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Assessing a novel way to measure step count while walking using a custom mobile phone application

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Hurt CP1, Lein DH Jr1, Smith CR2, Curtis JR3, Westfall AO4, Cortis J5, Rice C5, Willig JH6. PLoS One. 2018 Nov 6;13(11):e0206828. doi: 10.1371/journal.pone.0206828. eCollection 2018.

Abstract

Author information 1 Department of Physical Therapy, University of Alabama at Birmingham, Birmingham, Alabama, United States of America. 2 School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, United States of America. 3 Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, Alabama, United States of America. 4 Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama, United States of America. 5 CGI Group Inc, Montreal, Quebec, Canada. 6 Division of Infectious Disease, University of Alabama at Birmingham, Birmingham, Alabama, United States of America.

Abstract INTRODUCTION: Walking speed has been associated with many clinical outcomes (e.g., frailty, mortality, joint replacement need, etc.). Accurately measuring walking speed (stride length x step count/time) typically requires significant clinician/staff time or a gait lab with specialized equipment (i.e., electronic timers or motion capture). In the present study, our goal was to measure "step count" via smartphones through novel software and to compare with step tracking software that come standard with iOS and Android smartphones as a first step in walking speed measurement.

METHODS: A separate calibration and validation data collection was performed. Individuals in the calibration collection (n = 5) walked 20m at normal and slow speed (<1.0 m/s). Appropriate settings for the novel mobile application were chosen to measure step count. Individuals in the validation (n = 52) collection walked at 6m, 10m, and 20m at normal and slow walking speeds. We compared step difference (absolute difference) from observed step counts to native step tracking software and our novel software derived step counts. We used generalized estimated equation adjusted (participant level) negative binomial regression models of absolute step difference from observed step counts, to determine optimal settings (calibration) and subsequently to gauge performance of the shake algorithm settings and native step tracking software across different distances and speeds (validation).

RESULTS: For iOS/iPhone 6, when compared to observed step count, the shake service (software driven approach) significantly outperformed the embedded native step tracking software across all distances at slow speed, and for short distance (6m) at normal speed. On the Android phone, the shake service outperformed the native step tracking software at slow speed at 6 meters and 20 meters, while its performance eclipsed the native step tracking software only at 20 meters at normal speed.

DISCUSSION: Our software based approach outperformed native step tracking software across various speeds and distances and carries the advantage of having adjustable measurement parameters that can be further optimized for specific medical conditions. Such software applications will provide an effective way to capture standardized data across multiple commercial smartphone devices, facilitating the future capture of walking speed and other clinically important performance parameters that will influence clinical and home care in the era of value based care.