Sensor-based technology for upper limb rehabilitation in patients with multiple sclerosis: A randomized controlled trial

1 Introduction

Multiple sclerosis (MS) is an inflammatory, neurodegenerative, demyelinating disorder of the Central Nervous System characterized by focal lymphocytic infiltration that leads to the damage of myelin and axons (Compston et al., 2008). The clinical course of the pathology is unpredictable, different from patients to others, and may change over time (Compston et al., 2002). Related to the varying possible distribution of damaged areas, patients with MS present a wide range of neurological symptoms that may compromise many core functions performances. One of the most important function impairments is represented by upper extremities function limitations that characterize up to 76% of patients with MS influencing the activities of daily living, both in early stages of the disease and in the later stages and leading to important disabilities (Einarsson et al., 2006; Goodkin et al.,1988; Johansson et al., 2007; Kamm et al., 2012). Neurorehabilitation often represents the only treatment available to improve some functional symptoms and intensive multidisciplinary rehabilitation is recommended for all patients with MS (Grasso et al., 2005). Historically, individuals with MS were advised not to exercise as it was clinically observed that some symptoms, like fatigue, worsened after exercise (Smith et al.,2009). However, recent studies showed that regular exercise in MS patients may led to achieve some health benefits (Slawta et al.,2003), including an increase of muscle strength and of mobility an improving of Quality of Life (Svensson et al., 1994; Petajan et al., 1996; Mostert et al., 2002; DeBolt et al., 2004; Motl et al., 2008; Smith et al.,2009; Tramontano et al., 2018). Technological devices are increasingly used in the rehabilitation treatment of subjects with neurological disorders (Prange et al., 2006) and could be good candidates for neuromotor rehabilitation of patients with MS, as they allow the design of personalized training protocols and permit quantitative measurement of the motor performances during training (Carpinella et al., 2009; Morone et al., 2016).

The PABLO^® Upper Extremity is a sensor-based device product by Tyromotion for unilateral and bilateral training of the upper limb. Thanks to interactive therapy games with audio-visual feedback, it allows to train the movement of shoulders, elbows, and wrist and to measure the strength of hand functions and the active range of motions of the upper extremity. Three sensors permit to detect the accelerations of movements in the three dimensions of the space and one sensor detects the hand’s force applied during the movements. Obtained data were registered in an electronic database as quantitative measures and may be used to monitor individual data and therapy progress. PABLO^®-Tyromotion is considered a neurocognitive task-oriented approach of rehabilitation and required an active participation of patients.

Based on these considerations, our hypothesis is that an audio-visual feedback hand training performed with PABLO^®-Tyromotion in addition to the conventional neurorehabilitation, may increase the effectiveness of conventional therapy in upper limbs functions of patients with MS. For that purpose, the aim of this study was to evaluate the effects of a sensor-based technology for motor rehabilitation of the upper limbs in MS people.

2 Methods

2.1 Trial design

This was a two-arm single-blind randomized controlled trial (Fig. 1). The aim was to investigate the effects of a robotic-trained motor rehabilitation performed with PABLO^®-Tyromotion on upper limbs’ functions in MS patients. The guidelines for Good Clinical Practice, and the Consolidated Standards of Reporting Trials (CONSORT), were followed. The trial was approved by the Local Ethics Committee of Fondazione Santa Lucia CE/PROG.631; all participants gave their written informed consent for the participation in the study. A researcher who was not involved in the intervention sessions assessed the patients’ eligibility to participate, based on the inclusion and exclusion criteria. Participants were randomly assigned to one of two groups: experimental (TYRg) or control group (CTRLg).

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Fig. 1

Flow Chart. TYRg = Experimental Group; CTRLg = Control Group.

3 Outcomes

At enrollment, clinical and demographic data were collected. A blinded examiner assessed primary and secondary outcomes. All patients were evaluated at baseline (T0) and after 4 weeks of training (T1). The primary outcome was the changes in functionality of the upper limb measured in 9 Hole Peg Test (9HPT) (Tijsma et al. 2017; Solaro et al., 2019), at 1 month. Secondary outcome measures were carried out to assess the ability in the daily living activities using the Modified Barthel Index (MBI) (Mahoney et al.,1965; Castiglia et al., 2017); the quality of life, using the Multiple Sclerosis Quality of Life-54 (MSQoL-54) (Vickrey et al.1995; Solari et al.,1999); the functional mobility in gait, balance, and transfers using the Rivermead Mobility Index (RMI) (Vaney et al.,1996; Franchignoni et al.,2003); the severity of the fatigue and its effects on daily living activities and life participation using the Fatigue Severity Scale (FSS) (Krupp et al., 1989; Ottonello et al., 2016) and the upper limb strength using the Medical Research Council scale (MRC) (Paternostro-Sluga et al., 2008). All clinical scale scores were collected by a researcher not aware of the allocation group and not involved in the intervention sessions.

4 Sample size

The sample size complied with the minimum number of participants recommended by a power analysis performed on preliminary data (α= 0.05; β= 0.8; ES = 0.5) for nonparametric between-group comparisons (Cohen, 1977). This sample-size estimation procedure recommends that at least 15 patients be included in each group (Waliño-Paniagua, 2019)

5 Blinding

A researcher not involved in the intervention sessions carried out the randomization. Block randomization was performed with a computer-generated randomization list using a block size. Allocation concealment was ensured by using an automatic random number generator (www.random.org). The researcher responsible for the randomization process deposited the list in a secure web-based storage.

6 Statistical analysis

All the statistical analyses were carried out with the IBM SPSSS Statistic Software version 23, IBM Corp. Armonk, NY, U.S.A. Data were reported in terms of means and standard deviations. The paired T-test and the Wilcoxon signed ranks test were used for the within-subjects comparison for both groups at times T0–T1. The Mann-Whitney U-test was used to compare data between groups at T0 and T1. The significance was considered for p < 0.05. The descriptive analysis was performed using [(T1score - T0score/maximalscore - T0score) ×100] (Shah et al. 1990) in order to calculate the percentages of effectiveness in the two groups.

7 Results

Thirty-one patients met the inclusion criteria and were enrolled in the study. One subject dropped out before the end for reasons not related to the study. The statistical analysis was performed using the data of thirty (TYRg = 14, CTRLg = 16) subjects. There were no significant between-group differences in demographics and clinical data or in outcome measures at baseline (T0).

As reported in Table 2 both groups showed an overall improvement at T1, no significant between-group differences in clinical scales scores were found. The within-subject analysis showed a statistically significant improvement in both groups, in the MBI and in the RMI scores and a significant improvement in MSQoL only in the TYRg.

The analysis of effectiveness revealed that, compared with baseline (T0), the improvement percentage in all clinical scale scores was greater in the TYRg than the CTRLg (Table 3). Furthermore, in the between-subjects analysis the RMI score improvement is statistically significant in the TYRg compared to CTRLg.

MRC scores show a statistically significant improvement in 20 out of the 22 evaluated districts for TYRg and 12 out of the 22 for CTRLg as reported in Table 4. No side effects were reported.

The aim of this study was to evaluate the effects of a sensor-based training performed with PABLO^®-Tyromotion for upper limb rehabilitation in MS patients.

The improvement in terms of arm motor recruitment and in functional recovery is significant in the treatment group as well as in the control group. The percentage of improvement in all clinical scales score was greater for treatment TYRg than CTRLg and, in the between-subjects analysis the RMI score improvement is statistically significant in the TYRg compared to CTRLg. Probably the improvement of the RMI could be related to the enhancement of the upper limb’s function supported by the MRC scores which show statistically significant improvement in 20 out of the 22 evaluated districts for TYRg and 12 out of the 22 for CTRLg.

In add only TYRg (and not CTRLg) reported significant improvements in the quality of life (MSQoL) in pre-post analysis. Speculating on our results, we might hypothesize that task enjoyment offered by the PABLO^®-Tyromotion training could be associated with intrinsic motivation (Ryan et al., 2000) which is considered a durable form of motivation that doesn’t decrease much over time (Vansteenkiste et al., 2006). Consequently, there is more enthusiasm about enhancing enjoyment in the context of rehabilitation. The interactive-games based training provided by the PABLO^® may have increased the patient motivation and the visual feedback may have affected the individual awareness of upper limb abilities leading to a major improvement in upper limb’s activities.

Other possible mechanism that could enhance the subject’s performance during experimental training, is the possibility to have a visuo-feedback. Indeed, this is not only a possibility to have more intensive and motivating task-oriented training but also the possibility to engage more muscle firing during training as demonstrated in robot-assisted arm movement with and without visual exergame feedback (Li et al., 2014).

Whereas technological and robot-assisted rehabilitation efficacy benefit from an initial moderate evidence of efficacy as demonstrated by the recent updated Cochrane revision (Mehrholz et al., 2018), literature involving subjects affected by multiple sclerosis is rare. At the light of this consideration, clinical randomized clinical trial even in case of non-positive or negative results are important to create aggregate data that could be analyzed in specific metanalysis. In particular, more than the evaluation of the superiority or not with respect to the conventional training is important to evaluate the best candidate for a specific device and to establish the correct frequency, intensity, and dose of the training (Morone et al., 2020).

Our results are in accordance with previous studies regarding upper limb robotic training in subjects with MS. In particular, Feyes et al showed non-significant differences between a proximal end-effector robot vs conventional therapy in an RCT (Feys et al., 2015). Similar results were obtained from Vergaro et al with an end effector robot in a RCT with a crossover design (Vergaro et al., 2010). Similarly, Gandolfi et al demonstrated the same efficacy of a distal end effector upper limb robot vs conventional therapy (Gandolfi et al., 2018). In add, other studies (case series and case control study) demonstrated the feasibility of the robotic therapy and reported initial evidence of efficacy (Carpinella et al., 2009; Gijbels et al., 2011; Carpinella et al., 2012).

Ozdogar et al demonstrated that a video-based exergaming was applied using a game console (Microsoft Xbox One and Kinect motion sensor is almost as effective as conventional rehabilitation regarding improving upper extremity functions, cognitive functions, fatigue, depression, and health-related quality of life in MS patients) (Ozdogar et al., 2020). This is important because PABLO^® of tyromotion embedded some elements of the exergaming, triggering cognitive capacity and some aspects of executive functions as attention, working memory, visuo-motor alert, dual tasking (Jakob et al., 2018).

According to a recent study (Solaro et al., 2020) our results, demonstrated that sensor-based did not improve manual dexterity measured in 9HPT. Indeed, in our sample, the 9HPT improvement is rather large in comparison to the control group but not significantly (p > 0.05) and the changes in test scores at T1 were less than 20% to define it clinically relevant (Feyes et al., 2017). This result may be partly due to the difficulty in performing a task that requires coordination of the entire limb, including the shoulder. Thus, although the wrist mobility limited and the hand is not actively participating in the reaching exercise.

Furthermore, several studies (Tramontano et al., 2016; Tramontano et al., 2018; Bonnì et al., 2019; Tramontano et al., 2019) reported that subjects with central nervous system lesions have great recovery potential if they follow a repetitive, frequent, intense and oriented rehabilitation in terms of functional recovery. In view of this, PABLO^®-Tyromotion results useful to introduce in the rehabilitation program as it permits to allow repetitive training with increasing difficult and variable intensity adaptable to the needs of each patient.

9 Limitation of the study

This study has certain limitations. First, the primary endpoint of the study (9HPT variations) does not reach the minimal detectible changes. Second, our sample included patients with different disability levels. Although the patients have shown themselves available and pleasantly satisfied with the training with PABLO^®-Tyromotion, we didn’t use any validated scale to measure enjoyment and intrinsic motivation (van der Kooij et al., 2019). In addition, future instrumental assessments of muscles’ activity may be considered.

10 Conclusions

The use of an audio-video-feedback training in the upper limb rehabilitation could be a useful complementary strategy in patients with MS. Future studies on larger populations are needed to better understand the clinical value of sensor-based training for hand rehabilitation in MS patients.