The problem that this study addressed is the rise of internet gaming disorder (IGD) globally, including within the United States and countries such as China—and, the resultant need for more data on the prevalence of adult men and women meeting criteria for a diagnosis of IGD, as well as data on related comorbidities and psychosocial issues. A global sample (N=231) met the study inclusion criteria (i.e., play video games at least once a week at a minimum, consider themselves involved in Internet gaming, and have been gaming for the past six months—while of interest were findings with an English Speaking (ES) sample, and a Chinese Mandarin Speaking (CMS) sample. The study sample of convenience recruited via a social media campaign was 62.4% (n=63) male in the ES sample, and 55.4% (n=72) male in the CMS sample. The ES sample had a mean age of 29.34 (SD=8.396, Min=18, Max=52), and the CMS sample had mean age of 25.65 (SD=7.514, Min=18, Max=57). While the CMS sample indicated they were Asian (99.2%, n=129), the ES sample was diverse: 58% White (n=59), 17.8% (n=59) Asian, and 11.9% (n=12) Black.
The main study findings reveal a prevalence of Internet Gaming Disorder (IGD of 0% (n=101) for the ES sample, and .8% (n=1) for the CMS sample. As another main study finding, for the CMS sample, participants met more DSM-5 criteria for IGD (out of the 9 total criteria), when they were male, experienced anxiety in the past year, and were engaged in more violence due to gaming. For the ES sample, study participants met more DSM-5 criteria for IGD (out of the 9 total criteria), when they did not have a partner, had a higher income, were engaged in more violence due to gaming, engaged in a higher level of help seeking for personal/emotional support, and had a lower level of perceived social support. In essence, this constitutes the provision of risk profiles and descriptions of those most vulnerable to IGD. This study contributes to those efforts to conduct research on the DSM-5 criteria for IGD (APA, 2013).
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/d8-js7c-nx64 |
| Date | January 2019 |
| Creators | Torez, Miguel |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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