黑料网

Internet Addiction: A Review

Clinical Services, National Institute of Psychiatry “ Ramon de la Fuente Muniz”, Mexico City, Mexico

*Corresponding Author:

Josue Alberto Vasquez-Medina
Clinical Services
National Institute of Psychiatry “Ramón de la Fuente Muñiz”
Mexico City, Mexico
Tel: +52(55)41605343
E-mail: mdjvm@yahoo.com.mx

Received September 13, 2012; Accepted November 15, 2012; Published November 22, 2012

Citation: Pezoa-Jares RE, Espinoza-Luna IL, Vasquez-Medina JA (2012) Internet Addiction: A Review. J Addict Res Ther S6:004. doi:10.4172/2155-6105.S6-004

Copyright: © 2012 Pezoa-Jares RE, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Visit for more related articles at Journal of Addiction Research & Therapy

Abstract

Internet addiction (IA) is an increasingly prevalent clinical entity in adolescents and young adults, but can affect people of all ages. IA can lead to dysfunction in social, academic and work domains, and people affected by it usually share a comorbid psychiatric disorder. Although recognized for more than 15 years, IA continues to generate controversy on academic and clinical circles, and there has been no consensus regarding its terminology, classification and diagnosis. In the last years, considerable clinical and neurobiological research has been done on the subject, showing interesting findings. Treatment alternatives are available, although some have more evidence-based support than others. The following review attempts to describe available data on IA, thus hoping to create awareness in health professionals regarding this condition.

Keywords

Internet addiction; Neurobiology; Addiction; Internet; Social network; Internet use disorder; DSM-5

Introduction

The Internet has profoundly changed our everyday experience. Although initially conceptualized as a telecommunications standard to interconnect military computers worldwide [1], it has become an integral part of modern life. We use the Web to find information and perform many activities, although for a great majority of Internet users the primary purpose for going online is to connect with others. The Internet has become a part of our daily lives, and who we are determines how we use it [2].

There are several activities that can be performed on the Internet: surfing, e-mailing, downloading, social networking, blogging, navigating in virtual worlds, gaming, chatting, and others [3]. All of these can be used for work, leisure or interpersonal communication. Despite its inherent benefits, the Internet is not without its problems, especially when its use becomes excessive. Over the last decade, in parallel to the flourishing popularity of the Internet, the number of research studies addressing the addictive potential of the Internet has steadily increased. Regardless of this fact, there is still controversy whether addiction to the Internet is a real, unique problem or just a transient social phenomenon that all modern technologies have gone through [4].

In hope of creating awareness on the existence of this emerging phenomenon, this text attempts to give a comprehensive overview of Internet addiction (IA) as a clinical entity. It will address the conception and controversies of IA, along with a description of available data on epidemiology, classification, diagnosis and comorbidity. Also, recent research concerning neurobiological findings and treatment of subjects with IA will be reviewed. Given the rising popularity of Facebook and other social networks, a thought on social network addiction is made. Lastly, we give a reflection on policy and regulatory implications addressing the IA problem.

Conception of using the Internet as an ‘Addiction’

The introduction of affordable personal computers, the growth of Internet access and its rising popularity have led to concerns over its excessive use [5,6]. If performed in a maladaptive pattern and high frequency, Internet use could lead to psychological, family, academic or work dysfunction [7].

The first reports regarding excessive use of computers date back to the 1970s [8], and by the 1980s it was reported that computer games might have an addictive potential [9]. However, it was not until the 1990s that the Internet was considered as a tool that could lead to addiction. Griffiths [10] included Internet use as one of many behaviors that could lead to a ‘technological addiction’, defined as a non-chemical addiction involving human-machine interaction, which could be passive or active, with inducing and reinforcing features.

The first to suggest an addiction to the Internet, although ironically, was the New York psychiatrist Ivan Goldberg. In 1995, he elaborated a symptom list for what he called ‘Internet Addiction Disorder’, analogous to the criteria for substance dependence from the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) [11]. However, it was Kimberly Young who was the first person to investigate this phenomenon in a methodical manner, defining a person as Internet ‘dependent’ or ‘nondependent’ based on modified criteria for DSM-IV pathological gambling [12]. Her pioneering research has become a point of reference for subsequent investigations addressing the phenomenon of Internet addiction around the world [13-15].

Definition of ‘Addiction’

Addiction is a chronic and relapsing disease resulting from adaptive changes in brain structure and function, in which the social context where it has developed and expressed is critical [16]. Addiction entails complex biological and environmental interactions that have made treatment particularly difficult. Attempts to understand and treat addiction as a purely biological or a purely environmental problem have not been successful [17]. Therefore, a comprehensive definition of addiction should encompass both views. Griffiths postulates a ‘components model’, which defines addiction as the sum of related features: salience, mood modification, tolerance, withdrawal, conflict and relapse [18,19]. These components reflect both biological and psychosocial features of the addictive phenomena, and all of them must be present to consider any behavior as an addiction.

Controversies about the Term ‘Internet Addiction’

As with any emerging field of research, one of the problems encountered by studying the issue of IA relates to terminology. There is no consensus among the various investigators as to how to call this phenomenon, and different terms have been proposed: Internet dependency [11], Internet addiction [12] , pathological Internet use [20], problematic Internet use [21], compulsive computer use [22], virtual addiction [23], Internet use disorder [24].

When the concept of IA was first introduced, a debate emerged among clinicians and academics around the contention that only chemical substances which are ingested could be termed ‘addictive’ [25]. Traditionally, the dependence on exogenous drugs causing neuroadaptation of the brain reward circuitry has served as a primary definition of addiction. However, several behaviors (such as gambling, buying, or eating) are capable of producing short-term stimulation of the reward system, which in vulnerable individuals could lead to an addictive state [26,27]. Therefore, IA could be viewed as a “behavioral addiction”, which is the result of moving towards a definition of addiction based more on behavior [28]. It is our view that to keep using the term ‘Internet addiction’ is still valid, given that there is a great deal of literature using this term, and it has become popular both in academic circles and the media. For these reasons, we will continue to designate the phenomenon as ‘Internet addiction’ throughout the rest of this article.

Epidemiology

Prevalence data on IA are limited by methodological difficulties concerning diagnosis and heterogeneity of diagnostic instruments. Also, the different studies assessing prevalence must be considered in the context of the conceptual approach to the problem (i.e., substance use VS. impulse-control disorder). Studies that evaluate IA based on DSM-IV criteria for substance use found that prevalence ranged from 9.8% to 15.2% [29,30]. When using Young’s Diagnostic Questionnaire, modeled after DSM-IV criteria for pathologic gambling (an impulse control-disorder) prevalence estimates are 2%-37.9% [13,31]. Table 1 summarizes epidemiological data from several studies addressing IA [13,20,29-49]. All these studies are transversal in nature, so reported prevalences are for current-disorder only. Although different prevalence rates are reported across studies, there are consistent findings among them that permit some generalizations.

Table 1: Epidemiologic Data on the Prevalence of Internet Addiction.

Cultural and technological differences between countries play a major role in the prevalence of IA. This is reflected in the fact that the phenomenon has been most studied in Asia, whereby South Korea currently appears to be the worst affected country, [11,50] followed by China, where government appeals to promote a “healthy online culture” have led gaming companies to develop restrictions on number of hours per play to online gamers [50,51].

The focus of different studies has been on younger populations, although some have also taken into account adult populations [32,34,36]. This may reflect the view that this is primarily a disorder of children, adolescents and early adulthood. While the natural history of IA is unknown, there might be age-related differences. Brenner presented results from a survey of 563 Internet users who admitted problematic use, in which older users reported experiencing fewer problems than younger users [52]. More research is needed on the characteristics of IA across the lifespan.

Even though some authors have reported an increased prevalence of IA in females, [12,31] the great majority of studies show a male preponderance. It has been suggested that this gender distribution may be explained by the fact that males, especially adolescents and young adults, use the Internet more than females and are more likely to express interest in games, pornography and gambling, activities that have all been associated with IA [20].

Classification

Given the popularity of the Internet, detecting and diagnosing IA is often difficult, as its legitimate business and personal use often mask addictive behavior [53]. To date, IA is not formally included in any of the major psychiatric diagnostic systems, and its detractors claim that modern technologies have gone through a phase of reserved acceptance due to fears of negative effects, which fail to realize. Therefore, the technology itself is not problematic, except for some individual cases when personality profiles or particular life circumstances play a major role [4].

Regardless of the above mentioned view, research on IA has provided evidence that it is a real problem and that it can be conceptualized as an impulse control disorder, [54-56] an addictive disorder, [57-59] or both [60,61]. An adequate theoretical approach is important for assessment, diagnosis, and treatment of the problem.

Impulse-control disorders are characterized by repetitive behaviors and impaired inhibition of these behaviors. Additionally, there is usually a pattern of engaging in the abnormal behavior in spite of adverse consequences (i.e. criminal changes, impairment of normal functioning, etc.) [62]. Problematic Internet use has been described as more impulsive and ego-syntonic in nature, and individuals with this problem have described an increasing sense of tension or arousal before successfully logging onto the Internet, which was difficult or impossible to resist, and a relief of that tension that was often pleasurable as they logged on [54]. Furthermore, there is an association between measures of IA and disorders such as kleptomania, trichotillomania, intermittent explosive disorder, pyromania, and compulsive buying, all of them impulsive in nature [56]. These data support the inclusion of IA as an impulse control disorder.

On the other hand, several impulse-control disorders, such as pathological gambling and kleptomania, have similarities to substance addictions on its behavioral and biological components [63,64]. Diagnostic criteria for impulse control disorders overlap with those for substance dependence, [64] and there is growing evidence indicating that behavioral addictions resemble substance addictions in natural history, phenomenology, tolerance, comorbidity, genetics, neurobiological mechanisms and response to treatment [26]. If an addictive disorder perspective is considered, IA has the following four components: 1) Excessive use, which is often associated with a loss of a sense of time or a neglect of basic drives; 2) Withdrawal, including feelings of anger, tension, and/or depression, when the computer is inaccessible; 3) Tolerance, including the need for better computer equipment, more software, or more hours of use; and 4) Negative repercussions, including arguments, lying, poor achievement, social isolation, and fatigue. All of the above components are criteria for substance dependence [25]. Furthermore, recent neuroimaging findings have associated Internet overuse with neurobiological changes, [65] similar to those observed in addictions. Although much more research is needed, evidence shows that IA shares much more similarities with addictive disorders than with impulse control disorders.

Diagnosis

IA should be distinguished from normal computer use, the most characteristic symptom being excessive ‘non-essential’ time spent online. Internet addicts develop urges to use the Internet when offline and computer usage significantly preoccupies their time and thoughts. Subjects with IA repeatedly try to cut back, however they can feel anxious, irritable, or depressed when not online. A sense of tension or arousal may develop before logging on to the Internet, with a sense of relief obtained once successfully logged on. More important, the excessive Internet usage leads to significant impairment in work, school, family or social domains. There is not awareness that the disorder is problematic, consequently, only a few voluntarily seek treatment, being brought to the attention of mental health professionals by family or significant others.

A clinical interview should always be the primary method of assessment for IA. During the interview, a thorough history of the patient and a mental status exam should be completed. The psychiatric history of the patient should be carefully explored because many individuals could meet criteria for other psychiatric disorders, such as major depression, anxiety disorders or impulse control disorder. Information on biological, psychological and social factors should be reviewed to allow the mental health professional to understand how the Internet has impacted these domains. The interview should address the level of motivation to change, as well as the impact of the behavior on the patient’s life [6,66]. Also, it is important to bear in mind that organic disorders can manifest with features of IA, although other odd behaviors could be presented as well [67].

Some authors have suggested sets of criteria to diagnose IA. According to the 8-Item Diagnostic Questionnaire developed by Young, [12] an individual would be Internet dependent by fulfilling five or more criteria. Beard and Wolf [55] modified Young’s criteria, recommending that all of the first five criteria are required for diagnosis of IA and that at least one of the last three criteria are required in diagnosing IA. Shapira et al. [68] elaborated on criteria modeled after DSM-IV impulse control disorder, and emphasized that excessive Internet use does not occur during periods of hypomania or mania, based on their findings on elevated comorbidity with bipolar disorder or schizoaffective disorder and IA [54]. Based on clinical experience and previously published diagnostic criteria, Tao et al. [61] proposed seven items of symptom criteria, plus three additional criteria: 1) exclusion, by not accounting for psychotic disorders or bipolar I disorder; 2) clinically significant impairment; and 3) course, with duration of IA in excess of 3 months, with at least 6 hours of non-essential internet usage per day. The DSM-5 Substance-Related Disorders Work Group have proposed criteria which take into account withdrawal and tolerance, although they consider Internet gaming as the only activity with addictive potential [24].

There are various standardized instruments that have been developed for assessing IA, although none have shown adequate reliability and validity across countries [63,66]. The Internet Addiction Test (IAT) is by far the most frequently used. It contains Young’s original 8-item questionnaire along with 12 new items and uses simplified terminology. It was designed to assess which areas of an individual’s life might be affected by their excessive Internet use [69]. This instrument has been validated in different countries, and has been used in several epidemiologic studies (Table 1). Since the conception of the IAT, other assessment instruments have been developed, such as the Chen Internet Addiction Scale, [70] Compulsive Internet Use Scale, [71] Problematic Internet Use Questionnaire, [72] Generalized Problematic Internet Use Scale, [73] Internet-Related Addictive Behavior Inventory [52] and Pathological Internet Use Scale, [20] among others.

Comorbidity

It is not clear whether IA represents a manifestation of an underlying disorder, or is a discrete disease entity [63]. To account for the association between IA and psychiatric symptoms, four mechanisms can be considered: 1) Psychiatric symptoms may lead to the onset or persistence of IA; 2) IA may precipitate psychiatric symptoms; 3) IA and psychiatric symptoms may increase vulnerability to each other; 4) Shared risk factors (genetic or environmental) lead to the onset or persistence of psychiatric symptoms and IA [74].

Some personality traits have been associated with IA. In adolescents assessed with the Tridimensional Personality Questionnaire (TPQ), Ko et al. reported that high scores on novelty-seeking and low reward-dependence are predictors for IA and substance use experience, whereas high harm-avoidance predicts IA alone [75]. The same group demonstrated that Internet-addicted college students have lower reward-dependence and higher novelty-seeking [76]. High levels of neuroticism and low extraversion have also been associated with IA [77].

Te Wildt et al. [78] assessed adult subjects with IA using the Structured Clinical Interview for DSM-IV (SCID) and a variety of questionnaires. In their sample, 76% suffered from any depressive syndrome, 40% suffered major depressive disorder, 24% had a comorbid anxiety disorder, 8% had a history of substance abuse, 36% had a comorbid personality disorder, with personality types from cluster B dominant in 56% of cases. Compared to controls, the patient group also presented significantly higher levels of impulsivity and dissociation.

Bernardi and Pallanti [79] sought for psychiatric comorbidities in adults with IA evaluated with SCID-I and II, among other instruments. Clinical diagnoses included 14% attention deficit and hyperactivity disorder, 7% hypomania, 15% generalized anxiety disorder, 15% social anxiety disorder; 7% dysthymia, 7% obsessive compulsive personality disorder, 14% borderline personality disorder, and 7% avoidant personality disorder. Shapira et al. [54] noted that a significant percentage of patients with IA in a clinical setting had a bipolar spectrum disorder. This finding has been corroborated recently by Müller and Wölfling [80], suggesting these IA patients might represent a distinct subgroup.

A study by Ha et al. [81] evaluated Korean children and adolescents using semistructured interviews, the Kiddie-Sads-Present and Lifetime Version (K-SADS-PL) and the SCID. Children showed higher prevalence of ADHD or subthreshold ADHD, whereas adolescents showed more depressive symptoms. In another study with adolescents by the same authors [82], obsessive-compulsive and depressive symptoms correlated with IA. In adolescents, IA can be useful as a predictor variable for substance use experiences [58].

Neurobiologic Basis

From a biopsychosocial perspective, addiction is the result of an interaction between many factors including biological and/or genetic predisposition, psychological constitution (i.e. personality factors, unconscious motivations, attitudes, expectations and beliefs, etc.), social environment and the nature of the activity itself [19]. The integration of all these factors results in behaviors, which, if maladaptive and repetitive, have the potential to become an addiction (Figure 1).

Recently, several studies have started to address research on IA using neuroscientific methods. Molecular genetics have been introduced to the field by evaluating associations between IA and some polymorphisms. Also, there are studies evaluating electrophysiological parameters and different modalities of magnetic resonance imaging, such as voxel-based morphometry, diffusion tensor imaging and functional imaging.

Molecular Genetics

Although no twin studies have investigated the heritability of IA to date, it has been shown that another behavioral addiction, pathological gambling, presents heritability estimates close to 50% [83]. If IA is considered a behavioral addiction, it is possible to imply a genetic susceptibility for the disorder.

Lee et al. [84] examined the association between excessive Internet use, harm avoidance, and serotonin transporter gene expression. The promoter region of the serotonin transporter gen (5HTTLPR) plays a role in the regulation of serotonergic neurotransmission; the homozygous long allelic variant (L) is associated with higher concentrations of serotonin mRNA and with greater rate of reuptake than variants containing the short allelic variant (S). This study found the excessive Internet use group to have higher levels of harm avoidance and SS-5HTTLPR frequencies.

Figure 1:Biopsychosocial model of addiction.
From a Biopsychosocial perspective, addiction is the result of maladaptive behaviors which originate from an interaction between various biological, psychological and environmental factors

Another study assessed genetic polymorphisms of the dopaminergic system and temperament in adolescents with excessive Internet videogame play. Han et al. [85] tested for associations between reward-dependence scales and frequencies of 3 dopamine polymorphisms: Taq1A1 allele of the dopamine D2 receptor (DRD2 Taq1A1) and Val158Met in the Catecholamine-O Methyltransferase (COMT) genes. The COMT gene has two allelic forms: the valine variant (with high activity) and methionine variant (with low activity). The Taq1A1 allele has been associated with reduced DRD2 density in the striatum, as well as suppressed dopaminergic signaling in the caudate nucleus, thalamus, and hippocampus. In this study, the Taq1A1 allele, low activity COMT alleles and higher reward-dependence scores were significantly more prevalent in patients with excessive Internet videogame play, relative to a control group.

Montag et al. [86] studied the association between IA and the gene coding for the nicotinic acetylcholine receptor subunit alpha 4 (CHRNA4). It has been shown that CHRNA4 impacts dopaminergic neurotransmission, domains of cognition, attention and working memory. Furthermore, a specific CHRNA4 polymorphism (CC variant of rs1044396) has been associated with significantly higher trait anxiety and smoking. Results showed an association between subjects with occasional and frequent problems with the Internet and having the CC variant of the rs1044396 polymorphism.

Electrophysiology

Event-related potentials are a valuable measure for studying brain-behavior relationships. Reduced P300 amplitude and prolonged P300 latency have been consistently observed in patients suffering from substance dependence. In a study by Ge et al. [87] individuals with IA exhibited significantly longer P300 latencies and similar P300 amplitudes compared to control subjects. After three months of cognitive behavioral therapy, P300 latencies decreased significantly. These results suggest deficits in cognitive function in patients with IA and that psychological treatment may be effective.

Dong et al. [88] investigated response inhibition in subjects with IA by recording event-related brain potentials during a Go/NoGo task. The patient group exhibited a lower NoGo-N2 amplitude, higher NoGo-P3 amplitude and longer NoGo-P3 peak latency than a control group. This suggests that patients with IA have lower activation in the conflict detection stage, less efficiency in information processing and lower cognitive control. Another study by the same group assessed the executive control ability by recording event-related potentials during a color-word Stroop task. They found the IA group to have longer reaction time and more response errors in incongruent conditions compared with a control group, which reflects impaired executive control [89].

Neuroimaging

Functional magnetic resonance imaging (fMRI) studies have characterized functional changes in neuronal activity associated with IA. Right orbitofrontal cortex, right nucleus accumbens, bilateral anterior cingulate, medial frontal cortex, right dorsolateral prefrontal cortex, and right caudate nucleus have been found to be activated in subjects with Internet gaming addiction [90]. While performing a reality-simulated guessing task, Internet addicts show increased activation in orbitofrontal cortex in ‘gain’ trials and decreased anterior cingulate activation in ‘loss’ trials. The brains of adolescents with IA show reactions to disembodiment-related stimuli, which is evidenced by higher activation in the thalamus, bilateral precentral area, bilateral middle frontal area, right parahippocampal area, and areas near the right temporo-parietal junction and left temporo-parieto-occipital junction [91].

Fractional anisotropy (FA) is a diffusion tensor imaging (DTI) measure that is highly sensitive to microstructural changes. Studies assessing white matter changes in subjects with IA have been performed, showing higher FA (indicating greater white matter integrity) in the thalamus, left posterior cingulate cortex and left posterior limb of the internal capsule. Also, reduced FA values have been observed within the right parahippocampal gyrus, orbito-frontal white matter, corpus callosum, cingulum, inferior fronto-occipital fasciculus, and corona radiata, internal and external capsules [92-94].

Studies using voxel-based morphometry (VBM) have shown that Internet addicts have decreased gray matter volume in the bilateral dorsolateral prefrontal cortex, supplementary motor area, orbitofrontal cortex, cerebellum, left anterior cingulate cortex, left posterior cingulate cortex, left insula, left lingual gyrus, both inferior temporal gyri, right middle occipital gyrus, and left inferior occipital gyrus, whereas an increase in volume has been observed in left thalamus gray matter [92,95,96].

The role of dopaminergic systems has been elucidated in some studies. Hou et al. [97] used single photon emission tomography (SPECT) with the radiotracer ^99mTc-TRODAT-1 to investigate striatal presynaptic dopamine transporter (DAT) density. In IA subjects, DAT expression level of striatum was significantly decreased. Kim et al. [98] assessed D2 dopamine receptor availability in Internet addicts using positron emission tomography (PET) and a radioligand, [11C] raclopride. Subjects had reduced dopamine D2 receptor availability in the bilateral caudate and left putamen compared with controls, and the degree of dopamine receptor availability was inversely correlated with the severity of IA.

Treatment

Despite its increasing importance as a major health problem, there is still a lack of evidence-based interventions for IA. However, both pharmacological and non-pharmacological approaches have been studied and recommended. There should be caution when diagnosing IA, but those who are correctly diagnosed should receive the benefit of whatever therapy is available and suitable for every individual case [25].

Pharmacological Treatment

There are reports evaluating the efficacy of Escitalopram [99], Naltrexone [57] or the combination of Citalopram/Quetiapine [100] in treating internet addicts. However, some clinical trials have been undertaken that evaluate the utility of pharmacological strategies in the treatment of IA.

One small trial assessed the efficacy and tolerability of Escitalopram in subjects with IA [101]. Escitalopram was started at 10 mg/day, then increased and maintained at 20 mg/day for 10 weeks, after which completers were randomly assigned to placebo or Escitalopram for 9 additional weeks. At the end of the 10^th week of open-label Escitalopram, primary measures of Internet use showed a statistically significant decrease, compared with baseline. However, at the end of the 9-weeks placebo-controlled phase, there were not differences in outcomes between the Escitalopram or placebo groups.

Another trial evaluated treatment with Bupropion to decrease craving for Internet game play [102]. Subjects with Internet videogame addiction were recruited, and were compared with healthy controls who had experience with, but were not addicted to, videogames. After a 6 week period of Bupropion sustained-release treatment, craving for Internet video game play and total game play time were decreased in the addiction group.

Some studies have evaluated treatment in subjects with IA and a comorbid psychiatric disorder. Bupropion 150-300 mg/day was evaluated in a prospective, randomized, double-blind clinical trial, in a male sample with comorbid online game addiction and major depressive disorder [103]. All participants were randomly assigned to Bupropion+education for internet use, or placebo+education for internet use. The study was designed as a 12-week prospective trial, including eight weeks of double-blind (bupropion or placebo) active treatment and a four-week post-treatment follow-up period. Results showed improvements in severity of online game addiction that was associated with parallel changes in depressive symptoms during bupropion treatment.

One trial examined the role of Methylphenidate treatment in individuals with comorbid ADHD and Internet videogame addiction [104]. Children diagnosed with ADHD who were Internet videogame players were treated with Methylphenidate 18–54 mg/day for 8 weeks. Some of the subjects scored high on measures of IA. Results showed that, after the treatment period, IA scores and usage times were significantly reduced. The changes in the IA scores were positively correlated with the changes in total and inattention scores.

Non-pharmacological Treatment

Cognitive behavioral therapy (CBT) has been modified to treat IA and has been shown to be effective. CBT helps Internet addicts recognize maladaptive cognitions, modify and reconstruct adaptive cognitions, and return to reality. Patients are taught to monitor their thoughts and identify those that trigger addictive feelings and actions while they learn new coping skills and ways to prevent a relapse [53].

Few rigorous studies have examined CBT alone for the treatment of IA. Young assessed the utility of CBT on reaching targeted goals associated with IA [105]. Goals were assessed over the course of 12 sessions and at 6 months after treatment termination. During treatment and for up to 6 months, patients improved their motivation to quit the problematic behavior, online time management, social isolation, sexual dysfunction, and abstinence from problematic online applications. Du et al. [106] investigated whether a multimodal school-based CBT is effective for adolescents after 6 months of its delivery, compared with no intervention at all. This involved 1) group CBT for patients; 2) psychoeducation for teachers; 3) group cognitive-behavioral parent training. The multimodal school-based intervention led to an improved emotional state, regulation ability, behavioral and self-management style in adolescents identified with IA. Currently there is an ongoing study evaluating the efficacy of a manualized short-term treatment program for Internet and computer game addiction; it is based on CBT, combining group with individual therapy [107].

Based on a psychotherapeutic approach used to treat substance use disorders, Orzack et al. [108] and Shek et al. [109] integrated the elements of motivational interviewing into their studies of the treatment of IA. Motivational interviewing is a directive, patient-centered counseling style for eliciting behavior change that is accomplished by helping patients explore and resolve ambivalence. It assumes that the responsibility and capability for change are within the patients, so it does not provide patients with solutions or problem solving until they have decided to change [110].

Another approach for treating IA has been Reality Therapy (RT), which is based on choice theory and control theory, which assumes that people are responsible for their lives and for what they do, feel, and think. If one is addicted to using the Internet, this is because of choice. RT states that the key to changing behavior lies in choosing to change our acting and thinking [110]. Jong-Un Kim evaluated the effect of a RT group counseling program on IA level and self-esteem in a sample of university students [111]. The treatment group received RT in 2 sessions per week for 5 consecutive weeks, whereas participants in a control group received no treatment. After 10 sessions, the treatment group showed significant decreases in IA measures, as well as increased self-esteem, compared with the control group.

Usually, any one of the above-mentioned approaches is not applied in isolation, but is instead combined with different schools of psychotherapy. Elements of groups and systemic family therapy have also been integrated into a multimodal approach in different studies. Therefore, multimodal psychotherapy includes various psychotherapeutic approaches, and it has been studied in different trials for the treatment of IA, showing improvement on Internet use measures, psychosomatic symptoms, quality of life, depressive symptoms, as well as increased functionality in family and social domains [110].

Social Network Addiction: An Emerging Problem?

Since its inception, the Internet has worked as a channel for communication in connected social networks. Regardless of the claims by Web 2.0 enthusiasts, who describe a paradigm shift that has resulted in the ‘social web’, the Internet has always been social. However, in the last years, certain Internet portals have emerged that are specifically designed to support and develop friendship, and whose overt purpose is to provide a context and appropriate tools for doing so [112].

Social network sites (SNS) are web-based services that allow individuals to 1) construct a public or semi-public profile; 2) articulate a list of other users with whom they share a connection; 3) view and traverse their list of connections and those made by others within the system. Since their introduction, SNS have attracted millions of users, many of whom have integrated these sites into their daily practices [113]. Among social networks, Facebook is by far the most popular. It has been estimated that Facebook will hit a billion active users by August 2012, which might be attributed to the growth of this portal in developing countries such as India and Brazil [114].  

Because of its popularity, there has been recent concern about potential abuse of social web. Research has shown that SNS use is positively associated with measures of IA [3]. It has been suggested that “Facebook addiction” or preferably “‘Social Networking Site addiction” could be another subcategory of the spectrum of Internet addiction disorders [115,116].

Very recently, Andreassen et al. [117] have developed a scale to measure Facebook addiction. It has been called Bergen Facebook Addiction Scale (BFAS). This scale contains 18 items, three for each of the six core features of addiction: salience, mood modification, tolerance, withdrawal, conflict, and relapse. The BFAS has acceptable psychometric properties in terms of internal consistency, factor structure and reliability. While very useful in the sense that researchers studying SNS currently have no psychometrically validated tools, it has been argued that clarification is needed on what SNS users are really addicted to. Although originally set up to facilitate communication, nowadays Facebook is a site on which people can do so much more, such as playing games, watching film or swapping photos [118].

According to Shaffer et al. ‘syndrome model of addiction’, [119] addictive phenomena should be understood as a syndrome with multiple expressions, that are the result of interacting biopsychosocial antecedents, different manifestations, and diverse consequences. As such, IA -and SNS addiction- could be viewed as different manifestation of the same underlying syndrome, with shared psychological and biological features. Recent studies suggest that there might be an association between SNS usage and substance use disorders, which could be explained with Shaffer’s model. In a sample of undergraduate students, Moreno et al. [120] found that subjects who chose to display references to intoxication or problem drinking on publicly available Facebook profiles were more likely to meet problem drinking criteria using the AUDIT score. Litt and Stock [121] have found that adolescents who perceive alcohol use as normative, evidenced by older peers’ Facebook profiles, are at higher risk for cognitions shown to predict alcohol use, in comparison to adolescents who do not see alcohol use portrayed as frequently on Facebook. Wilson et al. [122] sought to predict young adults’ use of SNS and addictive tendency toward the use of SNSs from their personality characteristics, evaluated by the NEO Five-Factor Personality Inventory. Their results showed that high scores on extroversion and low conscientiousness are associated with addictive tendencies to SNS. Although extraversion has been negatively correlated with measures of IA [123] and less consistently related to substance use and abuse, [124] low conscientiousness has been reported with diverse substance addictions [125]. An interesting study by Kanai et al. [126] sought to reveal brain regions associated with an individual’s online social network size by assessing gray matter variability with VBM. Their results showed that variation in the number of friends on Facebook strongly and significantly predicted gray matter volume in the right superior temporal sulcus, left middle temporal gyrus, entorhinal cortex and amygdala. Some of these structures are related with specific types of social cognition. Interestingly, impairments in social cognition have been observed in subjects with addiction, particularly alcoholism [127].

Unfortunately, the current literature on SNS addiction is scarce, and for the time being, we need to draw assumptions from indirect evidence. As technology continues to develop at a fast pace, and more people become acquainted with it (especially the young ones), it is expected that problems will become more prevalent. Clearly, more research is needed on this emerging field.

Policy Implications: Should the Internet Be Regulated?

In the last years, a point has been made for regulating the Internet in order to prevent people from becoming addicted to it. In Asian countries, two kinds of approaches have been proposed to address this problem (particularly excessive Internet gaming): a shutdown system or a fatigue system [128]. In the shutdown system, a government can force the stoppage of online traffic at a specific time, thereby preventing users from using a particular online tool. This approach has been used in Thailand and South Korea to counter Internet gaming. Under the fatigue system, online game users are only allowed to play several hours in a row and after that there is a penalty. This system has been implemented by the government of China.

Although well intentioned, and with the clear objective of protecting youth, the effectiveness and basic rights implications of these regulations are up to debate. Although government should not refrain from regulating the Internet at all, we believe that intervention on Internet freedom could create more problems than it tries to resolve. Furthermore, as already stated, any addiction is the result of an interaction between biological, psychological and environmental factors. We cannot assume that the Internet by itself creates addiction. Instead, IA might be a manifestation in people who, given their biopsychosocial antecedents, are addiction-prone individuals. Whichever effective policy to reduce rates of addiction cannot be focused on environmental issues only (either controlling Internet access/content or penalizing possession of drugs), but take an integral approach at the magnitude of the problem.

Even though, it should be noted that any attempt at regulation cannot be generalized to every nation or culture. Support for prohibition or regulation hinges on the relative importance different individuals and societies place on the magnitude of a given problem. Appropriate Internet regulatory policy will depend on the societal structure and prevailing technology of each country.

Conclusion

The study of IA is still in its beginnings; however, the last decade has seen an increased interest in the subject, with research pouring in data from different parts of the world. It is clear that IA leads to dysfunction in a range of life activities, such as time management, social relationships, work duties, and can even affect biological domains. Despite the growth in knowledge regarding IA, no consensus has been reached whether IA is a unique clinical entity or just the epiphenomenon of an underlying disorder. Furthermore, lack of conceptual clarity has hindered the development of adequate assessment instruments and, consequently, of diagnosis. However, research has shown that treatment alternatives are available, and exciting new findings on the neurobiology of IA will keep giving insights on the pathophysiology of this entity and, hopefully, allow the design of better therapeutic approaches. Although our current understanding of IA is not yet complete, it does not allow us to ignore the problem anymore. As technology continues to grow at its current speed, and new applications become available on the Web, failure to acknowledge IA will permit its silent and endemic spread, affecting millions of people, especially children and adolescents. Mental health professionals should be aware of the spectrum of IA, and work towards implementation of preventive, diagnostic and treatment strategies.

References

1. Glowniak J (1998)
2. Correa T, Hinsley AW, de Zúñiga HG (2010)
3. van Rooij AJ, Schoenmakers TM, van de Eijnden RJ, van de Mheen D (2010)
4. Hinic D (2011)
5. Song I, LaRose R, Eastin MS, Lin CA (2004)
6. Shaw M, Black DW (2008)
7. Cruzado Diaz L, Matos Retamozo L,Kendall Folmer R (2006)
8. Weizenbaum J (1976) Computer power and human reason : from judgment to calculation, W. H. Freeman, San Francisco.
9. Soper WB, Miller MJ (1983) Junk-time junkies: An emerging addiction among students. School Counselor 31: 40-43.
10. Griffiths M (1996)
11. teWildt BT (2011) Internet Dependency: Symptoms, Diagnosis and Therapy. In: Virtual Worlds and Criminality. In Cornelius, K. and Hermann, D. (eds). Springer Berlin Heidelberg, pp: 61-78.
12. Young KS (1998)
13. Johansson A, Götestam KG (2004)
14. Dowling NA, Quirk KL (2009)
15. Huang RL, Lu Z, Liu JJ, You YM, Pan ZQ, et al. (2009)
16. Leshner AI (1997)
17. Volkow ND, Fowler JS, Wang GJ (2003)
18. Griffiths M (1996)
19. Griffiths M (2005)
20. Morahan-Martin J, Schumacher P (2000)
21. Davis RA, Flett GL, Besser A (2002)
22. Black DW, Belsare G, Schlosser S (1999)
23. Greenfield DN (1999) Virtual addiction : help for netheads, cyberfreaks, and those who love them, New Harbinger, Oakland, Calif.
25. Chakraborty K, Basu D, Vijaya Kumar KG (2010)
26. Grant JE, Potenza MN, Weinstein A, Gorelick DA (2010)
27. Karim R, Chaudhri P (2012)
28. Holden C (2001)
29. Anderson KJ (2001)
30. Lavin MJ, Yuen CN, Weinman M, Kozak K (2004)
31. Leung L (2004)
32. Greenfield DN (1999)
33. Chou C, Hsiao MC (2000)
34. Kubey RW, Lavin MJ, Barrows JR (2001)
35. Lin SSJ, Tsai CC (2002)
36. Whang LS, Lee S, Chang G (2003)
37. Kaltiala-Heino R, Lintonen T, Rimpelä A (2004)
38. Yoo HJ, Cho SC, Ha J, Yune SK, Kim SJ, et al. (2004)
39. Niemz K, Griffiths M, Banyard P (2005)
40. Kim K, Ryu E, Chon MY, Yeun EJ, Choi SY, et al. (2006)
41. Aboujaoude E, Koran LM, Gamel N, Large MD, Serpe RT (2006)
42. Pallanti S, Bernardi S, Quercioli L (2006)
43. Jang KS, Hwang SY, Choi JY (2008)
44. Ghassemzadeh L, Shahraray M, Moradi A (2008)
45. Lam LT, Peng ZW, Mai JC, Jing J (2009)
46. Ghamari F, Mohammadbeigi A, Mohammadsalehi N, Hashiani AA (2011)
47. Durkee T, Kaess M, Carli V, Parzer P, Wasserman C, et al. (2012)
48. Poli R, Agrimi E (2012)
49. Shek DTL, Yu L (2012)
50. Block JJ (2008)
51. Lee M (2007)
52. Brenner V (1997)
53. Young K (2009)
54. Shapira NA, Goldsmith TD, Keck PE Jr, Khosla UM, McElroy SL (2000)
55. Beard KW, Wolf EM (2001)
56. Mazhari S (2012)
57. Bostwick JM, Bucci JA (2008)
58. Fisoun V, Floros G, Siomos K, Geroukalis D, Navridis K (2012)
59. Yen JY, Ko CH, Yen CF, Chen CS, Chen CC (2009)
60. Ko CH, Yen JY, Chen CC, Chen SH, Yen CF (2005)
61. Tao R, Huang X, Wang J, Zhang H, Zhang Y, et al. (2010)
62. Dell'Osso B, Altamura AC, Allen A, Marazziti D, Hollander E (2006)
63. Weinstein A, Lejoyeux M (2010)
64. Brewer JA, Potenza MN (2008)
65. Yuan K, Qin W, Liu Y, Tian J (2011)
66. Beard KW (2005)
67. Mendez MF, Shapira JS (2011)
68. Shapira NA, Lessig MC, Goldsmith TD, Szabo ST, Lazoritz M, et al. (2003)
69. Widyanto L, McMurran M (2004)
70. Yen CF, Ko CH, Yen JY, Chang YP, Cheng CP (2009)
71. Meerkerk GJ, Van Den Eijnden RJ, Vermulst AA, Garretsen HF (2009)
72. Pulido Rull M, Escoto de la Rosa R,Gutiérrez Valdovinos D (2011)
73. Caplan SE (2002)
74. Yen JY, Ko CH, Yen CF, Chen SH, Chung WL, et al. (2008)
75. Ko CH, Yen JY, Chen CC, Chen SH, Wu K, et al. (2006)
76. Ko CH, Hsiao S, Liu GC, Yen JY, Yang MJ, et al. (2010)
77. Hardie EA, Tee MY (2007) Excessive internet use: the role of personality, loneliness and social support networks in internet addiction.
78. te Wildt BT, Putzig I, Drews M, Lampen-Imkamp S, Zedler M, et al. (2010)
79. Bernardi S, Pallanti S (2009)
80. Muller KW, Wolfling K (2012) Clinical data on co-morbid bipolar symptoms in patients with internet addiction. Bipolar Disorders 14: 108-108.
81. Ha JH, Yoo HJ, Cho IH, Chin B, Shin D, et al. (2006)
82. Ha JH, Kim SY, Bae SC, Bae S, Kim H, et al. (2007)
83. Slutske WS, Zhu G, Meier MH, Martin NG (2010)
84. Lee YS, Han DH, Yang KC, Daniels MA, Na C, et al. (2008)
85. Han DH, Lee YS, Yang KC, Kim EY, Lyoo IK, et al. (2007)
86. Montag C, Kirsch P, Sauer C, Markett S, Reuter M (2012)
87. Ge L, Ge X, Xu Y, Zhang K, Zhao J, et al. (2011)
88. Dong G, Zhou H, Zhao X (2010)
89. Dong G, Zhou H, Zhao X (2011)
90. Ko CH, Liu GC, Hsiao S, Yen JY, Yang MJ, et al. (2009)
91. Dong G, Huang J, Du X (2011)
92. Yuan K, Qin W, Wang G, Zeng F, Zhao L, et al. (2011)
93. Dong G, DeVito E, Huang J, Du X (2012)
94. Lin F, Zhou Y, Du Y, Qin L, Zhao Z, et al. (2012)
95. Zhou Y, Lin FC, Du YS, Qin LD, Zhao ZM, et al. (2011)
96. Han DH, Lyoo IK, Renshaw PF (2012)
97. Hou H, Jia S, Hu S, Fan R, Sun W, et al. (2012)
98. Kim SH, Baik SH, Park CS, Kim SJ, Choi SW, et al. (2011)
99. Sattar P, Ramaswamy S (2004)
100. Atmaca M (2007)
101. Dell'Osso B, Hadley S, Allen A, Baker B, Chaplin WF, et al. (2008)
102. Han DH, Hwang JW, Renshaw PF (2010)
103. Han DH, Renshaw PF (2012)
104. Han DH, Lee YS, Na C, Ahn JY, Chung US, et al. (2009)
105. Young KS (2007)
106. Du YS, Jiang W, Vance A (2010)
107. Jäger S, Müller KW, Ruckes C, Wittig T, Batra A, et al. (2012)
108. Orzack MH, Voluse AC, Wolf D, Hennen J (2006)
109. Shek DT, Tang VM, Lo CY (2009)
110. Huang XQ, Li MC, Tao R (2010)
111. Jong-Un Kim (2008)
112. Merchant G (2011)
113. Boyd DM, Ellison NB (2007)
114. Lyons G (2012)
115. Karaiskos D, Tzavellas E, Balta G,Paparrigopoulos T (2010)
116. Kuss DJ, Griffiths MD (2011)
117. Andreassen CS, Torsheim T, Brunborg GS, Pallesen S (2012)
118. Griffiths MD (2012)
119. Shaffer HJ, LaPlante DA, LaBrie RA, Kidman RC, Donato AN, et al. (2004)
120. Moreno MA, Christakis DA, Egan KG, Brockman LN, Becker T (2012)
121. Litt DM, Stock ML (2011)
122. Wilson K, Fornasier S, White KM (2010)
123. Mottram AJ, Fleming MJ (2009)
124. Pelling EL, White KM (2009)
125. Terracciano A, Löckenhoff CE, Crum RM, Bienvenu OJ, Costa PT Jr (2008)
126. Kanai R, Bahrami B, Roylance R, Rees G (2012)
127. Uekermann J, Daum I (2008)
128. Park BW, Ahn JH (2010)