The addictive use of video games is recognized as a problem with clinical relevance and is included in international diagnostic manuals and classifications of diseases. The association between “Internet addiction” and mental health has been well documented across a range of investigations. However, a major drawback of these studies is that no controls have been placed on the type of Internet use investigated. The aim of this study is to review systematically the current literature in order to explore the association between Internet Gaming Disorder (IGD) and psychopathology. An electronic literature search was conducted using PubMed, PsychINFO, ScienceDirect, Web of Science and Google Scholar (r.n. CRD42018082398). The effect sizes for the observed correlations were identified or computed. Twenty-four articles met the eligibility criteria. The studies included comprised 21 cross-sectional and three prospective designs. Most of the research was conducted in Europe. The significant correlations reported comprised: 92% between IGD and anxiety, 89% with depression, 85% with symptoms of attention deficit hyperactivity disorder (ADHD), and 75% with social phobia/anxiety and obsessive-compulsive symptoms. Most of the studies reported higher rates of IGD in males. The lack of longitudinal studies and the contradictory results obtained prevent detection of the directionality of the associations and, furthermore, show the complex relationship between both phenomena.

In recent years, the evidence regarding Internet Gaming Disorder (IGD) suggests that some personality traits are important risk factors for developing this problem. The heterogeneity involved in problematic online gaming and differences found in the literature regarding the comorbid psychopathology associated with the problem could be explained through different types of gamers. Clustering analysis can allow organization of a collection of personality traits into clusters based on similarity. The objectives of this study were: (1) to obtain an empirical classification of IGD patients according to personality variables and (2) to describe the resultant groups in terms of clinical and sociodemographic variables. The sample included 66 IGD adolescent patients who were consecutive referrals at a mental health center in Barcelona, Spain. A Gaussian mixture model cluster analysis was used in order to classify the subjects based on their personality. Two clusters based on personality traits were detected: type I “higher comorbid symptoms” (n = 24), and type II “lower comorbid symptoms” (n = 42). The type I included higher scores in introversive, inhibited, doleful, unruly, forceful, oppositional, self-demeaning and borderline tendency traits, and lower scores in histrionic, egotistic and conforming traits. The type I obtained higher scores on all the Symptom Check List-90 items-Revised, all the State-Trait Anxiety Index scales, and on the DSM-5 IGD criteria. Differences in personality can be useful in determining clusters with different types of dysfunctionality.

Gaming Disorder (GD) is characterized by a pattern of persistent and uncontrolled gaming behavior that causes a marked impairment in important areas of functioning. The evolution of the worldwide incidence of this disorder warrants further studies focused on examining the existence of different subtypes within clinical samples, in order to tailor treatment. This study explored the existence of different profiles of patients seeking treatment for GD through a data-driven approach. The sample included n = 107 patients receiving treatment for GD (92% men and 8% women) ranging between 14 and 60 years old (mean age = 24.1, SD = 10). A two-step clustering analysis approach explored the existence of different underlying GD profiles based on a broad set of indicators, including sociodemographic features, clinical course of the condition (e.g., onset or evolution), psychopathological symptoms, and personality traits. Two GD profiles emerged. The first cluster grouped together patients who presented with a lower psychological impact (n = 72, 66.1%), whereas the second cluster comprised patients with a higher psychological impact (n = 35, 32.7%). Cluster comparisons revealed that those patients presenting the higher impact were older, with a later onset of pathological gaming patterns, and more pronounced psychopathological symptoms and dysfunctional personality profiles. GD severity is influenced by specific demographic, clinical, and psychopathological factors. The identification of two separate profiles provides empirical evidence that contributes to the conceptualization of this disorder, as well as to the development of reliable and valid screening tools and effective intervention plans focused on the precise characteristics of the treatment-seeking patients.

Background and Aims: Compulsive sexual behavior (CSB) is characterized by a persistent pattern of failure to control sexual impulses, resulting in repetitive sexual behavior over a prolonged period that causes marked discomfort in personal, family, social, school, work or in other functional areas. The evolution of the worldwide incidence of this disorder warrants further studies focused on examining the characteristics of the affected people. The purpose of this study was to compare online compulsive sexual behavior (when the problematic sexual practices were online) and non-online compulsive sexual behavior (when the problematic sexual practices were in-person) patients (OCSB and non-OCSB, respectively), and healthy controls in terms of sexual behavior, sociodemographic variables and psychopathology and personality characteristics.Method: A sample of 80 CSB male patients consecutively admitted to our Behavioral Addictions Unit and 25 healthy male controls, participated in the study. The CSB group was comprised by 36 online CSB patients (mean age 42.25, SD: 10.0) and 44 non-online CSB patients (mean age 43.5, SD: 11.9). Scores on the Sexual Compulsivity Scale, Temperament and Character Inventory-Revised, Symptom CheckList-90 Items-Revised, State-Trait Anxiety Index, and additional demographic, clinical, and social/family variables related to sexual behaviors between the three groups were compared.Results: When compared with healthy controls, both clinical groups showed higher psychopathology in all measures as well as higher harm avoidance and self-transcendence and lower self-directness and cooperativeness. When comparing OCSB and non-OCSB patients, results showed that non-OCSB patients exhibited higher prevalence of sexually transmitted diseases, higher percentage of homosexual and bisexual orientation and higher scores in anxiety and in sexual impulse control failure.Conclusion: Both online and non-online CSB patients may experience a variety of comorbid psychological and medical problems. Patients with non-OCSB may suffer more consequences that are negative. Therefore, these results should be considered when designing the most convenient therapeutic approach. Whether sexual orientation plays a role in treatment needs and treatment response in CSB, should be further explored in future studies.