Feasibility of Two New Software Modules for the Rehabilitation of Patients With Neuromuscular Upper Limb Impairments

Introduction: In the recent past, medical training systems using virtual reality (VR) have been introduced to neurorehabilitation to train motor function deficits in patients. The usage of VR-based training systems is based on the evidence of neuroplasticity, which is responsible for recovery of patients suffering from motor dysfunction. Such systems are increasingly used to encourage purposeful limb movements in a VR environment and its efficacy has been found comparable with conventional therapeutic intervention. VR training systems, e.g. the YouGrabber® (YG), will increasingly also be used at home. Therefore, it is essential to integrate valid and reliable assessment tools to monitor the recovery process.

Objectives: The aim of the clinical study is to evaluate the usability, feasibility and validity of the digital version of the ActionResearchArmTest (d-ARAT) using the YG system as a platform. Additionally, the feasibility and usability of the implementation of two rehabilitation measures that only recently became integral part of neurorehabilitation, e.g. Action Observation (AO) and Motor Imagery (MI), into the YG training software will be evaluated. Patients \& methods: This observational study is designed as a single-arm trial for testing the assessment software including pre- to post rehabilitation comparison of a training involving AO and MI. Therefore, 75 adult patients with Parkinson's disease, MS, Stroke, traumatic brain injury or Guillain-Barré syndrome will be included. 30 out of the 75 patients will take part in the 4-week training on the enhanced VR-based system with a total of 16 training sessions of 45 min each. Primary outcomes will be the score on the System Usability Scale (SUS) and the ARAT as well as the d-ARAT scores. Secondary outcomes will be hand dexterity (Box-and-Block Test), upper limb activities of daily living (CAHAI) and quality of life (EQ-5D-5L).

Hypothesis: The study was designed to evaluate the d-ARAT and the training software modules for the YG system. Currently AO and MI specific tasks are being integrated and the ARAT subscales will be implemented on the basis of the redesigned glove equipped with new sensors. The results are expected to give recommendations for necessary modifications. They might also contribute knowledge concerning the application of AO and MI tasks within VR training.