Haptic vs sensorimotor training in the treatment of upper limb dysfunction in multiple sclerosis: A multi-center, randomised controlled trial

Journal Pre-proof Haptic vs sensorimotor training in the treatment of upper limb dysfunction in multiple sclerosis: A multi-center, randomised controlled trial

Claudio Solaro, Davide Cattaneo, Angelo Basteris, Ilaria Carpinella, Alice De Luca, Margit Mueller, Rita Bertoni, Maurizio Ferrarin, Vittorio Sanguineti PII:

S0022-510X(20)30079-4

DOI:

https://doi.org/10.1016/j.jns.2020.116743

Reference:

JNS 116743

To appear in:

Journal of the Neurological Sciences

Received date:

26 June 2019

Revised date:

11 February 2020

Accepted date:

17 February 2020

Please cite this article as: C. Solaro, D. Cattaneo, A. Basteris, et al., Haptic vs sensorimotor training in the treatment of upper limb dysfunction in multiple sclerosis: A multi-center, randomised controlled trial, Journal of the Neurological Sciences (2019), https://doi.org/ 10.1016/j.jns.2020.116743

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© 2019 Published by Elsevier.

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis

Haptic vs sensorimotor training in the treatment of upper limb dysfunction in Multiple Sclerosis: a multi-center, randomised controlled trial 1,2

Claudio Solaro MD, 3 Davide Cattaneo PT, PhD,

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Angelo Basteris PhD, 6 Ilaria

Carpinella, 4 Alice De Luca, 2 Margit Mueller PT, 3 Rita Bertoni PT, 6 Maurizio Ferrarin PhD, 4 Vittorio Sanguineti PhD Department of Rehabilitation Mons L Novarese Hospital Moncrivello Italy

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Department of Head and Neck, ASL 3 Genovese, Genoa, Italy

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Department of Neurorehabilitation, Don Gnocchi Foundation IRCCS, Milan, Italy

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Department of Informatics, Bioengineering, Robotics and Systems Engineering,

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University of Genoa, Genoa, Italy

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Recover Injury Research Centre, Griffith University, Gold Coast, Australia

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Department of Biomedical Technology, Don Gnocchi Foundation IRCCS, Milan,

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Italy

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Corresponding author:

Prof. Claudio Solaro Department of Rahabilitation Mons L Novarese Hospital Trompone street 13040 Moncrivello (ITALY) Ph +39 0161 426337 Fax +39 0161 426336 E-mail: [email protected] Running head: Robot-based haptic training in Multiple Sclerosis

Characters in running head: 42 (max 42) Words in abstract: 250 (max 250) Words in Manuscript body: 3989 (max 4000) Number of Figures and Tables: 5+1 (max 6) Number of references: 39 (maximum 60)

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis

Abstract Backgroud In multiple sclerosis (MS) exercise improves upper limb functions, but it is unclear what training types are more effective. Objective. This study compares robot-assisted training based on haptic or sensorimotor exercise. Methods. 41clinically definite MS subjects

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with upper limb impairment were randomised into two groups: (i) Haptic and (ii) Sensorimotor. Subjects in the Haptic performed a robot-assisted training protocol designed to

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counteract incoordination and weakness. The task –interaction with a virtual mass-spring system against a resistive load– requires coordination skills. Task difficulty and magnitude of

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resistive load were automatically adjusted to the individual impairment. Subjects in the

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Sensorimotor performed reaching movements under visual control; the robot generated no forces. Both groups underwent eight training sessions (40 min/session, 2 sessions/week).

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Treatment outcome were 9HPT and ARAT scores.

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Results.The average 9HPT score decreased from 74±9 s to 61±8 s for the Haptic and from 49±6 s to 44±6 s. We found a significant Treatment (p=0.0453) and Time differences

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(p=0.005), but no significant Treatment×Time interactions although we found that the absolute change was only significant in the Haptic group (p=0.011). We observed no significant changes in the ARAT score. Participants tolerated treatments well with a low drop-out rate. In the subjects evaluated at after 12 week (11 subject in sensory-motor and 17 in haptic group) no retention of the effect was found. Conclusions. Task oriented training may improve upper limb function in persons with MS especially in prevalent pyramidal impaired subjects without maintain the effects after three months.

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis MeSH keywords: Multiple Sclerosis, Neurological Rehabilitation, Upper Extremity Clinical trial registration number: NCT02711566 (clinicaltrial.gov)

Introduction Multiple Sclerosis (MS) results in a wide variety of symptoms and functional deficits, including disturbed vision and sensation, muscle weakness and lack of movement coordination. Dysfunctions of the upper extremities occur in at least 66% of persons

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with MS1 . 76% have problems with manual dexterity and 44% experience proble ms with activities of daily living (ADL), leading to reduced functional independence

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and quality of life 3 .

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Positive outcomes of motor training programs on arm and hand have been reported

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for a variety of interventions, subjects conditions, and outcome measures, but only few are supported by RCT evidence4-7 .

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During the last decade, the potential of robots in the treatment of persons with

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neuromotor impairments due to neurological dysfunction has raised considerable . Robot devices interact with subjects by assisting or perturbing their

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movements8 , monitor performance and quantify impairment9 . Clinical trials involving stroke survivors have demonstrated that robot-assisted rehabilitation may lead to improved motor control, at least at the upper limb, however, no effects were observed in functions like manipulation of actual objects and in activities of daily living10-11 . Robots may turn out to be particularly appropriate for MS, because the high interindividual variability of these subjects points at a need for tailoring exercises to the individual symptoms, and robots can be easily programmed for this purpose. Based on previous studies suggesting that adaptation to novel environments may improve upper limb functions12 . The pyramidal and cerebellar involvement represent the may cause of upper limb lost of function in MS. , we developed a robot-assisted training

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis exercise that was specifically designed to deal with incoordination and muscle weakness since several studies found that muscle weakness is associated to manual dexterity impairments and that manual dexterity is a predictor of social participation restrictions in MS 13-14 . We assess the efficacy and tolerability of robotic ‘haptic’ training as compared to a training protocol of equal intensity and duration, based on a purely sensorimotor task

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and on the same virtual environment.

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis

Methods Study design The patients were recruited from 1st January 2013 to 31th December 2015 at the Neurology Unit Dept Head and Neck Genova and Don Gnocchi foundation, Milan. The study is a two-center, randomized double-blind parallel design, with three assessments at T0 (baseline – week 1), T1 (end of treatment – week 4), T2 (follow- up Treatments were administered in two participating centres, i.e.

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– week 16).

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University of Genoa and Don Gnocchi Foundation, Milan.

Subjects were randomly assigned using a randomization list made before the

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beginning of the study to either robot-based haptic training (‘Haptic’) or purely

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sensorimotor training (‘Sensorimotor’) groups. Randomization was automated through a computer program and conducted with a block size of four subjects. It was

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managed by one trained neurologist, principal investigator in this study (C.S.), with

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no access by other experimenters. In all subjects the treatment was applied to the most

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affected hand (as assessed by the 9HPT test, see below), but both hands were assessed at T0-T1-T2.Assessment included a variety of clinical scales (see below for details). One single examiner evaluated all subjects in each center. The examiner and the subjects were blind to treatment type. Subjects were forbidden to describe their symptoms to the examiner. All side effects were recorded by the examiner with an ad hoc interview and collected in a database. Each subject signed a consent form and the study protocol was approved by the ethical committees of both ASL3 ‘Genovese’ and Don Gnocchi Foundation. Subjects

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis The inclusion criteria were: both sexes, age >18 years, definite MS 15 , no relapses or worsening greater than 1 point at the Expanded Disability Status Scale (EDSS) 16 in the last three months - EDSS<7.5, Ashworth score17 at the upper limb lower than 2, Nine-Hole Peg Test (9HPT)18 between 30 s and 180 s., right hand dominace, MMSE > 24. The exclusion criteria were: previous treatment with robot-assisted exercise, presence of severe nystagmus, visual acuity less than 4/10 (evaluated using Eye Chart 16 lines) and major orthopaedic disorders interfering with the execution of the

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protocol.

One trained examiner evaluated all subjects within each center before treatment (T0)

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with the following clinical scales: EDSS to assess overall disability, Modified Fatigue

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Impact Scale (MFIS)19 to assess fatigue, Scripps Neurological Rating Scale (NRS)20

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to assess motor impairments, 9HPT18 and Action Research Arm Test (ARAT)21 to assess arm/hand dexterity and function.

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The last two scales were taken as outcome measures (see below) and were also

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assessed after treatment (T1) and at follow-up (T2).

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Based on neurological examination and on the Scripps’ NRS score, for each subject the evaluating physician classified the type of impairment of the treated arm into three categories: pyramidal (PYR), cerebellar (CBL) or mixed (MIX). No other occupational therapies or physioterapic intervention were allowed during the study period Exercise protocol The exercise protocol was organized into epochs. Every epoch consisted of 24 movements (four repetitions for each of the six possible directions, presented in random order). One session lasted at least 40 minutes, but epochs were never interrupted. Hence each session could include a variable number of epochs. The whole

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis treatment involved a total of eight sessions (two sessions per week, held in nonconsecutive days). To quantify the degree of retention, subjects underwent a follow- up assessment three months after completion of the exercise protocol.

Outcomes

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As the primary outcome measure, we took the pre-post treatment change of the 9HPT score.

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As secondary outcome measure, we took the number of responders i.e. the subjects who experienced an improvement greater than 20% at the 9HPT, a threshold that has

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been proposed as clinically significant22-23 . We also focused on changes (absolute and

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relative) in the ARAT scale.

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Secondary instrumental outcome: an indirect indication of whether and how subjects

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in the Haptic group improved their performance over sessions is represented by the temporal evolution of the performance score and of task difficulty, which is quantified by the magnitudes of resistive (K r) and tool stiffness (K m). The former measures the magnitude of the resistive load that subjects can afford and is therefore related to force generation capability. Tool stiffness determines the difficulty of the control of the mass-spring system, therefore it can be taken as a measure of movement coordination capability. The same evaluation was repeated with the non-treated hand as internal control. The sample size was determined defining a response to treatment as a reduction in the 9HPT score greater than 20% of the baseline score (estimated from personal data),

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis and assuming a 5% of responding patients in the control arm, a sample size of 40 subjects (20 per arm) provides an 80% power to detect an increase in the percentage of responding patients in the treated arm up to 48% (significance level 5%). Subjects were randomly assigned using a randomization list made before the beginning of the study to either robot-based haptic training (‘Haptic’) or purely sensorimotor training (‘Sensorimotor’) groups. Randomization was automated

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through a computer program and conducted with a block size of four subjects. It was managed by one trained neurologist, principal investigator in this study (C.S.), with

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no access by other experimenters

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Experimental apparatus

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The treatments were delivered through two planar robotic manipulandums, specifically

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designed for motor learning studies and robot-assisted rehabilitation24 . Subjects in the two groups were trained, with the same organization, duration, number

Task

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of repetitions and visual display of information.

Subjects in both treatment groups sat on a chair and grasped the handle of the manipulandum with their most affected hand; see Figure 1. Figure 1 near here Subjects in the Sensorimotor group had to perform fast-and-accurate reaching movements in different directions25 . Targets (displayed as green circles on a black background,  2 cm) were arranged on the vertices of a equilateral triangle (side

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis length: 22.5 cm). Both forward and backward movements were recorded, which amounts to a total of six movement directions (0°, 60°, 120°, 180°, 240°, 300°). The current position of the hand was continuously displayed (red circle,  1 cm). The manipulandum was only used to record hand movements, but throughout the movement it generated no forces. Experimental and computational studies have suggested that smooth trajectories of the

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point mass result from hand movements with a peculiar two-peaked speed profile26-27. The use of tasks that challenge sensorimotor coordination skills seems of paramount

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importance, as demonstrated in robot-assisted approaches aimed to facilitate learning of this motor skill28 .

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The task of the subjects in the Haptic group was similar to that performed by the

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Sensorimotor group, but reaching was mediated by a virtual ‘tool’, consisting of a virtual point mass connected to the subjects’ hand through a linear spring.

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Specifically, subjects were required to move the virtual point mass as fast as possible

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through suitable hand motions, so that the mass ends up and stops on the ‘target’ area.

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In this task, difficulty is determined by the stiffness of the virtual spring: with high stiffness, the task is little more difficult than simple reaching. To challenge muscle weakness symptoms we additionally connected a second virtual spring between the hand and the starting point. This second spring generates a force directed toward the start position, thus opposing movement. The robot was programmed to render the forces generated by the spring- mass system and by the resistive spring at the subject’s hand. Tool- mediated reaching movements were performed in different directions. Targets were arranged as in the ‘Sensorimotor’ group, but only the current position of the point

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis mass was continuously displayed. The task was considered as successfully accomplished when the virtual mass was held on the target area for at least 150 ms. If a subject could not reach the target within a 7 s time limit, the robot itself completed the movement. In both treatment groups, at the end of each movement a performance score (range: 010) was displayed. The score was calculated as a combination of a movement duration

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and a path length component29 . The task performed by the Haptic group may be made more challenging in terms of the

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required skills by decreasing the value of the mass-spring stiffness. Likewise, varying

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the resistive stiffness allows to manipulate the amount of resistive force that subjects

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must counteract. The estimated stiffness magnitudes were kept constant for the whole session, but the adaptation procedure was repeated at the beginning of each session.

Statistical analysis

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See Appendix for further details

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For all clinical outcome measures we ran a 2-way repeated- measures ANOVA, with Treatment (Sensorimotor vs Haptic) and Time (T0 vs T1) as factors (respectively, between- and within-subjects). We additionally made the following (planned) comparisons: (i) Treatment effects on the initial (T0) parameter values to determine if the two groups differed in terms of initial performance; (ii) Treatment effects on the change (T1-T0) to determine if the two groups differed in terms of the magnitude of change, if any. The effect of treatment type on the number of responders was evaluated through the Fisher’s exact probability test. A per-protocol approach was used. In particular, to assess retention we ran a separate ANOVA with Treatment and

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis Retention (T0 vs T2) as factors. This analysis was limited to the participants that were not lost to follow-up. The same procedure was repeated with the non-treated hand. In the Haptic group, we also looked at the relation between the observed changes in the task difficulty parameters (resistive and tool stiffness) and the type of impairment. Specifically, we ran a 2-way repeated-measures ANOVA with Time (T0 vs T1) and Impairment (Pyramidal – PYR, Cerebellar – CBL, or Mixed – MIX) as factors

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(respectively, within- and between-subjects). In all cases, we took p=0.05 as threshold

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for statistical significance.

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis

Results Among the 41 participants (20 in the Haptic group and 21 in the Sensorimotor group), two subjects (1 Haptic, 1 Sensorimotor) did not complete the training protocol and were excluded from all further analysis. Three add itional subjects (all Sensorimotor) completed the training protocol, but did not attend the post-training evaluation session. Hence a total of 36 subjects (19 Haptic, 17 Sensorimotor) completed both the

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training protocol and the post-training evaluation. 27 subjects (17 Haptic, 10 Sensorimotor) participated in the follow-up evaluation (Figure 2). Figure 2 near here

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Table 1 summarizes the subjects’ demographic and baseline characteristics. No

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statistical differences in demographic data were found.

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Table 1 near here

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Primary outcome measure: 9HPT change

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The average 9HPT score decreased from 74±9 s (mean±SE) to 61±8 s for the Haptic group, and from 49±6 s to 44±6 s for the Sensorimotor group. Quantitatively, the

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Haptic group resulted in an average reduction of the 9HPT score of -13±5 s (corresponding to a -15±4% change). In comparison, the Sensorimotor group resulted in a -5±4 s reduction (-8±5 % change). Statistical analysis revealed significant Treatment (p=0.0453) and Time differences (p=0.005), but no significant Treatment×Time interactions. Contrast analysis revealed that subjects in the two treatment groups had significantly different initial scores (p=0.032) – the Sensorimotor group exhibited a better initial performance than the Haptic group. As regards the 9HPT change with Time, the overall effect was significant only in absolute (p=0.0055), and relative terms (p=0.0009). However, when looking at the

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis two treatment groups separately, we found that the absolute change was only significant in the Haptic group (p=0.011). A similar result was observed when looking at the relative change (Haptic group: p=0.002; Sensorimotor group: not significant). Retention of the improvement after three months from completion of the exercise protocol was assessed by limiting the analysis to the subjects that were not lost at follow-up (a total of 27/36 subjects). In both treatment groups the 9HPT score returned to baseline levels (non-significant difference between follow-up and baseline

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level). These results are summarized in Figure 3 (top). Figure 3 near here

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We repeated the same analysis with the non-treated hand. The initial 9HPT score was,

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respectively, 50±10 s (Haptic group) and 36±3 s (Sensorimotor group). The final

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score was 48±10 s (Haptic group) and 36±3 s (Sensorimotor group). The average change was +0.4 s and -2.1 s, corresponding to a -5% and +1% change (respectively,

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Haptic and Sensorimotor group). Statistical analysis confirmed no significant e ffect of

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Treatment, Time or their interaction.

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Secondary outcome measures: clinical We found a 9HPT improvement (decrease after treatment) in 15/19 (79%) subjects in the Haptic group and in 10/17 (59%) subjects in the Sensorimotor group. In terms of the response to treatment (defined as a reduction of 20% in the 9HPT score), we had 6/19 (32%) responding subjects in the Haptic group (3 PYR, 2 CBL, 1 MIX) and 3/17 (18%) in the Sensorimotor group (1 PYR, 1 CBL and 1 MIX). However, the number of responders did not differ significantly (χ 2 test) between the two groups. We observed a worsening (increased 9HPT after treatment) in 4/19 subjects in the Haptic group and in 7/17 subjects in the Sensorimotor group.

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis Figure 3 (bottom) summarizes the distribution histograms of the absolute and relative 9HPT changes. The ARAT score increased from 47±1 to 50±1 in the Haptic group, and from 51±1 to 53±1 in the Sensorimotor group, but we found no significant Treatment, Time or interaction effects. From a descriptive point of view, considering only the responders (ie the subjects exhibiting a 9HPT increase of more than 20%), 4/6 subjects in the Haptic group and 2/3 in the Sensorimotor group showed an improvement in the

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ARAT score. Further, 3/6 in the Haptic group and 1/3 in the Sensorimotor group showed an improvement greater than three points. Considering the responders,

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subjects in both treatment groups (3/6 in the Haptic group and 2/3 in the Sensorimotor

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group) had MFIS values greater than 38 at baseline, suggesting that fatigue was a

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disabling symptom limiting their daily activities, but did not prevent response to

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session.

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treatment. No subjects in both groups subjectively reported fatigue at the end of any

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Secondary outcome measures: instrumental Figure 4 displays trajectories and velocity profiles of both hand and mass for a typical subject.

Figure 4 near here Although on session 1 this subject practiced with stiffness values next to those corresponding to the easiest task (i.e., K r=20 N/m for the resistive spring and Km=500 N/m for the tool spring), he/she could not complete the task within the maximum duration in 20% of the eight epochs in that session. A high number of peaks in both the hand and mass speed profiles (Figure 4e and Figure 4g), and prolonged oscillations of the mass around the target (Figure 4c) point at a poor control of the virtual object. In

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis the last session, despite a significantly greater task difficulty (K r and K m were respectively greater and smaller than in session 1), in all 13 epochs the same subject could complete the task within the maximum time duration, with a reduced number of peaks in the speed profile and smaller oscillations around the targets. The magnitudes of K r and Km can be taken as measures of, respectively, force generation capability and sensorimotor incoordination. Therefore, Figure 4 suggests

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that both aspects improved with training. This finding was confirmed when all subjects were pooled together. With training, K r increased of an average 39±11%. The change

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is statistically significant in both absolute (p=0.006) and relative terms (p=0.0037). Similarly, K m decreased of an average -13±3%. Again, the change is statistically

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significant in both absolute (p=0.0008) and relative terms (p=0.0009).

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We observed no significant interactions between the effect of training (Time factor)

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and the type of impairment (Pyramidal – PYR, Cerebellar – CBL, or Mixed – MIX). Contrast analysis revealed that the initial magnitude of K m, but not K r, exhibits a

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significant effect of the type of impairment (p=0.0166); see Figure 5. Figure 5 near here

Specifically, subjects with mostly cerebellar impairment (CBL) have a greater value of Km than subjects with motor impairment (PYR). Furthermore, the effect of exercise is only significant in the PYR group (resistive stiffness: p=0.0478;

tool stiffness:

p=0.0398), but not in the MIX and CBL groups, although in the MIX group this result might be partially due to a greater inter-subject variability. The average score also increased significantly over sessions (p<0.001). Contrast analysis revealed a significant improvement over the whole therapy protocol (p=0.0001), despite the fact that the mechanism of adaptation led to a gradual increase

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis in exercise difficulty. Further, as task difficulty was adjusted at the beginning of each session according to the individual performance in the initial trials of each session, this

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result also suggests that subjects exhibited good inter-session retention.

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis

Discussion We compared the efficacy and activity of robot-based haptic training with sensorimotor training in the treatment of upper limb dysfunction in persons with MS. A few pilot studies have suggested that MS subjects may indeed benefit from robot therapy. In one study30 , subjects exhibited improved movement performance after a robot-assisted training protocol involving the adaptation to a resistive, positiondependent force field. In a similar study12 , a 4-week adaptive training protocol with

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force fields tailored on individual impairments led to significant improvements in motor coordination and a clinically significant reduction (-24%) of the 9HPT score. In

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contrast, training with a robot exoskeleton for weight support31 or performing 3D

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movements within a virtual environments32 resulted in a gradual improvement of

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performance, especially in the most impaired subjects, but no significant

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improvements in the 9HPT score were observed at population level.

The upper extremity is rarely the object of dedicated interventions. Two pilot

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studies and two recent reviews 35,36 on rehabilitation interventions on upper limb

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functions in MS, suggested that multidisciplinary and robot-based rehabilitation has the potential to improve upper limb capacity. However, Gandolfi et al37 did not demonstrate that robot-assisted hand training has an effect on upper limb activity and function.

Haptic training is superior to Sensorimotor training Although exercise led to an overall improvement of the 9HPT score (significant effect of exercise), only Haptic treatment led to a statistically significant change in the 9HPT score. Haptic treatment also resulted in a greater number of responders with

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis respect to training based on reaching movements (‘Sensorimotor’ treatment) (Haptic: 6/19; Sensorimotor: 3/17). The magnitude of the observed 9HPT improvement in this treatment group (an average 15% decrease) is rather large in comparison to similar studies 12,30,31,38 . The effect is specific to the treated arm and does not transfer to the untreated arm. In contrast, we found no significant treatment effects in the ARAT scale. This may be partly due to a ceiling effect.

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In conclusion, Haptic treatment is slightly superior to Sensorimotor treatment in improving upper limb dexterity

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The aim of robotic treatment was to improve upper limb coordination. While the

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9HPT is primarily a measure of manual dexterity, performing the task requires

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coordination of the entire limb, including the shoulder. Thus, although the wrist is immobilized and the hand is not actively participating in the reaching exercise as

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showed in figure 2, the 9HPT improvement could be related to the amelioration in

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upper limb and shoulder coordination.

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Moreover the sample size was underestimated probably related to the greater magnitude of effect of sensorimotor training. In future group stratification by age, fatigue, disease type and degree of impairment, may provide further evidences on the effects of motor rehabilitation in MS. Motor adaption is greater in subjects with non-cerebellar symptoms The haptic training protocol was designed to treat subjects with a wide variety of impairments, and to automatically adapt to the individual proportions of incoordination and force generation capabilities. Nevertheless, we explored the relationship between the type of impairment (pyramidal, cerebellar or mixed) and treatment outcome. We found a significant relation between impairment type and the

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis self-adjusted task difficulty (K r and K m values). Specifically, we observed that at the beginning of the trial, the subjects with cerebellar (CBL) impairment needed a greater magnitude of the mass-spring (tool) stiffness (K m) in order to complete the task. Across training sessions, only subjects with a pyramidal (PYR) impairment exhibited a significant adaptation of task difficulty (both tool and resistive stiffness).

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contrast, subjects with a prevalent cerebellar (CBL) or mixed impairment (MIX) showed no (or at least less) adaptation. Adaptation of task difficulty is indicative of

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improved task performance and suggests that subjects with a prevalent motor impairment exhibit the greatest improvements. Overall, the above observations

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suggest that the training outcome depends on the type of impairment, which

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highlights the importance of subjects selection.

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We observed no correlation between the adaptation of task difficulty and treatment outcome (as reflected by the 9HPT change). Similarly, we found no relation between

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type of impairment and the treatment outcome (9HPT change). These findings will

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need to be confirmed by a more specific study as the number of subjects per

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impairment type was too small to grant solid conclusions. However, they suggest that there may be no automatic transfer of the improved task performance to the overall upper limb functionality. Consistent with previous findings31 , we found no evidence of retention of the observed improvement – after three months subjects return back to the pre-treatment levels equally consistent with previous findings30 is the lack of effect on the untreated arm. Overall, our results suggest that the pyramidal, but not subjects with cerebellar components may predict the capacity to adapt to task difficulty.

Tolerability and acceptance

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis In both groups of subjects the treatment protocols turned out to be safe and well accepted by the participants. Several subjects (23/36) reported fatigue (MFIS>38) at the baseline evaluat ion, including about 50% of the responders. Hence, baseline fatigue affected neither the capacity to complete the exercise protocol nor to the primary outcome. Further, no subject reported subjective fatigability (scored using NRS from 0 to 10) at the end of

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any session.

In conclusion, the study represents the first double blind head-to-head study on robot

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therapy for upper limb in MS patients.

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The strengths of the study were that robot- mediated training protocol may provide

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exercises for upper limb deficits tailored to specific subjects impairments, adapting task parameters after each sessions and a very low rate of drop-out at T1. Our results

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suggest that exercises based on motor skill learning involving haptic interaction is

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more effective than a simple sensorimotor control training, although retention is very

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low with loss of the performance improvement after three months. Moreover, fatigue at baseline was not effector related to the capacity to underwent to robot-therapy. The main limitation of the study was that the primary endpoint of the study (9HPT variations) do not reach statistical significance, partially related to the magnitude of the effect of the sensorimotor training. Other limitations of the study were the high rate of subjects lost at T2 (16 weeks) and the lack of an ad-hoc cognitive evaluation. Future studies on larger populations are needed to corroborate the results and to confirm relationship between pyramidal system involvement and positive outcome.

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis

Acknowledgements This work was partly supported by a grant from the Italian Multiple Sclerosis Foundation .

Authors contributions VS, CS, MF and DC conceived the study and designed the study protocol. VS and AB programmed the robot device. CS and DC recruited the subjects. AB, MM, AD, IC,

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RB, DC carried out the experiments. VS, AB, CS and DC analyzed the results. VS, DC, AB and CS wrote the manuscript. All authors have read and approved the final

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version of the manuscript.

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Conflicts of interest

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The authors declare that they have no conflicts of interest.

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis

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4. Jonsdottir J, Bertoni R, Lawo M, Montesano A, Bowman T, Gabrielli S. Serious games for arm rehabilitation of persons with multiple sclerosis. A randomized controlled pilot

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study. Mult Scler Relat Disord. 2018 Jan;19:25-29.

5. Ortiz-Rubio A, Cabrera-Martos I, Rodríguez-Torres J, Fajardo-Contreras W, Díaz-

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Pelegrina A, Valenza MC. Effects of a Home-Based Upper Limb Training Program in

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Patients With Multiple Sclerosis: A Randomized Controlled Trial. Arch Phys Med Rehabil. 2016 Dec;97(12):2027-2033.

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6. Rice IM, Rice LA, Motl RW. Promoting Physical Activity Through a Manual Wheelchair

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Propulsion Intervention in Persons With Multiple Sclerosis. Arch Phys Med Rehabil.

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2015 Oct;96(10):1850-8.

7. Kamm CP, Mattle HP, Müri RM, Heldner MR, Blatter V, Bartlome S, Lüthy J, Imboden D, Pedrazzini G, Bohlhalter S, Hilfiker R, Vanbellingen T Home based training to improve manual dexeterity in patients with multiple sclerosis: a randomized controlled trial. Mult Scler. 2015 Oct;21(12):1546-56. 8. Marchal-Crespo L, Reinkensmeyer DJ. Review of control strategies for robotic movement training after neurologic injury. J Neuroeng Rehabil. 2009;6:20. 9. Balasubramanian S, Colombo R, Sterpi I, Sanguineti V, Burdet E. Robotic assessment of upper limb motor function after stroke. Am J Phys Med Rehabil. 2012;91(11 Suppl 3):S255-269.

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis 10. Lo AC, Guarino PD, Richards LG, et al. Robot-assisted therapy for long-term upper-limb impairment after stroke. N Engl J Med. 2010;362(19):1772-1783. 11. 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):107-121. 12. Vergaro E, Squeri V, Brichetto G, et al. Adaptive robot training for the treatment of incoordination in Multiple Sclerosis. J Neuroeng Rehabil. 2010;7:37.

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13. Lamers I, Cattaneo D, Chen CC, Bertoni R, Van Wijmeersch B, Feys P. Associations of upper limb disability measures on different levels of the International Classification of

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Functioning, Disability and Health in people with multiple sclerosis. Phys Ther. 2015 Jan;95(1):65-75.

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14. Cattaneo D, Lamers I, Bertoni R, Feys P, Jonsdottir J. Participation Restriction in People

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With Multiple Sclerosis: Prevalence and Correlations With Cognitive, Walking, Balance, and Upper Limb Impairments. Arch Phys Med Rehabil.2017 Jul;98(7):1308-1315.

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15. Polman CH, Reingold SC, Banwell B, et al. Diagnostic criteria for multiple sclerosis:

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2010 revisions to the McDonald criteria. Ann Neurol. 2011;69(2):292–302. 16. Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an expanded disability

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status scale (EDSS). Neurology. 1983;33(11):1444-1452. 17. Ashworth B. Preliminary Trial of Carisoprodol in Multiple Sclerosis. Practitioner. 1964;192:540-542.

18. Rudick RA, Cutter G, Baier M, et al. Use of the Multiple Sclerosis Functional Composite to predict disability in relapsing MS. Neurology. 2001;56(10):1324-1330. 19. Flachenecker P, Kumpfel T, Kallmann B, et al. Fatigue in multiple sclerosis: a comparison of different rating scales and correlation to clinical parameters. Mult Scler. 2002;8(6):523-526. 20. Sipe JC, Knobler RL, Braheny SL, Rice GP, Panitch HS, Oldstone MB. A neurologic rating scale (NRS) for use in multiple sclerosis. Neurology. 1984;34(10):1368-1372.

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis 21. Platz T, Pinkowski C, van Wijck F, Kim IH, di Bella P, Johnson G. Reliability and validity of arm function assessment with standardized guidelines for the Fugl-Meyer Test, Action Research Arm Test and Box and Block Test: a multicentre study. Clin Rehabil. 2005;19(4):404-411. 22. Schwid SR, Goodman AD, McDermott MP, Bever CF, Cook SD. Quantitative functional measures in MS: what is a reliable change? Neurology. 2002;58:1294 - 1296. 23. Bosma LV, Kragt JJ, Brieva L, et al. Progression on the Multiple Sclerosis Functional

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Composite in multiple sclerosis: what is the optimal cut-off for the three components? Mult Scler. 2010;16(7):862-867.

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24. Casadio M, Sanguineti V, Morasso PG, Arrichiello V. Braccio di Ferro: a new haptic workstation for neuromotor rehabilitation. Technol Health Care. 2006;14(3):123-142.

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25. Solaro C, Brichetto G, Casadio M, et al. Subtle upper limb impairment in asymptomatic

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multiple sclerosis subjects. Mult Scler. 2007;13(3):428-432. 26. Dingwell JB, Mah CD, Mussa-Ivaldi FA. Experimentally confirmed mathematical model

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for human control of a non-rigid object. J Neurophysiol. 2004;91(3):1158-1170.

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27. O'Malley MK, Gupta A, Gen M, Li YF. Shared control in haptic systems for performance enhancement and training. J Dyn Syst-T Asme. 2006;128(1):75-85.

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28. Patoglu V, Li Y, O'Malley M. On the Efficacy of Haptic Guidance Schemes for Human Motor Learning. Paper presented at: Medical Physics and Biomedical Engineering World Congress; September 7-12, 2009; Munich, Germany. 29. Dounskaia N, Shimansky Y. Strategy of arm movement control is determined by minimization of neural effort for joint coordination. Exp Brain Res. 2016 Jun;234(6):1335-50. 30. Carpinella I, Cattaneo D, Abuarqub S, Ferrarin M. Robot-based rehabilitation of the upper limbs in multiple sclerosis: feasibility and preliminary results. Journal of rehabilitation medicine. 2009;41(12):966-970.

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis 31. Gijbels D, Lamers I, Kerkhofs L, Alders G, Knippenberg E, Feys P. The Armeo Spring as training tool to improve upper limb functionality in multiple sclerosis: a pilot study. J Neuroeng Rehabil. 2011;8:5. 32. Feys P, Coninx K, Kerkhofs L, et al. Robot-supported upper limb training in a virtual learning environment: a pilot randomized controlled trial in persons with MS. J Neuroeng Rehabil. 2015;12:60 33. Mark VW, Taub E, Bashir K, et al. Constraint-Induced Movement therapy can improve progressive

multiple

sclerosis.

Preliminary findings.

Mult Scler.

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hemiparetic

2008;14(7):992-994.

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34. de Sire A, Bigoni M, Priano L, Baudo S, Solaro C, Mauro A. Constraint-Induced Movement Therapy in multiple sclerosis: Safety and three-dimensional kinematic of

upper

limb

activity.

A

randomized

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analysis

single-blind

pilot

study.

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NeuroRehabilitation. 2019;45(2):247-254

35. Dixit S, Tedla JS. Effectiveness of robotics in improving upper extremity functions

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Apr;129(4):369-383

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among people with neurological dysfunction: a systematic review. Int J Neurosci. 2019

36. Duret C, Mazzoleni S. Upper limb robotics applied to neurorehabilitation: An overview

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of clinical practice. NeuroRehabilitation. 2017;41(1):5-15 37. Gandolfi M, Valè N, Dimitrova EK, et al. Effects of High-intensity Robot-assisted Hand Training on Upper Limb Recovery and Muscle Activity in Individuals With Multiple Sclerosis: A Randomized, Controlled,Single-Blinded Trial. Front Neurol. 2018 Oct 24;9:905 38. Carpinella I, Cattaneo D, Bertoni R, Ferrarin M. Robot training of upper limb in multiple sclerosis: comparing protocols with or without manipulative task components. IEEE Trans Neural Syst Rehabil Eng. 2012;20(3):351-360

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis

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Journal Pre-proof Robot rehabilitation in Multiple Sclerosis Table 1 Subjects’ demographic and baseline characteristics. PP: primary progressive. SP: secondary progressive. RR: relapsing-remitting. EDSS: Expanded Disability Status Scale. MFIS: Modified Fatigue Impact Scale. Subjects with MFIS>38 were considered as fatigued 32. Where not indicated otherwise, values are mean±SD

Age, years Female, % Disease duration, years Disease course, %

Sensorimotor n=21 n=17 (81%) n=11 (52%)

53±10 (33-67) 58% 15±10

46±10 (26-69) 59% 13±8

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Eligible Completed treatment Completed follow-up

Haptic n=20 n=19 (95%) n=17 (85%)

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8 7 4 6±1 41±21 59%

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Symptoms Cerebellar (CBL) Pyramidal (PYR) Mixed (MIX) EDSS (0-10) MFIS (0-84) Fatigued, %

4 (24%) 7 (41%) 6 (35%)

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PP 9 (47%) SP 8 (42%) RR 2 (10%)

8 5 4 5±1 35±16 44%

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Journal Pre-proof Robot rehabilitation in Multiple Sclerosis

Figure 1. Top: Experimental set-up. Bottom: Schematic representation of the task

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis Figure 2 Study diagram. R=randomization T1( 4 weeks), T2 (12 weeks)

52 patients were assessed for eligibility

11 not meet eligibility criteria

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Received Haptic treatment (n=20) as allocated

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R

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41 underwent randomization

Received Sensorimotor treatment (n=21) as allocated

Withdrawn at T1 (n=4) - Did not complete treatment (n=1) - Lost to posttreatment evaluation (n=3)

Completed trial at T1 (n=19)

Completed trial at T1 (n=17)

Lost at FU (T2) (n=2)

Lost at FU (T2) (n=6)

Completed evaluation at T2 (n=17)

Completed evaluation at T2 (n=11)

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Withdrawn at T1 (n=1) - Did not complete treatment (n=1) - Lost to posttreatment evaluation (n=0)

5

5

0

0

" 9HPT [%]

" 9HPT [%]

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis

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-15 SENSORIMOTOR HAPTIC

PRE

POST

-20

3M

PRE

POST

3M

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Figure 2 Effect of the treatments. Top: Average 9HPT change with respect to PRE assessment, just after treatment (POST) and at follow-up (3M), for treated (left) and non-treated arm (right). Bottom: Distribution of the 9HPT change (left: absolute; right: relative), for subjects in the Sensorimotor (top) and in the Haptic treatment group (bottom). The black lines denote the threshold (0 s or 0% ) for improvement. The red line indicates the threshold value (-20% ) for a clinically significant improvement.

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis

SESSION 8

hand

SESSION 1

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hand

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hand velocity

c

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mass velocity

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mass

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a

g

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Figure 4 Hand and mass trajectories and s peed profiles in the third epoch of session 1 (Kr =20 N/ m, Km =477 N/ m) and the l ast epoch of session 8 (Kr =34 N/ m, Km =216 N/m), for a typical subject (S2). Forward and back ward movements toward/ from the three targets are displ ayed with respect to a common starting point, which results in six different directions

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis

500

500 PYR CBL MIX

460

400

350

440 420 400 380 360 340

300

1

2

3

4

5

6

7

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320 300

8

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MIX

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45 40

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35

30

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50

50

1

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TOOL STIFFNESS [Nm]

TOOL STIFFNESS [N/m]

450

250

PRE POST

480

2

3

4

5

6

7

35 30 25 20 15 10 5

8

0

CBL

MIX

PYR

SESSION

Figure 5 Resistive stiffness (top) and object stiffness (bottom) for all subjects during the complete therapy cycle, subdivided into subjects symptoms, namely cerebellar (CBL, black), pyramidal (PYR, red) and mixed (MIX, blue). Bar plots compare initial (PRE, beginning of session 1) and final values (POST, beginning of session 8). Error bars denote the standard errors.

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis Appendix The task of the subjects in the Haptic group was similar to that performed by the Sensorimotor group, but reaching was mediated by a virtual ‘tool’, consisting of a virtual point mass (m=5 kg) connected to the subjects’ hand through a linear spring (stiffness range: K m=200-500 N/m). Difficulty is determined by the stiffness of the virtual spring, Km: with high stiffness,

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the task is little more difficult than simple reaching. To challenge muscle weakness symptoms we additionally connected a second, much weaker virtual spr ing (stiffness

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range: K r=20-70 N/m) between the hand and the starting point. Tool- mediated reaching movements were performed in different directions. Targets were arranged

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exactly as in the ‘Sensorimotor’ group, but only the current position of the point mass

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was continuously displayed (red circle,  1 cm). The task was considered as successfully accomplished when the virtual mass was held on the target area (speed

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less than 0.01 m/s) for at least 150 ms. In both treatment groups, at the end of each

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movement a performance score (range: 0-10) was displayed. The score was calculated

weights:

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as a combination of a movement duration and a path length component 31 , with equal

é T -T ù é L -L ù score = 5× ê max ú + 5× ê max ú ë Tmax - Tmin û ë Lmax - Lmin û

where T is movement duration and L is the normalized path length of the mass (Sensorimotor group: hand) trajectory. T

min

and Tmax (Lmin and Lmax) are the threshold

durations (path lengths) that result, respectively, in a maximum and minimum score. Based on preliminary tests, we set Tmin =1 s, Tmax=4 s, Lmin =1 and Lmax=3 (times the nominal movement amplitude). A greater performance score denotes a better performance, resulting from a combination of reduced movement time and decreased

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis path curvature. Auditory feedback was also provided after task completion (pleasant or unpleasant sounds depending on the score). The virtual environment was developed

under H3DAPI (www.h3dapi.org,

SenseGraphics, Kista, Sweden), an open-source environment for graphics and haptics rendering. We used an automatic procedure to adjust the magnitudes of K m and K r to individual

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subjects. More specifically, at the beginning of each treatment session, for each subject we estimated the K m and K r magnitudes that approximately resulted in score=6. To do

QUEST: a

Bayesian

adaptive

psychometric

method.

Percept Psychophys.

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DG.

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this, we used an adaptive procedure based on the QUEST method (Watson AB, Pelli

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1983;33(2):113-120).

To adjust the tool stiffness, K m, to each individual subject, in each training session we

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applied the adaptive procedure to movements 1-24, in which K r was set to zero (i.e., no

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resistive load). In movements 25-49 we used the same procedure to adjust K r; in these

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trials K m was kept constant to its final level

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Journal Pre-proof Robot rehabilitation in Multiple Sclerosis

Figure 3. Top: Experimental set-up. Bottom: Schematic representation of the task

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis

Figure 2 Study diagram. R=randomization T1( 4 weeks), T2 (12 weeks)

52 patients were assessed for eligibility

11 not meet eligibility criteria

al

Pr

Received Haptic treatment (n=20) as allocated

e-

pr

R

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41 underwent randomization

Received Sensorimotor treatment (n=21) as allocated

Withdrawn at T1 (n=4) - Did not complete treatment (n=1) - Lost to posttreatment evaluation (n=3)

Completed trial at T1 (n=19)

Completed trial at T1 (n=17)

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Withdrawn at T1 (n=1) - Did not complete treatment (n=1) - Lost to posttreatment evaluation (n=0)

Lost at FU (T2) (n=2)

Completed evaluation at T2 (n=17)

Lost at FU (T2) (n=6)

Completed evaluation at T2 (n=11)

5

5

0

0

" 9HPT [%]

" 9HPT [%]

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis

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PRE

POST

-20

3M

PRE

POST

3M

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Figure 4 Effect of the treatments. Top: Average 9HPT change with respect to PRE assessment, just after treatment (POST) and at follow-up (3M), for treated (left) and non-treated arm (right). Bottom: Distribution of the 9HPT change (left: absolute; right: relative), for subjects in the Sensorimotor (top) and in the Haptic treatment group (bottom). The black lines denote the threshold (0 s or 0% ) for improvement. The red line indicates the threshold value (-20% ) for a clinically significant improvement.

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis

SESSION 8

hand

SESSION 1

b

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f

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hand

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hand velocity

c

e

mass velocity

pr

mass

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a

g

h

Figure 4 Hand and mass trajectories and s peed profiles in the third epoch of session 1 (Kr =20 N/ m, Km =477 N/ m) and the l ast epoch of session 8 (Kr =34 N/ m, Km =216 N/m), for a typical subject (S2). Forward and back ward movements toward/ from the three targets are displ ayed with respect to a common starting point, which results in six different directions

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis

500

500 PYR CBL MIX

460

400

350

440 420 400 380 360 340

300

1

2

3

4

5

6

7

pr

320 300

8

SESSION

MIX

PYR

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PRE POST

45 40

Pr

RESISTIVE STIFFNESS [Nm]

CBL PYR MIX

45

40

al

35

30

rn

RESISTIVE STIFFNESS [N/m]

CBL

50

50

1

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25

20

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TOOL STIFFNESS [Nm]

TOOL STIFFNESS [N/m]

450

250

PRE POST

480

2

3

4

5

6

7

35 30 25 20 15 10 5

8

0

CBL

MIX

PYR

SESSION

Figure 5 Resistive stiffness (top) and object stiffness (bottom) for all subjects during the complete therapy cycle, subdivided into subjects symptoms, namely cerebellar (CBL, black), pyramidal (PYR, red) and mixed (MIX, blue). Bar plots compare initial (PRE, beginning of session 1) and final values (POST, beginning of session 8). Error bars denote the standard errors.

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis Table 2 Subjects’ demographic and baseline characteristics. PP: primary progressive. SP: secondary progressive. RR: relapsing-remitting. EDSS: Expanded Disability Status Scale. MFIS: Modified Fatigue Impact Scale. Subjects with MFIS>38 were considered as fatigued [25]. Where not indicated otherwise, values are mean±SD

46±10 (26-69) 59% 13±8

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8 7 4 6±1 41±21 59%

4 (24%) 7 (41%) 6 (35%)

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9 (47%) 8 (42%) 2 (10%)

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53±10 (33-67) 58% 15±10

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Age, years Female, % Disease duration, years Disease course, % PP SP RR Symptoms Cerebellar (CBL) Pyramidal (PYR) Mixed (MIX) EDSS (0-10) MFIS (0-84) Fatigued, %

Sensorimotor n=21 n=17 (81%) n=11 (52%)

Pr

Eligible Completed treatment Completed follow-up

Haptic n=20 n=19 (95%) n=17 (85%)

8 5 4 5±1 35±16 44%

Journal Pre-proof Robot rehabilitation in Multiple Sclerosis

Highlights Comparison between robot-assisted training based on haptic or sensorimotor exercise

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Task oriented training may improve upper limb function in persons with MS expecially in prevalent pyramidal impaired subjects

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