Positive and Negative Attitudes Toward Artificial Intelligence and E-Healthy Diet Literacy Among Adults: A Partial Least Squares Structural Equation Modeling Study.

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Τίτλος:	Positive and Negative Attitudes Toward Artificial Intelligence and E-Healthy Diet Literacy Among Adults: A Partial Least Squares Structural Equation Modeling Study.
Συγγραφείς:	Özenir Ç; Department of Nutrition and Dietetics, Kırıkkale University, 71100 Kırıkkale, Türkiye., Altınsoy C; Department of Nutrition and Dietetics, Recep Tayyip Erdoğan University, 53100 Rize, Türkiye., Çubuk M; Department of Nutrition and Dietetics, Erzincan Binali Yıldırım University, 24100 Erzincan, Türkiye., Kılınç FN; Department of Nutrition and Dietetics, Kırıkkale University, 71100 Kırıkkale, Türkiye., Çakır B; Department of Nutrition and Dietetics, Kırıkkale University, 71100 Kırıkkale, Türkiye.
Πηγή:	Nutrients [Nutrients] 2026 Sep 07; Vol. 18 (17). Date of Electronic Publication: 2026 Sep 07.
Τύπος έκδοσης:	Journal Article
Γλώσσα:	English
Στοιχεία περιοδικού:	Publisher: MDPI Publishing Country of Publication: Switzerland NLM ID: 101521595 Publication Model: Electronic Cited Medium: Internet ISSN: 2072-6643 (Electronic) Linking ISSN: 20726643 NLM ISO Abbreviation: Nutrients Subsets: MEDLINE
Imprint Name(s):	Original Publication: Basel, Switzerland : MDPI Publishing
Ιατρικοί όροι (MeSH):	Diet, Healthy/psychology , Artificial Intelligence , Health Literacy* , Health Knowledge, Attitudes, Practice*, Humans ; Adult ; Female ; Male ; Middle Aged ; Cross-Sectional Studies ; Young Adult ; Surveys and Questionnaires ; Least-Squares Analysis ; Reproducibility of Results
Περίληψη:	Background/Objectives: This study aimed to examine the association between e-healthy diet literacy (e-HDL) and general attitudes toward artificial intelligence (AI) among adults. Methods: This cross-sectional study included 610 adults aged 19-64 years. Data were collected through face-to-face interviews using a questionnaire consisting of five sections, including sociodemographic characteristics, digital nutrition information practices, AI use in nutrition and dietetics, the e-Healthy Diet Literacy Questionnaire (e-HDLQ), and the General Attitudes toward Artificial Intelligence Scale (GAAIS). Descriptive statistics, validity and reliability analyses, correlation analyses, and Partial Least Squares Structural Equation Modeling (PLS-SEM) were performed. Statistical significance was accepted at p < 0.05. Results: The mean age of the participants was 33.07 ± 11.36 years, and 50.8% were female. The measurement model demonstrated acceptable reliability and convergent validity. Structural model analysis showed that positive attitudes toward AI were positively associated with e-HDL (β = 0.243, p < 0.001), whereas negative attitudes toward AI were negatively associated with e-HDL (β = -0.232, p < 0.001). Among the e-HDLQ dimensions, the strongest loading was observed for finding e-healthy diet information (β = 0.844), followed by applying e-healthy diet information (β = 0.641), digital healthy eating literacy (β = 0.636), judging e-healthy diet information (β = 0.308), and understanding e-healthy diet information (β = 0.277). Together, positive and negative attitudes toward AI explained 12.2% of the variance in e-HDL (R² = 0.122), indicating a modest explanatory contribution. Conclusions: Positive and negative attitudes toward AI were independently associated with e-HDL among adults. Although the explained variance was modest, AI attitudes may represent one of several factors associated with digital nutrition literacy, warranting further investigation in future studies.
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Grant Information:	020260070210513 Recep Tayyip Erdoğan University
Contributed Indexing:	Keywords: PLS-SEM; artificial intelligence; digital nutrition literacy; e-healthy diet literacy; general attitudes toward artificial intelligence; nutrition literacy
Entry Date(s):	Date Created: 20260915 Date Completed: 20260915 Latest Revision: 20260916
Update Code:	20260916
PubMed Central ID:	PMC13567288
DOI:	10.3390/nu18172931
PMID:	42739102
Βάση Δεδομένων:	MEDLINE

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ISSN:	2072-6643
DOI:	10.3390/nu18172931