Combined Cognitive-Motor Rehabilitation in Virtual Reality Improves Motor Outcomes in Chronic Stroke - A Pilot Study

Abstract

Stroke is one of the most common causes of acquired disability, leaving numerous adults with cognitive and motor impairments, and affecting patients' capability to live independently. Virtual Reality (VR) based methods for stroke rehabilitation have mainly focused on motor rehabilitation but there is increasing interest toward the integration of cognitive training for providing more effective solutions. Here we investigate the feasibility for stroke recovery of a virtual cognitive-motor task, the Reh@Task, which combines adapted arm reaching, and attention and memory training. 24 participants in the chronic stage of stroke, with cognitive and motor deficits, were allocated to one of two groups (VR, Control). Both groups were enrolled in conventional occupational therapy, which mostly involves motor training. Additionally, the VR group underwent training with the Reh@Task and the control group performed time-matched conventional occupational therapy. Motor and cognitive competences were assessed at baseline, end of treatment (1 month) and at a 1-month follow-up through the Montreal Cognitive Assessment, Single Letter Cancelation, Digit Cancelation, Bells Test, Fugl-Meyer Assessment Test, Chedoke Arm and Hand Activity Inventory, Modified Ashworth Scale, and Barthel Index. Our results show that both groups improved in motor function over time, but the Reh@Task group displayed significantly higher between-group outcomes in the arm subpart of the Fugl-Meyer Assessment Test. Improvements in cognitive function were significant and similar in both groups. Overall, these results are supportive of the viability of VR tools that combine motor and cognitive training, such as the Reh@Task. Trial Registration: This trial was not registered because it is a small clinical study that addresses the feasibility of a prototype device.

Keywords: cognitive rehabilitation; motor rehabilitation; stroke; task adaptation; virtual reality.

Figures

FIGURE 1

Experimental setup and VR task. (A) The user works on a tabletop and arm movements are captured by augmented reality pattern tracking. These movements are mapped onto the movements of a virtual arm on the screen for the execution of the cancelation task. (B) The target stimuli can be letters, numbers, and symbols in black or different colors. The target stimuli in this picture are ordered by increasing complexity.

FIGURE 2

Flow diagram of enrollment, intervention allocation, follow-up, and data analysis.

FIGURE 3

Task performance evolution over time in the Reh@Task. Data show the maximum difficulty level achieved per training session for the memory and attention tasks.

FIGURE 4

Task performance changes between the first and last training sessions for the memory and attention tasks in the Reh@Task. The whiskers indicate the most extreme data points that are not considered outliers. ∗∗ indicates p < 0.01.

FIGURE 5

Percentage of task mistakes depending on the category of stimulus being presented in the Reh@Task. The whiskers indicate the most extreme data points that are not considered outliers. Outliers are represented as +. ∗ or ∗∗ indicates p < 0.05 or p < 0.01, respectively.

FIGURE 6

Movement smoothness analysis for the VR group. (A) Example 2-min sample of movement trajectory of one patient. (B) Computed speed profile of the sample in (A). Movement sequence segments are identified in-between null acceleration points. (C) Movement smoothness changes between the first and last training sessions. The whiskers indicate the most extreme data points that are not considered outliers. ∗∗ indicates p < 0.01.

FIGURE 7

Changes over time of the x and y component of the Range of movement as assessed by the Reh@Task calibration. The whiskers indicate the most extreme data points that are not considered outliers. Outliers are represented as +. ∗∗ indicates p < 0.01.