ArticleBMC medical informatics and decision making2026
Toward tailored implementation of an artificial intelligence-based healthy dietary chatbot: differences in technology acceptance pathways by health consciousness and user characteristics.
Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) health chatbots are increasingly used for health education and health promotion, yet the mechanisms underlying their adoption and the heterogeneity across user groups remain insufficiently understood.
methodsDrawing on the Technology Acceptance Model (TAM), this study examined behavioral intention to use an artificial intelligence-based healthy dietary chatbot, incorporating interactivity and entertainment as key system features, and testing whether health consciousness and sociodemographic subgroups moderate technology acceptance pathways. An anonymous questionnaire survey was conducted after participants interacted with a LINE-based AI dietary chatbot. Structural equation modeling and subgroup analysis were applied to evaluate TAM relationships, moderation by health consciousness, and boundary conditions across demographic groups.
resultsPerceived ease of use and perceived usefulness significantly predicted behavioral intention. Interactivity was primarily associated with intention indirectly through perceived ease of use and perceived usefulness, whereas entertainment showed a stronger direct association with intention. Health consciousness moderated the interactivity-usefulness and entertainment-intention relationships. Multi-group analyses further indicated pathway heterogeneity: interaction-related effects were stronger among younger, female, and more highly educated users, whereas older users placed greater weight on enjoyment and ease of use, and males exhibited a stronger usefulness-intention link.
conclusionsOverall, the findings support the applicability of TAM to artificial intelligence-based health chatbot adoption and suggest the coexistence of cognitive, interactivity-driven and affective, entertainment-driven acceptance routes. The moderating roles of health consciousness and user characteristics underscore the importance of segment-specific design and dissemination strategies to enhance adoption.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.