RESUMO
Stroke is the third leading cause of disability in the world, and effective rehabilitation is needed to improve lost functionality post-stroke. In this regard, robot-assisted therapy (RAT) and transcranial direct current stimulation (tDCS) are promising rehabilitative approaches that have been shown to be effective in motor recovery. In the past decade, they have been combined to study whether their combination produces adjuvant and greater effects on stroke recovery. The aim of this study was to estimate the effectiveness of the combined use of RATs and tDCS in the motor recovery of the upper extremities after stroke. After reviewing 227 studies, we included nine randomised clinical trials (RCTs) in this study. We analysed the methodological quality of all nine RCTs in the meta-analysis. The analysed outcomes were deficit severity, hand dexterity, spasticity, and activity. The addition of tDCS to RAT produced a negligible additional benefit on the effects of upper limb function (SMD -0.09, 95% CI -0.31 to 0.12), hand dexterity (SMD 0.12, 95% CI -0.22 to 0.46), spasticity (SMD 0.04, 95% CI -0.24 to 0.32), and activity (SMD 0.66, 95% CI -1.82 to 3.14). There is no evidence of an additional effect when adding tDCS to RAT for upper limb recovery after stroke. Combining tDCS with RAT does not improve upper limb motor function, spasticity, and/or hand dexterity. Future research should focus on the use of RAT protocols in which the patient is given an active role, focusing on the intensity and dosage, and determining how certain variables influence the success of RAT.
RESUMO
Current neurorehabilitation models primarily rely on extended hospital stays and regular therapy sessions requiring close physical interactions between rehabilitation professionals and patients. The current COVID-19 pandemic has challenged this model, as strict physical distancing rules and a shift in the allocation of hospital resources resulted in many neurological patients not receiving essential therapy. Accordingly, a recent survey revealed that the majority of European healthcare professionals involved in stroke care are concerned that this lack of care will have a noticeable negative impact on functional outcomes. COVID-19 highlights an urgent need to rethink conventional neurorehabilitation and develop alternative approaches to provide high-quality therapy while minimizing hospital stays and visits. Technology-based solutions, such as, robotics bear high potential to enable such a paradigm shift. While robot-assisted therapy is already established in clinics, the future challenge is to enable physically assisted therapy and assessments in a minimally supervized and decentralized manner, ideally at the patient's home. Key enablers are new rehabilitation devices that are portable, scalable and equipped with clinical intelligence, remote monitoring and coaching capabilities. In this perspective article, we discuss clinical and technological requirements for the development and deployment of minimally supervized, robot-assisted neurorehabilitation technologies in patient's homes. We elaborate on key principles to ensure feasibility and acceptance, and on how artificial intelligence can be leveraged for embedding clinical knowledge for safe use and personalized therapy adaptation. Such new models are likely to impact neurorehabilitation beyond COVID-19, by providing broad access to sustained, high-quality and high-dose therapy maximizing long-term functional outcomes.
RESUMO
Introduction: Robot-assisted therapy for upper extremity (UE) impairments post-stroke has yielded modest gains in motor capacity and little evidence of improved UE performance during activities of daily living. A paradigm shift that embodies principles of motor learning and exercise dependent neuroplasticity may improve robot therapy outcomes by incorporating active problem solving, salience of trained tasks, and strategies to facilitate the transfer of acquired motor skills to use of the paretic arm and hand during everyday activities. Objective: To pilot and test the feasibility of a novel therapy protocol, the Active Learning Program for Stroke (ALPS), designed to complement repetitive, robot-assisted therapy for the paretic UE. Key ALPS ingredients included training in the use of cognitive strategies (e.g., STOP, THINK, DO, CHECK) and a goal-directed home action plan (HAP) to facilitate UE self-management and skill transfer. Methods: Ten participants with moderate impairments in UE function >6 months after stroke received eighteen 1-h treatment sessions 2-3/x week over 6-8 weeks. In addition to ALPS training, individuals were randomly assigned to either robot-assisted therapy (RT) or robot therapy and task-oriented training (RT-TOT) to trial whether the inclusion of TOT reinforced participants' understanding and implementation of ALPS strategies. Results: Statistically significant group differences were found for the upper limb subtest of the Fugl-Meyer Assessment (FMA-UE) at discharge and one-month follow-up favoring the RT group. Analyses to examine overall effects of the ALPS protocol in addition to RT and RT-TOT showed significant and moderate to large effects on the FMA-UE, Motor Activity Log, Wolf Motor Function Test, and hand portion of the Stroke Impact Scale. Conclusion: The ALPS protocol was the first to extend cognitive strategy training to robot-assisted therapy. The intervention in this development of concept pilot trial was feasible and well-tolerated, with good potential to optimize paretic UE performance following robot-assisted therapy.