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Translating the user-avatar bond into depression risk: A preliminary machine learning study.

Taylor Brown¹, Tyrone L Burleigh², Bruno Schivinski³

¹Applied Health, School of Health and Biomedical Sciences, RMIT University, Australia.

Journal of Psychiatric Research

|January 9, 2024

Summary

Gamers' connection to their in-game avatars can predict depression risk. Artificial intelligence models accurately identified depression risk using user-avatar bond data, offering insights into mental health.

Keywords:

Artificial intelligence Avatar Depression Internet gaming Machine learning

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Area of Science:

Psychology
Computer Science
Digital Health

Background:

A gamer's relationship with their in-game representation, the avatar, is linked to depression risk.
The user-avatar bond may offer insights into a gamer's overall mental health and potential offline struggles.
Examining this bond could reveal information about current or developing depression.

Purpose of the Study:

To investigate if the connection between individuals and their avatars can predict depression risk.
To determine if user-avatar bond provides cross-sectional and predictive information about depression risk.
To evaluate the efficacy of artificial intelligence classifiers in identifying depression risk based on user-avatar bond.

Main Methods:

Longitudinal data from 565 adults/adolescents were collected twice, six months apart.
Participants completed the User-Avatar-Bond (UAB) scale and Depression Anxiety Stress Scale.
Tuned and untuned artificial intelligence (AI) classifiers analyzed responses for concurrent and prospective depression risk prediction.

Main Results:

AI models accurately identified depression risk based on UAB, age, and gaming duration, both currently and prospectively.
Random forests AI models demonstrated superior performance.
Avatar immersion emerged as the most significant predictor in AI model training.

Conclusions:

User-avatar bond data can be translated into accurate, concurrent, and future depression risk predictions using AI classifiers.
AI-driven assessment of user-avatar bonds shows potential for mental health screening and early intervention.
Findings support the use of AI and avatar analysis for understanding and addressing gamers' mental well-being.