{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T09:17:38Z","timestamp":1787822258503,"version":"build-2784847793"},"reference-count":26,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Pers. Med.","JPM"],"abstract":"<jats:p>Background\/Objectives: Assessment of Internet Gaming Disorder (IGD) relies on retrospective self-reports and clinical interviews, which may be affected by recall and social desirability biases and may be insensitive to behavioral change. This study evaluated an artificial intelligence (AI)-enabled, privacy-preserving digital phenotyping framework for personalized IGD risk stratification under controlled simulation assumptions. Methods: A synthetic dataset of 1000 virtual user profiles was generated with a 20% elevated-risk prevalence and 5% balanced stochastic label noise. Four aggregated telemetry features were modeled: average session duration, sessions per week, Late-Night Index, and application-switching rate. Random Forest, Logistic Regression, and Gradient Boosting classifiers were evaluated using a stratified 80:20 hold-out split, five-fold cross-validation, playtime-only baselines, label-noise sensitivity analysis, and 200 synthetic realizations. Results: The primary Random Forest model achieved a balanced accuracy of 0.850, a sensitivity of 0.800, a specificity of 0.900, an area under the receiver operating characteristic curve (ROC-AUC) of 0.909, an average precision (AP) of 0.779, and a Brier score of 0.089. As an internal consistency check under the pre-specified synthetic signal structure, all-feature models showed higher performance than playtime-only baselines, and feature importance analyses recovered the encoded signal hierarchy. Performance declined with increasing label noise. Across 200 realizations, mean ROC-AUC values for the three all-feature models ranged from 0.888 to 0.904, with overlapping empirical 95% intervals. Conclusions: The framework demonstrates the methodological feasibility of transforming aggregated telemetry into interpretable risk signals while avoiding content-level monitoring. These findings are hypothesis-generating and do not establish clinical validity or diagnostic performance. Longitudinal validation in clinically characterized cohorts is required before deployment.<\/jats:p>","DOI":"10.3390\/jpm16090447","type":"journal-article","created":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T08:02:27Z","timestamp":1787817747000},"page":"447","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-Enabled Digital Phenotyping for Personalized Risk Stratification in Internet Gaming Disorder: A Privacy-Preserving Simulation Study"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3668-3518","authenticated-orcid":false,"given":"Athanasios","family":"Kranas","sequence":"first","affiliation":[{"name":"School of Science and Technology, Hellenic Open University, 26335 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9910-8569","authenticated-orcid":false,"given":"Evgenia","family":"Paxinou","sequence":"additional","affiliation":[{"name":"School of Science and Technology, Hellenic Open University, 26335 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ioannis","family":"Bazakidis","sequence":"additional","affiliation":[{"name":"School of Engineering, College of Science and Engineering, University of Edinburgh, King\u2019s Buildings, Edinburgh EH9 3FB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2320-2185","authenticated-orcid":false,"given":"Christina","family":"Koufopoulou","sequence":"additional","affiliation":[{"name":"Intensive Care Unit, General Hospital of Athens \u201cKAT\u201d, Nikis 2, 14561 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Petros","family":"Koufopoulos","sequence":"additional","affiliation":[{"name":"Cardiology Department, Sismanogleio General Hospital, Sismanogleiou 1, 15126 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3597-1187","authenticated-orcid":false,"given":"Georgios","family":"Feretzakis","sequence":"additional","affiliation":[{"name":"School of Science and Technology, Hellenic Open University, 26335 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9758-0819","authenticated-orcid":false,"given":"Vassilios S.","family":"Verykios","sequence":"additional","affiliation":[{"name":"School of Science and Technology, Hellenic Open University, 26335 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"American Psychiatric Association (2022). 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