Estimation of free-living walking cadence from wrist-worn sensor accelerometry data and its association with SF-36 quality of life scores. (29th June 2021)

• Record Type: Journal Article
• Authors: Karas, Marta; Urbanek, Jacek K; Illiano, Vittorio P; Bogaarts, Guy; Crainiceanu, Ciprian M; Dorn, Jonas F
• Is Part Of: Physiological measurement. Volume 42:Number 6(2021)
• Publication Date: 2021-06-29
• Subjects: accelerometry -- actigraphy -- digital health -- walking segmentation -- cadence -- wearable sensor -- gait; Physiology -- Measurement -- Periodicals; Patient monitoring -- Periodicals; 612
• DOI: 10.1088/1361-6579/ac067b
• Languages: English
• ISSNs: 0967-3334

Abstract

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.