Real Behavioural Data and Artificial Intelligence Algorithms/Multivariate Statistical Methods in Technological Addiction Analysis

Dear Colleagues,

In 1996, Young first conceptualised Internet addiction when the only access devices were personal computers. Shortly afterwards, in 1998, Kraut et al. put forward the idea that information and communication technologies specifically designed to enhance real-time connectivity had the paradoxical effect of causing isolation and impairing the emotional well-being of their users. At present, the smartphone (heir to the personal computer) has rapidly diffused among the world's population. Due to the growing number of applications (communication, health, entertainment, shopping, finance, education, etc.), it is foreseeable that its diffusion and use will continue to increase (e.g., use of social networks, video games), which will increase the likelihood of unregulated or addictive use.

Current studies on smartphone addiction/problematic use share some of the premises of early research on Internet addiction. For example, they share the idea that smartphone addiction can lead to negative consequences stemming from behavioural addictions (e.g., gambling), poor psychological adjustment or increased conflict with family and friends. In addition, the World Health Organisation (WHO, 2015) has suggested that excessive use of the Internet and electronic devices, including smartphones, can lead to physical health problems, injuries and accidents. Among the psychosocial consequences, WHO (2015) points to problems in areas such as violence (cyberbullying, aggressive behaviour), problems in social development (social isolation), sleep deprivation, risky sexual behaviour and other social or psychological problems.

Many of these studies also share a methodological limitation: they use a variable measurement strategy based exclusively on self-reports, which does not seem to be an important limitation when psychological or psychosocial aspects are being assessed. However, it has been shown that the evaluation of the use of the application from self-reports based on the user's estimation is systematically biased (recall problems, social desirability, etc.). To mitigate this bias, behavioural log data—e.g. time and frequency of use of the smartphone and its apps—are increasingly being used so that, for example, these patterns of use can be related to problematic use and addiction. Techniques that accurately and realistically record a person's mood and/or its evolution over time can also be used.

This Research Topic aims to further the study of the antecedents and/or consequences (psychosocial well-being, etc.) of addiction or problematic use of any technological device (smartphone, video game console, etc.) in longitudinal/cross-sectional research in which real behavioural data have been obtained that can be analysed using, for example, artificial intelligence algorithms (such as machine learning) or multivariate analysis techniques. We also welcome research with data obtained using any type of technique that is an improvement on traditional questionnaire data.

Dr. Alberto Urueña López
Guest Editor

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• real behavioral data
• smartphone addiction
• problematic smartphone use
• digital well-being
• psychosocial well-being
• cross-sectional studies/longitudinal studies
• artificial intelligence