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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14230%2F24%3A00135348" target="_blank" >RIV/00216224:14230/24:00135348 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0022395623006027" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0022395623006027</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jpsychires.2023.12.038" target="_blank" >10.1016/j.jpsychires.2023.12.038</a>

Result language

angličtina

Original language name

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

Original language description

Research has shown a link between depression risk and how gamers form relationships with their in-game figure of representation, called avatar. This is reinforced by literature supporting that a gamer's connection to their avatar may provide broader insight into their mental health. Therefore, it has been argued that if properly examined, the bond between a person and their avatar may reveal information about their current or potential struggles with depression offline. To examine whether the connection with an individuals' avatars may reveal their risk for depression, longitudinal data from 565 adults/adolescents (Mage = 29.3 years, SD = 10.6) were evaluated twice (six months apart). Participants completed the User-Avatar-Bond [UAB] scale and Depression Anxiety Stress Scale to measure avatar bond and depression risk. A series of tuned and untuned artificial intelligence [AI] classifiers analyzed their responses concurrently and prospectively. This allowed the examination of whether user-avatar bond can provide cross-sectional and predictive information about depression risk. Findings revealed that AI models can learn to accurately and automatically identify depression risk cases, based on gamers' reported UAB, age, and length of gaming involvement, both at present and six months later. In particular, random forests outperformed all other AIs, while avatar immersion was shown to be the strongest training predictor. Study outcomes demonstrate that UAB can be translated into accurate, concurrent, and future, depression risk predictions via trained AI classifiers. Assessment, prevention, and practice implications are discussed in the light of these results.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

50100 - Psychology and cognitive sciences

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Psychiatric Research

ISSN

0022-3956

e-ISSN

1879-1379

Volume of the periodical

170

Issue of the periodical within the volume

February

Country of publishing house

GB - UNITED KINGDOM

Number of pages

12

Pages from-to

328-339

UT code for WoS article

001158930000001

EID of the result in the Scopus database

2-s2.0-85182217778