Evidence map›Paper›PMID 34269686›Full record

ReviewJMIR mHealth and uHealth2021

Embodied Conversational Agents for Patients With Dementia: Thematic Literature Analysis.

Margherita Rampioni, Vera Stara, Elisa Felici, Lorena Rossi, Susy Paolini

Abstract readReview
In one paragraph

Review in JMIR mHealth and uHealth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–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

15 citing papers in PubMed.

  1. Trial
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  7. Designing Software for Genomics Medicine Service Leaders to Engage Stakeholders.Proceedings. IEEE International Conference on Healthcare Informatics · 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

5 authors.

Margherita RampioniModel of Care and New Technologies, IRCCS INRCA - National Institute of Health and Science on Aging, Ancona, Italy.ORCID 0000-0001-9235-9923
Vera StaraModel of Care and New Technologies, IRCCS INRCA - National Institute of Health and Science on Aging, Ancona, Italy.ORCID 0000-0001-7536-7606
Elisa FeliciModel of Care and New Technologies, IRCCS INRCA - National Institute of Health and Science on Aging, Ancona, Italy.ORCID 0000-0002-1665-9155
Lorena RossiModel of Care and New Technologies, IRCCS INRCA - National Institute of Health and Science on Aging, Ancona, Italy.ORCID 0000-0002-5688-105X
Susy PaoliniUnit of Neurology, IRCCS INRCA - National Institute of Health and Science on Aging, Ancona, Italy.ORCID 0000-0003-3364-7102

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAs the world's population rapidly ages, the number of older adults with cognitive impairment will also increase. Several studies have identified numerous complex needs of people with dementia, which assistive technologies still fail to support. Recent trends have led to an increasing focus on the use of embodied conversational agents (ECAs) as virtual entities able to interact with a person through natural and familiar verbal and nonverbal communication. The use of ECAs could improve the accessibility and acceptance of assistive technologies matching those high-level needs that are not well covered to date.

objectiveThe aim of this thematic literature analysis was to map current studies in the field of designing ECAs for patients with dementia in order to identify the existing research trend and possible gaps that need to be covered in the near future. The review questions in this study were as follows: (1) what research frameworks are used to study the interaction between patients with dementia and ECAs? (2) what are the findings? and (3) what are the barriers reported in these studies?

methodsSeparate literature searches were conducted in PubMed, Web of Science, Scopus, and Embase databases by using specific umbrella phrases to target the population (patients with dementia) and the technology-based intervention (embodied conversational agent). Studies that met the inclusion criteria were appraised through the Mixed Methods Appraisal Tool and then discussed in a thematic analysis.

resultsThe search process identified 115 records from the databases and study references. After duplicates (n=45) were removed, 70 papers remained for the initial screening. A total of 7 studies were finally included in the qualitative synthesis. A thematic analysis of the reviewed studies identified major themes and subthemes: the research frameworks used to gather users' perspectives on ECAs (theme 1), the insights shared by the 7 studies as well as the value of user involvement in the development phases and the challenge of matching the system functionalities with the users' needs (theme 2), and the main methodological and technical problems faced by each study team (theme 3).

conclusionsOur thematic literature analysis shows that the field of ECAs is novel and poorly discussed in the scientific community and that more sophisticated study designs and proofs of efficacy of the approach are required. Therefore, by analyzing the main topic of the narrative review, this study underscores the challenge of synchronizing and harmonizing knowledge, efforts, and challenges in the dementia care field and its person-centered paradigm through the user-centered design approach. Enabling strict collaboration between interdisciplinary research networks, medical scientists, technology developers, patients, and their formal and informal caregivers is still a great challenge in the field of technologies for older adults.

Indexed as

CaregiversDementiaAgedCommunicationHumanscognitivecognitive impairmentdementiadesign for older adultselderlyembodied conversational agentolder adults with dementiapatientspatient with dementiapersonal assistantvirtualvirtual agentvirtual companionvirtual personal assistant

Identifiers

PMID34269686
PMCPMC8325086

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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.