ArticleFrontiers in stroke2025
Predicting community walking after stroke.
Article in Frontiers in stroke, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Wearable Sensors in Gait Assessment for Parkinson Disease and Stroke in Real-World Environments: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Digital biomarkers derived from wearable sensors in neurorehabilitation: a scoping review.Frontiers in neurology · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: A key element of personalized stroke rehabilitation is early prediction of an individual's potential to walk in the community. Objective: We aim to determine the predictive value of patient characteristics, clinical test results, and Inertial Measurement Units (IMU) based balance, clinical gait and daily-life measures, measured at admission and discharge in clinical stroke rehabilitation, for community walking 6 months after stroke. Methods: Data were collected from people after stroke during clinical rehabilitation and at 6 months post stroke. The assessment during rehabilitation consisted of an IMU-based 2-min walk test (2MWT), three IMU-based balance tests, an IMU-based measurement of gait in daily life, and several standard clinical tests, including the Berg Balance Scale, Barthel Index, Functional Ambulation Categories, Motricity Index (MI), and Trunk Control Test (TCT). At 6-months, gait in daily life was measured with an IMU for two consecutive days. From this measurement, three gait features were calculated, namely the strides per day, and average and maximum gait speed. We assessed the predictive value of IMU-based balance, gait, and daily-life measures, the clinical tests and patient characteristics at admission and discharge for predicting daily-life measures at 6 months after stroke with univariate ordinary least squares regression. Subsequently, significant predictors were included in a multivariate ordinary least squares regression. Results: Thirty-five individuals after stroke were included. Ordinary least squares regression analysis indicated that age, gait features and strides per day at admission and discharge had significant predictive value for the step count at 6 months. For the average and maximum gait speed in daily life at 6 months, the 2MWT gait speed, TCT, MI and the baseline average and maximum gait speed in daily life were significant predictors. Multivariate analysis indicated that the outcomes at admission had more predictive value than the outcomes at discharge, with adjusted Conclusions: Age, trunk stability (TCT), affected leg strength (MI), and the clinical and daily-life gait had predictive value for community walking 6-months after stroke. Future research with a larger sample size is required to refine these findings.
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