A Systematic Review of Pharmacological Treatments for Internet Gaming Disorder
Bibliographic record
Abstract
OBJECTIVE: Internet gaming disorder (IGD) is an increasingly common behavioral addiction, with an estimated global prevalence of 3%. A variety of pharmacological treatments have been used to treat IGD, yet no review to date has synthesized clinical trials evaluating their efficacy. This systematic review therefore synthesized the literature reporting on clinical trials of pharmacological treatments for IGD. METHODS: We reviewed articles from MEDLINE, Embase, PubMed Central, CINAHL, and PsycINFO that were published as of March of 2022. A total of 828 articles were retrieved for review and 12 articles were included, reporting on a total of 724 participants. RESULTS: Most participants were male (98.6%), and all were currently living in South Korea. The most common drugs used to treat IGD were bupropion, methylphenidate, and a range of selective serotonin reuptake inhibitors. The Young Internet Addiction Scale was the most frequently used to measure gaming-related outcomes. All studies reported reduced symptoms of IGD from pre- to post-treatment. Across all clinical trials, IGD symptom reductions following the administration of pharmacological treatments ranged from 15.4% to 51.4%. A risk of bias assessment indicated that only four studies had a low risk of bias. CONCLUSION: Preliminary results suggest that a wide array of pharmacological interventions may be efficacious in the treatment of IGD. Future studies using double-blind randomized controlled trial designs, recruiting larger and more representative samples, and controlling for psychiatric comorbidities are needed to better inform understanding of pharmacological treatments for IGD.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".