World Wide Web

Demography

Addiction

Statistics

Virology

The Internet

Psychiatry

Computer science

Anxiety

Worry

Outbreak

Coronavirus disease 2019 (COVID-19)

Psychology

Mathematics

Infectious disease (medical specialty)

Sociology

Distress

Medicine

Clinical psychology

Latent class model

Disease

Pathology

Journal of Behavioral Addictions

Background and aims The present longitudinal study examined the changes in problematic internet use (problematic smartphone use, problematic social media use, and problematic gaming) and changes in COVID-19-related psychological distress (fear of COVID-19 and worry concerning COVID-19) across three time-points (before the COVID-19 outbreak, during the initial stages of the COVID-19 outbreak, and during the COVID-19 outbreak recovery period). Methods A total of 504 Chinese schoolchildren completed measures concerning problematic internet use and psychological distress across three time-points. Latent class analysis (LCA) was used to classify participants into three groups of problematic internet use comprising Group 1 (lowest level), Group 2 (moderate level), and Group 3 (highest level). Results Statistical analyses showed that as problematic use of internet-related activities declined among Group 3 participants across the three time points, participants in Group 1 and Group 2 had increased problematic use of internet-related activities. Although there was no between-group difference in relation to worrying concerning COVID-19 infection, Groups 2 and 3 had significantly higher levels of fear of COVID-19 than Group 1 during the COVID-19 recovery period. Regression analysis showed that change in problematic internet use predicted fear of COVID-19 during the recovery period. Conclusion The varied levels of problematic internet use among schoolchildren reflect different changing trends of additive behaviors during COVID-19 outbreak and recovery periods.

Internet addiction and psychological distress among Chinese schoolchildren before and during the COVID-19 outbreak: A latent class analysis