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Combining Experience Sampling and Mobile Sensing for Digital Phenotyping With m-Path Sense: Performance Study.

Koen Niemeijer1, Merijn Mestdagh1, Stijn Verdonck1

  • 1Faculty of Psychology and Educational Sciences, Katholieke Universiteit Leuven, Leuven, Belgium.

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Summary

m-Path Sense integrates experience sampling methodology (ESM) with mobile sensing for richer, continuous data collection. While challenges in passive data collection persist, this fusion offers a promising digital phenotyping approach for studying everyday behavior.

Keywords:
ambulatory assessmentdigital phenotypingecological momentary assessmentexperience samplingmHealthmobile healthmobile phonemobile sensingpassive sensingsmartphones

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Area of Science:

  • Digital phenotyping
  • Mobile sensing
  • Behavioral science

Background:

  • Experience sampling methodology (ESM) is a gold standard for real-world data.
  • Smartphone mobile sensing offers richer, continuous, and unobtrusive data.
  • Combining ESM and mobile sensing data is limited by current app functionalities.

Purpose of the Study:

  • To present and evaluate m-Path Sense, a novel platform for simultaneous ESM and mobile sensing.
  • To assess the performance, reliability, and user experience of the m-Path Sense platform.

Main Methods:

  • Combined m-Path (ESM) with Copenhagen Research Platform Mobile Sensing framework.
  • Developed mpathsenser R package for data extraction and linking.
  • Conducted a 3-week pilot study with 104 participants.

Main Results:

  • Collected 69.51 GB of data (31.10 MB/participant/day).
  • Achieved satisfactory sampling reliability for most sensors.
  • Identified OS background app limitations and mild battery drain as challenges.

Conclusions:

  • m-Path Sense fuses ESM and mobile sensing for enhanced behavioral studies.
  • Mobile sensing combined with ESM is a promising digital phenotyping approach.
  • Addressing passive data collection challenges is key for future research.