Improvement of Game Users’ Depressive Symptoms via Behavioral Activation in a Massive Multiplayer Online Game: Randomized Controlled Trial
Bibliographic record
Abstract
Background: Online games developed to improve mental health symptoms are reportedly effective among game users. However, it has not been verified whether massive multiplayer online games (MMOGs) developed for leisure purposes are effective in improving users' mental health symptoms. Objective: Based on 2 theoretical frameworks, this study examined whether MMOGs improve depression and social anxiety. First, behavioral activation theory posits that depressive symptoms improve through the repetition of reward-linked behaviors. Second, inhibitory learning theory suggests that exposure to social stimuli (eg, being the center of attention) previously perceived as threatening reduces fear responses over time. Methods: Participants were Pigg Party users with at least 3 months of previous experience. Overall, 1105 participants were randomly assigned to either the experimental (n=548) or waitlist groups (n=557). Participants in the experimental group were instructed to ring a friend's room bell (an action that clearly draws attention) on weekdays and to customize their avatars on weekends. Those completing ≥60% of the tasks received additional monthly rewards. The waitlist group received no interventions but was given random additional rewards. Both groups completed questionnaires on depression (Quick Inventory of Depressive Symptomatology) and social anxiety (Brief Liebowitz Social Anxiety Scale) at baseline, and again at 1, 2, and 3 months. Results: The experimental group showed a significantly higher frequency of bell ringing compared to that of the waitlist group (standardized mean difference [SMD]=0.13), whereas no significant difference was observed between the groups in avatar customization frequency. As predicted, the experimental group showed a significantly greater reduction in depressive symptoms, with a small effect size observed (SMD=-0.12). However, no significant difference was determined between groups in social anxiety symptoms. Conclusions: This study demonstrated that MMOGs, when combined with administrator-led interventions, can reduce users' depressive symptoms, albeit with a small effect size. Further studies are needed to test the intervention effects on social anxiety symptoms in MMOGs, with improved exposure scenarios.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".