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Reproducible Analysis Pipeline for Data Streams: Open-Source Software to Process Data Collected With Mobile Devices.

Julio Vega1, Meng Li1, Kwesi Aguillera1

  • 1Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States.

Frontiers in Digital Health
|December 6, 2021
PubMed
Summary

Researchers can now standardize mobile sensor data analysis with the Reproducible Analysis Pipeline for Data Streams (RAPIDS). This open-source tool enhances efficiency, reproducibility, and transparency in behavioral and clinical research.

Keywords:
digital biomarkersdigital healthdigital phenotypingmobile sensingsmartphonewearable

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Area of Science:

  • Digital Health
  • Computational Behavioral Science
  • Data Science

Background:

  • Longitudinal data from smartphones and wearables are crucial for modeling human behavior in research.
  • Current practices involve custom data processing and analysis code, leading to inefficiencies and lack of reproducibility.
  • Sharing code alongside publications is inconsistent, hindering collaborative progress and transparency.

Purpose of the Study:

  • To introduce the Reproducible Analysis Pipeline for Data Streams (RAPIDS) for standardizing mobile sensor data processing.
  • To provide an open-source, extensible, and tested solution for researchers.
  • To enhance the rigor, reproducibility, and transparency of mobile sensing data analysis.

Main Methods:

  • RAPIDS is a pipeline comprising R and Python scripts for data preprocessing, feature extraction, analysis, and visualization.
  • It utilizes reproducible virtual environments and a workflow management system.
  • The pipeline supports data from Android/iOS apps and wearable devices like Fitbit and Empatica, following a consistent project structure.

Main Results:

  • RAPIDS standardizes the preprocessing, feature extraction, analysis, visualization, and reporting of mobile sensor data streams.
  • The open-source code is documented, extensible, and tested for various mobile sensing applications.
  • It enables rigorous and reproducible mobile sensor data processing, saving researcher time and effort.

Conclusions:

  • RAPIDS offers a standardized, reproducible, and transparent approach to analyzing mobile sensor data.
  • The pipeline facilitates the sharing of analysis workflows, promoting collaboration and building upon previous research.
  • Adoption of RAPIDS can significantly improve the efficiency and reliability of behavioral and clinical research using mobile sensing data.