Internet Addiction: Problem Statement - For NCU PSY7102

Internet Addiction: Why It Matters

Laura Ann Collins

Northcentral University

Abstract

From 2000 to 2015, global internet usage has increased from 400 million users to an estimated 3.2 billion users (ICT Data and Statistics Division, 2015).  By the end of 2015, the ICT estimates that there will be over 7 billion cellular phone subscriptions (ICT Data and Statistics Division, 2015).  As the trend of internet connectivity is unlikely to reverse, understanding how internet usage fits into peoples’ lives has become of increasing interest. Specifically, since 1995, when psychiatrist Ivan Goldberg suggested that internet addiction (IA) might be clinically relevant (Hsu, Lin, Chang, Tseng, & Chiu, 2015), questions have arisen about what is adaptive use versus maladaptive use of internet.  While Cheng and Li (2014) found “considerable” national variance in prevalence of IA, the results of their research yielded a global prevalence estimate of 6.0% of IA among internet users.  As IA is still an emerging disorder, controversy about what exactly IA is and is not continues to impede progress toward detecting and treating IA.  With such a significant possible impact, it is essential that researchers solidify the conceptualization of IA.

Keywords:  IA, Internet Usage, Internet Addiction, pathological internet use

Internet Addiction: Why It Matters

The World Health Organization estimates that mood disorders affect 350 million people, bipolar disorder affects 60 million people, schizophrenia and psychotic disorders affect 21 million people, and dementia affects 47.5 million people (Mental Disorders, 2015).  Combining statistics from the International Telecommunications Union (an agency of The United Nations) and the research from Six percent of 3.2 billion internet users (192 million) peoples’ lives may be adversely impacted by IA. Therefore, IA for internet users, a disorder which has only been possible since the early 1980’s, may impact more people than bipolar disorder, dementia, and the psychotic disorders combined. 

If cellular phone subscribers also have potential for IA, then an additional 7 billion people are at risk.  With 6% prevalence, then 420 million people may experience IA – 70 more million people than those that experience mood disorders.  However, without data to validate or refute that cellular phone users are as at risk of IA as internet users, the application of Cheng and Li’s (2104) 6% global prevalence statistic may not be appropriate.  It is scenarios such as that that illustrate why definition of IA and a more solid conceptualization of the disorder is so necessary.

Why A Definition Matters…

            What is internet addiction?  Lee et al. (2012) suggested IA is “an inability to control Internet use, [which] can lead to serious impairment in psychological and social functioning” (p. 373) and therefore, IA is an impulse control disorder and behavioral addiction such as pathological gambling.  Kuss et al. (2014) defined IA as “the excessive engagement in online behaviours, including, gaming, but not restricted to it, accompanied by the presence of traditional addiction symptoms (American Psychiatric Association, 2013)” (p. 352).  Kuss et al. included internet gaming in their definition of IA, even though they questioned if IA and video game disorder should be considered separate disorders.  Lortie and Guitton (2013) suggest that IA is like other addictions in that biochemical and neurological changes evidenced in persons exhibiting symptoms of IA are similar to those seen in other forms of addiction and dependence.  They defined “dysfunctional internet use” as use which had characteristics of tolerance, loss of sense of time, increased real-life isolation, and negative real-life consequences (Lortie & Guitton, 2013). 

Cheng and Li (2014) defined IA as both neurological disorder and behavioral disorder.  They implied that IA is a stress response to decreasing quality of life as shown in national statistics on increased air pollution, increased commute time, and lower national income.  Cheng and Li correlated some of the “significant” variance in national prevalence of IA to national indices indicating quality of life.  Cheng and Li called for more extensive research on how IA is related to national indices of quality of life.

IA – Caused by a Learning Curve?

The internet seems to exist between food and gambling/substance use with respect to the option to choose exposure.  Connectivity is becoming part of daily life for many of the global citizens, much as food is a daily part of life.  Of course, one does not die without a certain amount of internet use.  However, internet use is not as optional as gambling or substance use would be.  The world is moving toward a digital society with information and communication technologies being significant drivers of global change (ICT Data and Statistics Division, 2015).  If one considers that the internet is a relatively new aspect of human life (say compared to telephones or letters), then it is possible that humans are in a period of learning to utilize this new resource. 

Wasinski and Tomczyk (2015) defined IA as “a specific state of mind in which addicted individuals lose their grasp of their real existence…. [with] them being deeply intellectually and emotionally absorbed by the virtual world” (p. 68).  Wasinski and Tomczyk evaluated data from the EU Kids Online Study in their research and determined that parental attitudes and actions regarding implemenation of boundaries for their childrens’ internet usage is a protective factor against development of IA. 

Suissa (2104) suggested that when consideration of the pervasiveness of internet use in modern life is introduced to the discussion, it then becomes essential to distinguish from use and abuse of the internet.  Suissa inquired if the medical model of IA is appropriate and suggested that psychoeducation and skills-training might prevent IA from occurring.  Suissa posited that IA cannot be separated from the sociological context of the society in which the individual is embedded. 

Considering Wasinski and Tomczyk’s (2015) and Suissa’s (2014) research, a risk factor for IA could be lack of societal regulation regarding proper utilization the resource of the internet and on proper integration of internet use into “real” life activities.  Inaba and Cohen (2011) stated that societies utilize norms and laws to control the amount of substance use and that these regulating factors have developed over generations.  In the case of internet use, there have not been generations of individuals who have practiced with the internet enough for regulations to develop. 

Discussion

Each of the preceding definitions and concepts of IA draw from a different theoretical etiology of IA.  Is IA a behavior problem associated with impulse control?  Is IA a brain disease?  Is IA a sociological phenomenon that proper training and education can ameliorate?  Is IA a stress response?  Is IA some combination of each of these, or something not related to any of the suggested definitions?  Is there even a pathological condition related to internet use?   These are all questions which are relevant to understanding how to consider internet use within the individual’s life. 

Why do conceptualization and definition matter?  The way researchers conceptualize a topic impacts the questions that are posed about the topic.  For example, when the assumption is that something is medically wrong, then societal factors may not be considered.  If IA is a brain disease, then the levels of air pollution or the amount of time spent on commuting may not be considered as part of the disorder.  Conversely, if IA is considered a stress response, then brain abnormalities or genetic susceptibility may not be investigated.

Definition matters because it is through definition that researchers create hierarchies of acceptable, excessive, and pathological.  The individual who spends 35 hours per week on the internet because he is a media consultant may have a very different diagnosis than the individual who spends 35 hours per week on Facebook.  However, neither individual may be addicted… or one may be addicted… or both may be addicted.  Context, variables, and criteria all are components of definition.

Definition matters, also, in allowing researchers to select appropriate tools for assessment.  Lortie and Guitton (2103) noted that “very few authors confirmed the criteria validity of their questionnaires, leading to the conclusion that existing questionnaires do not emphasize the need to distinguish between ‘normal’ everyday users and ‘pathological’ heavy users of the internet” (p. 1212).  When utilizing tools to assess for IA, different studies have used cut-off scores which are not validated to separate excessive use from addictive use (Laconi, Rodgers, & Chabrol, 2014).  This reduces the reliability and validity of the tools to screen for and assess IA.

Conclusion

Internet use is becoming a fact of global life.  The number of people who have access to the internet via computer or cellular phone will continue to increase over the coming years (ICT Data and Statistics Division, 2015).  Within the last couple of decades, concern about maladaptive internet usage has developed.  As a result, there has been an explosion of research, leading Laconi et al. (2014) to state “few areas of research have accumulated such a rich and varied body of assessment tools in such a short time-period, illustrating the vibrancy and creativeness of the field” (p.199). 

Whether or not IA exists… whether or not IA is a function related to social factors, is due to physical/medical factors, or to something else entirely… It is essential to define what is acceptable internet use, what is excessive internet use, and what is internet use which is harmful.  It is essential that harmful internet use is conceptualized.  Definition and conceptualization provide the bedrock upon which to ground research for ameliorating negative impact of harmful use.

References

Cheng, C., & Li, A. Y.-I. (2014). Internet addiction prevalence and quality of (real) life: A meta-analysis of 31 nations across seven world regions. Cyberpsychology, Behavior, and Social Networking, (17)12, 755-760. doi: 10.1089/cyber.2014.0317.

Hsu, W.-Y., Lin, S. S., Chang, S.-M., Tseng, Y.-H., & Chiu, N.-Y. (2015). Examining the diagnostic criteria for internet addiction: Expert validation. Journal of the Formosan Medical Association, 114, 504-508. doi: 10.1016/j.jfma.2014.03.010.

ICT Data and Statistics Division. (2015). ICT facts & figures. Retrieved from the International Telecommunication Union at https://www.itu.int/en/ITU-D/Statistics/Documents/facts/ICTFactsFigures2015.pdf

Inaba, D. S., & Cohen, W. E. (2011). Uppers, downers, all arounders: Physical and mental effects of psychoactive drugs [7th. ed.]. Medford, OR: CSN Productions, Inc.

Kuss, D. J., Shorter, G. W., van Rooij, A. J., Griffiths, M. D., & Schoenmakers, T. M. (2014). Assessing internet addiction using the parsimonious internet addiction components model - A preliminary study, 12, International Journal of Mental Health Addiction, 351-366. doi:10.1007/s11469-013-9459-9.

Laconi, S., Rodgers, R., & Chabrol, H. (2014). The measurement of internet addiction: A critical review of existing scales and their psychometric properties. Computers in Human Behavior, 41, 190-202. doi: 10.1016/j.chb.2015.04.056.

Lee, H., Choi, J.-S., Shin, Y.-C., Lee, J.-Y., Jung, H., & Kwon, J. (2012). Impulsivity in internet addiction: A comparison with pathological gambling. Cyberpsychology, Behavior, and Social Networking, 15(7), 373-377. doi: 10.1089/cyber.2012.0063.

Lortie, C. L., & Guitton, M. J. (2013). Internet addiction assessment tools: Dimensional structure and methodological status. Addiction, 108, 1207-1216. doi: 10.1111/add.12202.

Mental Disorders. (2015, October). Mental disorders (Fact sheet N°396). Retrieved from World Health Organization: http://www.who.int/mediacentre/factsheets/fs396/en/#

Suissa, A. J. (2014). Cyberaddictions: Toward a psychosocial perspective. Addictive Behaviors, 39, 1914-1918. doi: 10.1016/j.addbeh.2014.07.027.

Wasinski, A., & Tomczyk, L. (2015). Factors reducing the risk of internet addiction in young people in their home enviornment. Children and Youth Services Review, 57, 68-74. doi: 10.1016/childyouth.2015.07.022.