A machine learning approach to identifying important features for achieving step thresholds in individuals with chronic stroke.

ABSTRACT:

Background

While many factors are associated with stepping activity after stroke, there is significant variability across studies. One potential reason to explain this variability is that there are certain characteristics that are necessary to achieve greater stepping activity that differ from others that may need to be targeted to improve stepping activity.

Objective

Using two step thresholds (2500 steps/day, corresponding to home vs. community ambulation and 5500 steps/day, corresponding to achieving physical activity guidelines through walking), we applied 3 different algorithms to determine which predictors are most important to achieve these thresholds.

Methods

We analyzed data from 268 participants with stroke that included 25 demographic, performance-based and

SUBMITTER: Miller AE 

PROVIDER: S-EPMC9205506 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature