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Record W3113305110 · doi:10.2196/18473

Choice of Leisure Activities by Adolescents and Adults With Internet Gaming Disorder: Development and Feasibility Study of a Virtual Reality Program

2020· article· en· W3113305110 on OpenAlexvenueno aff
Narae Lee, Jae‐Jin Kim, Yu‐Bin Shin, Hyojung Eom, Min-Kyeong Kim, S.-H. Kyeong, Young Hoon Jung, Sarang Min, Joon Hee Kwon, Eun Joo Kim

Bibliographic record

VenueJMIR Serious Games · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
FundersNational Research Foundation of KoreaNational Research Foundation
KeywordsThe InternetAddictionPsychologyVirtual realityMental healthApplied psychologyClinical psychologyComputer sciencePsychotherapistWorld Wide WebPsychiatryHuman–computer interaction

Abstract

fetched live from OpenAlex

BACKGROUND: Excessive internet game use frequently leads to various physical, psychological, and social problems, and internet gaming disorder (IGD) has become a serious public health issue worldwide. Recently, virtual reality (VR) therapy has emerged as a promising method to increase psychological treatment motivation and accessibility. However, few studies have examined the potential of VR technology for the management of IGD, and VR content tailored to IGD characteristics remains scarce. OBJECTIVE: This preliminary study aimed to examine the potential of a VR-based program that was designed to help users identify their leisure time use patterns, especially those related to gaming, and to modify their gaming overuse by alternative activities provided in the VR content. Moreover, to investigate whether users' VR activities reflect various clinical variables of IGD in youth, we examined the relationships among the leisure time activity selection pattern, built-in response, and speech data obtained from the VR program, as well as symptom severity of internet gaming, psychiatric comorbidities, and motivation of participants reported through relevant questionnaire data. METHODS: Three types of VR content (understanding my daily activities at home, finding an alternative activity to internet gaming at home, expressing contradictory opinions toward a friend's gaming beliefs) were developed by simulating the daily situations in which patients with IGD can select alternative free-time leisure activities. We examined internet addiction, mental health problems, and motivation for 23 IGD and 29 control participants. Behavioral and self-rated responses from VR, such as alternative activity selection data and speech patterns (speech time, speech satisfaction, and speech accordance), and results from various questionnaires were compared between groups. The correlations between IGD behaviors in VR and real-life behaviors assessed by questionnaire measures were analyzed. RESULTS: Significant correlations were found between internet gaming behavior and user activity data, such as speech and activity selection pattern, in our VR program. Our results showed that the IGD group had fewer leisure activities and preferred game or digital activities to other types of activities compared to controls, even in VR. There was a positive relationship between the viability of alternative leisure activities the participants selected in VR and the amount of perceived satisfaction from that activity (r=.748, P<.001). Speech accordance in the IGD group was lower than in the control group and was correlated negatively with Internet Addiction Test and Internet Addiction Test-gaming scores (r=.300, P=.03) but positively with users' motivation (r=.312, P=.02). CONCLUSIONS: The results from our VR program can provide information about daily activity patterns of youths with IGD and the relationship between user VR activities and IGD symptoms, which can be useful in applying VR technology to IGD management.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.154
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.322
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations16
Published2020
Admission routes1
Has abstractyes

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