A text-based conversational agent for asthma support: Mixed-methods feasibility study

Darren Cook; Dorian Peters; Laura Moradbakhti; Ting Su; Marco Da Re; Bjorn W Schuller; Jennifer Quint; Ernie Wong; Rafael A Calvo

doi:10.1177/20552076241258276

A text-based conversational agent for asthma support: Mixed-methods feasibility study

Digit Health. 2024 Jun 17:10:20552076241258276. doi: 10.1177/20552076241258276. eCollection 2024 Jan-Dec.

Authors

Darren Cook¹, Dorian Peters¹, Laura Moradbakhti¹, Ting Su¹, Marco Da Re¹, Bjorn W Schuller¹, Jennifer Quint², Ernie Wong², Rafael A Calvo¹

Affiliations

¹ Dyson School of Design Engineering, Imperial College London, London, UK.
² Imperial College Healthcare NHS Trust, London, UK.

Abstract

Objective: Millions of people in the UK have asthma, yet 70% do not access basic care, leading to the largest number of asthma-related deaths in Europe. Chatbots may extend the reach of asthma support and provide a bridge to traditional healthcare. This study evaluates 'Brisa', a chatbot designed to improve asthma patients' self-assessment and self-management.

Methods: We recruited 150 adults with an asthma diagnosis to test our chatbot. Participants were recruited over three waves through social media and a research recruitment platform. Eligible participants had access to 'Brisa' via a WhatsApp or website version for 28 days and completed entry and exit questionnaires to evaluate user experience and asthma control. Weekly symptom tracking, user interaction metrics, satisfaction measures, and qualitative feedback were utilised to evaluate the chatbot's usability and potential effectiveness, focusing on changes in asthma control and self-reported behavioural improvements.

Results: 74% of participants engaged with 'Brisa' at least once. High task completion rates were observed: asthma attack risk assessment (86%), voice recording submission (83%) and asthma control tracking (95.5%). Post use, an 8% improvement in asthma control was reported. User satisfaction surveys indicated positive feedback on helpfulness (80%), privacy (87%), trustworthiness (80%) and functionality (84%) but highlighted a need for improved conversational depth and personalisation.

Conclusions: The study indicates that chatbots are effective for asthma support, demonstrated by the high usage of features like risk assessment and control tracking, as well as a statistically significant improvement in asthma control. However, lower satisfaction in conversational flexibility highlights rising expectations for chatbot fluency, influenced by advanced models like ChatGPT. Future health-focused chatbots must balance conversational capability with accuracy and safety to maintain engagement and effectiveness.

Keywords: Digital health; WhatsApp; asthma; chatbots; conversational agents; ehealth; healthcare technology.