Digital choices as emotional Mirrors: Mapping psychosocial profiles through smartphone app usage patterns

This study examines how distinct patterns of smartphone application usage relate to psychosocial indicators, specifically anger, depression, and self-esteem, while identifying user clusters based on behavioral profiles. A two-stage analytical approach was employed. K-means clustering was first used to group 523 participants based on usage patterns across 130 applications, and Principal Component Analysis (PCA) was subsequently applied to summarize covariance structures among the psychosocial variables. The PCA yielded a two-dimensional psychological space that accounted for 90.1% of the variance and provided a descriptive framework for organizing users into relative psychosocial profiles. Results indicated that participants with similar demographic backgrounds exhibited different psychosocial characteristics depending on their digital usage patterns. Users who more actively engaged with productivity, health, or analog-content applications tended to show relatively more favorable psychosocial indicators, whereas users with more limited engagement in socially interactive applications tended to report higher levels of anger and depression and lower self-esteem. These findings should be interpreted as descriptive associations rather than causal effects. Overall, the study provides an exploratory account of how heterogeneous smartphone usage patterns are associated with variation in psychosocial characteristics in a non-clinical population, and suggests that everyday digital behavior may function as an observable behavioral correlate of stable psychosocial tendencies.