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From Digital Data to Psychological Insights: Making Sense of Mobile-Sensing Data through Integrative Preprocessing

Ramona Schoedel^1,2, Larissa Sust², Philipp Sterner^2,3

¹https://ror.org/05grahd76Charlotte Fresenius Hochschule, Germany.

|March 26, 2026

Summary

Mobile sensing with smartphones offers rich behavioral data but requires advanced preprocessing. This study reviews preprocessing methods, including data enrichment and aggregation, to extract meaningful psychological variables from complex mobile-sensing data.

Keywords:

data preprocessing digital behavioral data digital phenotyping mobile sensing variable extraction

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Area of Science:

Psychology
Digital Health
Behavioral Science

Background:

Traditional psychological research relies on questionnaires.
Smartphones enable real-world behavioral data collection via mobile sensing.
Mobile-sensing data is complex, high-dimensional, and requires advanced preprocessing.

Purpose of the Study:

Highlight challenges in mobile-sensing data preprocessing.
Review current preprocessing techniques for smartphone app usage logs.
Present preprocessing cases involving data enrichment and aggregation.

Main Methods:

Review of existing literature on mobile-sensing data preprocessing.
Presentation of three case studies with varying preprocessing complexity.
Analysis of data enrichment (integrating external/internal data) and data aggregation (summarizing data).

Main Results:

Identified potential pitfalls in mobile-sensing data preprocessing pipelines.
Discussed extensions to refine preprocessing for diverse data types and research questions.
Demonstrated mobile-sensing data's potential for nuanced behavioral variable extraction.

Conclusions:

Advanced preprocessing is crucial for unlocking the potential of mobile-sensing data.
Data enrichment and aggregation strategies enhance the extraction of psychological insights.
This work aims to inspire more sophisticated research questions using mobile-sensing data.