Estimation of free-living walking cadence from wrist-worn sensor accelerometry data and its association with SF-36 quality of life scores

Marta Karas, Jacek K. Urbanek, Vittorio P. Illiano, Guy Bogaarts, Ciprian M. Crainiceanu, Jonas F. Dorn

Research output: Contribution to journalArticlepeer-review

Abstract

Objective. We evaluate the stride segmentation performance of the Adaptive Empirical Pattern Transformation (ADEPT) for subsecond-level accelerometry data collected in the free-living environment using a wrist-worn sensor. Approach. We substantially expand the scope of the existing ADEPT pattern-matching algorithm. Methods are applied to subsecond-level accelerometry data collected continuously for 4 weeks in 45 participants, including 30 arthritis and 15 control patients. We estimate the daily walking cadence for each participant and quantify its association with SF-36 quality of life measures. Main results. We provide free, open-source software to segment individual walking strides in subsecond-level accelerometry data. Walking cadence is significantly associated with the role physical score reported via SF-36 after adjusting for age, gender, weight and height. Significance. Methods provide automatic, precise walking stride segmentation, which allows estimation of walking cadence from free-living wrist-worn accelerometry data. Results provide new evidence of associations between free-living walking parameters and health outcomes.

Original languageEnglish (US)
Article number065006
JournalPhysiological Measurement
Volume42
Issue number6
DOIs
StatePublished - Jun 2021

Keywords

  • accelerometry
  • actigraphy
  • cadence
  • digital health
  • gait
  • walking segmentation
  • wearable sensor

ASJC Scopus subject areas

  • Biophysics
  • Physiology
  • Biomedical Engineering
  • Physiology (medical)

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