Skip to main content

Short and long-term effects of robot-assisted therapy on upper limb motor function and activity of daily living in patients post-stroke: a meta-analysis of randomized controlled trials

Abstract

Objective

To investigate the effect of robot-assisted therapy (RAT) on upper limb motor control and activity function in poststroke patients compared with that of non-robotic therapy.

Methods

We searched PubMed, EMBASE, Cochrane Library, Google Scholar and Scopus. Randomized controlled trials published from 2010 to nowadays comparing the effect of RAT and control treatment on upper limb function of poststroke patients aged 18 or older were included. Researchers extracted all relevant data from the included studies, assessed the heterogeneity with inconsistency statistics (I2 statistics), evaluated the risk of bias of individual studies and performed data analysis.

Result

Forty-six studies were included. Meta-analysis showed that the outcome of the Fugl-Meyer Upper Extremity assessment (FM-UE) (SMD = 0.20, P = 0.001) and activity function post intervention was significantly higher (SMD = 0.32, P < 0.001) in the RAT group than in the control group. Differences in outcomes of the FM-UE and activity function between the RAT group and control group were observed at the end of treatment and were not found at the follow-up. Additionally, the outcomes of the FM-UE (SMD = 0.15, P = 0.005) and activity function (SMD = 0.32, P = 0.002) were significantly different between the RAT and control groups only with a total training time of more than 15 h. Moreover, the differences in outcomes of FM-UE and activity post intervention were not significant when the arm robots were applied to patients with severe impairments (FM-UE: SMD = 0.14, P = 0.08; activity: SMD = 0.21, P = 0.06) or when patients were provided with patient-passive training (FM-UE: SMD = − 0.09, P = 0.85; activity: SMD = 0.70, P = 0.16).

Conclusion

RAT has the significant immediate benefits for motor control and activity function of hemiparetic upper limb in patients after stroke compared with controls, but there is no evidence to support its long-term additional benefits. The superiority of RAT in improving motor control and activity function is limited by the amount of training time and the patients' active participation.

Introduction

Stroke is the main cause of mortality and disability worldwide [1]. Even though the mortality rate significantly decreased from 1990 to 2019 [2], a growing number of survivors are living with motor function loss and require nursing care [1]. Impairment of upper limb function is a common problem among post-stroke patients [3]. According to the International Classification of Functioning, Disability, and Health (ICF), upper limb function can be divided into body function and structures, activity (capacity and performance), and participation [4]. The impairment of motor function could limit activity and result in difficulty in reintegrating into society for poststroke patients [5]. Several approaches for the recovery of motor function exist, but the debate about the effect of these treatments is ongoing [6]. Traditional neurological treatments, such as Bobath, proprioceptive neuromuscular facilitation (PNF) therapy, and other upper limb exercises, are well known and are common treatments for rehabilitation. However, comparing with these traditional rehabilitation treatments, robotic devices may be advantageous in terming of the output of objective measures such as speed, torque, range of motion, position, and others to evaluate and monitor the patient's improvement, and the customization of treatment sessions regarding different levels of movement impairment of patients [7]. In addition, the advantage of these manual therapies most depends on the clinical skill of therapist and hardly be reproducible, whereas RAT has high-consistency and reproducibility to allow its widespread use[8]. Moreover, there is strong evidence supporting that intensive, highly repetitive, task-oriented training promotes motor function recovery after stroke [6]. The intensity and repetition of traditional rehabilitation programs carried out by physical and occupational therapists cannot reach such a level [9]; hence, assistance from rehabilitation tools is needed. Arm robots with specialized technological machines can effectively provide high-intensity, highly repetitive, functional, and precise exercises to better improve motor control function, strength, and accuracy of movement compared with traditional manual neurological treatments [9].

Although a better therapeutic effect of robot-assisted therapy (RAT) on motor and activity function has been reported [7,10,11,12,13], disparate effects and heterogeneities between trials were found depending on the phase of poststroke [14], the amount of training [15], the control system of the robots (e.g., patient-passive control robots versus patient-active control robots) [16] and the targeted joints of robots (e.g., proximal upper limb versus distal approach) [17], several meta-analyses have discussed the influence of stage of stroke [18,19,20,21,22] and the targeted joints of robots [20,22,23] on benefits of RAT on motor control and activity function, but few study focused on the level of impairment of patients, and the parameters of RAT such as amount of training time and the control system of the robots, thus we performed comprehensive analysis to discuss those factors to try to determine the optimal treatment parameters.

It is known that the control systems of arm robots can influence the therapeutic effect [16], the arm robots can be divided into patient-passive control robots and patient-active control robots according to the control strategies of robots. Patient-passive control robots mainly deliver automated practical movements to patients, and patient-active control robots can monitor and evaluate the physical parameters and performance of voluntary motion of patients [24] and then provide assistance as needed to complete the movement initiated by patients [25]. In the latter strategy, patients pay more attention to and put more effort into the training and more actively participate in the practice [26], which is essential for improving cortical activity, excitability and motor performance [[[[[27]]]]]. Active participation is influenced by the level of impairment, the mechanical properties of the robot, the control strategies, the training mode of the robot, the instructions of the therapist and various other factors, therefore, we conducted a subgroup analysis to investigate the effect of training mode and impairment level on the superiority of RAT.

Moreover, most clinical trials have focused on the outcomes post intervention, and few studies discussed the long-term effect of RAT on activity function at follow-up. However, the changes in motor and activity function were different at the end of treatment and at follow-up [28,29], and a previous study [30] found that the gains in the Fugl-Meyer Upper Extremity (FM-UE) and Functional Independence Measure (FIM) between the robotic group and the control group were significantly different at discharge but not at the six-month follow-up.

Therefore, we performed this systematic review to investigate the effect of RAT on motor control and activity and to further discuss whether the effect of RAT persists longer than the three-month follow-up and how the amount of training, level of impairment and training mode influence the effect, this research might provide evidence for therapist to determine the optimal parameter such as total training time and training mode for clinical application of RAT.

Methods

This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We have registered this review in PROSPERO (registered ID CRD42021189643).

Search Strategy and Selection of Studies:

We searched the literature in five databases (PubMed, Cochrane Library, EMBASE, Google Scholar and Scopus) for randomized controlled trials (RCTs) published from 2010 to nowadays. Our research is based on the following overarching participant, intervention, comparison and outcome (PICO) format:

Does robot-assisted therapy (RAT) (intervention) better improve upper limb motor control or activity (outcome) than non-robotic therapy (comparison) in adult poststroke patients (participant) after treatment or during the follow-up period (≥ three months)?

The search terms we used were “robot-assisted therapy" (robotic therapy (RT), exoskeleton, robot-supported, rehabilitation robot, robotic rehabilitation, robotic device, robot-aided rehabilitation), "upper limb" (upper extremity, arm, arm injuries, hand, hand injuries, shoulder, shoulder injuries, elbow, axilla elbow, forearm injuries, forearm, finger, finger injuries, wrist injuries, wrist), and "stroke" (middle cerebral artery infarction, intracranial hemorrhage, hemiplegia, cerebral vascular accident (CVA), cerebral vascular disorders, paresis).

Inclusion criteria

Two researchers independently evaluated the studies, and studies were included if they met the following criteria: (1) randomized controlled trials (RCTs); (2) the patients were over 18 years old; (3) the control group received the same amount of non-robotic therapy, such as usual care, conventional rehabilitation treatment, arm exercise, PT, OT, motor learning, self-guided therapy, task-oriented training, or home exercise program; the experimental group received RAT alone or RAT combined with additional treatments as a control group, for example in Hesse's study [31], patients in experimental group received RAT and individual arm therapy, and patients in control group only received individual arm therapy; (5) the results included at least one of the following measures: the Fugl-Meyer Upper Extremity (FM-UE), Barthel Index score (BI) or modified Barthel Index (mBI), Stroke Impact Scale (SIS), Frenchay Arm Test (FAT), ABILHAND Questionnaire and FIM for activity of daily living (ADL).

Methodological quality and risk of bias assessment

One researcher evaluated the methodological quality and risk of bias of the included studies for random allocation, concealment of allocation, blinding of participants, personnel and assessors, incomplete outcome data, selective reporting and other bias with the Cochrane risk-of-bias tool [32]. If all of the above quality standards were of low risk, indicating the overall risk of bias was low and the methodological quality of study was high and considered as Grade A; if one or more of the standards were of high or unclear risk, the overall risk was moderate and the study was rated as Grade B; if none of the standards was of low risk, the overall risk was high and the study was rated as Grade C.

Sensitivity analysis

We used the methodological features randomization produce, concealment of allocation, and blinding of assessors to test the robustness of the main results in a sensitivity analysis as described by Mehrholz [14] according to the instruction of the Cochrane Handbook for Systematic Reviews of Interventions [32]. We included trials with an adequate description of the randomization, a high quality of concealment of allocation and complete blinding of the assessors and analyzed the pooled effect of RAT on the outcomes of motor control and activity function.

Data extraction

Two researchers extracted the following data from the included studies: the number of subjects; age, time after stroke; intervention protocols (frequency and duration, involved joint); comparison group; the primary outcome (FMA or FM-UE) measuring motor control; the secondary outcomes (FIM, SIS, BI, mBI, the ABILHAND Questionnaire and FAT) measuring the ADL according to a previous study [14]; and the mean differences and standard deviations (SDs) of the outcomes at the end of treatment and/or follow-up (≥ three months after treatment). When an included study compared RAT with two different non-robotic therapies (e.g., RAT versus usual care or versus enhanced upper limb therapy [13]) or discussed two different training methods of RAT (e.g., planar or planar with vertical training versus conventional rehabilitation [33]), we found that the results between the intervention groups and control groups differed significantly and therefore considered them to be two individual groups, according to previous studies [20,34]. If the study did not show detailed data of the primary outcome or secondary outcome, we would contact with the author for the raw data, if not available, the study was excluded.

Data analysis

All data were recorded as the mean (SD). If the data were reported as 95% CI, the means and SDs were calculated using the appropriate statistical methods; if the data were reported as median/IQR, we conducted the author for data and calculated the mean, if the data were unavailable, the study was excluded. When the outcome was measured with the same scale, the mean difference was used; if not, the standard mean difference (SMD) was chosen to measure the effect [32]. Heterogeneity among studies was assessed using heterogeneity statistics (I2 statistic); P ≤ 0.1 and I2 ≥ 50% indicated significant heterogeneity[35]. The fixed-effects model was used when I2 < 50% or P > 0.1; if not (I2 ≥ 50% or P ≤ 0.1), the random-effects model was applied [36]. Four independent analyses were performed to evaluate the effect of RAT on upper-limb motor control and activity at the end of treatment and follow-up (≥ three months). Subgroup analyses were performed to investigate whether and how the poststroke phase and the training intensity (time per session × number of sessions, in hours) influenced the effect of RAT. There were no missing data in our study.

Results

The search retrieved 502 articles. After removal of duplicate articles, 328 articles were screened, of which 260 articles were excluded. Sixty-two articles were assessed for eligibility, and forty-six studies were eligible for inclusion. The flow diagram of the study selection is shown in supplementary material (Additional file 1: Fig. S1).

Characteristics of study

The study characteristics are described in Table 1. All included studies were RCTs published in 2010 to nowadays. The 46 included studies involved 2533 participants with a mean age ranging from 46.20 to 75.5 years old. Almost all (96.7%) patients had first-ever stroke, and 60% patients had ischemic stroke, 15.5% patients had hemorrhagic stroke, 40.7% patients had right hemiparesis, and 39.8% had left hemiparesis. The mean time poststroke ranged from 11 days to 8.5 years. The duration of RAT ranged from 10 days to 12 weeks, and the frequency ranged from two to ten sessions per week. The time spent engaged in RAT ranged from 30 to 180 min per session. The total number of RAT sessions ranged from 10 to 60. On average, patients received RAT four sessions per week for six weeks. The amount of treatment was presented using total time, and the cutoff time (15 h) was chosen according to a previous study in which the authors found that the difference in gains in FMA and FIM assessment between RAT and controls was not significant with a training time of 15 h and was significant with a training time of more than 15 h [10]. The control treatment group received the same amount of treatment as the intervention group. The arm robot used in the intervention group included the Mirror Image Movement Enabler (MIME), UL-EXO7, Amadeo Robotic System, InMotion ARM 2.0 Robot, Aremo Spring, Bi-Manu-Track, Myomo e100, Neuro-Rehabilitation Robot (NeReBot), electromyography (EMG)-driven robot, REJOYCE robot, Pneu-WREX, ReoGo system, and Gloreha robot, as described in Table 1. All included studies assessed motor control function with the FM-UE. Twenty-two studies assessed activity function using different measures, such as the FIM, SIS, BI and mBI.

Methodological quality and risk of bias

We used the Cochrane risk-of-bias tool to assess the methodological quality of the involved studies. Additional file 2: Fig. S2 and Additional file 3: Fig. S3 presented the assessment of the risk of bias of all individual studies in detail. Forty studies (86.96%) described the randomization procedure, and six studies [37,38,39,40,41,42] did not show detailed information on random sequence generation. There were twenty-nine (63.04%) trials with adequate allocation concealment and thirty-eight (82.61%) trials with blinding of the assessors. However, only seven (15.22%) studies reported blinding of participations and personnel because the therapists who carried out the intervention can hardly be blinded to the group allocation. Table 2 showed the methodological quality of involved studies, only one included study [42] were rated as Grade C, and others were rated as Grade B.

Meta-analysis

The outcomes of FM-UE (Additional file 4: Fig. S4) (SMD = 0.20, 95% CI 0.08 to 0.32, P = 0.001) and ADL (Additional file 6: Fig. S6) (SMD = 0.32, 95% CI 0.16 to 0.47, P < 0.0001) at the end-of-treatment were significantly higher in RAT group than controls, and the differences in outcomes of FM-UE (Additional file 5: Fig. S5) and ADL (Additional file 7: Fig. S7) between two groups were not found at the follow-up. Therefore, we pooled the outcomes of FM-UE and ADL at the end-of-treatment rather than at the follow-up in subgroup analyses. Additional file 8: Fig. S8 showed that there was no publication bias in those studies, sensitivity analysis (Additional file 9: Fig. S9, Additional file 10: Fig. S10, Additional file 11: Fig. S11, Additional file 12: Fig. S12) confirmed that the effect of RAT on the outcomes of the FM-UE and ADL at the end of treatment and follow-up was quite stable and not affected by the methodological quality.

The amount of training

The amount of treatment was estimated by total time as described in a previous study [10,43]. We found that there was a statistically significant difference in the motor control results at the end of treatment between RAT and controls in the subset with a total time > 15 h (Fig. 1) (SMD = 0.15, 95% CI 0.05 to 0.25, P = 0.005), but no significant difference was found when the total time was ≤ 15 h (SMD = 0.26, 95% CI − 0.02 to 0.55, P = 0.07). A significant difference in outcome of activity function at the end of treatment between RAT and controls (Fig. 2) was also detected when the total time was more than 15 h (SMD = 0.32, 95% CI 0.12 to 0.53, P = 0.002), and no statistically significant difference was observed when the total time was ≤ 15 h. (SMD = 0.25, 95% CI − 0.00 to 0.51, P = 0.05).

Fig. 1
figure 1

A subgroup analysis of the effect of RAT with different total training time versus non-robotic therapy on outcome of FM-UE at the end-of-treatment. The subgroup analysis showed that RAT better improved the outcomes of FM-UE at the end-of-treatment than controls when the total training time was more than 15 h (SMD = 0.15, 95% CI 0.05 to 0.25, P = 0.005), and had no significant clinical benefit with the total training time ≤ 15 h (SMD = 0.26, 95% CI − 0.02 to 0.55, P = 0.07)

Fig. 2
figure 2

A subgroup analysis of the effect of RAT with different total training time versus non-robotic therapy on outcome of ADL at the end-of-treatment. The subgroup analysis indicated that RAT better improved the outcomes of ADL at the end-of-treatment than controls with the total training time more than 15 h (SMD = 0.32, 95% CI 0.12 to 0.53, P = 0.002), and had no additional benefit with the total training time ≤ 15 h (SMD = 0.25, 95% CI − 0.00 to 0.51, P = 0.05)

Level of impairment

The level of impairment was evaluated according to the baseline FM-UE scores, and the participants were classified into mild to moderate (22–66) and severe (≤ 21) groups as described in a previous study [29]. In the subgroup analysis, contrast with the study conducted by Wu [22], we found RAT significantly improved the FMA-UE scores at the end-of-treatment in the patients with mild-to-moderate paralysis, compared with controls (Additional file 13: Fig. S13) (SMD = 0.26, 95% CI 0.09 to 0.42, P = 0.002), and the difference between two groups at the end-of-treatment was not significant in patients with severe paralysis (SMD = 0.14, 95% CI − 0.01 to 0.30, P = 0.08). In line with the result of FM-UE, the between-group difference in outcome of ADL at the end-of-treatment was also observed in patients with mild to moderate paralysis (Fig. 3) (SMD = 0.27, 95% CI 0.07 to 0.48, P = 0.009) and was not found in patients with severe paralysis (SMD = 0.21, 95% CI − 0.01 to 0.42, P = 0.06).

Fig. 3
figure 3

Comparison of the effect of RAT and non-robotic therapy on outcome of ADL at the end-of-treatment in patients with different level of impairment. The subgroup analysis showed that RAT significantly better improved the activity function in patients with mild to moderate paralysis (SMD = 0.27, 95% CI 0.07 to 0.48, P = 0.009), but had the same clinical effect as controls in patients with severe paralysis (SMD = 0.21, 95% CI − 0.01 to 0.42, P = 0.06)

The training mode

The training modes provided by the arm robots included patient-passive mode, patient-active mode and active resistance mode [44]. In the patient-active mode and active resistance mode, patients actively participate in the treatment, therefore, we considered them together as the patient-active group; while in several clinical trials, patients first received passive movement practice and then performed robot-assisted active tasks, thus, we considered them as the passive-active group. Figure 4 showed that the passive-active mode RAT group (SMD = 0.33, 95% CI 0.06 to 0.59, P = 0.01) and the patient-active mode RAT group (SMD = 0.17, 95% CI 0.03 to 0.31, P = 0.02) had the higher outcome of the FM-UE at the end of treatment, compared with control group; while the patient-passive mode RAT group (SMD = -0.09, 95% CI -1.04 to 0.86, P = 0.85) had the same outcome of the FM-UE as control group. The outcome of the ADL at the end-of-treatment was also significantly higher in the passive-active mode RAT group (Fig. 5) (SMD = 0.42, 95% CI 0.15 to 0.68, P = 0.002) and patient-active mode RAT group (Fig. 5) (SMD = 0.22, 95% CI 0.03 to 0.40, P = 0.02) compared to controls, and the difference in outcome of the ADL between RAT and control groups was not significant when RAT was applied in the patient-passive mode (SMD = 0.70, 95% CI -0.27 to 1.67, P = 0.16).

Fig. 4
figure 4

A subgroup analysis for the effect of RAT versus non-robotic therapy on outcome of FM-UE at the end-of-treatment in different training modes. The result indicated that RAT had better therapeutic effect on motor control function than controls when arm robots provide passive-active (SMD = 0.33, 95% CI 0.06 to 0.59, P = 0.01) and patient-active training (SMD = 0.17, 95% CI 0.03 to 0.31, P = 0.02)

Fig. 5
figure 5

A subgroup analysis of the effect of RAT versus non-robotic therapy on outcome of ADL at the end-of-treatment in different training modes. The meta-analysis suggested that RAT could better improve the activity function than controls when arm robot provide passive-active (SMD = 0.42, 95% CI 0.15 to 0.68, P = 0.002) and patient-active training (SMD = 0.22, 95% CI 0.03 to 0.40, P = 0.02)

Discussion

This systematic review demonstrated that RAT has the immediate benefits on motor control and activity function compared with non-robotic therapy. Moreover, we found the superiority of RAT in improving motor control and activity function was observed when it was supplied in passive-active mode or patient-active mode, with the amount of training more than 15 h and to patients with mild to moderate impairment.

In our study, we found that RAT could better improve the outcomes of the FM-UE and the activity function at the end-of-treatment compared with controls. Several reasons might account for this result. First, arm robots can simultaneously provide highly repetitive, interactive forms of training and multisensory stimulation for the paretic limb [45], and several robots can provide gravity support for the upper limb, allow patients to perform a complete functional movement with their own effort. Additionally, some arm robots can precisely assess the limb function such as interaction forces, range of motion and limb movement reports, and then provide biofeedback, thus increasing the objective of training and promoting recovery of motor control of the upper limb after stroke [46].

The differences in outcomes of the FM-UE and ADL between RAT and controls were significant at the end-of-treatment, but were not in the follow-up period, indicating the long-term effect of RAT was not better than controls. Consistent with our study, Masiero [47] and Susanto [48] conducted follow-up studies and found that although RAT could improve the FM-UE, the differences between RAT and control groups were nonsignificant. However, the small sample size (n = 11/n = 7) in our study might cause our result underpowered, the future research involving a larger sample is needed to investigate the long-term effect of RAT.

Considering the optimal total training time of RAT, this meta-analysis suggested that a larger amount (> 15 h) of RAT could better improve the motor control and activity compared with controls. In our study, we found that the differences in outcomes of the FM-UE and ADL between RAT and controls were significant when the total training time was more than 15 h and not significant when training time was less than 15 h, in consistent with a previous study [30] in which the authors found that the gains in the FMA and FIM were not different between the RAT and control group when the total training time was 15 h. Sehle’s [49] study found that RAT led to the higher motor excitability compared with control treatment, and the motor excitability was positively correlated with the amount of robot-assisted training. We speculate that when total training time is less than 15 h, the motor excitability induced by RAT is weak and couldn't successfully translate to clinical improvements, and the motor excitability becomes stronger enough to translate into clinical improvement with the total training time increasing.

The movement practice and application of robotic force are two interacting processes of RAT, and which process is more beneficial is controversial. A previous study [26] found that robotically finishing a movement for a patient with stroke did not show better improvement of function than usual movement practice, and using robotic forces to assist patients to complete correct movements could focus and intensify patients' effort and attention to the treatment, achieving better outcomes [50]. Active participation of the patients is critical for neuroplasticity, motor learning and rehabilitation [50,51], and studies have found that rehabilitation treatment integrated with patients' voluntary movement could facilitate the recovery of lost motor ability [16,52]. The level of patients' active participation is partially influenced by the control systems of robots and the paralysis level of patients. The control systems of robots can be roughly divided into patient-passive control and patient-active control [16]. Arm robots implementing patient-passive control are suitable for patients with severe paralysis, and provide passive mode training for them to passively execute repetitive movement along predefined trajectories, and the active participation of patients is often neglected during such patient-passive training mode [53]. Robots equipped with patient-active control, such as patient-cooperative control, assist-as-needed control, impedance-based control and EMG-signal-based control, can regulate the human–robot interaction based on the motion intention and disability level of patients [54], and the training modes provided by those patient-active controls include passive mode for patients with severe disability, active mode and active-resistance mode. In our study, we found that RAT could better improve motor and activity function in patients with mild to moderate impairment than controls, and RAT had the same effect as controls in patients with severe impairment. RAT showed significant benefits for motor control and activity compared with controls when it provided patient-active and passive-active training, whereas RAT had the similar effects with controls when it provided patient-passive training. As we known, patients with severe paralysis perform few voluntary movements in the treatment, indicating decreased active participation, and patients might pay more attention and effort in the patient-active and passive-active training than passive training, therefore, the above findings in our study demonstrated that the better therapeutic beneficial effect of arm robots might not result from providing passive automatic movement but mainly from assisting patients to complete voluntary movements, and the higher degree of patients' active participation cause better improvement in motor and activity function.

Even though there were significant differences in the outcomes of the FM-UE and ADL at the end of the intervention between RAT and controls, the overall effect size was small or medium in some subgroups, indicating that the beneficial therapeutic effect of arm robots was limited, which suggested that the clinical application must be used with caution regarding the amount of treatment, the impairment level of patients, and the training mode. In addition, almost all (96.7%) patients in our study had first-ever stroke, and the majority (60%) of them suffered from ischemic stroke; hence, the results might not be applicable for patients with recurrent stroke or hemorrhagic stroke.

There were several limitations in this meta-analysis and review as following: (1) As we known, the application of arm robot such as arm robot alone or RAT combined with controls may affect the differences in outcomes of motor control and activity between intervention and control group, but we have not further discussed this factor; (2) We only investigated the effect of total training time on effectiveness of RAT, however other parameters such as the number of repetitions, frequency and duration of RAT also influence its effect; (3) The small sample size in follow-up group may cause our results underpowered.

Conclusion

Our study suggest that RAT has the significant immediate beneficial effects on motor control and activity function of hemiparetic upper limb in patients after stroke, but there is no evidence to support its long-term effect. The superiority of RAT is influenced by the amount of training time, the training mode and the impairment level of patients. To achieve the best therapeutic effect, arm robots should be applied with training time more than 15 h, in patient-active mode or passive-active mode for patients with mild to moderate impairment.

Considering the application of arm robot, the number of repetitions, the frequency and the duration of robot-assisted training may also influence the effectiveness of RAT, future study should stratify the patients according to the those factors to further determine the optimal application and parameters of RAT.

Table 1 The characteristic of included studies
Table 2 The methodological quality assessment of included studies

Availability of data and materials

Not applicable.

Abbreviations

RAT:

Robot-assisted therapy

FM-UE:

Fugl-Meyer Upper Extremity

ICF:

International Classification of Functioning, Disability, and Health

PNF:

Proprioceptive neuromuscular facilitation

ADL:

Activity of daily living

FIM:

Functional Independence Measure

BI:

Barthel Index score

mBI:

Modified Barthel Index score

SIS:

Stroke Impact Scale

FAT:

Frenchay Arm Test

SMD:

Standard mean difference

EMG:

Electromyography

References

  1. Feigin VL, Forouzanfar MH, Krishnamurthi R, Mensah GA, Connor M, Bennett DA, et al. Global and regional burden of stroke during 1990–2010: findings from the Global Burden of Disease Study 2010. Lancet (London, England). 2014;383(9913):245–54.

    Article  Google Scholar 

  2. Ding Q, Liu S, Yao Y, Liu H, Cai T, Han L. Global, Regional, and National Burden of Ischemic Stroke, 1990–2019. Neurology. 2021.

  3. Pollock A, Farmer SE, Brady MC, Langhorne P, Mead GE, Mehrholz J, et al. Interventions for improving upper limb function after stroke. Cochrane Database Syst Rev. 2014;2014(11): Cd110820.

    Google Scholar 

  4. Maritz R, Aronsky D, Prodinger B. The International Classification of Functioning, Disability and Health (ICF) in electronic health records. A systematic literature review. Appl Clin Inform. 2017;8(3):964–80.

    PubMed  PubMed Central  Article  Google Scholar 

  5. Harris JE, Eng JJ. Paretic upper-limb strength best explains arm activity in people with stroke. Phys Ther. 2007;87(1):88–97.

    PubMed  Article  Google Scholar 

  6. Veerbeek JM, van Wegen E, van Peppen R, van der Wees PJ, Hendriks E, Rietberg M, et al. What is the evidence for physical therapy poststroke? A systematic review and meta-analysis. PLoS ONE. 2014;9(2): e87987.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  7. Lo AC, Guarino PD, Richards LG, Haselkorn JK, Wittenberg GF, Federman DG, et al. Robot-assisted therapy for long-term upper-limb impairment after stroke. N Engl J Med. 2010;362(19):1772–83.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. Terranova TT, Simis M, Santos ACA, Alfieri FM, Imamura M, Fregni F, et al. Robot-assisted therapy and constraint-induced movement therapy for motor recovery in stroke: results from a randomized clinical trial. Front Neurorobot. 2021;15.

  9. Masiero S, Poli P, Rosati G, Zanotto D, Iosa M, Paolucci S, et al. The value of robotic systems in stroke rehabilitation. Expert Rev Med Devices. 2014;11(2):187–98.

    CAS  PubMed  Article  Google Scholar 

  10. Burgar CG, Lum PS, Scremin AM, Garber SL, Van der Loos HF, Kenney D, et al. Robot-assisted upper-limb therapy in acute rehabilitation setting following stroke: Department of Veterans Affairs multisite clinical trial. J Rehabil Res Dev. 2011;48(4):445–58.

    PubMed  Article  Google Scholar 

  11. Byl NN, Abrams GM, Pitsch E, Fedulow I, Kim H, Simkins M, et al. Chronic stroke survivors achieve comparable outcomes following virtual task specific repetitive training guided by a wearable robotic orthosis (UL-EXO7) and actual task specific repetitive training guided by a physical therapist. J Hand Therapy. 2013;26(4):343-52;quiz52.

    Article  Google Scholar 

  12. Calabrò RS, Accorinti M, Porcari B, Carioti L, Ciatto L, Billeri L, et al. Does hand robotic rehabilitation improve motor function by rebalancing interhemispheric connectivity after chronic stroke? Encouraging data from a randomised-clinical-trial. Clin Neurophysiol. 2019;130(5):767–80.

    PubMed  Article  Google Scholar 

  13. Rodgers H, Bosomworth H, Krebs HI, van Wijck F, Howel D, Wilson N, et al. Robot assisted training for the upper limb after stroke (RATULS): a multicentre randomised controlled trial. Lancet (London, England). 2019;394(10192):51–62.

    Article  Google Scholar 

  14. Mehrholz J, Pohl M, Platz T, Kugler J, Elsner B. Electromechanical and robot-assisted arm training for improving activities of daily living, arm function, and arm muscle strength after stroke. Cochrane Database Syst Rev. 2015;2015(11): Cd006876.

    PubMed Central  Google Scholar 

  15. Flynn N, Froude E, Cooke D, Kuys S. Repetitions, duration and intensity of upper limb practice following the implementation of robot assisted therapy with sub-acute stroke survivors: an observational study. Disabil Rehabil Assist Technol. 2020:1–6.

  16. Wu Q, Wang X, Chen B, Wu H. Patient-active control of a powered exoskeleton targeting upper limb rehabilitation training. Front Neurol. 2018;9:817.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. Mazzoleni S, Sale P, Franceschini M, Bigazzi S, Carrozza MC, Dario P, et al. Effects of proximal and distal robot-assisted upper limb rehabilitation on chronic stroke recovery. NeuroRehabilitation. 2013;33(1):33–9.

    PubMed  Article  Google Scholar 

  18. Chien WT, Chong YY, Tse MK, Chien CW, Cheng HY. Robot-assisted therapy for upper-limb rehabilitation in subacute stroke patients: a systematic review and meta-analysis. Brain Behav. 2020;10(8): e01742.

    PubMed  PubMed Central  Article  Google Scholar 

  19. Bertani R, Melegari C, De Cola MC, Bramanti A, Bramanti P, Calabrò RS. Effects of robot-assisted upper limb rehabilitation in stroke patients: a systematic review with meta-analysis. Neurol Sci. 2017;38(9):1561–9.

    PubMed  Article  Google Scholar 

  20. Veerbeek JM, Langbroek-Amersfoort AC, van Wegen EE, Meskers CG, Kwakkel G. Effects of robot-assisted therapy for the upper limb after stroke. Neurorehabil Neural Repair. 2017;31(2):107–21.

    PubMed  Article  Google Scholar 

  21. Mehrholz J, Pohl M, Platz T, Kugler J, Elsner B. Electromechanical and robot-assisted arm training for improving activities of daily living, arm function, and arm muscle strength after stroke. Cochrane Database Syst Rev. 2018;9(9): Cd006876.

    PubMed  Google Scholar 

  22. Wu J, Cheng H, Zhang J, Yang S, Cai S. Robot-assisted therapy for upper extremity motor impairment after stroke: a systematic review and meta-analysis. Phys Therapy. 2021;101(4).

  23. Zhao M, Wang G, Wang A, Cheng LJ, Lau Y. Robot-assisted distal training improves upper limb dexterity and function after stroke: a systematic review and meta-regression. Neurol Sci. 2022;43(3):1641–57.

    PubMed  Article  Google Scholar 

  24. Lu Z, Tong KY, Shin H, Li S, Zhou P. Advanced myoelectric control for robotic hand-assisted training: outcome from a stroke patient. Front Neurol. 2017;8:107.

    PubMed  PubMed Central  Google Scholar 

  25. Colombo R, Pisano F, Micera S, Mazzone A, Delconte C, Carrozza MC, et al. Assessing mechanisms of recovery during robot-aided neurorehabilitation of the upper limb. Neurorehabil Neural Repair. 2008;22(1):50–63.

    CAS  PubMed  Article  Google Scholar 

  26. Kahn LE, Lum PS, Rymer WZ, Reinkensmeyer DJ. Robot-assisted movement training for the stroke-impaired arm: Does it matter what the robot does? J Rehabil Res Dev. 2006;43(5):619–30.

    PubMed  Article  Google Scholar 

  27. Lotze M, Braun C, Birbaumer N, Anders S, Cohen LG. Motor learning elicited by voluntary drive. Brain J Neurol. 2003;126(Pt 4):866–72.

    Article  Google Scholar 

  28. Conroy SS, Wittenberg GF, Krebs HI, Zhan M, Bever CT, Whitall J. Robot-assisted arm training in chronic stroke: addition of transition-to-task practice. Neurorehabil Neural Repair. 2019;33(9):751–61.

    PubMed  Article  Google Scholar 

  29. Straudi S, Baroni A, Mele S, Craighero L, Manfredini F, Lamberti N, et al. Effects of a robot-assisted arm training plus hand functional electrical stimulation on recovery after stroke: a randomized clinical trial. Arch Phys Med Rehabil. 2020;101(2):309–16.

    PubMed  Article  Google Scholar 

  30. Burgar CG, Lum PS, Erika Scremin AM, Garber SL, Machiel van der Loos HF, Kenney D, et al. Robot-assisted upper-limb therapy in acute rehabilitation setting following stroke: Department of veterans affairs multisite clinical trial. J Rehabil Res Dev. 2011;48(4):445–58.

    PubMed  Article  Google Scholar 

  31. Hesse S, Heß A, Werner CC, Kabbert N, Buschfort R. Effect on arm function and cost of robot-assisted group therapy in subacute patients with stroke and a moderately to severely affected arm: a randomized controlled trial. Clin Rehabil. 2014;28(7):637–47.

    PubMed  Article  Google Scholar 

  32. Higgins JP, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al. Cochrane handbook for systematic reviews of interventions. New Jersey: Wiley; 2019.

    Book  Google Scholar 

  33. Conroy SS, Whitall J, Dipietro L, Jones-Lush LM, Zhan M, Finley MA, et al. Effect of gravity on robot-assisted motor training after chronic stroke: a randomized trial. Arch Phys Med Rehabil. 2011;92(11):1754–61.

    PubMed  PubMed Central  Article  Google Scholar 

  34. Ferreira F, Chaves MEA, Oliveira VC, Van Petten A, Vimieiro CBS. Effectiveness of robot therapy on body function and structure in people with limited upper limb function: a systematic review and meta-analysis. PLoS ONE. 2018;13(7): e0200330.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  35. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ (Clinical research ed). 2003;327(7414):557–60.

    Article  Google Scholar 

  36. Cumpston M, Li TJ, Page MJ, Chandler J, Welch VA, Higgins JPT, et al. Updated guidance for trusted systematic reviews: a new edition of the Cochrane Handbook for Systematic Reviews of Interventions. Cochrane Database Syst Rev. 2019(10).

  37. Grigoras AV, Irimia DC, Poboroniuc MS, Popescu CD. Testing of a hybrid FES-robot assisted hand motor training program in sub-acute stroke survivors. Adv Electr Comput Eng. 2016;16(4):89–94.

    Article  Google Scholar 

  38. Villafañe JH, Taveggia G, Galeri S, Bissolotti L, Mullè C, Imperio G, et al. Efficacy of short-term robot-assisted rehabilitation in patients with hand paralysis after stroke: a randomized clinical trial. Hand (New York, NY). 2018;13(1):95–102.

    Article  Google Scholar 

  39. Yoo DH, Cha YJ, Kim SY, Lee JS. Effects of upper limb robot-assisted therapy in the rehabilitation of stroke patients. J Phys Ther Sci. 2013;25:407–9.

    Article  Google Scholar 

  40. Zengin-Metli D, Özbudak-Demir S, Eraktaş İ, Binay-Safer V, Ekiz T. Effects of robot assistive upper extremity rehabilitation on motor and cognitive recovery, the quality of life, and activities of daily living in stroke patients. J Back Musculoskelet Rehabil. 2018;31(6):1059–64.

    PubMed  Article  Google Scholar 

  41. Wu CY, Yang CL, Chen MD, Lin KC, Wu LL. Unilateral versus bilateral robot-assisted rehabilitation on arm-trunk control and functions post stroke: a randomized controlled trial. J Neuroeng Rehabil. 2013;10:35.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. Youssef TM, Ahmed GM, Amer HA, Battesha HHM, Elsherbini AIEM. Effect of robot-assisted therapy on upper extremity function in chronic stroke patients. Turk J Physiother Rehabil. 32:3.

  43. Wang L, Zheng Y, Dang Y, Teng M, Zhang X, Cheng Y, et al. Effects of robot-assisted training on balance function in patients with stroke: a systematic review and meta-analysis. J Rehabil Med. 2021;53(4): jrm00174.

    PubMed  Article  Google Scholar 

  44. Hsieh YW, Lin KC, Horng YS, Wu CY, Wu TC, Ku FL. Sequential combination of robot-assisted therapy and constraint-induced therapy in stroke rehabilitation: a randomized controlled trial. J Neurol. 2014;261(5):1037–45.

    CAS  PubMed  Article  Google Scholar 

  45. Zheng QX, Ge L, Wang CC, Ma QS, Liao YT, Huang PP, et al. Robot-assisted therapy for balance function rehabilitation after stroke: a systematic review and meta-analysis. Int J Nurs Stud. 2019;95:7–18.

    PubMed  Article  Google Scholar 

  46. Morone G, Cocchi I, Paolucci S, Iosa M. Robot-assisted therapy for arm recovery for stroke patients: state of the art and clinical implication. Expert Rev Med Devices. 2020;17(3):223–33.

    CAS  PubMed  Article  Google Scholar 

  47. Masiero S, Armani M, Ferlini G, Rosati G, Rossi A. Randomized trial of a robotic assistive device for the upper extremity during early inpatient stroke rehabilitation. Neurorehabil Neural Repair. 2014;28(4):377–86.

    PubMed  Article  Google Scholar 

  48. Susanto EA, Tong RK, Ockenfeld C, Ho NS. Efficacy of robot-assisted fingers training in chronic stroke survivors: a pilot randomized-controlled trial. J Neuroeng Rehabil. 2015;12:42.

    PubMed  PubMed Central  Article  Google Scholar 

  49. Sehle A, Stuerner J, Hassa T, Spiteri S, Schoenfeld MA, Liepert J. Behavioral and neurophysiological effects of an intensified robot-assisted therapy in subacute stroke: a case control study. J Neuroeng Rehabil. 2021;18(1):6.

    PubMed  PubMed Central  Article  Google Scholar 

  50. Pennycott A, Wyss D, Vallery H, Klamroth-Marganska V, Riener R. Towards more effective robotic gait training for stroke rehabilitation: a review. J Neuroeng Rehabil. 2012;9:65.

    PubMed  PubMed Central  Article  Google Scholar 

  51. Kaelin-Lang A, Sawaki L, Cohen LG. Role of voluntary drive in encoding an elementary motor memory. J Neurophysiol. 2005;93(2):1099–103.

    PubMed  Article  Google Scholar 

  52. Kim B, Deshpande AD. An upper-body rehabilitation exoskeleton Harmony with an anatomical shoulder mechanism: design, modeling, control, and performance evaluation. Int J Robot Res. 2017;36(4):414–35.

    Article  Google Scholar 

  53. Hu XL, Tong KY, Song R, Zheng XJ, BEng. A comparison between electromyography-driven robot and passive motion device on wrist rehabilitation for chronic stroke. Neurorehabil Neural Repair. 2009;23(8):837–46.

  54. Keller U, Hedel H, Klamroth-Marganska V, Riener R. ChARMin: the first actuated exoskeleton robot for pediatric arm rehabilitation. IEEE/ASME Trans Mechatron. 2016;21(5):2201–13.

    Article  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Natural Science Foundation Project of Chongqing (Grant Number cstc2019jcyj-msxmX0026); the Medical Scientific Research Projects Foundation of Chongqing (Grant Number 2018MSXM043); the National Natural Science Foundation of China (Grant Number 81900079); Natural Science Foundation of Chongqing (Grant Number cstc2020jcyj-msxmX0238); Chongqing medical scientific research project (Joint project of Chongqing Health Commission and Science and Technology Bureau) (Grant Number 2020MSXM036).

Author information

Authors and Affiliations

Authors

Contributions

ZLP assessed the bias of risk of included studies and wrote the manuscript, JGW extracted the data and performed the analysis, MJX revised the manuscript, WSR researched and evaluated the RCTs, and CL designed this study. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Li Cheng.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Fig S1.

Flow diagram of study selection.

Additional file 2: Fig S2.

Risk of bias summary for all included studies.

Additional file 3: Fig S3.

Risk of bias graph for all included studies.

Additional file 4: Fig S4.

Comparison of the effect of RAT and non-robotic therapy on outcome of FM-UE scale at the end-of-treatment. The result showed that RAT had the additional immediated benfits on motor control compared with controls (SMD = 0.20, 95% CI 0.08 to 0.32, P = 0.001).

Additional file 5: Fig 5.

Comparison of the effect of RAT and non-robotic therapy on outcome of FM-UE at the follow-up (≥ 3 months). The result showed that the long-term effect of RAT on motor control was same as controls (SMD = -0.07, 95% CI -0.21 to 0.07, P = 0.31).

Additional file 6: Fig S6.

Comparison of the effect of RAT and non-robotic therapy on results of ADL at the end-of-treatment. The results showed that RAT could better improve the activity function at the end-of-treatment than controls (SMD = 0.32, 95% CI 0.16 to 0.47, P < 0.0001).

Additional file 7: Fig S7.

Comparison of the effect of RAT and non-robotic therapy on results of ADL at the follow-up (≥ 3 months). The results indicated that long-term effect of RAT on ADL was similar with controls (SMD = 0.09, 95% CI -0.06 to 0.23, P = 0.25).

Additional file 8: Fig S8.

The funnel plots of the results of the FM-UE and ADL at the end-of-treatment and at the follow-up. (A). The funnel plot of the outcomes of the FM-UE at the end-of-treatment;(B). The funnel plot of the outcomes of the FM-UE at the follow-up;(C). The funnel plot of the outcome of the ADL at the end-of-treatment; (D). The funnel plot of the outcome of the ADL at the follow-up.

Additional file 9: Fig S9.

The sensitivity analysis of the outcomes of the FM-UE at the end-of-treatment.

Additional file 10: Fig S10.

The sensitivity analysis of the outcomes of the FM-UE at the follow-up.

Additional file 11: Fig S11.

The sensitivity analysis of the outcomes of ADL at the end-of-treatment.

Additional file 12: Fig S12.

The sensitivity analysis of the outcomes of the ADL at the follow-up.

Additional

file 13: Fig S13. The subgroup analysis of the effect of RAT versus non-robotic therapy on outcome of FM-UE at the end-of-treatment in patients with different level of impairment. The results indicated that RAT had the additional benefit on motor control in patients with mild-to moderate paralysis (SMD = 0.26, 95% CI 0.09 to 0.42, P = 0.002), and had no significant clinical benefits in patients with severe paralysis (SMD = 0.14, 95% CI -0.01 to 0.30, P = 0.08).

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, L., Jia, G., Ma, J. et al. Short and long-term effects of robot-assisted therapy on upper limb motor function and activity of daily living in patients post-stroke: a meta-analysis of randomized controlled trials. J NeuroEngineering Rehabil 19, 76 (2022). https://doi.org/10.1186/s12984-022-01058-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12984-022-01058-8

Keywords

  • Stroke
  • Rehabilitation
  • Robot-assisted therapy
  • Upper limb
  • Meta-analysis