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Methods for Step Count Data: Determining "Valid" Days and Quantifying Fragmentation of Walking Bouts.

Lisa Reider1, Jiawei Bai1, Daniel O Scharfstein1

  • 1Johns Hopkins Bloomberg School of Public Health, 415 N. Washington Street, Baltimore, MD, 21205, United States.

Gait & Posture
|August 18, 2020
PubMed
Summary
This summary is machine-generated.

A new algorithm identifies valid walking days from step count data. Fragmentation measures offer unique insights into activity and function beyond total steps, improving clinical research accuracy.

Keywords:
fragmentationorthopaedic traumavalid dayswalking bouts

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Area of Science:

  • Biomedical Engineering
  • Clinical Research Methodology
  • Data Science

Background:

  • Step count monitors are widely used in clinical research for assessing walking activity.
  • Accurate identification of valid daily data and analysis of walking patterns beyond total steps present significant analytical challenges.

Purpose of the Study:

  • To introduce a novel data-driven anomaly detection algorithm for identifying valid walking days.
  • To evaluate the utility of walking fragmentation measures in clinical research.

Main Methods:

  • A support vector machine (SVM) based anomaly detection algorithm was developed to identify and exclude invalid days from step count data.
  • Measures of walking fragmentation, including average steps per active minute (SCA), active to sedentary transition probability (ASTP), and sedentary to active transition probability (SATP), were computed.
  • The added value of fragmentation measures was assessed by their ability to explain variability in self-reported function (SMFA) beyond total steps (SC).

Main Results:

  • 39% of the initial 4,448 days were identified as invalid.
  • Fragmentation measures (SCA, ASTP, SATP) explained 25% more variability in self-reported function (SMFA) compared to step count (SC) alone.
  • Approximately 41% of the variability in fragmentation measures was unique and not explained by SC, indicating distinct information content.

Conclusions:

  • The developed SVM algorithm effectively identifies valid walking days, improving data quality in step count studies.
  • Walking fragmentation measures provide valuable, unique insights into activity patterns and functional outcomes.
  • This approach enhances the precision of activity assessment in clinical studies utilizing step count monitoring.