Smartphone Addiction, Use Preferences, and Depression Among Older Adults in the Digital Context: Machine Learning Analysis of Survey Data

Background: The increasing use of smartphones among older adults offers new opportunities for social connection but may also pose risks associated with adverse mental health outcomes, including depression. Objective: This study examined the relationship between smartphone use and depression among older adults in Guangzhou, China, to identify key predictors and complex configurations associated with depression. Methods: Using a hybrid analytic approach applied to survey data from 2585 older adults in Guangzhou, machine learning methods first identified the strongest predictors of depression. Subsequent fuzzy-set qualitative comparative analysis delineated distinct configurations associated with depression. Results: The analysis identified high levels of smartphone addiction and a low preference for interactive use as central configurations associated with depression. These patterns appeared to operate through 2 distinct mechanisms: structural exclusion related to resource limitations and agentic maladaptation associated with relational deficiencies. This underlying vulnerability, in combination with specific sociodemographic factors, delineated distinct depression-related typologies. High-risk profiles included unmarried men with limited educational attainment and socially withdrawn individuals with greater socioeconomic resources. Conclusions: Problematic smartphone use, particularly when it displaces social interaction, is significantly associated with depression among older adults. Interventions should therefore promote healthier interactive digital behaviors and provide targeted support for high-risk groups.