Upper Limb Robotic Rehabilitation Following Stroke: A Systematic Review and Meta-Analysis Investigating Efficacy and the Influence of Device Features and Program Parameters

Abstract

Background: Following stroke, upper limb impairment is common and frequently limits ability to perform everyday activities. Due to limited resources, current therapy levels are insufficient to optimise functional improvement. Robotic devices have potential to augment upper limb stroke rehabilitation, but knowledge regarding the optimal device features and intervention parameters is limited. This systematic review and meta-analysis aimed to determine the efficacy of upper limb robotic rehabilitation compared with conventional rehabilitation, and to critically explore the device features and programme parameters that influence rehabilitation outcomes. Methods: Six electronic databases were searched for RCTs that compared dose-matched robotic versus conventional rehabilitation following stroke, and measured activity level changes in upper limb outcomes. The efficacy of robotic compared with conventional rehabilitation was evaluated using random-effects (I² ≥ 50%) or fixed-effect (I² < 50%) models. A systematic categorization of robotic device features and intervention parameters was conducted to facilitate subgroup analyses and meta-regression, enabling exploration of how these factors influence rehabilitation outcomes. Results: The review included 54 studies, involving 2744 participants. Meta-analysis demonstrated that robotic rehabilitation had a small, statistically significant positive effect on upper limb capacity compared with conventional rehabilitation (SMD 0.14, 95% CI [0.02, 0.26]), however these gains were not maintained at follow-up (SMD 0.05, 95% CI [− 0.13, 0.24]). No significant differences were found between robotic and conventional rehabilitation for ADL outcomes either post-treatment (SMD 0.04, 95% CI [– 0.05, 0.13]) or at follow-up (SMD 0.05, 95% CI [− 0.13, 0.24]). Subgroup analyses provided crucial insights into the factors influencing robotic rehabilitation efficacy, revealing significant effects of device assistance (p = 0.0046), joints mobilized (p = 0.0133), degrees of freedom (p = 0.012), device laterality (p = 0.0048), and the number of devices used (p = 0.0001). Conclusions: The results suggest that robotic rehabilitation does not result in clinically meaningful improvement in either upper limb capacity or ADL performance. However, this study’s novel subgroup analyses highlight specific device features and intervention parameters that significantly influence efficacy. These findings provide critical guidance for the design, implementation, and future research of robotic rehabilitation.