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    Adult's food label awareness: scale development and validation
    (Emerald Group Publishing Ltd, 2026) Acikgoz, Inci; Kokturk, Seda Nur; Yardimci, Hulya
    PurposeThis study aimed to develop and validate the Food Label Awareness Scale (FLAS) to comprehensively assess adults' food label reading habits and expectations.Design/methodology/approachThe FLAS was developed based on a literature review and expert input. Initial item generation was followed by expert evaluations for content and face validity. Data were collected through structured face-to-face interviews using the preliminary version of the FLAS and a sociodemographic questionnaire. A total of 300 adults aged 20-65 years participated. Food label awareness was assessed based on the three subdimensions: (1) product packaging and ingredients, (2) nutrition and health and (3) product brand. In addition, participants' age, gender and education level were recorded. Cronbach's alpha assessed internal consistency. Reliability was supported by comparing the highest and lowest 27% of total scores. Associations between demographics and subdimension scores were tested using appropriate statistical methods.FindingsThe finalized 18-item FLAS demonstrated a three-factor structure with acceptable model fit and internal consistency. Older adults had higher awareness of packaging and ingredients. Lower education was associated with greater reliance on product brand.Research limitations/implicationsA primary limitation is the geographically restricted sample, which may limit the generalizability of the findings.Originality/valueThis study is original in developing a valid and reliable scale specifically measuring adults' awareness of food label reading - an area less explored in the literature, which mostly emphasizes general nutrition knowledge or label reading frequency.
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    Ecological and nutritional analysis of artificial intelligence-based sustainable dietary models
    (Academic Press Inc Elsevier Science, 2026) Kokturk, Seda Nur; Yardimci, Hulya
    Growing concerns over environmental sustainability and health have increased the demand for sustainable diets. Advances in artificial intelligence (AI) offer new opportunities for diet planning. Evaluating the nutritional adequacy and environmental impact of AI-generated sustainable diets is essential. This study aimed to compare the nutritional quality and ecological footprints of sustainable diets created by four popular AI tools (Gemini, Copilot, ChatGPT, and Grok). Each AI tool generated a sustainable diet plan for a healthy adult woman. Diets were evaluated for energy, nutrient content, antioxidant capacity, glycemic index, protein quality, fatty acid profiles, Healthy Eating Index-2015 scores, and ecological footprints. All diets showed low ecological footprints but did not meet energy targets (1800 kcal/day). Gemini had the most sustainable composition. Vitamin B12 was consistently below recommended levels in diets from Gemini, Copilot, and ChatGPT. All diets had adequate carbohydrate, protein, fat quality, and overall diet quality. Carbon footprint associated with protein, energy from protein, cholesterol, pyridoxine, folate, and vitamin C. Water footprint was linked to multiple nutrients, and fruits, meat, poultry, and fish; vegetable intake correlated with carbon footprint. AI-assisted diets have potential for sustainability but nutritional completeness concerns remain, especially for vulnerable groups. Further studies comparing AI and dietitian plans are needed to assess long-term effects.