Aimee Miller, M. A. Kaitlin O Brien, B. A. Department of Psychology California State University, Dominguez Hills

Aimee Miller, M. A. Kaitlin O Brien, B. A. Department of Psychology California State University, Dominguez Hills

George Marsh Applied Cognition Laboratory CSU Dominguez Hills Department of Psychology Founding Faculty Mentors: Dr. Larry Rosen, Ph.D. Dr. Mark Carrier, Ph.D. Dr. Nancy Cheever, Ph.D.

The Media and Technology Usage and Attitudes Scale (2013) Developed by the GMAC lab at CSU Dominguez Hills

Introduction Increasing popularity of the Internet Constantly accessing technology and information Potential problematic outcomes Internet Gaming Disorder DSM-5, 2013 Ethnicity & Internet Addiction Derbyshire et al., 2013 Asian populations Internet Addiction

Internet Addiction (IA) Definition Young, 1998 Problematic outcomes from pathological Internet use Behavioral and substance-related disorders Outcomes Social Academic

IA & Technology Use Frequency & Type of Technology Use Kuss et al., 2014 Frequency Type of participation in technology Internet Addiction Internet Addiction & Adolescents Carbonell et al., 2012; Xu et al., 2012 Frequency and duration predicts IA Duration of Internet use Participation in online gaming Use of social applications

IA & Executive Function Cognitive Control & Attention Systems Working memory Reasoning Task flexibility Problem solving Planning Decision making Inhibition Internet Addiction & Tasks Brand et al., 2014 Deficits in impulse control Impaired performance on decision making Lowered abilities for working memory tasks

Purpose & Research Question To determine how Internet Addiction develops What is the relationship between executive function, technology use, and Internet Addiction?

Hypotheses Hypothesis 1: Increased problems in executive functioning will predict symptoms and severity of IA Hypothesis 2: Increased technology use will predict symptoms and severity of IA Hypothesis 3: The relationship between IA and technology use is moderated by an individual s amount of prefrontal control

Participants 391 university students Age range: 20 60 years Ethnicity M = 26.5 years SD = 6.7 years 10% 6% 16% Other Asian Gender Females: 222 (56.8%) 49% 19% African American Hispanic Caucasian Males: 169 (43.2%)

Measures Internet Addiction Test (IAT) Young, 1998 Symptoms and severity of IA Media and Technology Usage and Attitudes Scale (MTUAS) Rosen et al., 2013 Type of device, frequency, attitudes Web Based Executive Functioning Questionnaire (Webexec) Buchanan et al., 2010 Executive function problems

Results Dependent Variable Internet Addiction (M = 33.50, SD = 11.36, α =.92) Independent Variables Step 1: Remove extraneous variables Age, Gender, Ethnicity, Median Income Step 2: Independent predictor variables Total Technology Use (M = 186.10, SD = 35.95, α =.90) Executive Dysfunction (M = 12.02, SD = 3.72, α =.86) Analysis of interaction between two quantitative predictors Moderated approach to multiple regression analysis

Table 1: Hierarchical Multiple Regression Model Summary Model Step R R 2 Change in R 2 F- score df pred df error Sig. Level 1 a.252.063.063 3.643 7 377.001 2 b.513.263.200 33.852 3 374.000 Dependent Variable: Internet Addiction a Predictors: Age, Gender, Median Income, Asian, Black, Hispanic, White b Predictors: Executive Dysfunction, Technology Use, Interaction Variable

Step 1: Summary of Hierarchical Multiple Regression Analysis of Variables Predicting Likelihood of Internet Addiction Variable Step 1 Age -.143** African-American/Black.071 Asian.249** Caucasian/White.110 Hispanic/Latino.191 Gender -.041 Median Income.068 *p <.05, **p <.01, ***p <.001. β

Step 2: Summary of Hierarchical Multiple Regression Analysis of Variables Predicting Likelihood of Internet Addiction Variable Step 2 Age -.074 African-American/Black.106 Asian.197* Caucasian/White.094 Hispanic/Latino.186 Gender -.029 Median Income.058 Technology Use.157** Executive Dysfunction.427*** Interaction (Tech Use x Executive Functioning) -.091* *p <.05, **p <.01, ***p <.001. β

Internet Addiction (Total Score) Figure 1: The Moderating Effects of Executive Dysfunction in Determining the Relationship Between Executive Dysfunction and Internet Addiction 60 50 40 30 20 10 Low EF Problems Medium EF Problems High EF Problems 0 Low Technology Use High

Discussion Hypothesis 1 Executive dysfunction predicts an increased likelihood of Internet addiction Hypothesis 2 Internet addiction is predicted by increased technology usage Hypothesis 3 Executive function moderates the relationship between technology use and Internet addiction

Limitations and Future Directions Limitations Participants did not endorse items consistent with prominent executive dysfunction Participants symptoms and severity were not indicative of Internet addiction disorder Future Directions Identify which technology related activities are most disruptive Implement neuroimaging and neuropsychological measures Study in progress

Acknowledgments Dr. Mark Carrier, Ph.D. Dr. Larry Rosen, Ph.D. George Marsh Applied Cognition Laboratory (GMAC Lab) CSU Dominguez Hills