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
Author Information
Marta Karas: Department of Biostatistics, Johns Hopkins University, 615 N Wolfe St, Baltimore, MD 21205, United States of America. ORCID
Jacek K Urbanek: Division of Geriatric Medicine and Gerontology, Department of Medicine, Johns Hopkins University, 2024 E Monument St, Baltimore, MD 21205, United States of America. ORCID
. 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.. 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.. 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.. 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.
