Does motivation matter in upper-limb rehabilitation after stroke? ArmeoSenso-Reward: study protocol for a randomized controlled trial

Abstract

BackgroundFifty percent of all stroke survivors remain with functional impairments of their upper limb. While there is a need to improve the effectiveness of rehabilitative training, so far no new training approach has proven to be clearly superior to conventional therapy. As training with rewarding feedback has been shown to improve motor learning in humans, it is hypothesized that rehabilitative arm training could be enhanced by rewarding feedback. In this paper, we propose a trial protocol investigating rewards in the form of performance feedback and monetary gains as ways to improve effectiveness of rehabilitative training.MethodsThis multicentric, assessor-blinded, randomized controlled trial uses the ArmeoSenso virtual reality rehabilitation system to train 74 first-ever stroke patients (< 100 days post stroke) to lift their impaired upper limb against gravity and to improve the workspace of the paretic arm. Three sensors are attached to forearm, upper arm, and trunk to track arm movements in three-dimensional space while controlling for trunk compensation. Whole-arm movements serve as input for a therapy game. The reward group (n = 37) will train with performance feedback and contingent monetary reward. The control group (n = 37) uses the same system but without monetary reward and with reduced performance feedback. Primary outcome is the change in the hand workspace in the transversal plane. Standard clinical assessments are used as secondary outcome measures.DiscussionThis randomized controlled trial will be the first to directly evaluate the effect of rewarding feedback, including monetary rewards, on the recovery process of the upper limb following stroke. This could pave the way for novel types of interventions with significantly improved treatment benefits, e.g., for conditions that impair reward processing (stroke, Parkinson’s disease).Trial registrationClinicalTrials.gov, ID: NCT02257125. Registered on 30 September 2014.Electronic supplementary materialThe online version of this article (doi:10.1186/s13063-017-2328-2) contains supplementary material, which is available to authorized users.

Brain Functional Networks Study of Subacute Stroke Patients With Upper Limb Dysfunction After Comprehensive Rehabilitation Including BCI Training

“…BCI-based training can be considered a type of motor learning to modify neuronal activities through sustained feedback and reward. Studies have identified feedback and reward as important contributors to neurorehabilitation (34)(35)(36)(37). However, the relationship between BCI training and feedback/rewardrelated regions of the brain has not been extensively investigated; thus, the efficacy and mechanisms of BCI-based training remain unclear, such as the effects on subacute stroke patients, alterations to sensorimotor area-related networks, and precise relationships with UL function.…”

Translation of robot-assisted rehabilitation to clinical service: a comparison of the rehabilitation effectiveness of EMG-driven robot hand assisted upper limb training in practical clinical service and in clinical trial with laboratory configuration for chronic stroke

“…Hence, positive patients’ motivation requires an encouraging environment and good interaction between the therapists and the patients. Some other studies also suggested that training devices which can provide rewarding schemes in a gaming environment had the ability to enhance the motivation of patients [ 44 , 45 ]. However, how to optimally apply motivational therapy into the rehabilitation to achieve better training outcomes is still unclear and need to be further investigated [ 46 ].…”

Magnetometer-Based Drift Correction During Rest in IMU Arm Motion Tracking

“…The ArmeoSenso system [31,32,33,34] is an upper limb rehabilitation platform for the home environment. It consists of a motion capture system based on the wearable sensors which are presented in this paper, a PC with a large screen for visual feedback and touch input, as well as a therapy gaming software (implemented in Unity3D, Unity Technologies Inc., San Francisco, USA).…”

“…A simple, rule-based classifier is used to detect whenever the user has returned to this rest pose and then the drift is corrected using the magnetometer. This tracking method is implemented in ArmeoSenso [31,32,33] (Figure 1), a VR home therapy system for upper-limb rehabilitation after neurological injury (a video demonstration of the system can be found online [34]). We present the tracking method and validate its long term accuracy and latency.…”