Addict Behav. 2018 Dec 7;90:421-427. doi: 10.1016/j.addbeh.2018.12.009. [Epub ahead of print]
Li G1, Hou G1, Yang D1, Jian H2, Wang W3.
Abstract
Associations between anxiety, depression and adolescent Internet addiction have been well documented in the literature; however, few published studies have examined these relationships considering the developmental trajectory courses of adolescent Internet addiction as well as the individual differences over time. Using a sample of 1545 Chinese adolescents and 3 waves of data over six months, we examined the longitudinal associations between anxiety and depression and Internet addiction, considering sex and obesity. We used latent growth curve modeling (LGCM) to examine the overall conditions of Internet addiction, and latent class growth modeling (LCGM) to determine adolescent developmental membership for Internet addiction. Both unconditional and conditional models were performed. Anxiety and depression were analyzed as time-varying variables, and sex and obesity as time-invariants in our conditional models. Overall, there was a linear decline in adolescent Internet addiction over the six months. Anxiety and depression positively predicted adolescent Internet addiction. Two developmental trajectory patterns for Internet addiction were determined (i.e., low/declining, high/declining). Anxiety was associated with adolescent Internet addiction for both groups of adolescents, but depression was associated with Internet addiction only for adolescents who followed a low/declining course of Internet addiction. Boys reported a higher mean score of Internet addiction at the initial status than girls, and boys also had a faster, declining rate of change over the six months than girls. Obesity was not a predictor of Internet addiction. The results spoke to the importance of considering mental health problems and sex in any intervention efforts to reduce adolescent Internet addiction. Limitations of the study were discussed.
KEYWORDS: Adolescence; Chinese students; Internet addiction; Longitudinal analysis
PMID: 30553156
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