Evidence map›Paper›PMID 34475154›Full record

ArticleBMJ open2021

Remote cognitive assessment of older adults in rural areas by telemedicine and automatic speech and video analysis: protocol for a cross-over feasibility study.

Alexandra König, Radia Zeghari, Rachid Guerchouche, Minh Duc Tran, François Bremond, Nicklas Linz, Hali Lindsay, Kai Langel, Inez Ramakers, Pascale Lemoine and 2 more

Abstract read
In one paragraph

Article in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

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

  1. Pooled it
  2. Trial
  3. Article
  4. Review
  5. Review
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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

12 authors.

Alexandra KönigSTARS Team, Institut National de Recherche en Informatique et en Automatique Centre de Recherche Sophia Antipolis Méditerranée, Sophia Antipolis, France alexandra.konig@inria.fr.ORCID 0000-0001-9960-9657
Radia ZeghariCobtek (Cognition-Behaviour-Technology) Lab, FRIS, Universite Cote d'Azur, Nice, France.
Rachid GuerchoucheSTARS Team, Institut National de Recherche en Informatique et en Automatique Centre de Recherche Sophia Antipolis Méditerranée, Sophia Antipolis, France.
Minh Duc TranSTARS Team, Institut National de Recherche en Informatique et en Automatique Centre de Recherche Sophia Antipolis Méditerranée, Sophia Antipolis, France.
François BremondSTARS Team, Institut National de Recherche en Informatique et en Automatique Centre de Recherche Sophia Antipolis Méditerranée, Sophia Antipolis, France.
Nicklas LinzGerman Research Centre for Artificial Intelligence Saarbrucken Branch, Saarbrucken, Germany.
Hali LindsayDeutsches Forschungszentrum fur Kunstliche Intelligenz GmbH Standort Saarbrucken, Saarbrucken, Germany.
Kai LangelJanssen Healthcare Innovation, Beerse, Belgium.
Inez RamakersMaastricht University, Maastricht, The Netherlands.
Pascale LemoineCentre Hospitalier de Digne-les-Bains, Digne-les-Bains, France.
Vincent BultingaireCentre Hospitalier de Digne-les-Bains, Digne-les-Bains, France.
Philippe RobertCobtek (Cognition-Behaviour-Technology) Lab, FRIS, Universite Cote d'Azur, Nice, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionEarly detection of cognitive impairments is crucial for the successful implementation of preventive strategies. However, in rural isolated areas or so-called 'medical deserts', access to diagnosis and care is very limited. With the current pandemic crisis, now even more than ever, remote solutions such as telemedicine platforms represent great potential and can help to overcome this barrier. Moreover, current advances made in voice and image analysis can help overcome the barrier of physical distance by providing additional information on a patients' emotional and cognitive state. Therefore, the aim of this study is to evaluate the feasibility and reliability of a videoconference system for remote cognitive testing empowered by automatic speech and video analysis. METHODS AND ANALYSIS: 60 participants (aged 55 and older) with and without cognitive impairment will be recruited. A complete neuropsychological assessment including a short clinical interview will be administered in two conditions, once by telemedicine and once by face-to-face. The order of administration procedure will be counterbalanced so half of the sample starts with the videoconference condition and the other half with the face-to-face condition. Acceptability and user experience will be assessed among participants and clinicians in a qualitative and quantitative manner. Speech and video features will be extracted and analysed to obtain additional information on mood and engagement levels. In a subgroup, measurements of stress indicators such as heart rate and skin conductance will be compared. ETHICS AND DISSEMINATION: The procedures are not invasive and there are no expected risks or burdens to participants. All participants will be informed that this is an observational study and their consent taken prior to the experiment. Demonstration of the effectiveness of such technology makes it possible to diffuse its use across all rural areas ('medical deserts') and thus, to improve the early diagnosis of neurodegenerative pathologies, while providing data crucial for basic research. Results from this study will be published in peer-reviewed journals.

Indexed as

SpeechTelemedicineAgedCognitionFeasibility StudiesHumansObservational Studies as TopicReproducibility of Resultsclinical trialsdementiahealth informaticsold age psychiatrytelemedicine

Identifiers

PMID34475154
PMCPMC8413472

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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.