Computer Addiction

Computer Addiction

Table of Contents Introduction The dawn of the computer and internet access has passed, and the world-wide-web is accessible to over 2 billion global usersl . This access has, in the last 10 years, increased fourfold2 (see footnote for website details that evidence growth) and become abundantly available through the wireless revolution of appliances; whereby mobile internet use has developed and grossly contributed towards the mass global access and usage of the internet.

The convenience and portability of such technology means that there is also an increased awareness into the associated pathologies that ne may encounter- whereby negative compared to positive consequences resulting from excessive computer use have been highlighted on opposite sides of the spectrum. This expansion, which is exponential and has momentum, should be examined from both perspectives; both positive and negative.

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There are undoubtedly numerous benefits of having such a wealth of information and knowledge at ones disposal. However, reports of detrimental psychological and even physiological outcomes have been well evidenced and supported by current and archived research. Cumulatively the evidence is concerning and further study which xamines possible contributions of IA may facilitate awareness and provide caution towards an individuals’ time spent online.

Addiction is a controversial and debated pathology, some disciplines regard non-chemical motivated behaviour to be not truly capable of causing addictive symptoms within medically defined parameters, meaning that pathological computer used would be void of this term ‘addiction'(Treuer, F?¤bi?¤n, & F??redi, 2001 ; Zhou et al. , 2011). However, as research shows, the neurological processes that show to be activated during TSO are ynonymous with the same areas associated with reward(Ko, Liu, et al. , 2009). The area also stimulated when pathological drug use is monitored and considered to be the mechanism that perpetuates drug addiction.

Research into neurological processes at action may provide insight into the nature of why the internet becomes a medium of pathological use and what draws the user into using the internet to a pathological level. A relationship is thought to exist between the amount of time a user spends online, and their propensity to possess addictive qualities (as per PIU Scale). The cause and effect phenomenon exists, which delivers the question as to whether one potentiates the other. Models of reward have been used to evaluate the essence of what may encourage users to maintain online activity for prolonged periods.

Research has discussed the qualitative aspects of online use, whereby concerns are highlighted to address the unbalance between online life and real life. It appears that a proportion of online users invest a world. It is proposed that quality of life is detrimentally affected because of this disproportionate use whereby the online users’ relationships have deteriorated and egatively impacted upon their health, employment and overall inability for pathological users to form stable real world relationships.

A possible domino effect is suggested to include a variety of mental health issues(Yen, Ko, Yen, Wu, & Yang, 2007); such as depression, poor self-esteem(Ayin & San, 2011) and even neglect leading to death3(Ko, Yen, Liu, Huang, & Yen, 2009). The latter of which may be a sparse incident, yet should be examined, as prevention is arguably optimal for any possible threat of re-occurrences. Currently there is no entry in the DSM for Internet Addiction; however, research is currently encouraging the importance of and considerations for the entry of such a disorder for the purpose of the DSM-V(Young, 2009).

There were 3 separate aims to consider for the purpose of this study. The 1st objective is to investigate as to whether the amount of time that is spent online (TSO) by a participant affects their score on an addiction questionnaire using the ‘Pathological Internet Usage’ Scale (Morahn-Martin & Schumacher, 1977). The 2nd aim is to address whether Participant Age (PA) can also be associated with addictive haracteristics, assessed using the PIU scale. The 3rd proposition predicts that there will be evidence of an interaction between PA and TOS.

All of these 3 study aims will be discussed after consideration and interpretation of statistical analysis. It was hypothesised that increased TSO would be positively correlated with a higher PIU Score. The 2nd hypothesis predicts that PA will have a significant effect on PIU Score. The 3rd hypothesis states that there will be an interaction between TSO and PA. Results Measures Addiction to the internet was assessed using the ‘pathological internet use’ scale Morahn-Martin & Schumacher, 1977).

This is a 13 item questionnaire in which individuals who score four or more affirmative answers are defined as pathological internet users. Each participant also provided their age and the amount of time that they spent online each day as additional measures. Materials Microsoft Word 2008 was used to write up research development. SPSS v. 18 was Regression was performed. A University of Liverpool computer was used to access processing and statistical software. Participants 60 self-selected participants were recruited by advertising a study into ‘Internet ehaviour’ on a popular internet forum (http://www. ow-europe. com/en/). Participants were first directed to a website hosted at the University of Liverpool psychology department were they indicated their age and the amount of time spent browsing the internet each day. Once done, participants were re-directed to a 2nd internet page were they were asked to complete the questionnaire. Upon completion of the study, all of the people who took part in the study received information explaining the aims of the investigation. Contact details were provided for further nquiry, and all participant data was confirmed as being confidential and anonymous.

Withdrawal from the study at anytime was additionally offered to each participant. Procedure Self-selected participants who were directed to the online addiction questionnaire where asked 13 questions and asked to respond accordingly. The sample group was considered most appropriate, as the target population for the purpose of the study was aimed at an online audience. The accessibility of the questionnaire enabled the participant to complete at their convenience and provided the researcher with an udited data set.

All responses were anonymously recorded to encourage realistic responses, yet were associated with each individual’s PA and TSO to enable. analysis. Scores were recorded into Microsoft Excel, which were then transferred onto SPSS v. 18 from a I-JOL computer and analysed for statistical significance. Once a priori assumptions were confirmed and accepted as being normally distributed, the data was exposed to a Multiple Regression analysis (see CD appendix).

Independent variables (PA and TSO) where checked used collinearity diagnostics (see Results, gure 1) to check for multi-collinearity, which was not an issue and factors were considered to be sufficiently independent of one-another. After significance levels were performed, a scatter plot was generated to examine the interaction between PA and TSO (See Results figure 2). Descriptive statistics were also generated (see Results, table 1) to facilitate further analysis and highlight trends in data which supports or contradicts relevant research.

Once all statistical analysis had been performed and examined visually by the researcher, an extensive literature review was performed to onsider interpretation and utilisation of the data for extrapolation purposes. Design The design used in this study was a between subjects independent design, using a multiple regression model for analysis. There were two variables: IVI, Time Spent Online (TSO) which was measured in hours; and IV2, Participant Age (PA). To glean statistical results, participants’ data was entered into SPSS v. 18 and a Multiple Regression4 (see footnote for online explanation of MR) was performed.

Interaction effects between independent variables, the MR was deemed the most appropriate test to use. The study was an independent between subjects design, with two independent factors (IV 1 = Age, IV2 = Time Spent Online). The dependent factor in this study was the score obtained from each participant from the PIU Scale. All data was at the required interval level and a minimum of 45 participants (actual used, 60). Table 1: Descriptive Statistics for each Independent Variable: Age and TSO and Dependent Variable: PIU Score. VARIABLE MEAN SD +/_ NUMBER 23. 52 6. 72 TSO 6. 82 3. 43 PIU score 5. 3 3. 29 The VIF score was bellow 10 (VIF = 2. 53, see CD appendix; Regression) so multi- ollinearity was not a factor, and Durbin-Watson did not deviate from 2 significantly (D-W = 1. 73; see appendix, Regression). When examining the effect of TOS on PIU Score, using the enter method on the regression analysis, a significant model emerged: F (2, 57) = 37. 907, p < 0. 0005. This model explains 56. 10% of the variance (adjusted . 556). The standardised beta co-effcient (P), for each 'V, was measured and then assessed. This gave an indication as to the individual impact of each IV on PIU Score.

PA and TSO were both found to significantly affect PIU score as individual entities. Figure 1: Scatter Plot. Interaction and Line of Fit between IVI (TSO) and IV2 (PA) The mean interaction plot (Figure 1) shows a negative correlation between TSO and PA. Younger users are evidently, in this sample, likely to spend an increased time spent online. The Line of fit equation shows that Participant Age can robustly predict the amount of Time Spent on Line and that a there is a definite relationship between both ‘Vs. Discussion The results of the statistical analysis indicate that all 3 of the study aims were found to be correct.

Hypothesis 1 stated that Time spent online would significantly affect PIU score, which was confirmed. Hypothesis 2 stated that PA would have an effect and TSO would interact with each other. Therefore, all 3 hypotheses are accepted and the null hypotheses rejected. Previous research has shown that TSO can negatively affect the users’ quality of life and their surrounding relationships. Notable deaths have occurred globally including; South Korea, China, USA, Vietnam and Canada. Co-morbidity between excessive internet use and other mental health disorders are evident (Ayin & San, 2011; Yen et al. 2007), although this may be merely a correlation and not evidence of cause & effect. Neurological correlates show enhanced reward and lessened sensitivity towards loss(Dong, Huang, & Du), which may mean that the rewards associated with online use outweighs the perceived consequences of possible loss; such as hygiene or neglect towards relationships. The neurological substrates that are activated associated with excessive online use occur within the reward dopamine centres including the nucleus accumbens (NA) and ventral tegmentum area (WA).

This area is also known to be active in the pathology of gambling, and most importantly drug addiction (Bernardi & Pallanti; Ko, Liu, et al. 2009). Therefore, if the same basic neurological substrates are responsible and effectively driving the behaviour for drug use and excessive computer use respectively, there surely should be an acceptance of computer addiction as a disorder. Internet addiction may be a disorder that carries the symptomology synonymous of addiction and its properties which is also capable of displaying physiological withdrawal and psychological obsession.

TSO and user age are both found to be significant factors when assessing risk for likelihood of excessive internet se (Chang & Man Law, 2008), which supports the statistical analysis found in the current study. Discussion Furthermore, identifying ‘at risk groups can only serve to provide insight which can help address and make aware the real world dangers of excessive computer use. It should also be considered that the internet may merely provide an unrealistic alternative world for individuals suffering with social phobias(Kim et al. , 2006) or conflict issues, and consequently a direct cause and effect should be extrapolated with caution.

Future research should consider alternative means of communicating he available support via an increased network in an attempt to remedy the disparity between virtual reality and actual reality. Ayin, B. , & San, S. V. (2011). Internet addiction among adolescents: The role of self- esteem. Procedia – Social and Behavioral Sciences, 15, 3500-3505. Bernard’, S. , & Pallanti, S. Internet addiction: a descriptive clinical study focusing on comorbidities and dissociative symptoms. Comprehensive Psychiatry, 50(6), 510-516. Chang, M. K. , & Man Law, S. P. (2008). Factor structure for Young’s Internet Addiction Test: A confirmatory study.

Computers in Human Behavior, 24(6), 2597-2619. Dong, G. , Huang, J. , & Du, X. Enhanced reward sensitivity and decreased loss sensitivity in Internet addicts: An fMRI study during a guessing task. Journal of Psychiatric Research, In Press, Corrected Proof. Kim, K. , Ryu, E. , Chon, M. -Y. , Yeun, E. -J. , Choi, S. -Y. , Seo, J. -S. , et al. (2006). Internet addiction in Korean adolescents and its relation to depression and suicidal ideation: A questionnaire survey. International Journal of Nursing Studies, 43(2), 185-192. KO, c. -H. , LILI, G. -c. , Hsiao, S. , yen, J. -Y. , yang, M. -J. , Lit-I, W. C. , et al. (2009). Brain activities associated with gaming urge of online gaming addiction. Journal of Psychiatric Research, 43(7), 739-747. Ko, C. -H. , Yen, J. -Y. , Liu, S. -C. , Huang, C. – F. , & Yen, C. -F. (2009). The Associations Between Aggressive Behaviors and Internet Addiction and Online Activities in Adolescents. Journal of Adolescent Health, 44(6), 598-605. Treuer, T. , F?¤bi?¤n, Z. , & F??redi, J. (2001). Internet addiction associated with features of impulse control disorder: is it a real psychiatric disorder? Journal of Affective Disorders, 66(2-3), 283-283. yen, J. -Y. , KO, c. -H. yen, C. +. wu, H. -Y. , & yang, M. -J. (2007). The Comorbid Psychiatric Symptoms of Internet Addiction: Attention Deficit and Hyperactivity Disorder (ADHD), Depression, Social Phobia, and Hostility. Journal of Adolescent Health, 41 (1), 93-98. References Young, K. (2009). Internet Addiction: Diagnosis and Treatment Considerations. Journal of Contemporary Psychotherapy, 39(4), 241-246. Zhou, Y. , Lin, F. -c. , Du, Y. -s. , Qin, L. -d. , Zhao, Z. -m. , Xu, J. -r. , et al. (2011). Gray matter abnormalities in Internet addiction: A voxel-based morphometry study. European Journal of Radiology, 79(1), 92-95.

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