Predicting Community Walking after Stroke

Richard Felius ^1,2* Michiel Punt ² Natasja C Wouda ^3,4 Marieke Geerars ⁵ Sjoerd M. Bruijn ² Jaap Van Dieen ²

• ¹ HU University of Applied Sciences Utrecht, Utrecht, Netherlands
• ² Department of Human Movement Sciences, Faculty of Behavioural and Movement Sciences, VU Amsterdam, Amsterdam, Netherlands, Netherlands
• ³ Center of Excellence for Rehabilitation Medicine, University Medical Center Utrecht, Utrecht, Netherlands, Netherlands
• ⁴ Department of neurorehabilitation, De Hoogstraat Rehabilitation, Utrecht, Netherlands
• ⁵ Physiotherapy Department Neurology, Axioncontinu, Rehabilitation Center de Parkgraaf, Utrecht, Netherlands

A key element of personalized stroke rehabilitation is early prediction of an individual's potential to walk in the community.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 six months after stroke.Data were collected from people after stroke during clinical rehabilitation and at six months post stroke. The assessment during rehabilitation consisted of an IMU-based two-minute 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 six-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 dailylife measures , the clinical tests and patient characteristics at admission and discharge for predicting daily-life measures at six months after stroke with univariate ordinary least squares regression. Subsequently, significant predictors were included in a multivariate ordinary least squares regression.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 six months. For the average and maximum gait speed in daily life at six 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 R 2 values for the strides per day, average and maximum gait speed models of 0.60, 0.42 and 0.53 respectively.Age, trunk stability (TCT), affected leg strength (MI), and the clinical and daily-life gait had predictive value for community walking six-months after stroke. Future research with a larger sample size is required to refine these findings.