The effects of error-augmentation versus error-reduction paradigms in robotic therapy to enhance upper extremity performance and recovery post-stroke: a systematic review

Stroke, also referred to as cerebrovascular accident (CVA), is one of the leading causes of disablement among adults [1, 2]. It is estimated that stroke costs the Canadian, United States and United Kingdom economy around $3.6 billion [3], $34 billion [4] and £9 billion [5] a year respectively in medical services, personal care and lost productivity. The disabilities resulting from stroke can affect all aspects of life including gross and fine motor ability, walking, activities of daily living (ADLs), speech and cognition [6]. Motor impairments are some of the most prevalent issues post stroke and restoring upper extremity function is one of the top priorities of people with stroke [7]. Compared to the lower extremity impairments, the upper extremity impairments are more likely to result in activities limitations (see International Classification of Functioning, Disability and Health (ICF) in Appendix 1) because tasks that involve the arm and hand often require a high level of fine motor control [8]. In fact, severe upper extremity impairments post-stroke often hinder the ability to take care for oneself and perform ADLs [9]. Although restoration of upper extremity motor functions is crucial for stroke patients to regain their independence, studies have shown that only 35 to 70% of people with stroke recover to the level of arm ability that is considered functional [10‐12] while more than 50% have persistent upper extremity impairments [13].

Studies in both human and animal models demonstrate the importance of motor learning in the process of motor recovery following an acquired brain lesion as both learning and recovery processes can induce cortical changes and reorganization [14]. Motor learning, which is “a set of processes associated with practice or experience that leads to relatively permanent changes in the ability to produce skilled action” [15], relies on an experience-dependent neural plasticity that is modulated by various factors such as task specificity, repetition, intensity, timing, salience, etc. [16]. Amongst different factors influencing the acquisition of motor skills, feedback is believed to be one of the key factors [15]. Feedback is the information that an individual receives as a result of his or her performance [17]. It can be either intrinsic or extrinsic, where intrinsic feedback is that experienced by the performer (e.g. sensory, visual feedback, etc.) and extrinsic (augmented) feedback is that provided by an external source, such as a therapist providing verbal or physical guidance [18, 19]. Extrinsic feedback can inform the performer about a success or failure on a task (knowledge of results) or about the quality of movement or task performance (knowledge of performance) [15].

Robotics is one of the advanced technologies that is increasingly used in post-stroke upper extremity rehabilitation [20]. Compared to conventional approaches, it offers the advantages of high convenience when providing task-oriented practice, as well as high accuracy in measuring outcomes of motor performance (e.g. trajectory straightness, movement speed, range of joint movement [21]). The latter outcomes can in turn be used to provide knowledge of performance as a source of feedback [22]. Two main paradigms of training on the use of feedback, arising from the literature on robotics, were proposed and tested as means to facilitate motor learning and improve motor performance: the error reduction (ER) paradigm and error augmentation (EA) paradigm. The ER paradigm, also known as haptic guidance, is to reduce the performance errors of a subject during a motor task [23], namely via the assistance provided by a robot so that the performer can stay within the optimal movement trajectory determined by the non-paretic arm or by the therapist [24]. This approach is based on the hypothesis that by demonstrating the correct movement trajectory to a person, he/she will be able to learn it by imitation [25]. The discovery of “mirror neurons” that were first identified using microelectrode recordings of single neurons in area F5 of monkey premotor cortex [26] prompted the researchers to believe that a similar mirror neuron system exists in humans, and that this mirror neuron system could play an important role in learning through imitation [27]. Furthermore, the theory of reinforcement-based learning suggests that positive/successful feedback is essential for motor learning to occur [28]. The ER paradigm also assumes that there is a unique optimal movement trajectory and any deviation from it is considered to be an error. According to the principle of abundance and the theory of use-dependent learning, however, having variance in how a motor action is performed does not necessarily impede the overall motor performance [29, 30].

A whole body of literature also suggests that motor learning can be an error driven process, a postulate that can be explained and supported by motor control theories such as the internal model theory [31] and the equilibrium point hypothesis [32]. In the internal model theory, it is hypothesized that subjects form an ‘internal model’ based on their anticipation of the effects of the environment on their motor actions, therefore the internal model acts as a feed-forward component of the motor control [31]. The detection of errors that occur during the motor performance play the role of a feedback component, as errors prompt the existing internal model to adapt in order to reduce errors [33‐36]. In the equilibrium point hypothesis, the errors occur in the subsequent movements following a change in the environment, but the motor system is able to correct these errors by adjusting the control variables based on information about the current motor system, joint positioning of the limbs, etc., thus resetting the activation thresholds (λ) of muscle and forming a new equilibrium point [32, 37]. Given the role of errors in motor learning, it was hypothesized that artificially increasing the performance error would cause learning to occur more quickly [25], an idea that is the foundation of the EA paradigm. In robotics, one of the commonly used technique to artificially increase performance error is to create a force-field that disturbs the limb motion during the movement [38].

While the theories and ideas that support ER vs. EA paradigms are distinct, both are currently being used, primarily in the form of haptic feedback, as part of clinical intervention studies for populations with deficits in motor recovery. Until this day, there is no consensus as to which of the two paradigms provides superior treatment effects in upper extremity motor recovery and performance among stroke survivors. Furthermore, while systematic reviews on the use of error modification in upper extremity rehabilitation after stroke were published in the recent years [39, 40], these exclusively focused on the EA paradigm and did not allow for a comparison between the two approaches. In this study, we conducted a systematic review on the use of EA and ER paradigms in the form of haptic feedback to enhance upper extremity motor recovery and performance in stroke survivors. The main research questions that were addressed are listed in PICO format (Population, Intervention, Comparison, and Outcome) and read as follows:

For the purpose of clarification, the comparison component of the first research question, “training without error modification,” refers to standard repetitive practice that does not involve any external force (reducing or amplifying errors) that provides feedback on the performance. The outcomes of both research questions, “upper extremity motor recovery and performance,” can include clinical measures of both upper extremity impairment and disability and kinematic measures of motor performance (for more details, refer to the section of inclusion and exclusion criteria).

This study completed, for the first time, a systematic review of interventions studies that compared the effectiveness of the EA training paradigm to standard repetitive practice without error modification, the ER paradigm to standard repetitive practice, and EA to ER at enhancing upper extremity motor recovery and performance in individuals with stroke. Thirteen studies were included in the review. The reason why EA was found to more effective than standard repetitive practice while ER was not could be due to the fact haptic guidance and assistive therapy are more effective in the initial stage of motor learning while error-based learning is more used in the later stage of learning. Indeed, it has been shown that in the initial stage of motor learning, motivation and positive reinforcement are believed to play a much more important role than being able to identify errors [28]. Since most participants in the reviewed studies are people with chronic stage of stroke, it is believed that they have already gone through the initial stage of motor relearning.

While some differences in clinical outcomes between training paradigms were statistically significant, it is also important to assess their clinical relevance and effect size in order to address the objectives of this review. Amongst clinical tests that assess motor recovery, the AMFM shows a minimal detectable change (MDC) of 5.2 [67] and a minimally clinically important difference (MCID) of ranging from 4.25 to 7.25 [68]. None of the reviewed studies on EA presented intervention gains that met the MDC or MCID for this test. In fact, only Takahashi and colleagues (2014) [56] who compared ER to standard practice had results that met the MDC and MCID for the AMFM, in both intervention groups. For the WMFT FAS and the WMFT time measure which reflect motor abilities in functional and timed tasks, none of the studies reviewed met the MCID (WMFT FAS ranging from 0.2 to 0.4 point; WMFT time measure ranging from 1.5 to 2.0 s [69]). The MCID for the ARAT (5.7 [70]) was attained only in Timmermans and colleagues’ study (2014) [57], both by the ER and standard practice groups. It is to be noted that no established MCID was found in Motor Assessment Scale, Motor Activity Log and Motor Status Score. Spasticity, as measured by the MAS, showed intervention induced changes that reached the MCID (1 point [71]) for ER and EA in two studies that compared the latter two approaches [54, 58]. The Box and Blocks test and ROM did not see any significant change in any of the intervention groups in the thirteen studies reviewed, presumably because arm trajectory control was specifically targeted in the interventions, as opposed to manual dexterity and joint mobility. In addition, the effect sizes of the differences in clinical outcomes in all thirteen studies were for most moderate or small. Collectively, these observations suggest that while EA was found to have superior effects over standard repetitive practice to improve upper extremity motor impairments and functional disability, it yet has to demonstrate that it can yield clinically meaningful changes in clinical outcomes of motor impairment and function. Such observations also raise important questions, being whether the intervention was delivered optimally (e.g. in terms of training intensity, duration, feedback sensory modality, stroke chronicity and baseline level of motor recovery, etc.) and whether the selected outcomes were actually best suited to capture the improvements brought up by the intervention.

To that effect, the EA training paradigm was further found to be more effective at improving kinematic outcomes that measure reaching trajectory control compared to both ER and standard repetitive practice. Indeed, two studies showed very large effect sizes on the difference between EA and standard repetitive practice, and between EA and ER [24, 63]. Furthermore, when comparing EA to ER, the only statistically significant difference that emerged was in the kinematic outcomes which were in favor of the EA group. In fact, although EA showed larger improvement than standard practice and although ER did not show significant difference compared to standard practice in terms of clinical outcomes, EA surprisingly did not appear to be better than ER at improving clinical outcomes. It has been shown that kinematic variables are highly responsive to changes in motor performance following training intervention [72] and that they can capture the quality of the movement which is another important aspect of motor abilities [73]. In the context of this study, this could suggest that EA is actually better than ER at improving the quality of movement which is mostly measured by the kinematic outcomes, but such improvement could not be detected by most of the examined clinical outcomes. From a broader perspective, these observations emphasize the need to deeply understand the mechanisms of action of error modification interventions and select outcome measures accordingly.

Besides factors related to the intervention itself (intensity, duration, etc), personal-related factors such as the site of lesion, stroke severity and chronicity also are factors that may have influenced the results of studies reviewed in this manuscript and ensuing conclusions. Unfortunately, most studies did not provide information on brain lesion location. Among the three studies that did provide this information [24, 52, 53], participants suffered stroke in a variety areas (e.g. cortical, sub-cortical, thalamus, basal ganglia, brain stem, etc.) and the distribution of the different sites of lesion amongst groups was not reported, making it impossible to analyse the effects of lesion location. As for stroke severity, among the studies that compared EA to repetitive practice, baseline AMFM scores did not seem to influence the results because participants who had AMFM scores ranging from 15 to 55 [52, 53, 60, 62, 63] all demonstrated larger improvement with the EA training. However, it was difficult to draw definite conclusions on ER vs. standard repetitive practice and EA vs. ER, as the number of studies in these two categories was small and studies used different outcome measures to assess stroke severity. Lastly, most of the studies only recruited chronic stroke survivors, making it difficult to appraise the effects of stroke chronicity while limiting the generalization of findings mainly to chronic stroke survivors.

Results of this review also highlighted contradictions across studies which could be due to an influence of participants’ personal factors on intervention outcomes. For instance, Takahashi and colleagues (2008) [56] suggested that full ER practice was better than half ER/half standard repetitive practice at improving AMFM and ARAT scores, a finding that was in contradiction with that of other studies [55, 57]. The full ER group, however, had an average onset of stroke of 1.2 years compared to 4.8 years for the other intervention group, and this suggests that time of stroke onset might be a factor that influences the motor recovery [56]. Moreover, the full ER group also had nine points less in baseline average AMFM scores compared to the other group [56], possibly leaving more room for improvement in the former group. We therefore suggest that at this point in time, a deeper investigation of patient-related factors on the intervention outcomes is warranted.

This systematic review has some limitations. The risk of bias among the selected studies is high as most of the selected studies have either short training period or small sample size. Another limitation lies in the fact that many studies did not provide numerical values for the standard deviations of their results, or the standard deviations had to be estimated from tables or figures, which may have affected the calculation of some effect sizes. Only one out of 13 studies [57] reported the effects of intervention on the arm use which is an important predictor of upper extremity motor recovery. It should also be noted that 6 out of 10 studies involving EA trainings may come from the same research group [24, 52, 53, 59, 60, 62]. Moreover, the main methodological quality assessment was done using the PEDro scale. Like many checklist-style appraisal tools, PEDro has a disadvantage of giving the same weighing (1 point) to every category of source of bias. However, depending on the types of study, not all sources of bias affect the internal validity equally. Finally, before starting this systematic review, the authors have planned to conduct experimental studies on the use of EA and ER on motor learning in the future, therefore this could act as a source of bias, although unwillingly.

In the future, clinical trials of strong methodological quality which include sensitive outcomes that capture changes in movement quality and patient functioning in activities of daily living are needed to further demonstrate the effects of error-modification therapies with a stronger level of evidence and to possibly achieve clinically meaningful changes. The influence of intervention-related factors such as training intensity and duration, as well as personal factors such as the site of lesion, severity of stroke and stroke chronicity on the error-modification intervention paradigms should further be explored. Finally, the emergence of virtual reality makes other modalities, namely visual and auditory feedback, potential alternatives to haptic feedback. These modalities could be cheaper and easier to implement than robotics, and it appears that more and more studies have begun to examine the effect of these feedback on motor learning. Therefore, the use of different modalities of feedback, such as visual, auditory and/or a combination of multiple sensory modalities, could also be investigated.