Purpose: A systematic review was performed to (1) evaluate the effectiveness of augmented visual feedback-based treatments for motor rehabilitation in Parkinson’s disease, and (2) examine treatment design factors associated with enhanced outcomes following these treatments.
Methods: Eight databases were searched from their start-date up to January 2017 using the key terms Parkinson’s Disease and augmented visual feedback. Two independent raters screened the abstracts and full articles for inclusion. Relevant data were extracted and summarized, and methodological quality of accepted articles was assessed.
Results: Eight single-group studies and 10 randomized control trials were included in the review. Augmented visual feedback-based treatments resulted in improved outcomes with small to large effect sizes post-treatment for the majority of impairment, activity, participation, and global motor function measures, and these improvements were often superior to traditional rehabilitation/education programs. Enhanced treatment outcomes were observed in studies that provided large amounts and high intensities of treatment; gamified feedback; and provided knowledge of performance feedback in real-time on 100% of practice trials.
Conclusion: Augmented visual feedback appears to be a useful motor rehabilitation tool in Parkinson’s disease; however, high-quality, rigorous studies remain limited. Future studies should consider factors that enhance rehabilitation outcomes when designing augmented visual feedback-based interventions. Implications for rehabilitation Augmented visual feedback is a useful tool for motor rehabilitation in Parkinson’s disease; augmented visual feedback-based treatments are often superior to traditional programs. These treatments are associated with improved outcomes in impairment, activity, participation, and global motor function domains. Rehabilitation professionals can optimize their use of augmented visual feedback-based treatments by providing large amounts and a high intensity of treatment, gamifying feedback, and providing knowledge of performance feedback in real-time and at a high frequency.