AI-enhanced conversational agents for personalized asthma support Factors for engagement, value and efficacy

TL;DR

This study explores factors influencing engagement with AI chatbots for asthma support, revealing patient preferences and barriers, and provides design recommendations to enhance efficacy and adoption of personalized health chatbots.

Contribution

It offers empirical insights from a large patient survey on what drives engagement with asthma chatbots and proposes guidelines for improving their design and implementation.

Findings

53% of adults with asthma are interested in chatbots

Patients with more serious asthma are more likely to engage

WhatsApp is preferred over other access methods

Abstract

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.