Conversational artificial intelligence HeAlth supporT in Atrial Fibrillation Self-Management (CHAT-AF-S): rationale...

Area of Science:

• Digital Health
• Artificial Intelligence in Healthcare
• Cardiology

Background:

• Atrial fibrillation (AF) significantly impacts patient quality of life, increases stroke risk, and mortality.
• Clinical guidelines advocate for digital technologies to enhance AF patient education and self-management.
• Conversational AI offers a novel approach to human-like interaction, potentially improving patient engagement in self-care.

Purpose of the Study:

• To assess the effectiveness of the Conversational artificial intelligence HeAlth supporT in Atrial Fibrillation Self-Management (CHAT-AF-S) intervention.
• To evaluate the impact of CHAT-AF-S on the quality of life for individuals diagnosed with AF.
• To explore the feasibility and acceptability of using conversational AI for AF self-management.

Main Methods:

• A 3-month randomized controlled trial (RCT) with 1:1 allocation involving 480 adult participants with documented AF.
• The primary outcome measure is the Atrial Fibrillation Effect on QualiTy-of-life overall score.
• A mixed-methods approach including a user experience survey and qualitative interviews for process evaluation.

Main Results:

• Primary outcome data collection is ongoing.
• User experience and qualitative data will provide insights into intervention feasibility and acceptability.
• The study adheres to intention-to-treat principles with blinded data analysis.

Conclusions:

• The CHAT-AF-S intervention holds promise for improving quality of life in AF patients through enhanced self-management.
• Findings will inform the integration of conversational AI into routine AF care pathways.
• Results will be disseminated through peer-reviewed publications and international conferences.