Stroke is the third leading cause of adult disability worldwide, with 101 million people living with long-term effects [1]. Amongst people who have had a stroke, around 80% experience upper limb impairment, with 65% still experiencing deficits six months post stroke [2, 3]. This impairment significantly affects individuals’ wellbeing and quality of life by limiting their ability to engage in daily activities [4]. Following a stroke some spontaneous motor recovery occurs, but further improvements usually rely on engagement in rehabilitation delivered by a specialised multidisciplinary team [5, 6]. Effective rehabilitation programmes emphasise high doses of therapy incorporating repetition, challenge, and task-specific practice to promote upper limb recovery [7,8,9]. However, the amount of rehabilitation currently being delivered is often insufficient to elicit optimal functional change, due to limited resourcing of rehabilitation services, shortages of clinicians and high caseloads [10]. Delivering optimum doses of rehabilitation therefore continues to be challenging for stroke services, necessitating a need for enhancing rehabilitation approaches.
The implementation of robotic devices offers a potential solution to address this shortfall by facilitating upper limb movements akin to conventional rehabilitation [11]. Robotic rehabilitation could improve functional outcomes for people recovering from stroke by enabling greater repetitions of upper limb movement, task specific practice and grading of the challenge level [12,13,14]. Rehabilitation robotics are classified according to their placement and the application of force to the upper limb, where exoskeleton devices have an external structural mechanism with the robot axes aligned with the anatomical axes of the wearer [15], whereas end-effector devices are attached to the wearer’s distal upper limb and generate forces at the interface [16].
In this dynamically evolving field, upper limb stroke rehabilitation robotics have been evaluated through randomised control trials (RCTs) to gauge efficacy compared to conventional rehabilitation. Previous systematic reviews have investigated the overall effectiveness of rehabilitation robotics for upper limb rehabilitation following stroke [17,18,19], primarily focusing on ‘body functions and structures’ and ‘activity’ level outcomes according to the International Classification of Functioning, Disability, and Health (ICF) [20]. ‘Body functions and structures’ are defined as the anatomical parts and physiological functions of body systems [20] and include measures of muscle strength and motor control. Systematic reviews evaluating ‘body functions and structures’ level outcomes have shown robotic rehabilitation may lead to significant improvement in hemiparetic upper limb muscle strength [17, 19] and motor control [17] compared with conventional rehabilitation in dose-matched [17] and non-dose matched trials [19]. Whereas Norouzi-Gheidari et al. [18] found comparable improvements in motor control and strength outcomes when robotic and conventional rehabilitation was dose-matched. However, additional sessions of robotic rehabilitation yielded better motor control outcomes compared with conventional rehabilitation alone [18], suggesting increased rehabilitation dosage may enhance ‘body function and structures’ outcomes.
Though there is some evidence to suggest that robotic rehabilitation may improve upper limb outcomes at the ‘body function and structures’ level, these improvements do not necessarily transfer to improved performance at the ‘activities’ level [21]. ‘Activities’ are defined by the ICF as the execution of a task or action by an individual [20]. Measures in this domain evaluates upper limb capacity of performing tasks such as unscrewing a lid or picking up an object [22], or evaluates performance during activities of daily living (ADLs) such as toileting or dressing. While some systematic reviews show improvements in upper limb capacity and ADL performance among people with stroke receiving robotic rehabilitation compared to conventional rehabilitation [19], dose matching remains inconsistent across trials. In dose-matched trials, Veerbeek [17] found no significant differences in upper limb capacity and ADL outcomes, and Norouzi-Gheidari [18] found no significant differences in ADL outcomes comparing robotic therapy with conventional rehabilitation. Consequently, the evidence for robotic rehabilitation’s efficacy at the ‘activity’ level remains uncertain. Favourable outcomes in non-dose matched trials with increased sessions raise questions about whether improvements stem from the robotic treatment itself or simply from the additional volume of rehabilitation delivered.
Beyond dose-matching inconsistencies there are also considerable variations in how robotic rehabilitation interventions are implemented. Studies have employed a range of robotic devices, each with distinct design features, and have implemented them within variable rehabilitation programmes and clinical environments. This has resulted in uncertainty about the best parameters for delivery [23]. To maximise robotic rehabilitation, there is a need to report on, and investigate, the specific features of robotic devices themselves, and the ways the robotic rehabilitation is delivered, which may lead to improved rehabilitation outcomes [24]. Researchers have begun to respond to this need by carrying out RCTs exploring whether features such as gamification of robotic devices [25], level of assistance provided by the device [26], provision of feedback from the device [27], or age of the user [28] has an impact on the effectiveness of robotic rehabilitation. A recent systematic review suggested that device type may be an important feature, with Moggio and colleagues [29] demonstrating that exoskeleton devices were more effective than end-effector devices for improving finger-hand muscle strength [29]. In contrast, Veerbeek’s [17] systematic review reported that end-effector devices were more effective compared to exoskeleton devices for improving motor control outcomes for all upper limb joints [17]. Veerbeek [17] also explored subgroups based on the joint targeted by the device, reporting significantly larger improvements in upper limb muscle strength following robotic rehabilitation targeting the shoulder and elbow joints together compared with devices targeting the elbow, shoulder, wrist, hand, the whole arm, or combinations of these. Mehrholz et al. [30] conducted a network meta-analysis categorising robotic devices by key features and found no significant impact on outcomes based on laterality, device type, device placement, or glove-finger-based design [30]. This small body of emerging evidence suggests some device features may be more important in driving intervention efficacy.
This literature shows that there is uncertainty regarding the effectiveness of upper limb robotic rehabilitation on ‘activity’ level outcomes following stroke, and limited evidence about the robotic device features and programme parameters that impact these outcomes, where many variables remain unexplored. Clarifying best delivery could enhance device design and implementation [23]. Therefore, this systematic review and meta-analysis aimed to 1) determine the efficacy of upper limb robotic rehabilitation on ‘activity’ level outcomes of upper limb capacity and ADL, in comparison with conventional rehabilitation in dose-matched trials, and 2) analyse the robotic device features and programme parameters which may contribute to improved robotic rehabilitation outcomes.
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