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Personal cost is responsible for 90.6% and primary screening for 66.4% of the total cost. Conclusion For the development of the program (from screening to rehabilitation) 530 513 IRR should be spent per capita. GB0-139 The cost of detection per client can vary due to differences in disease prevalence, especially treatment and rehabilitation costs. It is suggested to consider the variation of the prevalence in expanding the plan to the whole country. Integrating the services in primary health care lead to huge cost saving.Objective The purpose of the present study was to examine the validity and reliability of the Persian version of the Weight Control Strategies Scale among individuals engaged in weight loss or weight maintenance. Method This descriptive study conducted from October 2019 to February 2020 on social media networks. A total of 420 men and women were selected using consecutive sampling. They completed the Persian version of the Weight Control Strategies Scale and the Self-Compassion Scale. Data were analyzed using descriptive statistics, Cronbach's α, confirmatory factor analysis, and Pearson product-moment correlations. Results Internal consistency for the total score of the Persian version of Weight Control Strategies Scale was excellent and acceptable to good for all 4 subscales (in all cases over α = 0.70). Confirmatory factor analysis supported the factor structure of the original model of the scale, but, it was different from the model at the item level. Moreover, the Persian version of Weight Control Strategies Scale had good convergent validity. Conclusion Psychometrically speaking, the Persian version of the Weight Control Strategies Scale is a valid and reliable tool to assess the psychological and behavioral profile of individuals engaging in losing or maintaining weight, both for clinical and research purposes.Objective Recently, social media use has become prevalent in the daily lives of many adolescents. This study was performed to address adolescents' sleep quality and depression in relation to social media use. Method This cross-sectional cluster-sampling study was directed on 576 high school students in 2019 in Hamadan, Iran. Three standard self-reported questionnaires were used for recording sleep patterns (Pittsburgh Sleep Questionnaire Index (PSQI)), depression (Beck), and Electronic Media Use. Data was analyzed using SPSS. P-values less than 0.05 were considered as being significant. Results Among the adolescents 290 (50.3%) were female and the age median was 17. The average time of all Smart devices used was 7.5±4.4 hours per day. Among all students 62.3 % (359) said that they had their cell phone on in their bedroom when they sleep. In boys, the amount of social media use was significantly more than girls and poor sleep quality had a statically significant relationship with social media use (P-Value = 0.02). Additionally, there was a reverse correlation between the average use of electronic devices and sleep duration (Spearman's rho = 0.17; P-Value = 0.03), and a direct correlation between the average use in social media and depression (Spearman's rho = 0.171; P-Value less then 0.001). Conclusion In this important age group a high level of electronic devices use and its relationship with sleep quality, daily dysfunction, sleep duration and depression is worthy of issue awareness among health managers, parents and teachers for providing interventional programs, based on standard updated guidelines, in order to reduce the problem and familiarize adolescents and their parents, at home or school, with restrictions on using devices to view and participate in social media.Objective Bipolar I disorder is one of the most frequent mental disorders characterized by manic or mixed +/- depressive episodes. Drug treatment has been proved to diminish next episodes, but many other factors are important for exacerbating the conditions. This study aimed to investigate the effective factors on the time and number of episodes in these patients by applying the shared frailty model. Method In this retrospective longitudinal study, the information of 606 patients with bipolar I disorder, admitted for the first time in Ibn-e-Sina psychiatric hospital in Mashhad from the beginning of 2007 until the end of 2009 were used. These patients were followed up until the end of 2018 for readmission. The Cox model with gamma frailty and Bayesian approach were used to determine the effective factors of frequent recurrences. Results History of head trauma, substance abuse, and legal conflict had a positive impact on recurrences, while age had a negative effect on recurrences and the risk of recurrence was higher in younger people (P less then 0.05). The variance estimation of frailty effect was 0.97 that indicates a correlation between the recurrence intervals of bipolar I patients, owing to a heterogeneity among patients. Conclusion Based on the results, a higher risk of recurrence of bipolar I disorder was found in younger patients and those with a history of head trauma, substance abuse, and legal conflicts. Further investigations are required to account for the genetic factor and psychosocial exposure during critical periods applying this model.Objective Although comorbidity of psychotic disorders and substance use can lead to increase in mortality, less is known about the outbreak and predictors. Psychotic patients tend to be overlooked during assessment; hence, the possibility of an undertreated or missed condition such as increasing substance use. This investigation aimed to measure the prevalence of substance use in psychotic patients and to survey the powerful predictors. Method In a 1-year cross-sectional study, 311 psychotic patients were assessed using the Structured Interview Based on DSM-5 for diagnostic confirmation as well as questions surveying prevalence and possible predictors of substance use. Results Prevalence of substance use among psychotic patients was 37.9%. Several variables were identified as factors associated with drug abuse among the psychotic patients. These included male gender, younger age, being currently homeless, a history of imprisonment, and having family history of drug use. The strongest predictors of substance use, however, were family history of drug use, male gender, and being currently homelessness.