Authors
Daiana Colledani, Pasquale Anselmi, Lucia Monacis, Bruno Genetti, Daniele Fassinato, Luis J Gomez Perez, Adele Minutillo, Luisa Mastrobattista, Claudia Mortali
Published in
PloS one. Volume 21. Issue 8. Pages e0355595. Epub Aug 20, 2026.
Abstract
Hikikomori, or prolonged social withdrawal, is an issue of global relevance. The HRI-15 is a brief tool for its assessment. This study enhances its utility by adding clinical thresholds to identify risk and person-centered clinical profiles, and by developing a machine-learning(ML)-based computerized adaptive test (CAT) to enable rapid screening. Data from a national survey of Italian adolescents (N = 8,755) were used to conduct ROC analysis and latent profile analysis (LPA). The findings indicated that a score of ≥ 42 achieved optimal classification performance, and four profiles with distinct meanings and systematic differences in anxiety, depression, impulsivity, and risk behaviors were identified. A multivariate conditional inference tree was estimated to develop an ML-based adaptive version of the instrument. The CAT reduced administered items by 53% (7.03/15), accurately reproducing full-length scores (r = .77-.997) and the corresponding classification; alignment was assessed with latent transition analysis (entropy = .96). The procedure was integrated into an application supporting administration, scoring, and reporting (score, risk, profiles, graphs). Combining a cross-validated cutoff and clinically useful profiles with CAT and its automated administration and reporting application strengthens triage and personalization, enabling large-scale, multidomain screening and earlier intervention for hikikomori risk.
PMID:
42623430
Bibliographic data and abstract were imported from PubMed on 21 Aug 2026.
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