Managing Game-Related Conflict With Parents of Young Adults With Internet Gaming Disorder: Development and Feasibility Study of a Virtual Reality App
Bibliographic record
Abstract
BACKGROUND: Individuals with internet gaming disorder (IGD) report facing family conflicts repeatedly because of their excessive internet gaming. With recent advancements in virtual reality (VR) technology, VR therapy has emerged as a promising method for the management of various psychiatric disorders, including IGD. Given that several risk and protective factors for young people with addiction can be influenced by their interpersonal context, the potential utility of VR-based apps for managing family conflicts needs to be examined with reference to IGD management. However, few studies have evaluated potential treatment modules related to interpersonal conflict management, such as emotion regulation and taking the perspective of others. OBJECTIVE: This preliminary study aims to examine the potential use of a VR-based app in the management of game-related conflicts with parents of young adults with IGD and matched controls. METHODS: In total, 50 young male adults (24 with IGD and 26 controls) were recruited to participate in the study. We developed a virtual room where game-related family conflicts arise. Using this room, participants completed 2 VR tasks that required them to express anger and then implement coping skills (ie, risk/benefit assessment of stopping a game and taking parents' perspective) to deal with negative emotions in interpersonal conflict situations and to decrease one's gaming behavior. RESULTS: The results showed that immersion in our VR app tended to provoke negative emotions in individuals with IGD. In addition, after a risk/benefit assessment of stopping a game, the response of stopping a game immediately increased significantly in the IGD group, suggesting that patients' gaming behavior could be changed using our VR program. Furthermore, in individuals with IGD, longer gaming hours were associated with a lower level of perceived usefulness of the coping skills training. CONCLUSIONS: The findings of this study indicate that our VR app may be useful for implementing more desirable behaviors and managing gaming-related family conflicts in individuals with IGD. Our VR app may offer an alternative for individuals with IGD to learn how a vicious cycle of conflicts is developed and to easily and safely assess their dysfunctional thoughts behind the conflicts (ie, perceived unreasonable risks of stopping a game and thoughts acting as a barrier to taking the perspective of others).
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".