Evidence map›Paper›PMID 42761744›Full record

ReviewFrontiers in public health2026

From monitoring to intervention: a closed-loop digital health framework for gaming disorder.

Longji Li, Lifeng Zhang

Abstract readReview
In one paragraph

Review in Frontiers in public health, 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

2 authors.

Longji LiStrength and Conditioning Center, School of Physical Education, Chengdu Sport University, Chengdu, China.
Lifeng ZhangStrength and Conditioning Center, School of Physical Education, Chengdu Sport University, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gaming disorder has become a significant public health concern and represents a complex, dynamic condition that requires continuous risk monitoring throughout its progression. However, conventional assessment methods, which rely primarily on questionnaires and clinical interviews, are inherently static and retrospective, limiting dynamic monitoring and early risk identification. Although digital phenotyping, artificial intelligence (AI), and digital therapeutics have emerged as promising approaches, existing reviews have largely examined these technologies separately, lacking an integrated framework for intelligent gaming disorder management. This review systematically synthesizes current evidence on digital phenotyping, AI-driven risk prediction, and digital intervention strategies, and integrates them into a "Monitor-Predict-Intervene" closed-loop digital health framework. This study indicates that digital phenotyping enables continuous multimodal monitoring of behavioral, physiological, psychological, and social signals; AI facilitates dynamic risk stratification and trajectory prediction through multimodal data integration; and digital therapeutics, particularly Just-in-Time Adaptive Interventions (JITAIs), deliver personalized interventions based on evolving risk states. Collectively, these technologies establish a closed-loop digital health paradigm that supports continuous risk management across the progression of gaming disorder. Overall, this framework advances gaming disorder management from static assessment to continuous monitoring, from reactive treatment to proactive prevention, and from standardized intervention to individualized precision care, while providing a conceptual foundation and translational roadmap for future digital mental health research and clinical practice. Future research should further address challenges related to data privacy, algorithm interpretability, and clinical implementation to facilitate the development of scalable and intelligent digital health systems.

Indexed as

Artificial IntelligenceBehavior, AddictiveDigital HealthInternet Addiction DisorderVideo GamesDigital MediaHumansdigital phenotypingdigital therapygaming disorderprecision interventionrisk prediction

Identifiers

PMID42761744
PMCPMC13587035

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.