{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T13:13:12Z","timestamp":1788527592554,"version":"build-2803163510"},"reference-count":75,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T00:00:00Z","timestamp":1788480000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:p>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 \u201cMonitor\u2013Predict\u2013Intervene\u201d 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.<\/jats:p>","DOI":"10.3389\/fpubh.2026.1912606","type":"journal-article","created":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T12:18:49Z","timestamp":1788524329000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["From monitoring to intervention: a closed-loop digital health framework for gaming disorder"],"prefix":"10.3389","volume":"14","author":[{"given":"Longji","family":"Li","sequence":"first","affiliation":[{"name":"Strength and Conditioning Center, School of Physical Education, Chengdu Sport University","place":["Chengdu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lifeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Strength and Conditioning 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