Beyond Screen Time: Inferring Everyday Life Context from Diverse Smartphone Data

Smartphones serve as ubiquitous sensors of daily life, generating rich data streams. However, existing research often focuses narrowly on usage metrics from small or homogeneous samples, limiting our understanding of how phone data reflects broader real-world contexts. To address this gap, this paper introduces a hierarchical context inference framework designed to systematically map raw and multimodal data into two deep contextual dimensions: the Informational-Cognitive Context and the Physical-Situational Context. We empirically validated this framework through a large-scale, 14-day in-the-wild study involving a diverse cohort ( N = 539) spanning 15 occupations and 31 provincial regions in China. Applying our framework to this data reveals generalizable phenomena: the information environment frequently mixes multiple topics, shows high emotional fluctuation, and exhibits task-driven volatility rhythms. Physically, users own diverse Bluetooth peripherals but show focused usage, and our models reveal stable and cyclical phone interaction patterns across distinct operational states. Integrating these dimensions yields nuanced user personas that differ across occupational, hobby, and personality groups. This work contributes a validated framework for understanding the interplay between users' digital and physical worlds, supported by the release of a comprehensive multimodal dataset.