Anticipated acceptance of generative AI among older adults with chronic heart failure: a qualitative study guided by UTAUT

Chronic heart failure imposes a substantial disease burden on older adults, who face considerable challenges in post-discharge self-management. Generative artificial intelligence tools offer the potential to provide personalised, real-time health information support. However, how older adults with chronic heart failure perceive and respond to such tools remains poorly understood. This study aimed to explore the anticipated acceptance of generative artificial intelligence health management tools, and the factors shaping it, among older adults with chronic heart failure, guided by the Unified Theory of Acceptance and Use of Technology model. A qualitative descriptive design was adopted. Patients aged 60 years or older with chronic heart failure were recruited from the cardiology department of a tertiary teaching hospital in eastern China using maximum variation purposive sampling. Face-to-face semi-structured interviews were conducted between November and December 2025 until data saturation was achieved. All participants received the same standardised demonstration before the formal interview questions, including those with prior experience of generative artificial intelligence. Data were analysed using Braun and Clarke’s thematic analysis with a combined deductive–inductive approach. The study was reported following the Consolidated Criteria for Reporting Qualitative Research guidelines. Twenty participants aged 60 to 89 were included. Six themes were identified across two dimensions: a theory-aligned dimension illustrating the relevance of performance expectancy, effort expectancy, social influence, and facilitating conditions; and a contextual extension dimension comprising trust and risk perception, which emerged inductively from the data, and value trade-offs, which corresponds conceptually to the price value construct described in UTAUT2. Anticipated acceptance was distributed along a continuum from active adoption to deliberate resistance rather than forming a binary outcome. Older adults with chronic heart failure exhibited notable heterogeneity in their anticipated acceptance of generative artificial intelligence tools. Trust and risk perception emerged as an important factor beyond the four core constructs, while value trade-offs corresponded to the price value construct of UTAUT2. These findings suggest a potential navigating role for healthcare professionals in geriatric care, through tiered training, age-friendly interaction and education of family caregivers, although this requires evaluation in larger and more varied samples.