AI-enhanced conversational agents for personalized asthma support Factors for engagement, value and efficacy
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916857244549120 |
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| author | Moradbakhti, Laura Peters, Dorian Quint, Jennifer K. Schuller, Björn Cook, Darren Calvo, Rafael A. |
| author_facet | Moradbakhti, Laura Peters, Dorian Quint, Jennifer K. Schuller, Björn Cook, Darren Calvo, Rafael A. |
| contents | Asthma-related deaths in the UK 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 in order to provide health education, self-management support and bridges to care. 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? We present results from a patient survey (N=1257) devised by a team of asthma clinicians, patients, and technology developers, conducted to identify optimal factors for efficacy, value and engagement for a chatbot. Results indicate that most adults with asthma (53%) are interested in using a chatbot and the patients most likely to do so are those who believe their asthma is more serious and who are less confident about self-management. Results also indicate enthusiasm for 24/7 access, personalisation, and for WhatsApp as the preferred access method (compared to app, voice assistant, SMS or website). Obstacles to uptake include security/privacy concerns and skepticism of technological capabilities. We present detailed findings and consolidate these into 7 recommendations for developers for optimising efficacy of chatbot-based health support. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_16735 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-enhanced conversational agents for personalized asthma support Factors for engagement, value and efficacy Moradbakhti, Laura Peters, Dorian Quint, Jennifer K. Schuller, Björn Cook, Darren Calvo, Rafael A. Human-Computer Interaction Artificial Intelligence Computers and Society Emerging Technologies K.4.2; J.3 Asthma-related deaths in the UK 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 in order to provide health education, self-management support and bridges to care. 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? We present results from a patient survey (N=1257) devised by a team of asthma clinicians, patients, and technology developers, conducted to identify optimal factors for efficacy, value and engagement for a chatbot. Results indicate that most adults with asthma (53%) are interested in using a chatbot and the patients most likely to do so are those who believe their asthma is more serious and who are less confident about self-management. Results also indicate enthusiasm for 24/7 access, personalisation, and for WhatsApp as the preferred access method (compared to app, voice assistant, SMS or website). Obstacles to uptake include security/privacy concerns and skepticism of technological capabilities. We present detailed findings and consolidate these into 7 recommendations for developers for optimising efficacy of chatbot-based health support. |
| title | AI-enhanced conversational agents for personalized asthma support Factors for engagement, value and efficacy |
| topic | Human-Computer Interaction Artificial Intelligence Computers and Society Emerging Technologies K.4.2; J.3 |
| url | https://arxiv.org/abs/2507.16735 |