# Identifying Indicators for the Early Detection of Internet Addiction Among University Students

## TL;DR

This study identifies social media usage frequency as a key indicator for early detection of Internet addiction in university students.

## Contribution

The study introduces the number of social media usage situations as a novel indicator for detecting Internet addiction.

## Key findings

- IAT scores were significantly correlated with the number of social media usage situations.
- Multiple regression analysis showed that IAT scores can be predicted by social media use frequency.
- Higher usage situations correlate with a stronger tendency to lose control over Internet use.

## Abstract

Background: Internet use has been reported to cause disturbances in eating and sleeping habits. The results of a survey conducted by the Japanese Ministry of Education, Culture, Sports, Science and Technology indicated the need to investigate Internet addiction, which was defined as a dependence on the process of action, similar to gambling and shopping addictions. Therefore, this study aimed to identify the factors associated with a tendency to be dependent on the Internet and to examine the indicators for early detection of such dependence among university students.

Methodology: A questionnaire survey regarding the actual use of smartphones, the environment surrounding their use, and relationships with the tendency to depend on the Internet was conducted on 765 university students.

Results: Internet addiction test (IAT) scores were significantly correlated with the numbers of acquaintances communicated with on social media and the number of social media usage situations. The results of the multiple regression analysis showed that IAT scores could be predicted based on the number of social media use situations.

Conclusion: The findings of this study suggest that the greater the number of usage situations, the stronger the tendency to lose control of usage, and that the number of usage situations can be an indicator for identifying Internet addiction.