The use of machine learning to improve the identification and assessment of Internet-related disorders

The Internet’s growing significance has raised global concerns about Internet-related disorders. Organizations like the American Psychological Association (APA) and the World Health Organization (WHO) have already highlighted the potential negative effects of excessive Internet use on mental health. The project aims to address two key research priorities in the field of problematic use of the Internet (PUI), related to (a) contributing to their conceptualization and (b) improving their assessment. In this regard, four different studies targeting gaming disorder and cyberchondria were conducted. This project centrally focuses on using machine learning (ML) and traditional statistics to reach these objectives. In Study 1 we identified psychological factors that predict the level of cyberchondria (excessive use of the Internet to search for health-related information) during the pandemic. In Study 2, different gamer groups based on their profiles of passion for gaming were identified. We also investigated how gaming disorder symptoms are linked to harmonious and/or an obsessive passion for gaming. Study 3 used gaming disorder criteria to predict depression and well-being levels. It also identified predictors of gaming disorder level and their importance in the prediction of each DSM-5 criterion proposed for Internet gaming disorder. Finally, Study 4 warns against the misuse of algorithm-generated data in ML analyses and its negative impact on the conceptualization and assessment of a PUI. Results from the studies suggest that cyberchondria and gaming disorder can be understood within the same general framework. Nevertheless, additional models specific to each condition can enhance their understanding and provide important insights for their treatment and prevention interventions. Regarding their assessment, the project supports the notion of the transdiagnostic nature of the criteria proposed by the ICD-11 for the assessment of gaming disorder and their potential capacity to address the various forms of PUI. The project also demonstrates that ML methods offer a helpful and convenient instrument for psychological research topics such as PUI.