Explore the vast range of titles available.

A new understanding of substance abuse explores treatment issues based on subtypes of addicts, with a special focus on antisocial personality disorder and functional addiction. Professionals in the treatment of substance abuse have long recognized the dismal success rate in addressing this pervasive problem. A fresh view of addiction may offer long-sought answers. Intervention and treatment strategies can be made more effective, maintains veteran addiction educator and psychologist Gary L. Fisher, through identification of addict subtypes. That is the goal of Understanding Why Addicts Are Not All Alike: Recognizing the Types and How Their Differences Affect Intervention and Treatment. The book provides an in-depth, research-based analysis of three specific subtypes of substance abusers: addicts who fit the disease model, addicts with antisocial personality disorder, and functional addicts—those who lead otherwise successful lives. Particular attention is paid to the latter two groups, which have not been adequately studied previously. Characteristics of the three subtypes are illustrated through case studies that clearly demonstrate how subtype impacts prevention, intervention, and treatment. Most important, the book recommends practical intervention and treatment strategies that will enable concerned parties to identify—and help—each of these distinct groups.

This study was designed to investigate the revised and short version of the smartphone addiction scale and the proof of its validity in adolescents. In addition, it suggested cutting off the values by gender in order to determine smartphone addiction and elaborate the characteristics of smartphone usage in adolescents. A set of questionnaires were provided to a total of 540 selected participants from April to May of 2013. The participants consisted of 343 boys and 197 girls, and their average age was 14.5 years old. The content validity was performed on a selection of shortened items, while an internal-consistency test was conducted for the verification of its reliability. The concurrent validity was confirmed using SAS, SAPS and KS-scale. Receiver operating characteristics analysis was conducted to suggest cut-off. The 10 final questions were selected using content validity. The internal consistency and concurrent validity of SAS were verified with a Cronbach's alpha of 0.911. The SAS-SV was significantly correlated with the SAS, SAPS and KS-scale. The SAS-SV scores of gender (p<.001) and self-evaluation of smartphone addiction (p<.001) showed significant difference. The ROC analysis results showed an area under a curve (AUC) value of 0.963(0.888-1.000), a cut-off value of 31, sensitivity value of 0.867 and specificity value of 0.893 in boys while an AUC value of 0.947(0.887-1.000), a cut-off value of 33, sensitivity value of 0.875, and a specificity value of 0.886 in girls. The SAS-SV showed good reliability and validity for the assessment of smartphone addiction. The smartphone addiction scale short version, which was developed and validated in this study, could be used efficiently for the evaluation of smartphone addiction in community and research areas.

While considerable efforts have been made to understand the neurobiological basis of substance addiction, the potentially “addictive” qualities of repetitive behaviors, and whether such behaviors constitute “behavioral addictions,” is relatively neglected. It has been suggested that some conditions, such as gambling disorder, compulsive stealing, compulsive buying, compulsive sexual behavior, and problem Internet use, have phenomenological and neurobiological parallels with substance use disorders. This review considers how the issue of “behavioral addictions” has been handled by latest revisions of the Diagnostic and Statistical Manual of Mental Disorders (DSM) and the International Classification of Diseases (ICD), leading to somewhat divergent approaches. We also consider key areas for future research in order to address optimal diagnostic classification and treatments for such repetitive, debilitating behaviors.

This paper sets out to clarify the unique features of exercise addiction. It begins by examining how this addiction can be distinguished from compulsions and impulse control disorders both of which, like an addiction, involve excessive behavior that creates adverse effects. Assessment of exercise addiction also requires that clinicians be attuned to other forms of excessive behavior, especially eating disorders that can co-occur with exercise. Finally in an effort to clarify exercise addiction, this paper uses the four phases of addiction to examine the attributes of exercise that define it as a healthy habit distinct from an addiction. The paper ends with a discussion of the implications of these topics for effective assessment and treatment.

This review summarizes neurobiological and genetic findings in behavioural addictions, draws parallels with findings pertaining to substance use disorders, and offers suggestions for future research. Articles concerning brain function, neurotransmitter activity, and family history and (or) genetic findings for behavioural addictions involving gambling, Internet use, video game playing, shopping, kleptomania, and sexual activity were reviewed. Behavioural addictions involve dysfunction in several brain regions, particularly the frontal cortex and striatum. Findings from imaging studies incorporating cognitive tasks have arguably been more consistent than cue-induction studies. Early results suggest white and grey matter differences. Neurochemical findings suggest roles for dopaminergic and serotonergic systems, but results from clinical trials seem more equivocal. While limited, family history and genetic data support heritability for pathological gambling and that people with behavioural addictions are more likely to have a close family member with some form of psychopathology. Parallels exist between neurobiological and genetic and family history findings in substance and nonsubstance addictions, suggesting that compulsive engagement in these behaviours may constitute addictions. To date, findings are limited, particularly for shopping, kleptomania, and sexual behaviour. Genetic understandings are at an early stage. Future research directions are offered.

The ICD-11 was introduced in January 2022. In chapter 6, “Mental, behavioral and neurodevelopmental disorders” we find the section “Disorders due to substance use and addictive behaviors” (section 6C4). Changes from the ICD-10 in this section include broadening the categories of harmful use and dependence, including more types of substances, and the addition of more behavioral addictions (gaming disorder). These changes have been discussed and debated [1].

Substance addiction (or drug addiction) is a neuropsychiatric disorder characterized by a recurring desire to continue taking the drug despite harmful consequences. Non-substance addiction (or behavioral addiction) covers pathological gambling, food addiction, internet addiction, and mobile phone addiction. Their definition is similar to drug addiction but they differ from each other in specific domains. This review aims to provide a brief overview of past and current definitions of substance and non-substance addiction, and also touches on the topic of diagnosing drug addiction and non-drug addiction, ultimately aiming to further the understanding of the key concepts needed for a foundation to study the biological and psychological underpinnings of addiction disorders.

Background Internet gaming has gained widespread popularity in China, yet the classification of problematic gaming subtypes based on personality traits remains limited. This study aimed to employ latent profile analysis (LPA) to identify distinct groups of online gamers and compare key variables across these groups. Methods An online survey was conducted within 5593 internet gaming users, including a demographic questionnaire, queries on internet gaming usage, the Video Gaming Dependency Scale, and the Chinese Big Five Inventory-brief version. LPA was applied to identify distinct user groups, followed by an examination of associations between profile membership and auxiliary variables. Results LPA identified three gamer categories for problematic gaming: “high-risk” (64.78%), “medium-risk” (3.22%), and “low-risk” (32%). High-risk gamers allocated more time and financial resources to gaming for escapism and leisure purposes. The medium-risk group sought enjoyable experiences, exhibiting traits that were intermediate between the high- and low-risk groups. High-risk gamers demonstrated elevated levels of neuroticism, accompanied by lower scores in other Big Five personality traits. In contrast, medium-risk gamers scored low across all Big Five dimensions, while low-risk gamers achieved higher scores in all traits except neuroticism. Notably, the low-risk group reported forming the fewest new online friendships, despite sharing similar social motivations with the other groups. Conclusion Traits such as low neuroticism and high conscientiousness serve as protective factors against gaming addiction, while being unmarried or an only child provides additional safeguards. Conversely, increased time and financial investment in gaming activities are associated with a heightened risk of addiction. These findings are crucial for identifying high-risk gamers and informing the development of targeted interventions.

Despite the fact that more and more clinical case studies and research reports have been published on the increasing problem of Internet addiction, no generally accepted standardized tool is available to measure problematic Internet use or Internet addiction. The aim of our study was to create such a questionnaire. On the basis of earlier studies and our previous experience with Young’s (1998a) Internet Addiction Test, initially, we created a 30-item questionnaire, which was assessed together with other questions regarding participants’ Internet use. Data were collected online from 1,037 persons (54.1% of them male; mean age, 23.3 years; SD, 9.1). As a result of reliability analysis and factor analysis, we reduced the number of items to 18 and created the Problematic Internet Use Questionnaire (PIUQ) containing three subscales: obsession, neglect, and control disorder. Cronbach’s α of the PIUQ is .87 (Cronbach’s α of the subscales is .85, .74, and .76, respectively). The test-retest correlation of the PIUQ is .90. The PIUQ proved to be a reliable measurement for assessing the extent of problems caused by the “misuse” of the Internet; however, further analysis is needed. nt]mis|This research was supported by Grant KAB-KT-02-13 from the Ministry of Children, Youth, and Sport in Hungary.

Adverse effects on wellbeing associated to the excessive usage of video games have prompted the introduction of Internet Gaming Disorder (IGD) as provisional diagnosis by the American Psychiatric Association [APA] in 2013, and the formal classification of Gaming Disorder (GD) by the World Health Organisation [WHO] in 2019. Despite these milestones, ongoing debate surrounds the diagnostic validity and cross-cultural applicability of these classifications. Consequently, the aims of the present review involve to (i) comparably introduce the WHO and the APA suggested criteria related to disordered gaming, whilst taking into consideration the available psychometric evidence internationally; (ii) illustrate the suggested criteria’s broader strengths and weaknesses and; iii) identify areas of priority for further empirical research to contribute to the available knowledge in the field identify areas of priority for further empirical research to contribute to the available knowledge in the field, whilst concurrently synthesizing the available evidence through the lenses of the recently proposed Cyber-Developmental Framework. With the increasing prevalence of disordered gaming and screen-related addictive behaviours as significant mental health concerns globally, this review highlights the need for enhanced diagnostic precision and greater consistency in assessment methodologies across diverse community, clinical, and national populations.