Towards an automated approach for understanding problematic gaming.

INTRODUCTION

Video games have become increasingly popular worldwide, attracting billions of gamers across diverse demographics. While studies have highlighted their potential benefits, concerns about problematic gaming behaviors have also emerged. Conditions such as Internet Gaming Disorder (IGD) have been recognized by major health organizations, necessitating accurate diagnostic tools. However, existing methods, primarily reliant on self-report questionnaires, face challenges in accuracy and consistency. This paper proposes a novel technological approach to provide gaming behavior indicators, aiming to offer precise insights into gamer behavior and emotion regulation.

METHODS

To attain this objective, we investigate quantifiable gaming behavior metrics using automated, unobtrusive, and easily accessible methods. Our approach encompasses the analysis of behavioral telemetry data collected from online gaming platforms and incorporates automated extraction of gamer emotional states from face video recordings during gameplay. To illustrate the metrics and visualizations and demonstrate our method's application we collected data from two amateur and two professional gamers, all of whom played Counter-Strike2 on PC. Our approach offers objective insights into in-game gamer behavior, helping health professionals in the identification of patterns that may be difficult to discern through traditional assessment methods.

RESULTS

Preliminary assessments of the proposed methodology demonstrate its potential usefulness in providing valuable insights about gaming behavior and emotion regulation. By leveraging automated data collection and visualization analysis techniques, our approach offers a more comprehensive understanding of gamer behavior, which could enhance diagnostic accuracy and inform interventions for individuals at risk of problematic gaming behaviors.

CONCLUSION

Our findings demonstrate the valuable insights obtainable from a tool that collects telemetry data, emotion regulation metrics, and gaming patterns. This tool, utilizing specific indicators, can support healthcare professionals in diagnosing IGD and tracking therapeutic progress, potentially addressing challenges linked to conventional IGD assessment methods. Furthermore, this initial data can provide therapists with detailed information on each player's problematic behaviors and gaming habits, enabling the development of personalized treatments tailored to individual needs. Future research endeavors will focus on refining the methodology and extending its application in clinical settings to facilitate more comprehensive diagnostic practices and tailored interventions for individuals at risk of problematic gaming behaviors.