The Influence of Social Media and Video Game Usage on Psychiatric Disorders Shah-Nijah Fields GPSY 500 Regent University Abstract Technology has influentially impacted human interaction and has advanced society in many of areas

The Influence of Social Media and Video Game Usage on Psychiatric Disorders
Shah-Nijah Fields
GPSY 500
Regent University

Technology has influentially impacted human interaction and has advanced society in many of areas. However, the integration of technologic usage as increased researchers’ awareness on its correlation with the development of addictive behaviors and its link to psychological disorders. In a cross sectional study by Andreassen, Griffiths, Kuss, Mazzoni, Billieux, Demetrovics ; Pallesen (2016), 23,533 participants completed five online survey based assessments that evaluated the symptoms in attention-deficit hypertension disorder (ADHD), obsessive-compulsive disorder (OCD), anxiety and depression to see they are able to explain the variances in addictive use of video gaming and social media. Results from this study showed a positive correlation between the symptoms in addictive technological behaviors and psychological disorders. In addition to the correlation coefficient analysis results, the hierarchical regress analyses showed that sex, relationship status, educational level explained 11 and 12% of variance in addictive social media usage whereas ADHD, OCD, anxiety and depression explained 7 and 15% of the variance in addictive video game usage. Findings from this study extended empirical knowledge on the roles of mental health symptoms in correlation to addictions to operating technological devices and media.

Technology has made a remarkable impact on society by making essential contributions to help enhance social exchanges, businesses, and entertainment. However, these same advancements has also been linked to the development of psychological disorders associated with addictions. In the article, The Relationship Between Addictive Use of Social Media and Video Games and Symptoms of Psychiatric Disorders: A Large Cross-Sectional Study, Andreassen, Griffiths, Kuss, Mazzoni, Billieux, Demetrovics ; Pallesen (2016), studied the correlation between psychological disorders and the addictive usage of video games and social media. In this study Addictive behaviors were defined by being excessively worried about online engagement, overly compelled by an overpowering impulse to participate, and dedicating long periods of time to online interactions to which it impairs areas of importance (Andreassen, 2016). In addition, the evaluated psychological disorders are attention deficit hyperactivity disorder (ADHD), obsessive –compulsive disorder (OCD), anxiety, and depression.

Based on previous studies associated to the psychological disorders of interest and the link between the two addictive technological behaviors, Andreassen et al. (2016) formulated a study evaluating five hypotheses to further empirical research. The first hypotheses consisted of a positive correlation between video gaming social networking symptoms (Andreassen et al (2016). Previous studies have shown that males are more prone to get addicted to video games whereas females are prone to excessive social media, online shopping, and texting usage (Andreassen et al., 2016). The second hypothesis predicts younger females will score higher on assessments screening addictive symptoms in social media whereas younger males will score higher on addictive video game usage (Andreassen et al., 2016). In addition previous studies has shown that ADHD is linked to frequent video game usage Andreassen et al. (2016). The third hypothesis predicts ADHD will be associated to the addictive behaviors in video gaming and social media usage. OCD was also examined in this study, which influenced the hypothesized notion that symptoms related to OCD will correlate to addictive symptoms used in social media and in video gaming (Andreassen et al., 2016). The fifth hypothesis is associated to anxiety and depression and is hypotheses that these disorders will have a positive connection to the addictive technological behaviors examined in the study (Andreassen et al., 2016).

Method and Measurements
In this study 23,533 Norwegian individuals completed an online cross-sectional survey based study focused on several addictive behaviors. Participants were able to access the assessment through a link published in an online advertisement posted in a Norwegian newspaper. After one week of advertisement, data was collected from 41,970 participants (N=41,970). However, due the limited amount of information provided or the failure to complete the whole assessment 18, 437 participant’s (n= 18,437) data was deleted. After the deletion of the limited data, the sample size consisted of 23, 533 respondents ranging from the ages of 16-88 years of age. Out of the comprised data, the participants consisted of 15,299 women and 8234 men participants. Statistically, Women accounted for 65% of the study whereas males accounted for 35 percent.
The instruments utilized in this study consisted of six survey-based assessments that incorporated a point based likert scale for measurement. The Bergen Social Media Addiction Scale (BSMS) adapted elements for the Bergen Facebook Addiction scale and replaced the word “facebook” with the word “social media” and measured the essential elements associated to addiction (Andressan et al., 2016). This assessment incorporated a 5-point scale that used answers ranging from very rarely to vary often. The achieved scores from this assessment can fall between 6 to 30 point and measures behaviors within the last past year. On the other hand, The Game addiction Scale (GAS), consist of a seven-item questionnaire that assessed the indications of addictive behavior in video gaming. Originally, this assessment was formulated to test adolescents, but has been administrated to variety of age groups. Similar to the BMSM, GAS consist of a 5 point scale that required answers that ranged from Never to often but yields results from 7 to 35.
The third assessment used was the Adult ADHD Self-Report Scale I, which encompassed an 18 questionnaire that aligns with the DSM-IV criteria of symptoms associated with ADHD In adults (Andressan, et al., 2016). Similar to the other assessments this assessment also consist of a 5 point answering selection ranging from Never to very with the ability to accumulate a score from 18 to 90. In addition, the Obsession-Compulsive Inventory –Revised (OCI-R) was the fourth assessment incorporated in this study. It utilized 18 elements that measure six fundamental symptoms of OCD. These symptoms consist of checking, ordering, neutralizing, washing, obsessing, and hoarding (Andressan, et al., 2016). Similar to the other assessement used in this study, OCI-R incorporates a 5-point scale ranging from not at all to extremely. Not at all receives a score of 1 point leading up to extremely, which receives 5 points. The higher a participant score on this assessment suggests the person is disturbed by the symptoms associated with OCD.
The last assessment integrated in this study was the Hospital Anxiety and Depression Scale (HADS). This assessment measured unsomatic indications of anxiety and depression by using a 14-item two factorized scale. A two factored scale means that half of the items measure non-somatic symptoms in anxiety, while the other half of items measured nonvegetative symptoms in depression. It is important to note that all six assessments undergone a Cronbach Alpha measurement to help distinguish internal consistency and reliability. All assessment showed good internal consistency. The Cronbach’s a = .88 for the BSMS measure, .89 for GAS, .87 for ASRS- Version I, .87 for OCI-R, and .82 and .75 for HADS.
Results from this study were achieved by encompassing a correlation coefficient to evaluate the interconnections between each set of the study’s variables (Andressan, et al., 2016). A Pearson product moment correlation coefficient is denoted by r and measures the strength of linearity between two variables. Its range of value starts at +1 and goes to -1 (Mertler & Reinhart, 2017). After the Pearson correlation of coefficient was preformed, a linear categorized regression analyses was conducted on video gaming and social networking which represented dependent variables. A regression analyses allowed Andressan, et al., (2016) to examine the interactions between the desired variables. In addition, age, sex, education, and relationship status was included in the first step of the analysis whereas ADHD, OCD, anxiety and depressive symptoms were included in the second step.
According to the results of the correlation coefficient analysis, addictive usage to both video game and social media usage was positively interrelated. Simultaneously, they both positively correlated to the other measured variables in the study too. Moderate to high correlations where shown in both addictive technological behaviors when measure with ADHD, anxiety, and OCD. However, depression showed a lower correlation value in addictive usage in video games, whereas a higher correlation value was obtained when depression related to addictive use of social media (Andressan,et al., 2016) .
In the regression analysis for addictive use of social media, regressed variables sex, relationship status, educational level explained 11.6% of the addiction in social media usage whereas ADHD, OCD, anxiety and depression explained 14.9% of the variance (Andressan,et al., 2016). As a whole, this part of the study explained 26.5% of the total variance. The second step of the regression analysis displayed the results for addictive use to video games. The same regressed variables in step 1 (sex, relationship status, educational level) explained 11.4%, while ADHD, OCD, anxiety and depression explained 6.6% of the variance related to addictive use of video games (Andressan et al., 2016). As a whole, the independent variable for addictive use of video games explained 17.9%.
Results from this study indicated that fundamental symptoms found within psychological disorders are correlated with social media and video game addictions. As predicted in the second hypothesis, females showed a higher association to addictions in social media usage whereas males showed a higher correlation to addictions involving video gaming (Andressan et al., 2016). In addition, ADHD and OCD displayed a positive interaction between these two variables. It should be noted that Andressan et al. (2016) was the first study to examine the relationship between ADHD and addictive technological performances. When it comes to anxiety and depression, this study was also able to display a positive association to addictive usage to both social media and video gaming. However when the regression analysis controlled the associated the demographic factors, anxiety had a positive correlation to addictions in social media usage and a negative correlation in addition in video game usage (Andressan et al., 2016). These results were opposite for depressive measure. When Andressan et al. (2016), analyzed the influence of the demographic factors related to this study, being single was influential to addictive behaviors in modern technology. Single females positively correlated to addictive social networking usage whereas single males were positively correlated to addictive video game usage. Due to these results, the limitations of this study may have attracted younger and female participants due to the web based surveys. Consequently, further empirical studies concerning this epidemic may require differentiating addictions in social networking and video gaming as separate entities instead of grouping in the same category as Internet addictions.

Mertler, C. A., & Vannatta, R. A. (2017). Advanced and multivariate statistical methods: Practical application and interpretation. New York, NY: Routledge, Taylor & Francis Group.

Schou Andreassen, C., Billieux, J., Griffiths, M. D., Kuss, D. J., Demetrovics, Z., Mazzoni, E., & Pallesen, S. (2016). The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: A large-scale cross-sectional study.Psychology of Addictive Behaviors : Journal of the Society of Psychologists in Addictive Behaviors, 30(2), 252-262. doi:10.1037/adb0000160