Comparing Step-Counting Algorithms for High-Resolution Wrist Accelerometry Data in Older Adults in the ARIC Study.

ABSTRACT:

Background

Step counting from wrist accelerometry data is widely used in physical activity research and practice. While several open-source algorithms can estimate steps from high-resolution accelerometry data, there is a critical need to compare these algorithms and provide practical recommendations for their use in older adults.

Methods

A total of 1 282 Atherosclerosis Risk in Communities study participants (mean age 83.4, 60% female) wore ActiGraph GT9X wrist devices for 7 days, collecting 80-Hz triaxial accelerometry data. Five open-source step-counting algorithms (ADEPT, Oak, Step Detection Threshold [SDT], Verisense, and Stepcount) were applied to this data. Step count distributions and their cross-sectional associations with health outcomes were compared.

Results</

SUBMITTER: Gao S 

PROVIDER: S-EPMC12061852 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature