Psychological research has long centered around questionnaire assessments, but now digital devices, especially smartphones, enable the collection of real-world behavioral data through mobile sensing. While this data collection method offers unique opportunities, it also introduces new methodological challenges, as mobile-sensing data are highly complex and high in dimensionality (i.e., timestamped events with millisecond resolution), requiring advanced preprocessing to derive psychologically meaningful variables. This article highlights these challenges by reviewing the current state of data preprocessing based on app usage logs from smartphones. Afterward, it presents three preprocessing cases that vary in complexity across the dimensions of data enrichment-which involves adding context to raw data by integrating information from external and internal sources (including ecological momentary assessments)-and data aggregation-which entails summarizing data in different ways, from basic descriptive statistics to sophisticated machine-learning models. For each case, potential pitfalls are identified, and extensions are discussed to refine our preprocessing pipelines and accommodate different data types and research questions. By outlining these preprocessing strategies, this manuscript demonstrates the rich potential of mobile-sensing data for extracting nuanced behavioral variables beyond simple person-level summaries and aims to inspire the development of more advanced research questions based on sensing data.
Schoedel et al. (Thu,) studied this question.