ArticlePloS one2022
A machine learning approach to identifying important features for achieving step thresholds in individuals with chronic stroke.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.
- Contextual factors associated with walking performance after stroke: a systematic review and meta-analysis.Frontiers in neurology · 2025Pooled it
- Human-AI Cooperation in Healthcare and Rehabilitation.Delaware journal of public health · 2026Article
- Predicting patient-reported outcome of activities of daily living in stroke rehabilitation: a machine learning study.Journal of neuroengineering and rehabilitation · 2023Article
- Predicting later categories of upper limb activity from earlier clinical assessments following stroke: an exploratory analysis.Journal of neuroengineering and rehabilitation · 2023Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundWhile 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.
objectiveUsing 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.
methodsWe analyzed data from 268 participants with stroke that included 25 demographic, performance-based and self-report variables. Step 1 of our analysis involved dimensionality reduction using lasso regularization. Step 2 applied drop column feature importance to compute the mean importance of each variable. We then assessed which predictors were important to all 3 mathematically unique algorithms.
resultsThe number of relevant predictors was reduced from 25 to 7 for home vs. community and from 25 to 16 for aerobic thresholds. Drop column feature importance revealed that 6 Minute Walk Test and speed modulation were the only variables found to be important to all 3 algorithms (primary characteristics) for each respective threshold. Other variables related to readiness to change activity behavior and physical health, among others, were found to be important to one or two algorithms (ancillary characteristics).
conclusionsAddressing physical capacity is necessary but not sufficient to achieve important step thresholds, as ancillary characteristics, such as readiness to change activity behavior and physical health may also need to be targeted. This delineation may explain heterogeneity across studies examining predictors of stepping activity in stroke.
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