Evidence map›Paper›PMID 41812152›Full record

ArticleJMIR human factors2026

AI-Enhanced Conversational Agents for Personalized Asthma Support in People With Asthma: Factors for Engagement, Value, and Efficacy in a Cross-Sectional Survey Study.

Laura Moradbakhti, Dorian Peters, Jennifer K Quint, Björn Schuller, Darren Cook, Rafael A Calvo

Abstract read
In one paragraph

Article in JMIR human factors, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

6 authors.

Laura MoradbakhtiDyson School of Design Engineering, Faculty of Engineering, Imperial College London, South Kensington Campus, 25 Exhibition Road, London, SW7 2AZ, United Kingdom.ORCID 0000-0001-8886-6906
Dorian PetersInstitute for Technology and Humanity, University of Cambridge, Cambridge, United Kingdom.ORCID 0000-0002-4767-4198
Jennifer K QuintDyson School of Design Engineering, Faculty of Engineering, Imperial College London, South Kensington Campus, 25 Exhibition Road, London, SW7 2AZ, United Kingdom.ORCID 0000-0003-0149-4869
Björn SchullerDepartment of Computing, Faculty of Engineering, Imperial College London, London, United Kingdom.ORCID 0000-0002-6478-8699
Darren CookDyson School of Design Engineering, Faculty of Engineering, Imperial College London, South Kensington Campus, 25 Exhibition Road, London, SW7 2AZ, United Kingdom.ORCID 0000-0002-6810-0281
Rafael A CalvoDyson School of Design Engineering, Faculty of Engineering, Imperial College London, South Kensington Campus, 25 Exhibition Road, London, SW7 2AZ, United Kingdom.ORCID 0000-0003-2238-0684

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Asthma-related deaths in the United Kingdom are the highest in Europe, and only 30% of patients access basic care. There is a need for alternative approaches to reaching people with asthma to provide health education, self-management support, and better bridges to care. Objective: This study aimed to examine patients' interest in using a chatbot for asthma and to identify factors that influence engagement. Automated conversational agents (specifically, mobile chatbots) present opportunities for providing alternative and individually tailored access to health education, self-management support, and risk self-assessment. But would patients engage with a chatbot, and what factors influence engagement? Methods: We present results from a patient survey (N=1257) developed by a team of asthma clinicians, patients, and technology developers, conducted to identify optimal factors for efficacy, value, and engagement with an asthma chatbot. Results: Results indicate that most adults with asthma (53%) are interested in using a chatbot. The patients most likely to do so are those who believe their asthma is more serious and are less confident in their self-management. Results also indicate enthusiasm for 24/7 access, personalization, and for WhatsApp (Meta) as the preferred access method (compared to app, voice assistant, SMS text messaging, or website). Conclusions: Obstacles to uptake include security and privacy concerns and skepticism of technological capabilities. We present detailed findings and consolidate these into 7 recommendations for developers to optimize the efficacy of chatbot-based health support.

Indexed as

Artificial IntelligenceAsthmaSelf-ManagementAdolescentAdultAgedCross-Sectional StudiesFemaleHumansIntelligent SystemsMaleMiddle AgedSurveys and QuestionnairesUnited KingdomYoung AdultAIartificial intelligenceasthmachatbotconversational agentdigital healthpsychological needsself-determination theoryWhatsApp

Identifiers

PMID41812152
PMCPMC12978652

What OpenQuestion holds

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LicenceCC BY
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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.