Development and Validation of the AI Health Acceptability Evaluation (AI-HAE) in Older Adults

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Abstract
Artificial intelligence (AI) is increasingly used in health care, yet little is known about user acceptability, particularly among older adults. To address this gap, the AI Health Acceptability Evaluation (AI-HAE) was developed to assess perceptions of AI in health management contexts. Thirteen items were created based on the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and AI-specific considerations (eg, trust, transparency, privacy). A total of 404 adults aged 60 to 81 years completed an online survey. Psychometric evaluation included exploratory and confirmatory factor analyses, internal consistency, and known-groups validity. Participants were 52.2% female and predominantly White (88.4%), with high rates of smartphone (90.1%) and PC/laptop ownership (90.1%). Exploratory factor analysis supported the removal of one reverse-coded item with low factor loading, resulting in a 12-item structure. Confirmatory factor analysis supported a unidimensional model with strong item loadings and excellent fit indices and internal consistency. Exploratory subgroup comparisons indicated higher AI acceptability among younger participants, males, individuals with higher education, and those with prior AI experience; no differences were observed by race or income. The AI-HAE is a valid and reliable instrument for assessing AI acceptability in health care among older adults. Its strong psychometric properties support its use in evaluating readiness, informing intervention design, and guiding equitable implementation of AI-driven health solutions.

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