I have zero grasping ability because first my finger spasticity needs to be cured so I can do the first step, which is to open the hand.
Effect of task-oriented training assisted by force feedback hand rehabilitation robot on finger grasping function in stroke patients with hemiplegia: a randomised controlled trial
Journal of NeuroEngineering and Rehabilitation volume 21, Article number: 77 (2024)
Abstract
Background
Over 80% of patients with stroke experience finger grasping dysfunction, affecting independence in activities of daily living and quality of life. In routine training, task-oriented training is usually used for functional hand training, which may improve finger grasping performance after stroke, while augmented therapy may lead to a better treatment outcome. As a new technology-supported training, the hand rehabilitation robot provides opportunities to improve the therapeutic effect by increasing the training intensity. However, most hand rehabilitation robots commonly applied in clinics are based on a passive training mode and lack the sensory feedback function of fingers, which is not conducive to patients completing more accurate grasping movements. A force feedback hand rehabilitation robot can compensate for these defects. However, its clinical efficacy in patients with stroke remains unknown. This study aimed to investigate the effectiveness and added value of a force feedback hand rehabilitation robot combined with task-oriented training in stroke patients with hemiplegia.
Methods
In this single-blinded randomised controlled trial, 44 stroke patients with hemiplegia were randomly divided into experimental (n = 22) and control (n = 22) groups. Both groups received 40 min/day of conventional upper limb rehabilitation training. The experimental group received 20 min/day of task-oriented training assisted by a force feedback rehabilitation robot, and the control group received 20 min/day of task-oriented training assisted by therapists. Training was provided for 4 weeks, 5 times/week. The Fugl-Meyer motor function assessment of the hand part (FMA-Hand), Action Research Arm Test (ARAT), grip strength, Modified Ashworth scale (MAS), range of motion (ROM), Brunnstrom recovery stages of the hand (BRS-H), and Barthel index (BI) were used to evaluate the effect of two groups before and after treatment.
Results
Intra-group comparison: In both groups, the FMA-Hand, ARAT, grip strength, AROM, BRS-H, and BI scores after 4 weeks of treatment were significantly higher than those before treatment (p < 0.05), whereas there was no significant difference in finger flexor MAS scores before and after treatment (p > 0.05). Inter-group comparison: After 4 weeks of treatment, the experimental group’s FMA-Hand total score, ARAT, grip strength, and AROM were significantly better than those of the control group (p < 0.05). However, there were no statistically significant differences in the scores of each sub-item of the FMA-Hand after Bonferroni correction (p > 0.007). In addition, there were no statistically significant differences in MAS, BRS-H, and BI scores (p > 0.05).
Conclusion
Hand performance improved in patients with stroke after 4 weeks of task-oriented training. The use of a force feedback hand rehabilitation robot to support task-oriented training showed additional value over conventional task-oriented training in stroke patients with hand dysfunction.
Clinical trial registration information
NCT05841108
Background
Stroke is a leading cause of morbidity worldwide and the primary cause of motor impairment [1]. More than 80% of stroke patients with hemiplegia experience hand dysfunctions, which not only affects the use of their arms and hands in activities of daily living (ADL), but also limits their participation in social life and quality of life [2, 3].
Being the basic function of the hand, grasping plays a very important role in the activities of daily life. Simple functional activities of daily living, such as eating, dressing, grooming, and drinking, rely on the grasping function of the fingers [4]. However, grasping is a complex process that requires proper grasping force and motor control ability. When grasping, it is necessary to gradually open the fingers to form an appropriate configuration of the target object (“preshaping”). The fingers then continue to open wider than the size of the target object and stop opening at approximately 60–70% of the movement, after which they enclose the object, and finally contact its surface for grasping with appropriate force [5]. However, the grasping force and hand motor control ability are often insufficient in stroke patients, which seriously reduces the quality of movement when grasping objects in activities of daily life. It seems that finger grasping training is particularly important for improving the ability of daily living in stroke patients with hand dysfunction.
Rehabilitation therapy is considered the foundation of stroke treatment to improve the motor skills and quality of life of survivors [6]. Furthermore, repetitive training is an effective method to facilitate recovery from stroke and assist in restructuring neural networks. As a newer rehabilitation method, hand rehabilitation robots are potential tools for stroke rehabilitation treatment because they can support stable and consistent training with highly repetitive movements compared with conventional therapy [7]. However, the commonly used hand function rehabilitation robots in clinical practice are typically based on the spatiotemporal tmovement trajectory predefined by the robot computer control system, allowing patients to passively complete repeated training without requiring their active contribution, resulting in low active participation of patients [8]. A bigger problem is that most rehabilitation robots still do not apply effective input and feedback channels of sensorimotor information. In this kind of robot training, patients can only rely on visual feedback to judge the object’s size and weight to be grasped, and lack other available sensory stimuli and feedback, which affects their movement adjustment and motor control, and is not conducive to completing more accurate grasping movements [9].
Force feedback rehabilitation robots can compensate for these defects. It is a new generation of rehabilitation robots based on force feedback technology. When the wearer begins to grasp an object, information from the tactile sensors determines how much additional force the wearer needs to grasp the object, and the glove ‘strengthens’ the hand accordingly [10]. On the one hand, it can apply proportional compensation to assist the patient in completing grasping movements. On the other hand, it can provide effective force feedback information for patients, so that they can further adjust their movements according to the feedback information to achieve more accurate grasping movements. Previous studies have shown that force feedback hand rehabilitation robot training improves grip strength and hand performance in patients with spinal cord injury, articular rheumatism, and other diseases, as well as in older adults [10]. Therefore, using force feedback hand rehabilitation robots for finger grasping training in stroke patients with hemiplegia is expected to be an effective method for improving their subjective initiative and grasping function.
In addition to repetitive exercise training, another requirement for successful rehabilitation is a goal-oriented and task-specific training program to help patients use the affected side and voluntarily perform motor functions, and there are a variety of physical intervention approaches [11]. Of those, task-oriented training has been reported to be effective in improving the functional motor skills required to perform ADLs in stroke patients [12]. Task-oriented training is a therapeutic model based on the systems theory of motor control, which uses a functional approach in rehabilitating neurological patients and teaches task-specific strategies to help them adapt to changing environments [13]. This approach involves having patients practice a skill essential for achieving the goal of a task to facilitate problem-solving by enhancing their ability to adapt to various situations and developing an effective reward strategy [14,15,16]. In addition, for maximal learning, the approach involves behaviourally motivating patients using tasks related to their daily lives and emphasising the interaction between patients and their environment. Van Peppen et al. stated that repetitive and focused task-oriented training improved the recovery of upper limb function and enhanced motor patterns, dexterity, and agility in the upper limb [17]. The treatment effects of task-oriented training methods for stroke-related limb dysfunction have been widely recognised and supported by authoritative guidelines and systematic reviews [18, 19].
Based on the characteristics of the force feedback hand rehabilitation robot and the task-oriented training method, this study combined them to explore the effectiveness and added value of the combination of force feedback hand rehabilitation robot and task-oriented training to provide an effective rehabilitation treatment method for the recovery of hand function in stroke patients with hemiplegia and to provide a reference for the clinical application of relevant force feedback hand rehabilitation robots.
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