Background: The aim of the study was to compare the effect of 8 weeks of online Zumba exercises and walking on body composition, eating attitude and body image perception in overweight and obese women during the pandemic. Material and methods: A cohort of 150 obese women aged between 20 and 40 years old were classified as Control (n=50), Walking (n=50) and Zumba (n=50) groups. All groups received dietary counselling, while the Zumba and walking groups received 8 weeks of exercise intervention. Bioimpedance, Body Image Scale and Three-Factor Nutrition Questionnaire-Revised 18 Items Scale were used to assess body composition, body image satisfaction and eating behavior, respectively. Paired Sample T test and ANOVA analysis were used at the significance level of α=0.05. Results: Walking and Zumba exercise groups had lower BMI, fat mass, fat percentage and waist-to-hip ratio. The body image increased significantly in both walking and Zumba groups, while a decline was determined in the control group at the end of the study. Uncontrolled eating, emotional eating and sensitivity to hunger decreased in walking and Zumba groups, while there was no significant difference in cognitive restrain for all three groups. Conclusions: It can be concluded that 8-week home-based online Zumba exercise with self-monitoring via Smartphone apps was as effective as walking for improving body composition and eating attitudes of overweight and obese women..

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.

Research problem/aim: This case study investigates the impact of achieving an ideal body weight (77 kg) through an 18-month individualized exercise and nutrition program on the quality of life of an obese male adolescent diagnosed with infantile scoliosis, who started the program at the age of 13.5 weighing 143 kg and was followed until the age of 15. The study aimed to explore physical, psychological, and social changes during this transformation process. Method: A mixed-method case study design was employed, and criterion sampling was used for participant selection. The independent variable was the personalized program, and the dependent variable was the change in quality of life. Quantitative data were collected through 49 bioelectrical impedance measurements at regular intervals. Qualitative data were obtained via semi-structured interviews with the participant and his mother. Open-ended responses were grouped under main themes and analyzed through content analysis. Findings: Findings were categorized under six themes: physical functioning, emotional well-being, academic engagement, mental health, family relationships, and social interaction. Improvements were noted in endurance, flexibility, mobility, emotional balance, body image, and school participation. Conclusions: The study concludes that individualized interventions, when supported by active family involvement, can foster holistic development and promote long-term well-being in adolescents with obesity and comorbid skeletal conditions.