Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleAçık Erişim2026Scopus SJRQ1Ecological and nutritional analysis of artificial intelligence-based sustainable dietary models
Köktürk, Seda Nur; Yardımcı, Hülya
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.
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İncele - MakaleYalnız künye2026Scopus SJRQ1Effect of ultra-processed food consumption on premenstrual syndrome across different body mass index categories: a cross-sectional analysis
Köktürk, Seda Nur; Taş Yıldırım, Esra; Yardımcı, Hülya
Purpose – This study aimed to examine the association between ultra-processed food (UPF) consumption and premenstrual syndrome (PMS) across different body mass index (BMI) categories in women. Design/methodology/approach – A total of 480 women aged 20–49 years participated in the study between May 2024 and May 2025 in 3 cities in Türkiye. PMS symptoms were assessed using the Premenstrual Syndrome Scale (PMSS), and UPF intake was evaluated according to the NOVA classification using a food frequency questionnaire. Anthropometric measurements, including height and body weight, were taken by researchers, and BMI was calculated for each participant. Findings – The mean BMI of the participants was 24.2 ± 4.9 kg/m2, with an average daily UPF intake of 858.0 ± 133.3 g. Higher UPF intake was significantly associated with increased reporting of PMS, with 7.0% higher odds among normal-weight women, 19.0% higher odds among overweight women, and 41.0% higher odds among women with obesity. Moreover, as UPF consumption increased from the lowest (Q1) to the highest quartile (Q4), the odds of experiencing PMS symptoms were 2.65 times (OR: 2.65, %95 CI: 1.52–4.61), 2.85 times (OR: 2.85, %95 CI:1.60–4.95), and 3.07 times (OR: 3.07, %95 CI: 1.71–5.49) higher, respectively (p = 0.001). Research limitations/implications – These findings underscore a significant association between UPF consumption and PMS symptoms, particularly among women with obesity. Given the high prevalence of PMS and its substantial impact on women's well-being, dietary interventions aimed at reducing UPF intake may represent a promising approach for PMS management and prevention. Originality/value – To the best of our knowledge, this study is the first to examine the relationship between UPF intake and self-reported PMS presence across different BMI categories. 2026
- MakaleAçık Erişim2026Scopus SJRQ1Assessment of malnutrition in hospitalized children: Comparison of stamp, PYMS and strong-kids screening tools
Mutlu, Elif; Köktürk, Seda Nur; Ersoy Söke, Nursena; Yardımcı, Hülya
Aim Early identification of malnutrition risk in hospitalized children is essential for timely nutritional intervention. This study aims diagnostic performance of three pediatric malnutrition screening tools validated in Turkish: STAMP, PYMS, and STRONG-Kids. Methods In this cross-sectional study, 148 hospitalized children were included. Nutritional risk was evaluated using STAMP, PYMS, and STRONG-Kids screening tools. Anthropometric measurements were performed, and height-for-age, Body Mass Index (BMI)-for-age, and Middle Upper Arm Circumference (MUAC)-for-age z-scores were calculated. The sensitivity and specificity of each tool were assessed in identifying chronic and acute malnutrition. Receiver operating characteristic (ROC) curve analysis was used to assess the discriminative ability of the screening tools. Results Chronic malnutrition was identified in 12.8% of patients and acute malnutrition in 15.5%. STAMP and STRONG-Kids showed the highest sensitivity for detecting chronic malnutrition (84.2% and 84.3%), while PYMS had the highest specificity. STRONG-Kids demonstrated the greatest sensitivity for acute malnutrition and low MUAC z-scores (100.0%). ROC analysis showed that PYMS had the highest Area Under the Curve (AUC) for acute malnutrition, whereas STRONG-Kids had the highest AUC for chronic malnutrition, although all tools demonstrated limited discriminative ability for chronic malnutrition. Conclusion STAMP and STRONG-Kids were more sensitive in detecting malnutrition risk, whereas PYMS showed higher specificity. Tool performance varied according to anthropometric indicators. Implications to practice In clinical practice, selecting the screening tool according to the targeted malnutrition type and available anthropometric indicators may improve early detection and allow more effective prioritization of nutritional interventions in hospitalized children.
