Freshman year mental health symptoms and level of adaptation as predictors of Internet addiction: a retrospective nested case-control study of male Chinese college students

Psychiatry Res. 2013 Dec 15;210(2):541-7. doi: 10.1016/j.psychres.2013.07.023. Epub 2013 Jul 26.

Abstract

A retrospective nested case-control study was designed to explore whether freshman year mental health status and level of adaptation are predictors of Internet addiction. The study cohort was 977 college students at a university in northwest China. In the first college year, the students' mental health status and adaptation level were assessed using the Chinese College Student Mental Health Scale (CCSMHS) and the Chinese College Student Adjustment Scale (CCSAS). In the following 1-3 years, 62 Internet-addicted subjects were identified using Young's 8-item diagnostic questionnaire. Controls were matched for demographic characteristics. Using logistic regression analysis, freshman year mental health status, including factors such as somatization, anxiety, depression and self-contempt, and freshman year adaptive problems were found to be causal factors and predictors of Internet addiction. Freshman with features of depression, learning maladaptation and dissatisfaction could be an important target-intervention population for reducing Internet addiction.

Keywords: College freshmen; Internet addiction; Retrospective nested case-control design; Risk factors.

MeSH terms

  • Adaptation, Psychological*
  • Adolescent
  • Adult
  • Anxiety / psychology
  • Asian People / psychology*
  • Asian People / statistics & numerical data
  • Behavior, Addictive / ethnology
  • Behavior, Addictive / psychology*
  • Case-Control Studies
  • China / epidemiology
  • Depression / psychology
  • Female
  • Humans
  • Internet*
  • Male
  • Mental Health
  • Predictive Value of Tests
  • Regression Analysis
  • Retrospective Studies
  • Somatoform Disorders / psychology
  • Students / psychology*
  • Students / statistics & numerical data
  • Surveys and Questionnaires
  • Universities