Evidence map›Paper›PMID 36729586›Full record

ArticleJMIR research protocols2023

Assessing the Feasibility of a Text-Based Conversational Agent for Asthma Support: Protocol for a Mixed Methods Observational Study.

Rafael A Calvo, Dorian Peters, Laura Moradbakhti, Darren Cook, Georgios Rizos, Bjoern Schuller, Constantinos Kallis, Ernie Wong, Jennifer Quint

Open access · goldAbstract read
In one paragraph

Article in JMIR research protocols, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.6field-weighted citation impact, top 17% of its field
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

7 citing papers in PubMed, 8 citations in OpenAlex.

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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

9 authors at 2 institutions in 1 country.

Rafael A Calvo *Dyson School of Design Engineering, Imperial College London, London, United Kingdom.ORCID https://orcid.org/0000-0003-2238-0684
Dorian Peters *Dyson School of Design Engineering, Imperial College London, London, United Kingdom.ORCID https://orcid.org/0000-0002-4767-4198
Laura Moradbakhti *Dyson School of Design Engineering, Imperial College London, London, United Kingdom.ORCID https://orcid.org/0000-0001-8886-6906
Darren Cook *Dyson School of Design Engineering, Imperial College London, London, United Kingdom.ORCID https://orcid.org/0000-0002-6810-0281
Georgios Rizos *Department of Computing, Imperial College London, London, United Kingdom.ORCID https://orcid.org/0000-0003-2483-5574
Bjoern Schuller *Department of Computing, Imperial College London, London, United Kingdom.ORCID https://orcid.org/0000-0002-6478-8699
Constantinos Kallis *Faculty of Medicine, National Heart & Lung Institute, Imperial College London, London, United Kingdom.ORCID https://orcid.org/0000-0003-0866-5421
Ernie Wong *Faculty of Medicine, National Heart & Lung Institute, Imperial College London, London, United Kingdom.ORCID https://orcid.org/0000-0002-3081-9696
Jennifer Quint *Faculty of Medicine, National Heart & Lung Institute, Imperial College London, London, United Kingdom.ORCID https://orcid.org/0000-0003-0149-4869
Imperial College London · GBDyson (United Kingdom) · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDespite efforts, the UK death rate from asthma is the highest in Europe, and 65% of people with asthma in the United Kingdom do not receive the professional care they are entitled to. Experts have recommended the use of digital innovations to help address the issues of poor outcomes and lack of care access. An automated SMS text messaging-based conversational agent (ie, chatbot) created to provide access to asthma support in a familiar format via a mobile phone has the potential to help people with asthma across demographics and at scale. Such a chatbot could help improve the accuracy of self-assessed risk, improve asthma self-management, increase access to professional care, and ultimately reduce asthma attacks and emergencies.

objectiveThe aims of this study are to determine the feasibility and usability of a text-based conversational agent that processes a patient's text responses and short sample voice recordings to calculate an estimate of their risk for an asthma exacerbation and then offers follow-up information for lowering risk and improving asthma control; assess the levels of engagement for different groups of users, particularly those who do not access professional services and those with poor asthma control; and assess the extent to which users of the chatbot perceive it as helpful for improving their understanding and self-management of their condition.

methodsWe will recruit 300 adults through four channels for broad reach: Facebook, YouGov, Asthma + Lung UK social media, and the website Healthily (a health self-management app). Participants will be screened, and those who meet inclusion criteria (adults diagnosed with asthma and who use WhatsApp) will be provided with a link to access the conversational agent through WhatsApp on their mobile phones. Participants will be sent scheduled and randomly timed messages to invite them to engage in dialogue about their asthma risk during the period of study. After a data collection period (28 days), participants will respond to questionnaire items related to the quality of the interaction. A pre- and postquestionnaire will measure asthma control before and after the intervention.

resultsThis study was funded in March 2021 and started in January 2022. We developed a prototype conversational agent, which was iteratively improved with feedback from people with asthma, asthma nurses, and specialist doctors. Fortnightly reviews of iterations by the clinical team began in September 2022 and are ongoing. This feasibility study will start recruitment in January 2023. The anticipated completion of the study is July 2023. A future randomized controlled trial will depend on the outcomes of this study and funding.

conclusionsThis feasibility study will inform a follow-up pilot and larger randomized controlled trial to assess the impact of a conversational agent on asthma outcomes, self-management, behavior change, and access to care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/42965.

Indexed as

artificial intelligenceasthmabehavior changechatbotconversational agenthealthhealth educationwell-being

Identifiers

PMID36729586
PMCPMC9936366
OpenAlexW4315697466

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.