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

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.

Automated summary

The study evaluates the stride segmentation performance of ADEPT for subsecond-level accelerometry data collected in the free-living environment using a wrist-worn sensor, expanding the existing pattern-matching algorithm and finding a significant association between walking cadence and SF-36 quality of life measures.