To date, a number of epidemiological studies estimated the prevalence rates of Pathological Gambling. Estimates are varying according to methodological and regional characteristics. Stucki and Rihs-Middel summarized 33 prevalence studies in a review (Stucki and Rihs-Middel 2007). Restricted to 12-month prevalence, the review presented weighted mean prevalence rates from 0.8 % to 1.8 %, depending on measuring tools. Prevalence estimates in Europe were lower (0.2–0.8 %) than in US-American studies (0.5–3.5 %). This is in the same range as a recent epidemiological survey in Germany, the “Pathological Gambling and Epidemiology”-study (PAGE) with 15,023 respondents which found 12-month prevalence rates of 0.3 % and lifetime prevalence to be 0.6 % with increased rates among males, younger age groups, and individuals with migration background (Meyer et al. 2014).

Estimates on Internet Addiction or IGD have to be regarded with caution because of various diagnostic assessment instruments and diagnostic thresholds. As a consequence, prevalence estimations differ widely. One paper found prevalence rates between 1 and 14 % (Tao et al. 2010). A systematic review of problematic Internet use of studies on US-youth ranged from 0 to 26 % (Moreno et al. 2011). Sample selection bias is very likely to be a major cause of divergent prevalence estimates. One pitfall is that most studies come from convenience samples recruited via online solicitations or in sub-populations such as students. In these studies, probability of study inclusion was obviously likely to be confounded with the problem behaviour to be measured and such approaches tend to lead to overestimation. Few studies are representative for the population under study and few data are general population based. Studies focusing on excessive computer gaming found lower rates compared to those on the broader diagnosis of Internet Addiction. In addition, prevalence rates are higher in younger cohorts and as well in Asian countries. With respect to the general population, four studies on Internet Addiction have been published and finding rates ranging from 0.3 % (Aboujaoude et al. 2006) to 2.1 % (Müller et al. 2013).

In the absence of a consensus concerning criteria to define and tools to assess Internet Addiction, one study used a statistical approach by performing a latent class analysis in a large general population sample (Rumpf et al. 2014b). In the entire sample aged 14–64, 1 % was classified as having Internet Addiction. Percentages were higher in younger age groups with up to 4 % in participants aged 14–16. There were no overall gender differences while males reported Internet Gaming as main activity and females Social Networks. Unemployment and migration background were related to Internet Addiction.

Pathological gamblers are known to show high rates of co-morbid psychiatric disorders, similarly to individuals with substance use disorders (Crockford and el-Guebaly 1998). The worldwide largest representative study with data for Pathological Gambling, the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) also assessed Alcohol and Drug Use, Mood and Anxiety Disorders, and Personality Disorders (Petry et al. 2005). Of the participants with Pathological Gambling during lifetime, 73.2 % had also a lifetime Alcohol Use Disorder. Additionally, 49.6 % suffered from a Mood Disorder during lifetime, and 41.3 % had an Anxiety Disorder. An Antisocial Personality Disorder was diagnosed in 23.3 % of the participants. Furthermore, an Obsessive–Compulsive Personality disorder was diagnosed in 28.5 % of the pathological gamblers. In a 3-year-follow-up, 53.8 % of the population with Gambling Disorders had developed an incident Axis I disorder (Chou and Afifi 2011).

In another US study, the National Comorbidity Survey (NCS-R), Kessler and colleagues showed that 96.3 % of the pathological gamblers had also suffered of at least one Axis-I disorder during their lifetime (Kessler et al. 2008). The Odds Ratios (OR) were 3.7 for any Mood Disorder, 3.1 for any Anxiety Disorder, and 5.5 for any Substance Disorder.

The German PAGE-study also assessed co-morbid psychiatric disorders. Of the pathological gamblers, 85.1 % had any co-morbid psychiatric disorder (without tobacco dependence) during lifetime with the highest rates for Alcohol Use Disorders (61.7 %) and Mood Disorders (46.8 %), followed by Anxiety Disorders (38.3 %) (Bischof et al. 2013). Compared to a general population sample, pathological gamblers showed a 3.7 times higher risk for Alcohol Use Disorders, a 3.1 times higher risk for a Mood Disorder, and an OR of 3.8 for Anxiety Disorders.

Taken together and as confirmed by a systematic review by Lorains and colleagues, there is a significant psychiatric comorbidity in pathological gambling, with Substance Use Disorders to be the most prevalent, followed by Mood Disorders and Anxiety Disorders (Lorains et al. 2011).

Quite a number of studies have analysed psychiatric comorbidity of Internet Addiction/Internet Gaming Disorder. A systematic review identified 20 studies most of them coming from Asian countries. Of all studies, 75 % reported significant correlations of problematic Internet use with Depression, 57 % with Anxiety, 100 % of the studies with symptoms of ADHD, 60 % with obsessive-compulsive symptoms, and 66 % with hostility or aggression. None of the studies included in this review reported associations between problematic Internet use and Social Phobia (Carli et al. 2013). The weakest association was found for hostility/aggression and the strongest for depression while associations were higher among males.

Of special interest are studies with longitudinal study designs to analyse if specific characteristics in terms of comorbidity are risk factors for the development of Internet Addiction or other outcomes. One study conducted follow-ups of a sample of adolescents from ten junior high schools in Taiwan over a period of 2 years (Ko et al. 2009a). Aim was to evaluate if psychiatric comorbidities or personality characteristics predict the onset of Internet Addiction. Among those without this disorder at the baseline assessment but with Internet Addiction at follow-up, Depression, ADHD, Social Phobia, and hostility were found as predictors. Regardless of gender, ADHD and hostility were the strongest predictors. As a shortcoming it has to be mentioned that the assessment of the comorbid disorders were based on rather brief questionnaires instead of in-depth diagnostic interviews. Another study focused on gaming and followed-up school children in Singapore over a period of 24 months (Gentile et al. 2011). This study used a longitudinal latent class approach to identify distinct groups of participants who started, continued or stopped to be pathological Internet gamers within the follow-up period or who never had problematic gaming. Predictors of pathological gaming were lower social competence and empathy, poorer emotional regulation skills and greater impulsivity. Important to notice is that depression, anxiety, social phobia (as well as lower school performance) were found to be sequelae of the pathological gaming not precursors. This is very important because Internet Addiction or Internet Gaming Disorder is often regarded as a symptom of another (underlying) disorder. These data speak against this hypothesis. Although studies are rare, to date it can be summarized that psychiatric comorbidity may as well play a role in the development of Internet Addiction as well as being a consequence.

One German study has followed-up individuals from a large general population sample (Rumpf et al. 2014b) exploring signs of excessive internet use and psychiatric comorbidity. In those who fulfilled at least 5 DSM-5 criteria for Internet Gaming Disorder and who reported that gaming was their main activity in the Internet, high proportions of comorbid disorders were found: Substance Dependence 46.7 %, Mood Disorders 46.7 %, Anxiety Disorders 23.3 %, Cluster A personality disorder 4.%, Cluster B personality disorder 12.0 %, Cluster C personality disorder 24.0. Findings were comparable for other Internet activities showing that between 28 % and 33 % (depending on main activity) had at least one personality disorder.

A recently published Cochrane Collaboration meta-analysis assessing the efficacy of psychological interventions in the treatment of pathological gamblers (PG) reported a superiority of cognitive behavioural therapy over other psychological treatments (Cowlishaw et al. 2012). This is very similar to the treatment of chemical addictions (Magill and Ray 2009). However, in the case of PG given the small samples and the high variation of therapeutic procedures within the interventions, the reported therapy effects should be interpreted with caution.

Overall, patients with PG can be treated safely and effectively either within a psychiatric clinic (i.e. inpatient and day patient treatment) or on an outpatient basis. The treatment choice depends on the symptom severity and other comorbidities. The overall aim of the intervention is to motivate and support patients in the achievement of gambling abstinence as well as to help them in taking responsibility in managing their problems. The treatment of pathological gamblers generally involves group as well as individual settings. The key elements in the treatment of PG are:

The process of working on an individual explanatory model together with the patient improves the person’s understanding of his/her dysfunctional gambling behaviour. Furthermore, in doing so helps the patient to learn about neurobiological, genetic as well as social factors influencing and maintaining PG.

Often, patients exhibit a negative self-concept that becomes apparent in negative core beliefs such as being terrible and worthless. These beliefs in turn induce negative feelings and physical tension that maintain the vicious circle to use gambling as a coping strategy. Thus, gambling-associated triggers (situations, feelings, or gambling stimuli) activate the dopaminergic reward system in the brain and entail hedonistic feelings. This in turn leads to the ignorance of negative long-term consequences.

The issue that most patients are not aware of the variety of triggers inducing craving for gambling leads to a relapse in many cases. Therefore, the therapist assesses these underlying situational processes together with the patient, trying to underline the connections between the triggers (e.g. an interpersonal conflict with the spouse), the cognitive, emotional and behavioural reactions as well as the short-term and long-term consequences. This behavioural assessment, as the SORC model differentiates between S—Stimulus or antecedent conditions that trigger gambling (e.g. an interpersonal conflict with the spouse), O—Organismic variables related to the problematic behaviour (e.g. the patient is harm avoidant), R—(Responses): physical (e.g. tension in shoulders, increased heart frequency), emotional (e.g. feelings of anxiety, anger, sadness, anxiety to loose someone, craving), cognitive (e.g. thoughts of wanting to go out of this conflict) and behavioural (gambling) as well as C—Consequences of the problematic behaviour.