Using Meta-regression to Explore the Potential Influential Factors of Treatment Effects of Children and Adolescents with Internet Addiction: An Updated Meta-analysis

• Main Authors: Pei-Qi Chen, 陳沛齊
• Other Authors: 張玉坤
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/4dt2xz

碩士 === 淡江大學 === 數學學系碩士班 === 106 === According to a summary report: “A Survey on Broadband Internet Usage in Taiwan, 2016” by the TWNIC showed that the overall internet rate in Taiwan is as high as 84.8%. Another survey conducted by TrendGo and the Institute of Applied Statistics of Fu Jen Catholic University from January to March 2011 showed that “The age group of the highest rate of Internet access is 15 to 19 years old, up to 100%. It’s about 58% for age below 12. And, it’s 99.9% for age between 12 to 14 year. For age between 20 to 24-year-olds, the Internet access rate is 99.6%. The majority of the Internet usage population is between 12 and 24 years old." There exist some potential problems behind this message that are worrying children and adolescent psychiatrists and educators the most. Because, according to the results of Internet addiction studies, it’s known that "Internet-addicts have poorer parent-child relationships and sever levels of depression than non-addicts." Another study on Internet addiction between the ages of 5 and 15 indicated that: “Internet-addicted group have both lower intelligence test score than non-addicted group and signifiantly lower scores on cognition scale in the Children''s Depression Inventory than non-addicted group.” The researchers further pointed out that brain development is most active in adolescence, and excessive use of the Internet may have a negative impact on adolescents'' cognitive ability. We used meta regression to explore the impact of different treatments or other potential prognostic factors on the treatment efficacy of Internet addiction. This study didn''t exclude other published meta analysis papers. Accordingly, it is named updated meta-analysis. Among the 14 collected papers, the prognostic variables that were sorted out were: year of publication, intervention methods, evaluation scale, nationality of the studied subjects, parent involvement, group therapy, randomized control trial, and the methods of effective size was calculated and mean age. There were more than half (56.3%) of the selected papers did not present the information of mean age (presented by eduction level instead). The final results were presented in two parts: mean age included and excluded. In the study results, we believe that the best fitted model is to include intervention methods and treated symptoms. The corresponging goodness-of-fit indices, named Adj R-squared and I-squared, were 0.1748 and 0.8947, respectively (including the mean age were 0.4833 and 0.8375, respectively). Among the intervention methods, the first two highest effect size were the psychotherapy plus other intervention methods. In other words, to improve the treatment effect of Internet addiction, the commonly used psychotherapy should be used with other methods (such as cognitive behavioral therapy or drugs). However, in this study, we find out that the one with lowest effect size was multi-level psychotherapy, after adjusting for the effect of treated symptoms (although, p=0.073). On the other hand, after adjusting for the effects of interventions, the first two highest effect sizes of treated symptoms were Internet addiction severity indicators and online time. Among them, most of the Internet addiction severity scales were including Internet compulsive, withdraw response and tolerance. In other words, the treatment of internet addiction must begin with how to effectively control online time.