PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
May 14, 2016Psychiatry and Clinical Neurosciences204 citationsOpen Access

Internet addiction: Prevalence and relation with mental states in adolescents

View Full Paper
KKKentaro KawabeFHFumie HoriuchiMOMarina Ochi

Key Points

Key points are not available for this paper at this time.

Abstract

AIM: Internet addiction disrupts the daily lives of adolescents. We investigated the prevalence of Internet addiction in junior high school students, elucidated the relation between Internet addiction and mental states, and determined the factors associated with Internet addiction in adolescents. METHODS: Junior high school students (aged 12-15 years) were assessed using Young's Internet Addiction Test (IAT), the Japanese version of the General Health Questionnaire (GHQ), and a questionnaire on access to electronic devices. RESULTS: Based on total IAT scores, 2.0% (male, 2.1%; female, 1.9%) and 21.7% (male, 19.8%; female, 23.6%) of the total 853 participants (response rate, 97.6%) were classified as addicted and possibly addicted, respectively. Total GHQ scores were significantly higher in the addicted (12.9 ± 7.4) and possibly addicted groups (8.8 ± 6.0) than in the non-addicted group (4.3 ± 4.6; P < 0.001, both groups). A comparison of the percentage of students in the pathological range of GHQ scores revealed significantly higher scores in the possibly addicted group than in the non-addicted group. Further, accessibility to smartphones was significantly associated with Internet addiction. CONCLUSION: Students in the addicted and possibly addicted groups were considered 'problematic' Internet users. Use of smartphones warrants special attention, being among the top factors contributing to Internet addiction.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Kawabe et al. (2016) studied this question.

synapsesocial.com/papers/6a205cb1268695cee1e7340ahttps://doi.org/10.1111/pcn.12402
Ask AI
Helpful
Bookmark
Share
View Full Paper