsWe conducted systematic searches in electronic databases, including Google search, Google Play Store, and iPhone Operating System App Store, using the search terms "Online OR Internet-based AND Memory OR Brain OR Dementia OR mild cognitive impairment OR MCI AND Test OR Screen OR Check."

resultsWe identified 3057 tools, of which 25 were included in the review. Most tools meeting the inclusion criteria assessed multiple cognitive domains. The most frequently assessed domains were memory, attention, and executive function. We then conducted an electronic survey with the developers of the tools to identify data relating to development and validation of each tool. If no response to the survey was received, Google (to identify gray literature), Google Scholar, and Medical Literature Analysis and Retrieval System Online were searched using key terms "(name of developer, if available)" AND "(the name of the tool)" to identify any additional data. Only 7 tools had any information concerning psychometric quality, and only 1 tool reported data on performance norms, reliability, validity, sensitivity, and specificity for the detection of cognitive impairment.

conclusionsThe number of cognitive self-assessment electronic health tools for cognitive impairment is increasing, but most are of uncertain quality. There is a need for well-validated tools and guidance for users concerning which tools provide reliable information about possible cognitive impairment that could warrant further investigation.

Indexed as

AdultAgedAged, 80 and overCognitive DysfunctionHumansInternetMiddle AgedPsychometricsReproducibility of ResultsSelf-AssessmentTelemedicinedementiaeHealthmHealthmild cognitive impairmentself-assessmenttelemedicine

Identifiers

PMID31951218
PMCPMC6996724

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.