Evidence map›Paper›PMID 29678927›Full record

ReviewEvidence-based mental health2018

Digital technologies for the assessment of cognition: a clinical review.

Amy Chinner, Jasmine Blane, Claire Lancaster, Chris Hinds, Ivan Koychev

Abstract readReview
In one paragraph

Review in Evidence-based mental health, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 2 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

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

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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

5 authors.

Amy ChinnerDepartment of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK.
Jasmine BlaneDepartment of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK.
Claire LancasterBig Data Institute, University of Oxford, Oxford, UK.
Chris HindsBig Data Institute, University of Oxford, Oxford, UK.
Ivan KoychevDepartment of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dementia is the most widespread form of neurodegenerative disorder and is associated with an immense societal and personal cost. Prevalence of this disorder is projected to triple worldwide by 2050 leading to an urgent need to make advances in the efficiency of both its care and therapy research. Digital technologies are a rapidly advancing field that provide a previously unavailable opportunity to alleviate challenges faced by clinicians and researchers working in this area. This clinical review aimed to summarise currently available evidence on digital technologies that can be used to monitor cognition. We identified a range of pervasive digital systems, such as smartphones, smartwatches and smart homes, to assess and assist elderly demented, prodromal and preclinical populations. Generally, the studies reported good level of agreement between the digital measures and the constructs they aimed to measure. However, most of the systems are still only in the initial stages of development with limited data on acceptability in patients. Although it is clear that the use of digital technology to monitor and support the cognitive domains affected by dementia is a promising area of development, additional research validating the efficacy, utility and cost-effectiveness of these systems in patient populations is needed.

Indexed as

Mobile ApplicationsSelf-Help DevicesBiomedical TechnologyCognitive RemediationDementiaHumansMonitoring, Physiologicdementiainformation technologyold age psychiatry

Identifiers

PMID29678927
PMCPMC10270380

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.