Evidence map›Paper›PMID 42257421›Full record

ReviewFuture science OA2026

Pharmacotherapy for online behavioral addictions: a systematic review.

Jennifer J Park, Bryan B Chen, Marc N Potenza

Abstract readReview
In one paragraph

Review in Future science OA, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Jennifer J ParkDepartment of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.ORCID 0000-0002-7988-4704
Bryan B ChenDepartment of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.
Marc N PotenzaDepartment of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.ORCID 0000-0002-6323-1354

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionIn response to the increasing prevalence of online behavioral addictions, researchers have investigated various treatment approaches, including pharmacotherapies. This systematic review aimed to summarize pharmacotherapeutic studies of online behavioral addictions.

methodsPubMed, MEDLINE, and Embase were searched to identify randomized controlled trials or quasi-experimental studies of pharmacotherapy for online behavioral addictions. Studies were excluded if they only targeted offline behavioral addictions. Study findings were summarized, and the Effective Public Health Practice Project Quality Assessment Tool was used for quality assessment.

resultsThis systematic review included 20 studies (1016 participants). Bupropion, selective serotonin reuptake inhibitors (SSRIs), and opioid receptor antagonists were most frequently studied and showed preliminary promise in reducing symptom severity. Many pharmacotherapeutic studies addressed co-occurring concerns, such as depression.

conclusionsPharmacotherapies for online behavioral addictions appear promising, but the evidence base was limited by small samples, few randomized controlled trials, and short follow-up periods. Given preliminary support for psychotherapy + pharmacotherapy for problematic gaming, future research may evaluate the efficacy of combined treatment for a broader range of online behavioral addictions. Future research should also investigate pharmacotherapies for emerging problematic online behaviors, such as AI-based sexual engagement and problematic cryptocurrency trading.

Indexed as

Compulsive sexual behavior disordergamblinginternet addiction disordermedicationpharmacotherapysocial mediatreatmentvideo games

Identifiers

PMID42257421
PMCPMC13251523

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.