Poor sleep quality is prevalent among university students and has been linked to unhealthy eating behaviors, including night eating and low mindful eating, which may negatively impact overall health and well-being. The purpose of this study is to evaluate the effects of mindful eating and night eating syndrome on sleep quality among female students in the Faculty of Health Sciences. This cross-sectional study was conducted with a total of 360 female university students aged 18 to 24 years. Data were collected through face-to-face surveys, and the Mindful Eating Questionnaire (MEQ), Night Eating Questionnaire (NEQ), and Pittsburgh Sleep Quality Index (PSQI) were administered. Among the participants, 40.8% were classified as good sleepers, while 59.2% were poor sleepers. Additionally, MEQscores were not statistically different between the two groups (p > 0.05), while NEQ total scores were significantly higher in participants with poor sleep quality (p = 0.003). Higher total PSQI scores were significantly associated with skipping meals (Beta = 0.101), alcohol consumption (Beta = 0.163), high BMI (Beta = 0.610), and NEQ score (Beta = 0.084). In conclusion, poor sleep quality among female university students is significantly associated with night eating behaviors, higher BMI, meal skipping, and alcohol consumption, highlighting the need for interventions targeting these behaviors to improve sleep quality and overall well-being.

Objective Intuitive eating is an adaptive eating style that makes a strong connection with internal physiological manifestations of hunger and satiety. This study was conducted to evaluate the effects of intuitive eating on body appreciation, body mass index, and nutritional behaviors in adults. Methods Sample consisted of 497 individuals. Data were collected online. The questionnaire form included a general information and physical activity evaluation form, anthropometric measurements, Intuitive Eating Scale-2, Body Appreciation Scale-2, and food frequency questionnaire. Results A statistically significant relationship was found between body mass index and eating for physical rather than emotional reasons, reliance on internal hunger and satiety cues, body-food choice congruence subscale scores, and total scale score. There was a correlation between body appreciation and unconditional permission to eat, reliance on internal hunger and satiety cues, body-food choice congruence subscale scores, and total scale score. The number of physically active intuitive eaters was significantly higher than non-intuitive eaters. A significant correlation was found between intuitive eating scores and frequency of consumption of fish, cake, biscuit, chocolate, granulated sugar, honey, jam, molasses, pastries and dairy-based desserts, sunflower oil, margarine, and sugary, carbonated drinks. Conclusion Intuitive eating can be an effective tool in sustaining and controlling weight loss, and with more studies, it may be possible to further spread this practice

Background and Objectives: Coronary artery disease (CAD) is a leading cause of morbidity and mortality worldwide. An early and accurate diagnosis is essential for effective clinical management and risk stratification. Recent advances in machine learning (ML) have provided opportunities to enhance the diagnostic performance by integrating multidimensional patient data. This study aimed to develop and compare several supervised ML algorithms for early CAD diagnosis using demographic, anthropometric, biochemical, and psychosocial parameters. Materials and Methods: A total of 300 adult patients (165 CAD-positive and 135 controls) were retrospectively analyzed using a dataset comprising 21 biochemical markers, body composition metrics, and self-reported eating behavior scores. Six ML algorithms, k-nearest neighbors (k-NNs), support vector machines (SVMs), artificial neural networks (ANNs), logistic regression (LR), naïve Bayes (NB), and decision trees (DTs), were trained and evaluated using 10-fold cross-validation. Model performance was assessed based on accuracy, sensitivity, false-negative rate, and area under the Receiver Operating Characteristic (ROC) curve (AUC). Results: The k-NN model achieved the highest performance, with 98.33% accuracy and an AUC of 0.99, followed by SVM (96.67%, AUC = 0.95) and ANN (95.33%, AUC = 0.98). Patients with CAD exhibited significantly higher levels of glucose, triglycerides (TGs), LDL cholesterol (LDL-C), and abdominal obesity, while vitamin B12 levels were lower (p < 0.001). Although emotional and mindful eating scores differed significantly between the groups, their contribution to model performance was limited. Conclusions: Machine learning models, particularly k-NN, SVM, and ANN, have demonstrated high accuracy in distinguishing CAD patients from healthy controls when applied to a diverse set of clinical and behavioral variables. This study highlights the potential of integrating psychosocial and clinical data to enhance CAD prediction models beyond traditional biomarkers.

Interest in studies examining the effect of temperament types on nutrition has recently increased. The aim of this study was to evaluate the relationship between nine types of temperament, anthropometric measurements, and nutrition in adults. This study was conducted on 1317 individuals aged between 18 and 55 years. Descriptive information, dietary habits and anthropometric measurements of the participants were questioned. The Nine Types of Temperament Scale was administered to the individuals and food consumption records were obtained with a 24-hour retrospective reminder method. Type 2 scores of obese participants were higher than those of underweight and normal body weight; Type 8 scores of overweight participants were higher than those of normal body weight. Daily dietary intake of protein, riboflavin, folate, vitamins K, C, calcium, iron, and cholesterol were negatively associated with Type 1 score; protein, magnesium, iron, zinc intake, and water consumption were negatively associated with Type 2 score. Type 3 score was negatively associated with dietary CHO (%), dietary magnesium, iron, and zinc intake and positively associated with water consumption. The results of the study indicate significant relationships between temperament types, dietary habits, and anthropometric measures. In this context, considering temperament types when planning dietary patterns of individuals may be a new approach.