| Title: |
AI-Enhanced Conversational Agents for Personalized Asthma Support in People With Asthma: Factors for Engagement, Value, and Efficacy in a Cross-Sectional Survey Study |
| Authors: |
Moradbakhti, L.; Peters, D.; Quint, J. K.; Schuller, B.; Cook, D.; Calvo, R. A. |
| Publisher Information: |
JMIR Publications Inc. |
| Publication Year: |
2026 |
| Collection: |
City University London: City Research Online |
| Subject Terms: |
QA75 Electronic computers. Computer science; R Medicine (General) |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| File Description: |
text |
| Language: |
English |
| Relation: |
https://openaccess.city.ac.uk/id/eprint/37224/8/humanfactors-2026-1-e80979.pdf; Moradbakhti, L., Peters, D., Quint, J. K. , Schuller, B., Cook, D. https://openaccess.city.ac.uk/view/creators_id/darren=2Ecook.html orcid:0000-0002-6810-0281 orcid:0000-0002-6810-0281 Calvo, R. A.view all authorsEPJS_limit_names_shown_load( 'creators_name_37224_et_al', 'creators_name_37224_rest' ); (2026). AI-Enhanced Conversational Agents for Personalized Asthma Support in People With Asthma: Factors for Engagement, Value, and Efficacy in a Cross-Sectional Survey Study. JMIR Human Factors, 13, article number e80979. doi:10.2196/80979 https://doi.org/10.2196/80979 |
| DOI: |
10.2196/80979 |
| Availability: |
https://openaccess.city.ac.uk/id/eprint/37224/; https://doi.org/10.2196/80979 |
| Rights: |
cc_by_4 |
| Accession Number: |
edsbas.497686E2 |
| Database: |
BASE |