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Sensor wide association studies in digital medicine.
Nico Steckhan1,2,3, Felix Broghammer4, Dylan Powell5
1Digital Health - Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany. nico.steckhan@hpi.de.
NPJ Digital Medicine
|May 30, 2026
Summary
Sensor-Wide Association Studies (SWAS) enable rigorous analysis of digital health data. This approach facilitates hypothesis generation for digital epidemiology and personalized medicine.
Area of Science:
- Digital health
- Genomics
- Epidemiology
Background:
- Genomics association studies transformed health research by analyzing large feature sets.
- Digital medicine generates high-dimensional, longitudinal data from various sensors.
Purpose of the Study:
- Introduce Sensor-Wide Association Studies (SWAS) for analyzing digital sensor data.
- Establish SWAS as a reproducible foundation for digital epidemiology and personalized medicine.
Main Methods:
- Structured, feature-wide scans of sensor-derived features against clinical phenotypes.
- Utilize transparent feature documentation and longitudinal modeling.
- Implement principled control of multiplicity for robust findings.
Main Results:
- SWAS offers a systematic approach to hypothesis generation from digital health data.
- Addresses challenges in analyzing high-dimensional, longitudinal sensor data.
Conclusions:
- SWAS provides a framework for reproducible digital epidemiology.
- Enables personalized medicine through sensor data analysis.
