ArticleDigital health
Preparing for aging: Understanding middle-aged user acceptance of AI chatbots through the technology acceptance model.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Therapeutic Interaction Features of AI Chatbots in Depression Interventions: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Determinants of Healthcare Professionals' Intentions to Use Telemedicine for Elderly Care in Algeria: A Technology Acceptance Model Approach.Geriatrics (Basel, Switzerland) · 2026Article
- Patient Perceptions on the Use of Artificial Intelligence in Creating Clinical Research Documents: Survey Study.JMIR AI · 2026Article
- Factors Associated with AI Use in a Norwegian Sample.Behavioral sciences (Basel, Switzerland) · 2026Article
- The Role of AI in Improving Digital Wellness Among Older Adults: Comparative Bibliometric Analysis.JMIR AI · 2026Article
- Why should I use social chatbots? On potential users' acceptance and the role of anthropomorphism.Frontiers in psychology · 2026Article
- Predicting older adults smart healthcare adoption using an extended TAM.Scientific reports · 2025Article
- Demand analysis of transitional care for patients undergoing minimally invasive cardiac interventions with AI-driven solutions: a mixed-methods approach.BMC nursing · 2025Article
- Factors influencing older adults' adoption of AI voice assistants: extending the UTAUT model.Frontiers in psychology · 2025Article
- Facilitators and barriers to mobile health adoption among older adults with hip fractures: A systematic review.Digital healthReview
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Authors and funding
7 authors.
Funding
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Abstract
Background: Preparing for aging with personalized technology is crucial due to the growing elderly population. Artificial Intelligence (AI), notably AI chatbots in healthcare, has transformed technology by simulating human-like conversations. Research on middle-aged adults' acceptance of AI chatbots is limited. Assessing middle-aged individuals' intentions to use AI is vital for enhancing AI competency among the elderly and guiding future interventions. Objective: This study aims to explore the acceptance of middle-aged individuals toward AI chatbots and influencing factors and verify the usability of Technology Acceptance Model 2 (TAM2) in the use of AI technology in middle-aged people, also to inspire the design of future intelligent systems and online interventions for improving the health and well-being of the aging population. Methods: A cross-sectional design and snowball sampling method were utilized to conduct an online questionnaire survey among middle-aged adults. The questionnaire was compiled based on TAM2 and was created using the online survey platform. SPSS 26.0 software was used for statistical analysis. Results: A total of 259 valid questionnaires were included in the final data analysis. The study reported the Cronbach's α of 0.94 for the questionnaire. We found that perceived ease of use, subjective norm, and user image significantly influence users' intention to use AI chatbots. Notably, perceived usefulness emerged as a complete mediator in the relationship between subjective norm and intention to use, highlighting its central role in shaping user perceptions. The study also revealed a moderate acceptance level among middle-aged adults, emphasizing the need for targeted interventions. Conclusions: This study emphasized the importance of customizing AI technology to improve its adoption among middle-aged adults, providing valuable guidance for developers and policymakers. The findings indicated the need for effective aging preparation that includes technological competency, suggesting that future planning should encompass comprehensive preparations for aging to enhance AI competency among the middle-aged population.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.