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Determining the utility of a smartphone-based gait evaluation for possible use in concussion management.

David R HowellVipul LugadeMikhail TaksirWilliam P Meehan
Published in: The Physician and sportsmedicine (2019)
Objectives: Our was objectives were to (1) assess the validity of a smartphone-based application to obtain spatiotemporal gait variables relative to an established movement monitoring system used previously to evaluate post-concussion gait, and (2) determine the test-retest reliability of gait variables obtained with a smartphone.Methods: Twenty healthy participants (n = 14 females, mean age = 22.2, SD = 2.1 years) were assessed at two time points, approximately two weeks apart. Two measurement systems (inertial sensor system, smartphone application) acquired and analyzed single-task and dual-task spatio-temporal gait variables simultaneously. Our primary outcome measures were average walking speed (m/s), cadence (steps/min), and stride length (m) measured by the inertial sensor system and smartphone application.Results: Correlations between the systems were high to very high (Pearson r = 0.77-0.98) at both time points, with the exception of dual-task stride length at time 2 (Pearson r = 0.55). Bland-Altman analysis for average gait speed and cadence indicated the average disagreement between systems was close to zero, suggesting little evidence for systematic bias between acquisition systems. Test-retest consistency measures using the smartphone revealed high to very high reliability for all measurements (ICC = 0.81-0.95).Conclusions: Our results indicate that sensors within a smartphone are capable of measuring spatio-temporal gait variables similar to a validated three-sensor inertial sensor system in single-task and dual-task conditions, and that data are reliable across a two-week time interval. A smartphone-based application might allow clinicians to objectively evaluate gait in the management of concussion with high ease-of-use and a relatively low financial burden.
Keyphrases
  • cerebral palsy
  • palliative care
  • physical activity
  • machine learning
  • healthcare
  • young adults
  • big data
  • mild traumatic brain injury
  • artificial intelligence
  • preterm birth
  • clinical evaluation