CrossShift: Quantifying Interpersonal Differences in Mobile Sensing for Mental Health

Abstract

Mobile sensing systems for mental health leverage ubiquitous behavioral data to monitor states like stress and anxiety, enabling just-in-time interventions and context-aware care. However, the real-world scalability of these systems is severely limited by poor cross-user generalization—a challenge often attributed to interpersonal differences but rarely quantified systematically. We present CrossShift, a structured empirical framework for decomposing and measuring user-level covariate, label, conditional, and concept shifts in mobile mental-health sensing. CrossShift unifies shift testing, shift quantification, model evaluation, and shift-to-generalization attribution within a common analytical framework tailored to this domain. We evaluate our framework across eight diverse datasets, comprising three newly collected and five public mobile sensing repositories. Our analysis reveals that phone usage, sleep and social-interaction features are the most consistent sources of covariate shift across users. Crucially, we find that concept shift—the variation in the mapping from behavior to mental state—is the primary driver of performance degradation. Our findings suggest that scalable mobile mental health sensing requires moving beyond one-size-fits-all models toward shift-aware system design. We further discuss how CrossShift can support out-of-distribution user detection and selective personalization in real-world deployments.