Person-Specific Changes in Motor Performance Accompany Upper Extremity Functional Gains After Stroke

Abstract

In animal models, hundreds of repetitions of upper extremity (UE) task practice promote neural adaptation and functional gain. Recently, we demonstrated improved UE function following a similar intervention for people after stroke. In this secondary analysis, computerized measures of UE task performance were used to identify movement parameters that changed as function improved. Ten people with chronic post-stroke hemiparesis participated in high-repetition UE task-specific training 3 times per week for 6 weeks. Before and after training, we assessed UE function with the Action Research Arm Test (ARAT), and evaluated motor performance using computerized motion capture during a reach-grasp-transport-release task. Movement parameters included the duration of each movement phase, trunk excursion, peak aperture, aperture path ratio, and peak grip force. Group results showed an improvement in ARAT scores (p = 0.003). Although each individual changed significantly on at least one movement parameter, across the group there were no changes in any movement parameter that reached or approached significance. Changes on the ARAT were not closely related to changes in movement parameters. Since aspects of motor performance that contribute to functional change vary across individuals, an individualized approach to upper extremity motion analysis appears warranted.

A robotic object hitting task to quantify sensorimotor impairments in participants with stroke

“…Many studies highlight differences between motor function of participants with stroke as compared to healthy controls [14,22,26,41-46], but these studies are based on group-level comparisons. In general, such studies match each participant with stroke to a healthy control participant with the same age and sex.…”

Need for Speed

“…Although previous studies of grip force control in people with stroke have used custom-made devices equipped with strain gauges 47, 48 , we chose to use a pressure sensor because it does not require that participants place their hand or fingers on specific locations. Instead, each participant’s natural grasping performance was measured by the pressure sensor, which covered the entire surface of the vertical cylinder 46 . Using this method, we determined precisely when the hand contacted the object, and quantified grip force applied to the object.…”

“…Aperture path ratio quantified the efficiency of thumb and index finger movement during the reach phase, and was calculated as follows 46 . $$\text{Aperture}\text{Path}\text{Ratio}=\frac{\text{Sum}\text{of}\text{the}\text{absolute}\text{values}\text{of}\text{all}\text{changes}\text{in}\text{aperture}\text{during}\text{the}\text{reach}\text{phase}}{(\text{Peak}\text{aperture}-\text{aperture}\text{at}\text{beginning}\text{of}\text{reach})+(\text{Peak}\text{aperture}-\text{aperture}\text{at}\text{end}\text{of}\text{reach})}$$…”

Grip Type and Task Goal Modify Reach-to-Grasp Performance in Post-Stroke Hemiparesis

“…Total force measured by the sensor during each trial was exported as a time-series and further analyzed using MATLAB. Pressure sensor technology is a newer method for measuring grip force (DeJong, Birkenmeier, & Lang, In Press) and was selected because it affords more natural grasping performance for subjects without requiring specific hand/finger placement on discrete sensors. A limitation of the pressure sensor system is that it measures only grip (normal) forces, not load (tangential or shear) forces.…”