Me, myself and my computer: Internet Addiction.
“If addiction is judged by how long a dumb animal will sit pressing a lever to get a ‘fix’ of something, to its own detriment, then I would conclude that netnews is far more addictive than cocaine.”
Attributed to Robert Stämpfli.
Cyber Addiction: Defining the Concept
‘Discomgoogolation’, a neologistic term from the findings of a survey of British Internet users, is meant to refer to a distressing condition, characterised by stress and anxiety at not being able to access the Internet (Swaminath, 2008). The survey opined that 76% of Britons could not live without the Internet, with over half of the population using the web between one and four hours a day and 19% of people spending more time online than with their family in a week.
The Internet is an integral part of modern life and for the vast majority of Internet users its benefits far outweigh the adverse consequences secondary to excessive use (Murali & George, 2007). It is estimated that 76.8% of the developed world is using the Internet (ITU World Telecommunication/ICT Indicators database, 2013) and some people are becoming preoccupied with the Internet, are unable to control their use, and are jeopardising employment and relationships (Beard & Wolf, 2001). The concept of ‘Internet Addiction’ (IA) has been proposed as an explanation for uncontrollable, damaging use of this technology.
One of the first to use the term IA was Goldberg (1995), who specialises in treating people with difficult-to-manage mood disorders who fabricated and posted a list of Internet Addiction Disorder (IAD) symptoms as a critique of the restrictive approach of the DSM to identifying clinical disorders (Goldberg, 1995) Young (1998) was first to describe excessive and problematic Internet use as an addictive disorder (with features such as tolerance, preoccupation and inability to cut back) and she is credited with formally coining the term ‘internet addiction disorder’ (Murali & George, 2007).
The term ‘Cyber Addiction’ can encompass a variety of behaviours in relation to many different mediums from the Internet to video games (Young, 1998). This essay will be using the term ‘cyber addiction’ to refer to the overuse of the Internet in its different forms. Various names have been given to the uncontrolled use of the internet such as ‘computer addiction’, ‘online addiction’, ‘cyber addiction’, ‘pathological internet use’, ‘excessive internet use’, ‘internet addiction disorder’, ‘net addiction’, ‘cyberspace addiction’, ‘problematic internet use’, ‘technologic addiction’, ‘compulsive internet use’ and ‘internet behaviour addiction’ (Hall and Parsons 2001; Caplan 2002; Davis et al. 2002; Whang et al. 2003; Lee and Shin 2004; Hur 2006; Widyanto and Griffiths 2007).
Researchers have attempted to identify subtypes or sub-categories of Internet addiction. Davis (2001) subdivided problematic Internet use into two types: specific (overuse of a particular function or application) and generalised (‘multidimensional‘ overuse of the internet). Young (1999) categorised Internet addiction into five types: cybersexual addiction; cyberrelationship addiction; net compulsion (e.g. gam6Iin9or shopping on the internet); information overload (e.g. compulsive database searching); and computer addiction (excessive game-playing). This suggests that there are a wide range of complex behaviours and motivations underlying online addictions from sexual gratification to monetary rewards. One commonality across both of these definitions consider rewards reinforcing behaviours and neurological influences on IA/IAD; these be reviewed and discussed in the context of these sub-categories of IA.
Although IA is a subject attracting extensive attention, debates on its existence are still continuing (Korkeila et al. 2009; ?enormanc? et al., 2012). This review will consider the context of these debates; review the current clinical approaches to identifying behaviour that could be considered as IA, and outline possible explanations and treatments for the disorders. It will conclude with discussion on possible directions for future research and reflect on the concept of IA (or any of other terms used in the literature) and review if clinicians should to be ‘pathologising’ these behaviours or focusing instead at the underlying behaviours behind Internet usage.
Classification of Internet Addiction
Currently, IA or IAD are not explicitly referred to in either the diagnostic manuals predominately used by clinicians (DSM-5 and ICD-10). However, the most recent version of the DSM (2013) does make reference to Internet gaming in the ‘Conditions for Further Study’ section:
“Other excessive behavioural patterns, such as Internet gaming, have also been described, but the research on these and other behavioural syndromes is less clear. Thus, groups of repetitive behaviours, which some term behavioural addictions, with such subcategories as “sex addiction,” “exercise addiction,” or “shopping addiction,” are not included because at this time there is insufficient peer-re viewed evidence to establish the diagnostic criteria and course descriptions needed to identify these behaviours as mental disorders.” DSM-5, pg.781, 2013.
The most recent version of the DSM proposes nine criteria that could be used to diagnose a patient as suffering from ‘Internet Gaming Disorder’ (see Fig.1). Further to this, the International Classification of Diseases 11th Revision (ICD-11) may bring about changes in the way addiction is discussed, presented and coded. Reports by expert committee members show a discussion around the terms ‘addiction’ and ‘abuse’, and the possibility of dropping the terms in favour of less pejorative and stigmatising identifiers such as ‘dependence’ and ‘harmful use’, according to the thirty-fifth report of the WHO committee (World Health Organisation, 2012). There is speculation that Internet Use and Gaming Disorder will be included in the ICD-11 that is scheduled for release in 2015.
Robins and Guze (1970) followed by Feighner et al. (1972) proposed formal criteria for establishing the validity of any psychiatric diagnoses and the criteria that are developed to signpost clinicians when considering the classification of behaviours presented to them. Their suggestions were that all diagnostic criteria should have the following:
- Clinical descriptions (including symptom profiles, demographic characteristics, and typical precipitants),
- Laboratory studies (including psychological tests) supporting the diagnostic criteria,
- Delimitation from other disorders (by means of exclusion criteria),
- Follow-up studies (including evidence of diagnostic stability), and
- Family studies.
Currently, the literature only provides evidence for IA / IAD for the first two criteria with clinical descriptions (Young, 1998; Griffiths, 1998; Davis, 2001; Block, 2008) and laboratory studies (Hall and Parsons 2001; Caplan 2002; Davis et al. 2002; Whang et al. 2003; Lee and Shin 2004; Hur 2006; Widyanto and Griffiths 2007). Even meeting these two criteria, there is still great debate as to the diagnostic criteria that should be met to be diagnosed with IA / IAD (Murali and George, 2007).
Despite this, Block (2008) recommended the inclusion of IAD in DSM-V. He opines that conceptually, the diagnosis is a compulsive–impulsive spectrum disorder that involves online and/or offline computer usage and consists of at least three subtypes: excessive gaming, sexual preoccupations, and E-mail/text messaging (Swaminath, 2008).
The current arguments as to the criteria for IA / IAD and possible sub-categories of the disorders (Young, 1998; Griffiths, 1998; Davis, 2001) do share several components that are similar to more general definitions of addiction or substance dependence (Griffiths, 1997):
- Excessive use, often associated with a loss of sense of time or a neglect of basic drives,
- Withdrawal, including feelings of anger, tension, and/or depression when the computer is inaccessible,
- Tolerance, including the need for better computer equipment, more software, or more hours of use, and
- Negative repercussions, including arguments, lying, poor achievement, social isolation, and fatigue. (Block, 2008)
In addition to demonstrating the other criteria, it has been proposed that a diagnosis of IA must include symptoms for at least 3 months and at least 6 hours of non-essential Internet use per day (Tao et al., 2010).
Griffiths (2000) observes the scepticism among the academic community regarding the concept of ‘Internet Addiction’ but points out the acceptance of pathological gam6Iin9as an addiction has created a precedent for other excessive behaviours, such as Internet addiction. In addition Widyanto and Griffiths (2006) state that Internet addiction has frequently been conceptualised as a behavioural addiction, operating on a modified principle of classic addiction models (Panayides and Walker, 2012).
Diagnostic and Assessment Tools For IA/IAD
Even without a unified definition of IA/IAD many diagnostic and assessment tools have been developed by researchers to identify the prevalence rates and demography at most at risk of meeting the criteria for IA/IAD.
Young’s Diagnostic Questionnaire (Young 1998)
Young (1998) introduced her diagnostic questionnaire (the YDQ) for ‘Internet addiction’, with eight dichotomous items (see Fig.2), adapted from DSM-IV, from the criteria used for pathological gambling. She suggested a cut-off score of five, arguing that this cut off score is consistent with the number of criteria used for pathological gam6Iin9and is seen as an adequate number of criteria to differentiate normal from pathological addictive Internet use (Panayides and Walker, 2012).
Beard & Wolf’s (2001) Diagnostic Criteria for Maladaptive Internet Use.
Beard and Wolf had issues following Young’s development of her criteria and suggested that one concern is how objective the criteria she set was and how much of the criteria is based on self report. Some of the criteria (i.e., preoccupied with the Internet or feeling restless, moody, depressed, or irritable) can be reported or denied by a patient influencing the accuracy of the diagnosis. Further to this they questioned if pathological gambling, which is an impulse control disorder in DSM-IV, is the most accurate diagnostic criteria to use as a basis for diagnosing Internet addiction. Following these critiques they suggested an 8-point criteria where all of the first five had to be met with one of the last three (see Fig.3).
The Generalised Problematic Internet Use Scale (Caplan, 2002)
The GPIUS is based on Davis’ (2001) theoretical definition of ‘Problem Internet Use’, which focuses on cognitions and behaviours relating to Internet usage and how this may affect psychosocial health, producing negative consequences. The GPIUS psychometric considers seven factors that are taken into consideration:
- Mood alteration (using the Internet to change one’s mood)
- Social benefits (Perceived positive social benefits from online community)
- Negative outcomes (Problem dealing with friends/family due to Internet use)
- Compulsive use (Inability to control use, guilty feelings)
- Excessive time online (Losing track of time when using the Internet)
- Withdrawal (Difficulty spending time away from the Internet)
- Social control (A sense of social control with interacting with others online)
It is suggested that this measure is more reliable than both Young’s (1995) and Beard and Wolf’s (2001) measures and research (Caplan, 2002) found a Cronbach’s alpha reliability score of 0.78 to 0.85 and also found that all subscales correlate significantly with measure of psychosocial well-being.
Internet Consequences Scale (Clark et al., 2004)
This is a 38-item Likert-type scale used to assess the consequences of Internet use. It consists of 3 sub-scales: physical consequences (7 items); behavioural consequences (15 items) and psychosocial consequences (16 items). Clark et al found that this instrument has good content validity and reliability (Murali & George, 2007).
Beard (2005) reviewed these measures and found that there are three issues that reviewers need to be aware of when using these to operationalise IA/IAD in research. Firstly, although each author has tested their respective measures for reliability there is little objective and rigorous testing of reliability and validity in the literature. Second, all of these measures are based on self-report and consequently can fall victim to social desirability when patients are responding to the requests. Further to this many of the scales contain measures that are ambiguous and open to wide interpretation by the individual completing the psychometric. Finally, as identified earlier, the current literature is still at odds with each other as to a ‘gold standard’ for the criteria that an individual needs to meet to be diagnosed with IA/IAD therefore any measure relies in its own theoretical framework.
Prevalence Rates and Impact of Internet Addiction
There is a variety of research investigating prevalence rates of IA/IAD in different contexts and geographic locations. As one would expect, with differing views on the definition and criteria required to be classified as having IA/IAD there are varying results on the prevalence and demographic spread across different populations. The extent of this is demonstrated by reviewing the research in the area. Shotton (1991) noted that addicted computer users were mainly male, highly educated and introverted. However, subsequent studies (Griffiths, 1997, 1998; O’Reilly, 1996; Young, 1998) reported contrasting findings: dependent Internet users were mostly middle-aged women on home computers (Murali & George, 2007).
Some of the most interesting research on IA has been published in South Korea. Following ten cardiopulmonary-related deaths in Internet cafés (Choi, 2007) and a game-related murder (Koh, 2007), South Korea considers Internet addiction one of its most serious public health issues (Ahn, 2007). Using data from 2006, the South Korean government estimates that almost a quarter of a million children (2.1%; ages 6–19) suffer with some form of over exposure or IA disorder depending on the criteria that is used (Choi, 2007). About 80% of those needing treatment may need medications, and between 20% and 24% require hospitalisation (Ahn, 2007) (Block, 2008).
Weinstein et al. (2010) suggested that the prevalence rate of IA for studies published in North America and Europe range from 1.5% to 8.2%. Supporting this, Konstantinos et al. (2008) found that Internet users in Greece have an IA prevalence rate of 8.2%. In contrast Peukert et al. (2010) found that 1.5% – 3.5% of German teens show signs of Internet addiction or excessive use. Among these adolescents, IA is correlated with higher rates of depression, anxiety, and lower school achievement. Research from mainland Europe found that 1% of Norwegians are addicted to the Internet with an additional 5% are at risk of developing IA with the highest rate of addiction is in the 16-29 year old group (4% addicted, 19% at risk) (Bakkan et al., 2008). These figures are supported by Kaltiala-Heino et al. (2004) who found that among daily users of the Internet, 5% of boys and girls from Finland were classified as being addicted to the internet.
The figures from Asian research consistently provide higher rates of prevalence. Park et al. (2009) found that 10% of South Korean youth could be considered to be at high risk for IA. Leund et al. (2009) found similar rates of with 96% of teenagers in China utilising IM and 10% of these meeting the criteria to be classified as IM addicts.
Reviewing the differing prevalence rates it would suggest that location is a dominant factor influencing the rate of diagnosis of IA/IAD. This can even be taken to a level of different demography within the same countries and the sex of the individual. Liu et al. (2010) found that 7% of Chinese elementary and middle school students suffer from IA. The rate is higher in males (10%) than in females (4%). The rate is higher for rural students (8%) than for city students (5%). It is suggested that people aged between 11 and 24 are most at risk of developing IA/IAD with 18% of British students considered to be pathological internet users, whose excessive use of the internet was causing academic, social, and interpersonal problems (Niemz et al., 2005).
Considering the impact of IA/IAD, Kubey (2001) found that ‘synchronous’ applications (for example chat and multi-user games which involve a real-time interaction with another person) were more damaging to academic performance than ‘asynchronous’ applications such as email. Other research suggests that senior high school students who use the Internet to excess report and subsequently exhibit significantly more psychiatric symptoms than students who use the Internet less frequently. In addition, excessive users appear to have a distinctive personality profile when compared with nonusers, minimal, and moderate users (Yang, 2005).
The impact of IA behaviour is not only linked to aggression and academic performance, research has examined how marital relationships can result in separation and divorce due to IA. The ACE Model (Anonymity, Convenience, Escape) of Cybersexual Addiction provides a workable framework to help explain the underlying cyber-cultural issues increasing the risk of virtual adultery (Young et al., 2000).
A Brief Overview of Explanations for Internet Addiction
Uses and gratifications is perhaps the dominant paradigm for explaining media exposure in the field of communication studies. It has been applied to a wide range of conventional mediums (LaRose et al., 2001). Uses and gratifications theory assumes that audiences actively seek out media in a goal-directed way that provides them with the means of gratifying a wide variety of needs (Katz, Blumler, Gurevitch, 1974; Palmgreen et al., 1985). With its emphasis on active media use and its ability to span both mass and interpersonal communication, uses and gratifications was initially regarded as a natural paradigm for understanding the Internet (Morris & Ogan, 1996).
Korgaonkar and Wolin (1999) found that factors of escapism, information control, interactive control (relating to the ability to control the presentation of information), socialisation, and economic motivation differentiated light (less than an hour per day) and heavy Internet users. Charney and Greenberg (2001) established eight gratification factors for the Internet (keep informed, diversion and entertainment, peer identity, good feelings, communication, sights and sounds, career, and “coolness”), and two of these (keep informed and communication) explained 36% of the variance in weekly time spent on the net (LaRose et al., 2001). Social-cognitive theorists propose self-regulation as the key to understanding physiological addictions (Bandura, 1999), and behavioural addictions might be conceptualised with similar mechanisms, absent the physiological craving. Internet addictions represent a suspension of normal self-regulatory processes (LaRose et al., 2001).
Research into the neurological reward pathways has provided evidence to suggest that IA behaviours may cause biological reinforcements encouraging the behaviour to be replicated. Dopaminergic neurotransmission may be involved in learning, reinforcement of behaviour, attention, and sensorimotor integration (Koepp et al., 1998). Recent research has implicated the role of striatal dopaminergic system in the behavioural maladaptations associated with excessive Internet video game play (Han et al., 2007). The reward-deficiency hypothesis suggests that those who achieve less satisfaction from natural rewards turn to substances to seek an enhanced stimulation of reward pathways (Blum et al, 1996). Internet use provides immediate reward with minimal delay, mimicking the stimulation provided by alcohol or drugs. (Marali & George, 2007)
Personality theories in this area suggest that individuals may use the Internet to escape their reality and enhance their self-esteem. According to Shotton (1991), introverted, educated, technologically sophisticated males are more prone to develop pathological Internet use. Individuals who have low self-esteem have a greater propensity to Internet addiction. Shy individuals use the Internet to overcome their deficiencies in social skills, communication and social relationships.
Through a meta-analysis Douglas et al. (2008) reviewed articles published between 1996 and 2006 and proposed a conceptual model of IA. According to this model, excessive Internet use is determined by internal requirements and an individual’s motivation. However, personal inclination is also important (antecedents such as being in environments allowing internet, duration of exposure, and feelings of marginalisation). Further to this addict profiles such as refusal of excessive internet use as being a problem and having very little or no social life and/or self-confidence can further compound IA / IAD.
In addition to these predictors the impact of the online environment and the attractiveness of this (‘pull factors’) such as the ease of access, opportunities to make gains, and perceived improvements in ability to communicate ‘socially’ also signpost those who are suffering from IA. Push factors ease the relationship between the excessive use of Internet and the severity of the negative effects. Besides academic, social, economic and occupational effects and physical effects such as changes in sleeping patterns, the negative effects of IA may also involve deviant behaviours (?enormanc?, 2012).
The individual’s awareness of the problem of IA / IAD may facilitate the use of control strategies to prevent the addiction. Some individuals are more likely to adopt behaviours deviated from the normal than others, thus a direct connection was proposed between the antecedents and the behaviours deviated from the normal (Fig. 4) (Douglas et al. 2008).
Future Research Directions
With the recent publication of the DSM-5 (2013) and debates raging about what is going to be included in the next revision of the ICD (ICD-11, expected in 2015) future research will need to focus on rationalising the criteria that one has to meet to be diagnosed with IA/IAD. As has already been addressed the prevalence rates of IA/IAD vary massively depending on which of the many criteria are used and the development of ever more reliable diagnostic measures. A common framework should be developed and a more consistent approach to measuring IA/IAD is needed.
Currently, this area is plagued with contradictions as a result of the inconsistent approach that researchers have taken. Griffiths (2000) argued that most of the individuals who use the Internet excessively are not addicted to the Internet itself, but use it as a medium to fuel other addictions. In contrast, it is also acknowledged that there are some case studies that seem to report an addiction to the Internet itself (e.g., Young, 1996; Griffiths, 2000).
Walther & Reid (2000) in their critique of research in the area raise several important issues that should be taken into account.
- Researchers need to consider the nature of people’s activities on the Internet, rather than simply the extent of their use of it.
- A formal and theoretical understanding of the Internet’s particular allure is required.
- Researchers need to do a better job of collecting and analysing data about Internet use in a standardised way.
Researchers must also understand that spending a lot of time online may be productive, rather than dysfunctional, behaviour, especially in the evolving context of an ever increasing variety of methods to access resources online and the need to move with these developments. Research should also include the development of models that identify or explain the motivation underling online behaviour. For example, The ACE Model developed by Young (1999) explains how Accessibility, Control, and Excitement play a significant role in the development of Internet compulsions.
Further studies on large population with broad range of age are needed to detect the specific temperament and genetic predisposing polymorphisms that are involved in IA/IAD.
Me, myself and my computer: Discussion
“The only reason to make the distinction [between habit and addiction] is to persecute somebody” (Szasz, 1973)
There is an irony that mental health experts agree that the Internet can provide a valuable service to people looking for support groups, treatment options, and other help. Web sites, newsgroups, and E-mail lists all are powerful resources for people to find the information and help they need (Swaminath, 2008). There is even a burgeoning literature on the use of the Internet to deliver treatments for other addictive behaviours (Squires, 2005; Lenihan, 2007).
Labelling an unfamiliar activity as an addiction or a ‘craze’ has, at times in the past, been a response when large numbers of people (usually young) are engaged in activities with which the labeller is unfamiliar or cannot understand the justification for the behaviour. In the 20th century, jazz, rock and roll, and role-playing games were all the subject of such moral panics (Waldron, 2005; Boyd, 2006). It seems likely that, with the increase in diversity in both those using the Internet and the range of activities available online, the idea of a homogeneous ‘internet addiction’ will seem increasingly anachronistic and one-dimensional (Lenihan, 2007).
Defining behaviour as a disorder, the act of carving it out as a distinct entity, is not just a technical convenience. Even the most atheoretical and syndromal of diagnoses can become reified and appropriated by wider social and political forces, eventually carrying a burden of meanings that were not originally envisaged (American Psychiatric Association, 1994; Warden et al, 2004).
Among a small but growing body of research, the term addiction has extended into the psychiatric lexicon to identify problematic Internet use associated with significant social, psychological, and occupational impairment. Anecdotal evidence has suggested that mental health practitioners’ report increased caseloads of clients whose primary complaint involves Internet (Young et al., 2000). The issue of IA/IAD will not go away and as we move into a time where the Internet encroaches on more-and-more of our lives it is an area of psychological research that will need to develop and evolve. Vital to this development is a consistent approach to the research and a move towards a standard definition or set of diagnostic criteria of IA/IAD.
However, it is important to realise that “healthy enthusiasms add to life; addictions take away from it” (Griffiths in Davies, 2007). The question that we need to approach with caution is: where do we draw the line?
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