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Using cross-sectional survey data from 2,275 community-dwelling adults aged 60 years or older in Guangzhou, this exploratory study applied XGBoost with SHAP analysis to identify predictive correlates, followed by K-means clustering to examine subgroup heterogeneity among participants classified as high risk. Social participation and depressive symptoms were associated with the risk with nonlinear patterns. Two exploratory profiles were identified: the Socially Active profile (40.7%), characterized by high participation and more regular non-kin contacts, and the Emotionally Withdrawn profile (59.3%), characterized by elevated depressive symptoms, fewer regular non-kin contacts, and entertainment-oriented use. 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