The Diagnostic Criteria For Behavioral Addictions (Goodman 1990) Fixed

The idea that true addictions can exist even in the absence of psychotropic drugs (behavioral addictions) was popularized by Peele.[4] According to Peele, addicted individuals are dependent on a particular set of experiences, of which the reactions to a specific chemical substance is only one example.

A number of experts believe that behavioral addictions can be passive (e.g. television) or active (e.g. computer games), and usually contain inducing and reinforcing features which may contribute to the promotion of addictive tendencies.[16]

Based on Davis model, Urzack believed that people who suffer from behavioral addictions, were tired, depressed, lonely, bashful, shy, and usually have other types of addiction.[21] Young (1998) stated that individuals with behavioral addiction have certain symptoms and will undergo the same consequences as those with alcohol and drug addiction and other obsessive behaviors.[22]

From a neurobiological point of view, behavioral addictions that only indirectly affect the neurotransmitter systems of the brain, can serve as reinforcers comparable to pharmacological substances that directly affect these systems (e.g., dopaminergic system).[23,24] Indeed, recent findings support the assumption of common mechanisms that underlie the development and maintenance of both behavioral and substance-related addiction.[25]

From the psychological and psychiatric viewpoint, behavioral addictions include a collection of disorders, such as anxiety, depression, obsessive thoughts[31], withdrawal and isolationism, affective disorders, disturbances in social relationships, school problems such as educational failure and lack of interest in doing homework, occupational or interpersonal difficulties, isolation and negligence of friends and family or personal responsibilities, and mental or physical restlessness. In instances when the individual reduces or stops a specific behavior, excessive fatigue, lifestyle changes, significantly reduced physical activity, deprivation and changes in sleep patterns, impatience, sexual deviations, violence, eating disorder and withdrawal symptoms ensue.

Researchers describe excessive engagement in behaviors such as shopping, gambling, internet use, exercise, overeating, pornography, television viewing and sexual activity as behavioral or process addictions (Alavi et al. 20121), but the exact criteria that unite them are still highly debated.

Gambling disorder8 is the only officially-recognized behavioral addiction in the DSM-5 at present but the manual includes a proposed criteria for internet gaming disorder and suggests that more research is needed in order to classify gaming as an independent disorder.

Although behavioral addictions can be highly destructive, they are also very treatable. Cognitive-behavioral therapy (CBT) is one of the most effective addiction treatments because it focuses on changing thought patterns, which can help eliminate problem behaviors.

Background and aims: The use of mobile phones has become an integral part of everyday life. Young people in particular can be observed using their smartphones constantly, and they not only make or receive calls but also use different applications or just tap touch screens for several minutes at a time. The opportunities provided by smartphones are attractive, and the cumulative time of using smartphones per day is very high for many people, so the question arises whether we can really speak of a mobile phone addiction? In this study, our aim is to describe and analyze a possible case of smartphone addiction. Methods: We present the case of Anette, an 18-year-old girl, who is characterized by excessive smartphone use. We compare Anette's symptoms to Griffiths's conception of technological addictions, Goodman's criteria of behavioral addictions, and the DSM-5 criteria of gambling disorder. Results: Anette fulfills almost all the criteria of Griffiths, Goodman, and the DSM-5, and she spends about 8 hr in a day using her smartphone. Discussion: Anette's excessive mobile phone usage includes different types of addictive behaviors: making selfies and editing them for hours, watching movies, surfing on the Internet, and, above all, visiting social sites. The cumulative time of these activities results in a very high level of smartphone use. The device in her case is a tool that provides these activities for her whole day. Most of Anette's activities with a mobile phone are connected to community sites, so her main problem may be a community site addiction.

Our aim in this study is to describe and discuss a possible case of smartphone addiction. We try to fit the case to the criteria of behavioral addiction using Griffiths's (2000) and Goodman's (1990) conceptions and the DSM-5 criteria of gambling disorder (American Psychiatric Association, 2013).

An essential question is whether we can talk about mobile phone addiction in Anette's case or whether another diagnosis is more appropriate when describing her behavior. Anette fits to the addiction criteria conceptualized by Goodman (1990) in a format similar to that of DSM-III-R (American Psychiatric Association, 1987), as shown in Table 1. These criteria are general terms and not restricted by reference to a particular behavior; they are capable of determining whether a given behavioral syndrome (excessive smartphone use in this case) is an addictive disorder.

As we have seen previously, Anette's symptoms fit to Goodman's (1990) and Griffiths's (2000) criteria of behavioral addictions and also fit to the criteria of addiction as described in the DSM-5, which indicate that she may suffer from smartphone addiction. But before stating this, we must examine whether we could call Anette a mobile phone addict or whether she is addicted to an application or some function, which is provided by the cell phone (e.g., addicted to social networks or games). According to Anette, the average daily use is the following: at least 3-4 hr for social networking, 1.5 hr for surfing on the Internet, 1 hr for watching movies or series (in the evenings), 1 hr for games, 0.5 hr for listening to music, and 1 hr for taking and editing photos. The cumulative time of these activities results in a very high level of smartphone use. The most frequently used function (3-4 hr of social networking) can be called average among 18-year-olds; however, the 1-hr-long photo editing is also associated with social networking too, because she

Addiction is the term employed not only for excess consumption of substances, but also for problem behaviours like eating disorders, pathological gambling, computer addiction and pathological preoccupation with video games and sexual acts. No clear diagnostic criterion has been established with validity for behavioral addictions. Sexual addiction, including addiction to pornography is not included as a separate entity because of a lack of strong empirical evidence in this area. Different scales can be used for assessment of sexual addiction. Since there is an absence of established diagnostic criteria, the significance of validity of these scales is doubted. Several of the questions in these scales do not yield information about whether the diagnostic criteria are met or not. Pharmacotherapy, together with psychotherapy proves to have a better outcome in such patients as it helps to synthesize the role of developmental antecedents, reduce current anxiety, depression, guilt and to improve social adjustment.

Some forms of overeating show both behavioral and neurobiological similarities to substance use disorders. Accordingly, a possible addiction to food has been discussed for decades, and the debate has received increased scientific and public attention since the beginning of the twenty-first century. In 2009, the Yale Food Addiction Scale (YFAS) was developed in an attempt to provide a standardized self-report instrument for the assessment of food addiction based on the diagnostic criteria for substance dependence. Since then, the YFAS has been used in numerous studies and celebrates its fifth anniversary this year. This article presents an overview of the YFAS and its adaptations, which includes a detailed description of scoring instructions and a supplementary SPSS syntax. Furthermore, psychometric properties and correlates of the YFAS, as well as prevalence rates of food addiction diagnoses and symptoms in different populations, are reviewed. Finally, shortcomings of the scale and future directions for food addiction research and revisions of the YFAS are discussed.

With the objective of structuring the diagnostic criteria for sex dependence, Goodman (1990) proposes a methodological set similar to that presented in DSM III to characterize dependence disorder. According to the author, dependence has characteristics, such as:

Several studies have now followed the progress of very immature cohorts born in the 1980s and 1990s through to adolescence and adult life, and have sought to define the full spectrum of impairment, including psychiatric disorders. We place emphasis on population-based studies, particularly for cohorts born in the 1990s, because these reflect the most contemporaneous outcomes relevant to current public health concerns. In this article, we review clinical studies of outcomes in middle childhood and beyond and present an overview of behavioral and psychiatric morbidity in relation to neurodevelopmental correlates and early predictors of disorders in preterm populations.

Studies using diagnostic evaluations are required to provide definitive evidence of an increased prevalence of disorders in preterm/LBW populations. There is a relative paucity of such studies as psychiatric evaluations are costly and difficult to implement in large-scale investigations. Although the majority of those that exist have used questionnaires that yield symptom data corresponding with Diagnostic and Statistical Manual of Mental Disorders (DSM) diagnostic criteria (40), a small number have used more rigorous evaluations. Despite very different methodologies, these report similar prevalence estimates across a range of different populations (Table 1). 2b1af7f3a8