Background: Emotional conditions governing the family and patients’ perceived social support play important roles in the treatment or relapse process of the chronic disease. Objectives: The current study aimed to investigate the role of family expressed emotion and perceived social support in prediction of addiction relapse. Patients and Methods: The descriptive-correlation method was used in the current study. The study population consisted of the individuals referred to the addiction treatment centers in Ardabil from October 2013 to January 2014. The subjects (n = 80) were randomly selected using cluster sampling method. To collect data, expressed emotion test by Cole and Kazaryan, and Multidimensional Scale of Perceived Social Support (MSPSS) were used, and the obtained data was analyzed using the Pearson's correlation coefficient and multiple regression analyses. Results: Results showed a positive relationship between family expressed emotions and the frequency of relapse (r = 0.26, P = 0.011) and a significant negative relationship between perceived social support and the frequency of relapse (r = -0.34, P = 0.001). Multiple regression analysis also showed that perceived social support from family and the family expressed emotions significantly explained 12% of the total variance of relapse frequency. Conclusions: These results have implications for addicted people, their families and professionals working in addiction centers to use the emotional potential of families especially their expressed emotions and the perceived social support of addicts to increase the success rate of addiction treatment. Keywords: Expressed Emotion; Social Support; Recurrence; Substance Addiction

Background and objective: There is an increased interest in exploring the association between fitness components with cognitive development in children in recent years. One of the scopes is to find the best exercise prescription to enhance health and cognition. Most of the studies so far have focused on cardiorespiratory fitness with little evidence on other fitness components. The present study aimed to explore the association between physical fitness (PF) and motor fitness (MF) with cognitive performance in children. Methods: Two hundred and six schoolboys (11.0 ± 0.8 y) underwent a battery of tests to measure information processing speed (i.e., simple and choice reaction time) and inhibitory control (i.e., Simon task). PF components (i.e., flexibility, muscular strength, and endurance) and MF components (speed and agility) were measured. Results: Multiple linear regression analysis adjusted for potential confounders (i.e., age, socioeconomic status, %fat and physical activity) revealed no relationship between flexibility, speed, muscular strength, and endurance with either information processing tasks or inhibitory control tasks. However, a positive association was observed between agility with both congruent reaction time and incongruent reaction time. Conclusions: No relationship was observed between the underlying fitness components with either information processing or inhibitory control. However, an association was observed between agility with inhibitory control.

Background: The rising prevalence of mental disorders, coupled with limited access to mental health services, underscores the urgent need for innovative solutions. Artificial Intelligence (AI) offers transformative potential in managing mental health conditions through multimodal data analysis. Objective: This study explores emerging applications of AI in early detection, personalized treatment, and the prevention of symptom escalation in mental disorders. Methods: A narrative review was conducted using comprehensive searches of PubMed, Scopus, and IEEE Xplore databases (2015– 2025). Selected sources included studies on natural language processing (NLP), deep learning, and the analysis of multimodal data (eg, voice, text, and biosensor inputs). A qualitative synthesis was employed to identify key patterns, challenges, and innovations. Findings: AI enhances early detection through concepts such as a “psychological digital signature” and reports high performance in some studies (reported accuracies vary widely, eg, up to ~91% in selected cohorts). However, many high-accuracy reports derive from single-site or limited datasets with variable external validation; therefore, these figures should be interpreted cautiously. We discuss study-specific limitations (sample size, validation methods, and population diversity) in the Methods and Critical Appraisal sections. Conclusion: AI provides a patient-centered, preventive framework for reimagining mental health care. However, its effective integration requires robust ethical standards and digital infrastructure. Ethical considerations are critically linked to clinical implementation, particularly regarding privacy, fairness, and transparency in AI-assisted decision-making.

This research was conducted in order to comparing self-esteem and self-concept of handicapped and Normal Students. Statistical society of this research comprised all the handicapped boy and girl students as well as the normal society included all the sighted students aged 10–20, who were studying at Ardabil province high–schools in 85–86 school– years. 20 handicapped students (10 girl handicapped and 10 boy handicapped) were selected randomly as a study group and 20 sighted students (10 girl handicapped and 10 boy handicapped) as a comparison group. They were tested individually by two subtests of Coppersmith^, self- esteem and self-concept^,beak & stiller scale at their schools. The results of multivariate variance (MANOVA) showed that there was different mean score between handicapped and Normal Students. At all the results proved that the function of the sighted group individuals in self-esteem subtests was better than the handicapped. But the function of the handicapped individuals in self-concept subtests was better than the handicapped.