Comparing Step Counting Algorithms for High-Resolution Wrist Accelerometry Data in NHANES 2011-2014

TL;DR

This study compares five high-resolution wrist accelerometry step counting algorithms using NHANES data, revealing high correlation but significant differences in population step distributions and mortality risk estimates.

Contribution

It provides a comprehensive comparison of open-source and proprietary algorithms on real-world data, highlighting their similarities and differences in predictive performance and population estimates.

Findings

High correlation among algorithms (median=0.91)

Similar predictive performance for mortality (median concordance=0.72)

Wide variation in population step count distributions

Abstract

Purpose: To quantify the relative performance of step counting algorithms in studies that collect free-living high-resolution wrist accelerometry data and to highlight the implications of using these algorithms in translational research. Methods: Five step counting algorithms (four open source and one proprietary) were applied to the publicly available, free-living, high-resolution wrist accelerometry data collected by the National Health and Nutrition Examination Survey (NHANES) in 2011-2014. The mean daily total step counts were compared in terms of correlation, predictive performance, and estimated hazard ratios of mortality. Results: The estimated number of steps were highly correlated (median=0.91, range 0.77 to 0.98), had high and comparable predictive performance of mortality (median concordance=0.72, range 0.70 to 0.73). The distributions of the number of steps in the population varied widely (mean step counts range from 2,453 to 12,169). Hazard ratios of mortality associated with a 500-step increase per day varied among step counting algorithms between HR=0.88 and 0.96, corresponding to a 300% difference in mortality risk reduction ([1-0.88]/[1-0.96]=3). Conclusion: Different step counting algorithms provide correlated step estimates and have similar predictive performance that is better than traditional predictors of mortality. However, they provide widely different distributions of step counts and estimated reductions in mortality risk for a 500-step increase.