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Summary: Psychological flexibility has recently attracted the attention of researchers in the field of sleep disorders; therefore, in the study, psychological flexibility was evaluated as a predictor or factor related to the presence/severity of insomnia. We included 2218 individuals selected from the randomized–control trial for behavioural therapy for insomnia and cross‐sectional studies, including 1797 individuals with insomnia and 421 controls without insomnia. All participants completed the DSM‐5‐based insomnia diagnosis interview, Insomnia Severity Index, Hospital Anxiety and Depression Scale, and Acceptance Action Questionnaire‐II. Linear regression and multinomial logistic regression models were used. Sex, education, occupation, marital status, anxiety, depression and psychological inflexibility were possible predictors or factors associated with the severity of insomnia. Multivariate linear regression analysis demonstrated that sex (훽 = 0.88; t = 2.80; p = 0.005), depression (훽 = 0.41; t = 10.7; p < 0.001), anxiety (훽 = 0.58; t = 14.1; p < 0.001) and psychological inflexibility (훽 = 0.09; t = 5.07; p < 0.001) were predictors of insomnia. The results of the multinomial logistic regression demonstrate that, in comparison to the absence of insomnia, insomnia at all levels (mild, moderate and severe) was associated with sex, anxiety and depression. Psychological inflexibility was only associated with severe insomnia (odds ratio = 1.04). These findings are important from a public health perspective because behavioural strategies designed to treat insomnia with a focus on psychological flexibility are low‐cost and may help improve sleep quality in adults with insomnia, which also influences mental health. [ABSTRACT FROM AUTHOR]

Hierarchical psychopathology contributes to providing a broader picture of the links between emerging personality structures such as the DSM‐5/ICD‐11 trait models and clinical disorders. The present study aimed to predict the specific and general clinical symptoms by the less studied constructs of the ICD‐11 model (negative affectivity, detachment, dissociality, disinhibition, and anankastia). Data from 642 young adults from Iran (63% female, 18–34 years) were collected by three mental symptom scales and the Personality Inventory for DSM‐5 (PID‐5), which was recently used to harmonize the constructs of the DSM‐5 and ICD‐11 trait models. Multiple linear regressions showed that the ICD‐11 model significantly predicted both the specific clinical symptoms (ranging from R2 = 0.15 to 0.40) and the general factor of clinical symptoms extracted by exploratory factor analysis (R2 = 0.40, all p < 0.001). Negative affectivity was the strongest construct correlated with both the specific symptoms (ranging from β = 0.36 to 0.69) and the general symptom factor (β = 0.59, all p < 0.001). Because the ICD‐11 trait model is a practical structure related to the clinical psychopathology in young adults, screening for maladaptive traits can help clinicians in case formulation for diagnosis and treatment. [ABSTRACT FROM AUTHOR]

This study investigated the relationship between five subconscious psychological factors and marital quality with data collected from 799 married adults in Iran. Participants completed two questionnaires assessing their subconscious factors and the quality of their marriages; questionnaires were based on the Imago theory and developed specifically for this study. Analysis of the data revealed a negative relationship between all five factors and quality of one's marital life. The presence of these factors could lower the quality of marital life. Additionally, among all five factors, defenses had the largest influence on overall marital quality. Therefore, subconscious factors significantly impact marriage quality. [ABSTRACT FROM AUTHOR]

Background: Psychopathology research mainly focused on the cross-sectional and longitudinal associations between personality and psychiatric disorders without considering the moment-to-moment dynamics of personality in response to environmental situations. The present study aimed to both cluster a young sample according to three mixed clinical conditions (poor sleep quality, depression, and somatization) and to predict the derived clusters by maladaptive personality traits and sex differences using a deep machine learning approach. Methods: A sample of 839 adults aged 18-40 years (64% female) from the west of Iran were clustered according to the mixed clinical conditions using the cluster analysis techniques. An Artificial Neural Network (ANN) modeling is used to predict the derived clusters by maladaptive personality traits and biological gender. A receiver operating characteristic (ROC) curve was used to identify independent variables with high sensitivity specific to the derived clusters. Results: The cluster analysis techniques suggested a fully stable and acceptable four-cluster solution for Depressed Poor Sleepers, Nonclinical Good Sleepers, Subclinical Poor Sleepers, and Clinical Poor Sleepers. The ANN model led to the identification of one hidden layer with two hidden units. The results of Area under the ROC Curve were relatively to completely acceptable, ranging from.726 to.855. Anhedonia, perceptual dysregulation, depressivity, anxiousness, and unusual beliefs are the most valuable traits with importance higher than 70%. Conclusion: The machine learning approach can be well used to predict mixed clinical conditions by maladaptive personality traits. Future research can test the complexity of normal personality traits connected to mixed clinical conditions. [ABSTRACT FROM AUTHOR]

Background: Scientific evidence and everyday experience show that sleep disturbances and self-regulation as a proxy of stress reactivity are linked. Particular personality traits such as neuroticism, internalizing and externalizing problems are also associated with sleep disturbances. Here, we combined self-regulation and personality traits and associated these variables with subjective sleep disturbances. Methods: A total of 846 adults (mean age: 33.7 years; 78.7% females) completed questionnaires covering sleep disturbances, self-regulation and personality traits. Results: Higher scores for sleep disturbances were associated with higher scores for externalization, internalization, and instability and with lower scores for stability (all trait variables) and with poorer self-regulation (state variable). The regression model showed that higher scores for externalization and internalization (traits), and lower scores for self-regulation (state) predicted higher scores for sleep disturbance. Next, self-regulation had both a direct effect on sleep disturbance, and an indirect effect via personality traits. Conclusions: Sleep disturbances were related to both state (i.e., self-regulation) and trait (e.g., internalization and instability) dimensions. The current data analysis leapfrogs the state–trait dichotomy discussion and reconciles the state-and-trait approach in the prediction of poor sleep, though self-regulation appeared to have both direct and indirect effects on sleep disturbances. [ABSTRACT FROM AUTHOR]

Background: The cut points of psychological tools to diagnose clinical conditions are not universal and depend on the region and prevalence of the disorder. Thus, we aimed to identify the cutoff points of the Persian original version of the personality inventory for DSM‐5 (PID‐5; 220 items) that would optimally distinguish nonclinical from clinical groups. Methods: Both nonclinical (N = 634, 73% female, 34.0 ± 10.8 years) and clinical (N = 454, 29% female, 29.5 ± 7.4 years) samples from the West of Iran participated in the study. Data were analyzed using receiver operating characteristic (ROC) and Youden's index was used to determine the cutoff scores across the PID‐5 domains and facets. The means and standard deviations of both the clinical male and female were compared with the nonclinical group using Cohen's d and independent t‐tests. Results: All the PID‐5 algorithms and facets significantly distinguished clinical from nonclinical samples with some unique findings for male and female samples. The mean score of all the PID‐5 algorithms and facets in the clinical male and female samples were respectively 1.0−2.0 SD and 0.5−1.0 SD above the mean for the nonclinical counterparts. A score higher than 1.5 on ranging from 0 to 3 in each domain or facet indicated clinical status. Conclusion: Raw cutting scores throughout the PID‐5 algorithms can be well used to diagnose any pathology of personality and the severity of the disorder in clinical patients. The cut scores provide a useful tool for the clinical use of the original version of PID‐5 in Iran. [ABSTRACT FROM AUTHOR]

Background: While there is sufficient evidence that children and adolescents who stutter reported more impaired sleep compared to children and adolescents who did not stutter, findings among adults who stutter (AWS) were scarce. Furthermore, stuttering is associated with issues related to verbal communication in a social context. As such, it was conceivable that AWS reported higher scores for social anxiety, compared to adults who do not stutter (AWNS). In the present study, we tested whether AWS reported higher sleep complaints compared to AWNS. We further tested whether scores for social anxiety and stuttering independently predicted sleep disturbances. Methods: A total of 110 AWS (mean age; 28.25 years, 27.30% females) and 162 AWNS (mean age; 29.40 years, 51.20% females) completed a series of self-rating questionnaires covering sociodemographic information, sleep disturbances and social anxiety. Adults with stuttering further completed a questionnaire on stuttering. Results: Compared to AWNS, AWS reported a shorter sleep duration, a lower sleep efficiency, higher scores for drug use in terms of sleep-promoting medications (significant p-values and medium effect sizes), and an overall higher PSQI score (significant p-values and large effect size), when controlling for age and social anxiety. Next, while p-values were always significant for subjective sleep quality, sleep disturbances, and daytime functioning, when controlling for age and social anxiety, their effect sizes were trivial or small. For sleep latency, the p-value was not significant and the effect size was trivial. Among AWS, higher scores for stuttering and older age, but not social anxiety, predicted higher sleep disturbances. The association between higher sleep disturbances and higher stuttering severity was greatest among those AWS with highest scores for social anxiety. Conclusions: When compared to AWNS, AWS self-reported higher sleep disturbances, which were associated with older age, and higher scores for stuttering severity, but not with social anxiety. Adults who stutter might be routinely asked for their sleep quality. [ABSTRACT FROM AUTHOR]

Objective/Background: Previous studies suggested that sleep problems were related to non-suicidal self-injury. The current systematic review investigated more thoroughly this relationship. Methods: PubMED and Embase databases were searched. The keywords were "self-injury" OR "self-harm" OR "non-suicidal self-injury" OR "self-injurious behavior" OR "self-destructive behavior" OR "self-mutilation" AND "sleep problem" OR "sleep disturbance" OR insomnia OR nightmare OR "poor sleep quality" or "sleep disorders." A total of 16 studies were included in the present review. Results: The pattern of results indicated that sleep problems such as short sleep duration, sleep disturbances, and poor sleep quality were associated with non-suicidal self-injury. Additionally, emotional dysregulation, depression, and post-traumatic stress disorder appeared to mediate this relationship. Above all adolescents and young adults with sleep disruptions were at higher risk of non-suicidal self-injury. Conclusions:g Interventions to improve sleep quality and sleep duration might concomitantly decrease the risk of non-suicidal self-injury. [ABSTRACT FROM AUTHOR]

Objective: Given the need for further studies on health-promoting behaviors, the present study aimed to investigate the antecedents of controlling risk factors for cardiovascular diseases (CVDs) according to the affective and emotional composite temperament (AFECT) model and the mediating role of self-regulation. Materials and Methods: The samples of this cross-sectional study included 776 people resident in Kermanshah in Western Iran in 2019. The participants were selected using a cluster sampling method. Data were collected using the controlling the risk factors for cardiovascular disease questionnaire (CRC), the short form self-regulation questionnaire, and the AFECT scale. The structural equation modeling was used to analyze the data. Results: The analysis results indicated that there was a significant correlation between dimensions of AFECT and self-regulation with the control of risk factors for CVDs. The results also indicated that the dimensions of AFECT did not directly affect the control of risk factors for CVDs, but the mediating role of self-regulation was confirmed in this regard. Conclusion: According to the results, the dimensions of AFECT could affect the control of risk factors for CVDs through self-regulation; hence, the role of self-regulation in controlling CVDs should be taken into account. [ABSTRACT FROM AUTHOR]

Background and Aims: The objectives of the study were (i) cluster analysis of risky behaviors; (ii) determining correlates; and (iii) comparing clusters with regard to the attitude toward mental health help seeking. Methods: The current cross-sectional study is a part of the first phase of the Persian Youth Cohort. From October 2014 to January 2017, 2990 individuals from Ravansar City in western Iran completed structured interviews and national and international standard questionnaires. The obtained data were analyzed using two-stage cluster analysis, multinomial logistic regression analysis, and Chi-square test. Results: This model provided three distinct clusters: (i) low-risk group with mild distress, (ii) high-risk group with high distress, and (iii) violent group with medium distress. Some sociodemographic and lifetime psychiatric disorders were the correlates of unhealthy clusters (P < 0.05). Compared to the reference cluster, a higher number of members in unhealthy clusters were suffering from medium to severe disability. Nevertheless, the participants in these clusters were less inclined to mental health help seeking. Conclusions: More than half of the youth were suffering from suicidal and violent behaviors. Since high-risk participants are less inclined to mental health help seeking, the health policymakers can successfully utilize the results in planning general health programs. [ABSTRACT FROM AUTHOR]