[ARTICLE] Counteracting learned non-use in chronic stroke patients with reinforcement-induced movement therapy – Full Text



After stroke, patients who suffer from hemiparesis tend to suppress the use of the affected extremity, a condition called learned non-use. Consequently, the lack of training may lead to the progressive deterioration of motor function. Although Constraint-Induced Movement Therapies (CIMT) have shown to be effective in treating this condition, the method presents several limitations, and the high intensity of its protocols severely compromises its adherence. We propose a novel rehabilitation approach called Reinforcement-Induced Movement Therapy (RIMT), which proposes to restore motor function through maximizing arm use. This is achieved by exposing the patient to amplified goal-oriented movements in VR that match the intended actions of the patient. We hypothesize that through this method we can increase the patients self-efficacy, reverse learned non-use, and induce long-term motor improvements.


We conducted a randomized, double-blind, longitudinal clinical study with 18 chronic stroke patients. Patients performed 30 minutes of daily VR-based training during six weeks. During training, the experimental group experienced goal-oriented movement amplification in VR. The control group followed the same training protocol but without movement amplification. Evaluators blinded to group designation performed clinical measurements at the beginning, at the end of the training and at 12-weeks follow-up. We used the Fugl-Meyer Assessment for the upper extremities (UE-FM) (Sanford et al., Phys Ther 73:447–454, 1993) as a primary outcome measurement of motor recovery. Secondary outcome measurements included the Chedoke Arm and Hand Activity Inventory (CAHAI-7) (Barreca et al., Arch Phys Med Rehabil 6:1616–1622, 2005) for measuring functional motor gains in the performance of Activities of Daily Living (ADLs), the Barthel Index (BI) for the evaluation of the patient’s perceived independence (Collin et al., Int Disabil Stud 10:61–63, 1988), and the Hamilton scale (Knesevich et al., Br J Psychiatr J Mental Sci 131:49–52, 1977) for the identification of improvements in mood disorders that could be induced by the reinforcement-based intervention. In order to study and predict the effects of this intervention we implemented a computational model of recovery after stroke.


While both groups showed significant motor gains at 6-weeks post-treatment, only the experimental group continued to exhibit further gains in UE-FM at 12-weeks follow-up (p<.05). This improvement was accompanied by a significant increase in arm-use during training in the experimental group.


Implicitly reinforcing arm-use by augmenting visuomotor feedback as proposed by RIMT seems beneficial for inducing significant improvement in chronic stroke patients. By challenging the patients’ self-limiting believe system and perceived low self-efficacy this approach might counteract learned non-use.

Trial registration

Clinical Trials NCT02657070.


After stroke, a neural shock leads to a learning process in which the brain progressively suppresses the use of the affected extremity [1]. This phenomenon is commonly referred to as learned non-use [2, 3]. Constraint-Induced Movement Therapy (CIMT) [1] implements a technique that aims to re-integrate the affected arm in the performance of Activities of Daily Living (ADLs) and reduce learned non-use. In order to achieve this goal, CIMT proposes to restrict the movement of the patient’s less-affected arm for about 90 % of the patient’s waking hours, which physically forces the use of the affected arm during performance of ADLs. Although a number of studies have shown the effectivity of CIMT [4], the high intensity of its protocols severely compromises its adherence [5] and can be physically and mentally tiring [6]. Moreover, its application is restricted to patients without severe cognitive impairments and with mild hemiparesis, which only accounts for about 15 % of all stroke cases [7]. Due to this limitations, several studies have tested variants of CIMT with reduced intensity protocols, giving rise to a Modified Constraint-Induced Movement Therapy (mCIMT) [8] and the so called Distributed Constraint-Induced Movement Therapy (dCIMT) [9]. However, the inclusion criteria of this type of therapy still remains excessively stringent [8, 10], and its efficacy at the chronic stage is unclear [11]. Given these limitations, there is a need for developing alternative methods that build on CIMT principles to foster the usage of the paretic limb, while mitigate its limitations.

A better understanding of the different factors determining hand selection could provide valuable insights for the development of new treatments that effectively counteract learned non-use and promote functional recovery. Previous studies have shown that the history of rewards may strongly bias action selection and habit learning [12, 13,14, 15]. Indeed, perceived self-efficacy, i.e. one’s own belief in his or her capabilities to successfully execute actions that are required for a desired outcome [16], appears to be an important driver for health behavior improvements [17]. In addition, the minimization of the expected cost/effort associated to a given action may as well regulate the decision making process [18]. The strong influence of these two factors on hand selection (i.e. expected cost and expected reward) may be sufficient to approximate the prediction of hand selection patterns, and may provide a direct explanation of our general preference for the execution of ipsilateral movements [19]. Following this line of research, we have shown in previous studies that hemiparetic stroke patients may be highly sensitive to failure when using the affected limb, therefore exposure to goal-oriented movement amplification in VR when using the affected extremity may serve as implicit reinforcement and promote arm use [20]. The resulting bias in hand selection patterns may rapidly emerge via action selection mechanisms, both reducing the expected cost and increasing the expected outcome associated to those movements executed with the paretic limb. It is generally known that motor learning is driven by motor error, and the high redundancy of the human motor system allows for the optimization of performance through decision making processes (i.e. effector selection). Thus, by virtually reducing sensorimotor error, these decision making processes can be modulated through intrinsic evaluation mechanisms [21, 22]. Previous studies have further proposed that a successful action outcome might reinforce not only the intended action but also any movement that drives the ideomotor system during the course of its execution [23, 24, 25]. This theory suggests that accidental success after action selection may be an effective mechanism for the spontaneous emergence of compensatory movements [26]. On this basis, by reducing sensorimotor feedback of those goal-oriented movements performed with the paretic limb, we may reinforce the future selection of the executed action. Indeed, a fMRI study on one stroke patient suggests that activations in the sensorimotor cortex of the affected hemisphere (the “inactive” cortex) were significantly increased simply by providing feedback of the contralateral hand [27]. This effect was also observed in healthy subjects [27]. In more recent studies, the effect of visuomotor modulations in motor adaptation has been also explored, showing that diminished error feedback and goal-oriented movement amplification does not necessarily compromise error-based learning [22, 28]. Building on these findings and grounding them on the Distributed Adaptive Control (DAC) theory of mind and brain, which proposes that restoring impaired sensorimotor contingencies is the key for promoting recovery [29], we propose a new motor rehabilitation technique that we term Reinforcement-Induced Movement Therapy (RIMT) [20]. This strategy is a combination of the following methods: 1) Shaping through training, while increasing the task difficulty according the patient’s performance; 2) limiting the use of the non-affected arm by introducing contextual restrictions in VR (i.e. restricted and symmetrically matched workspace for each arm); 3) providing explicit feedback about performance to the patient; and 4) augmenting goal-directed movements of the paretic limb in virtual reality (VR), in such a way that the patient executing the movement is exposed to diminished visuomotor errors, both in terms of distance and directional accuracy, thus increasing the expected action outcome (i.e. expected success) and decreasing the expected action cost (i.e. expected effort) [21]. While principles one to three of RIMT are similarly present in CIMT and Occupational Therapy protocols, the novelty of RIMT resides in its fourth principle: the provision of implicit reinforcement through the reduction of sensorimotor errors. This unique component of RIMT is the only variable that will be manipulated in the present study.

We hypothesize that by reducing visuomotor error within RIMT protocols, we may be able to boost the patients’ perceived performance of the paretic limb, leading to an increased use over time. Consequently, the increased spontaneous use of the paretic limb may facilitate intense practice and induce use-dependent plastic changes, therefore establishing a closed loop of recovery in which arm use and motor recovery reinforce each other. In this vein, a recent computational model of motor recovery suggested that there may be a functional threshold that predicts the use of the paretic limb after therapy [13, 30]. According to this model, only therapies that enable the patient to exceed a given functional threshold will recursively increase the spontaneous use of the paretic limb and induce functional improvement, leading to a complete motor recovery. This principle of use it or loose it can as well predict the effectiveness of RIMT. Furthermore, based on simulations from a computational model, we propose that reinforcement-based and constraint-based protocols can be combined to maximally promote the use of the paretic limb and induce functional gains in the chronic phase after the stroke. To test our hypothesis we conduct a randomized, double-blind, longitudinal clinical study with chronic stroke patients, and we analyze the effects of RIMT intervention on counteracting learned non-use and inducing motor recovery.

Fig. 1 Set-up and scenarios. a RGS setup in the hospital showing the transparent acrylic table in front of which the desktop computer with the Kinect (on a tadpole that elevates it above the screen) is placed. In order to use the second Kinect and the overhead projector on the scaffold above the table for the real world evaluation scenario, a white cover can be placed over the acrylic surface. During a training session, the user sits in a chair facing the screen while resting his/her arms on the table. b Spheroids scenario, where sets of colored spheres are launched towards the player who has to intercept them. c Whack-a-mole scenario, where the user freely chooses which limb to use in order to reach towards an appearing mole. d Collector scenario, where a set of patterned spheroids as indicated in the upper-left corner of the screen need to be collected. e Virtual evaluation scenario, an abstract version of the Whack-a-mole scenario, where the patient has to reach towards an appearing cylinder. f Real-world scenario, where the user has to reach towards randomly appearing dots that are projected from above on the table surface in front of him or her

Continue —> Counteracting learned non-use in chronic stroke patients with reinforcement-induced movement therapy | Journal of NeuroEngineering and Rehabilitation | Full Text

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