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Introduction: Many studies have been conducted to evaluate the prevention of overweight and obesity in children and adolescents. The aim of this study was to review these studies. Materials & Methods: At first, we searched the ISI and Pub Med databases for studies containing the key words "Health Education","Intervention", "prevention", "Programs", "Obesity" and "Overweight" plus the key words "Adolescent", "Children" and "Child'. Only, those papers in English language were selected. Finally, 20 appropriate papers were evaluated. Findings: Most of the programs and educational interventions had focused on the behaviors such as daily regular physical activity, consumption of fruits and vegetables, consumption of sugar-free drinks and limiting television watching. Educations were conducted by school staff and health educators and most of the interventions reported a positive impact of the programs and trainings. Discussuin & Conclusion: Educating the obesity preventive behaviors in children and adolescents have been largely successful. According to the studies, it seems that there is an urgent need to pay more attention to the using of models and theories and innovative strategies for better learning and creating healthy behaviors.

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Introduction: Osteoporosis is a systemic skeletal disorder characterized by the red-uction of bone mass, deterioration of bo-ne structure, increasing bone fragility, and increasing fracture risk. Prevention of osteoporosis is one of the most important issues in World Health Organization. This study was conducted to assess the know-ledge, perceived susceptibility and percei-ved severity of female teachers in the pre-vention of osteoporosis in Shahr-e-kord city. Materials & Methods: This study was a descriptive and analytical one in which the population under study included 384 female teachers of Shahr-e-kord city. Relevant data was gathered by means of a standard questionnaire based on the health belief model. The obtained data was analyzed by the statistical software, SPSS 16 and the statistical tests, Pearson and Spearman correlations. Findings: Most of the people were (51%) in the 32-42 years age group, 62.4 % were married and most of them had an educ--ational level of Bachelor's degree. Total score of knowledge was 74.87±28.08, total score of perceived susceptibility was 48.09±28.84 and total score of perceived severity was 53.85±28.54.There was a significant relationship between marital status, perceived susceptibility and perce-ived severity (P<0.05). Discussion & Conclusion: The results ind-icated that the teachers had an optimal knowledge toward the prevention of osteoporosis however had a relatively acc-eptable perceived susceptibility and percei-ved severity. These results approve the nec-essity of more education regarding to increase perceived susceptibility and per-ceived severity.

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Introduction: Given that a huge amount of cost is imposed on public and private hospitals from the department of liver diseases, it is necessary to provide a method to predict liver diseases. This study aimed to propose a hybrid model based on the Ant Lion Optimization algorithm and K-Nearest Neighbors algorithm to diagnose liver diseases.
 
Materials & Methods: This descriptive-analytic study proposed a hybrid model based on machine learning algorithms to classify individuals into two categories, including healthy and unhealthy (those with liver diseases). The proposed model has been simulated using MATLAB software. The datasets used in this study were obtained from the Indian Liver Patient Dataset available in the Machine Learning Repository at the University of Irvine, California. This dataset contains 583 independent records, including 10 features for liver diseases.
 
Findings: After pre-processing, the dataset was randomly divided into 20 categories of the entire dataset, which included different training and test data. In each category of the dataset, 90% and 10% of the data were used for training and test, respectively. Regarding all features, the results obtained the most accurate mode at 95.23%. Moreover, according to the criteria of specificity and sensitivity accuracy, the corresponding values were 93.95% and 94.11%, respectively. Furthermore, the accuracy of the proposed model along with five features was estimated at 98.63%.
 
Discussions & Conclusions: This model was proposed to diagnose and classify liver diseases along with an accuracy rate of higher than 90%. Healthcare centers and physicians can utilize the results of this study.