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Over the last years, the introduction of robotic technologies in gait rehabilitation of stroke patients has aroused great interest. Some studies have been conducted to evaluate the effects of robot-assisted training compared to conventional gait rehabilitation in patients with subacute stroke but no studies seem to investigate the effects of a combined robotic treatment (gait plus balance).
The aim of this study is to evaluate the efficacy of a combined gait and balance robotic rehabilitation compared robotic gait training alone.
Stroke is not only the third cause of death after cardiovascular disease and cancer, but also the first cause of disability in the world with a significant impact on individuals, their families and finances. Post-stroke disability involves mobility and balance, muscle strength, control of movement, and gait pattern functions. Although the majority of stroke patients learns to walk independently by 6 months after stroke, gait and balance problems persist through the chronic stage and may have a significant impact on patients' quality of life. Accordingly, the restoration and improvement of walking functions is a primary concern to obtain independence in daily life. For this reason, gait recovery is a realist goal in the rehabilitation of almost all patients with stroke. The recovery of a more fluid, safe and correct execution of motor tasks such as gait and stair climbing are a prerequisite for the patients to become autonomous in the activities of daily living.
Over the last years, the introduction of robotic technologies in gait rehabilitation of stroke patients has aroused great interest. Some studies have been conducted to evaluate the effects of robot-assisted training compared to conventional gait rehabilitation in patients with subacute stroke. The main results were obtained using robotic exoskeletons or a treadmill training with partial body weight support and only a few studies used an end-effector device. Preliminary studies have shown that end-effector Robot-Assisted Gait Training (RAGT) has produced promising effects on motor and functional outcomes in chronic and subacute strokes patients comparing with conventional treatment. Moreover, safe gait needs a continuous dynamic balance than it is possible that in gait robotic rehabilitation could be included a rehabilitation treatment of static and dynamic balance with a robotic proprioceptive platform. The hypothesis of the study is that a combined robotic treatment (gait plus balance) could produce more effects than just one robotic gait training.
Therefore, the aim of this study is to evaluate the efficacy of gait and balance robotic rehabilitation in subacute stroke patients in terms of clinical outcomes, balance measures and gait kinematics, comparing them with robotic gait training alone.
The patients following first ever stroke in sub-acute phase will be recruited and assessed both clinically and instrumentally (Gait Analysis and Balance evaluation) at baseline (T0), after 12 sessions (T1) and at the end of the training program (24 sessions: T2). The patients will be randomized into 2 groups and will conduct two different types of rehabilitation training: one group will perform, gait training using an end-effector robotic device for RAGT (Gait Group, GG); and the other group will receive a combined robotic treatment program with the same end-effector robotic system and a robotic proprioceptive platform (Balance Group, GHG). The rehabilitation program of both groups will be combined with conventional physiotherapy.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Experimental 1: Gait Group (GG) | Experimental | Gait Group (GG) will perform, in addition to conventional therapy, gait training using only an end effector robotic device for Robot-Assisted Gait Training (RAGT), 3 times/week for 12 sessions/month. During the training, patients will be asked to walk, at a varying speed, for 45 minutes and a partial Body Weight Support (BWS). Participants will start with 30-40% of BWS and an initial speed of 1.5 km/h; increasing to a maximum of between 2.2 and 2.5 km/ h and reducing the initial BWS to 15%. The therapist will provide any help during sessions if required. Over 45 minutes, the patient simulates a minimum of 300 steps; patients could rest during the session, though they will be asked to walk continuously for a minimum of 5 minutes during each session. |
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| Experimental 2: Balance Group (GHG) | Experimental | Balance Group (GHG) will receive, in addition to conventional therapy, a combined robotic treatment program with the same end-effector robotic system and a robotic proprioceptive platform, 3 times/week for 12 sessions/month. The time of the single session (45 minutes) is dived in gait training and balance training. The balance training will consist in static and dynamic exercises during sitting and standing position, dual-task exercises and exercises aimed to improve trunk control. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| GEO | Device | Robot-Assisted Gait Training (RAGT) The Robotic Group (RG) performs a Robot-Assisted Gait Training (RAGT) using an end-effector robotic device (G-EO system-Reha Technology-Olten, Switzerland). |
| Measure | Description | Time Frame |
|---|---|---|
| Change in Berg Balance Scale (BBS) | The Berg Balance Scale is a widely used clinical test of a person's static and dynamic balance abilities. The test takes 15-20 minutes and comprises a set of 14 simple balance related tasks, ranging from standing up from a sitting position, to standing on one foot. The degree of success in achieving each task is given a score of zero (unable) to four (independent), and the final measure is the sum of all of the scores. | Baseline (T0), Session 12 (4 weeks) (T1), Session 24 (8 weeks ) (T2) |
| Measure | Description | Time Frame |
|---|---|---|
| Change in Motricity Index (MI) | The MI aims to evaluate lower limb motor impairment after stroke, administrated on both sides. Items to assess the lower limbs are 3, scoring from 0 to 33 each: (1) ankle dorsiflexion with foot in a plantar flexedposition (2) knee extension with the foot unsupported and the knee at 90° (3) hip flexion with the hip at 90° moving the knee as close as possible to the chin. (no movement: 0, palpable flicker but no movement: 9, movement but not against gravity :14, movement against gravity movement against gravity: 19, movement against resistance: 25, normal:33). |
| Measure | Description | Time Frame |
|---|---|---|
| Gait analysis - Biomechanical data - Step width | Biomechanical data were collected by using the 8-camera SMART-D500 motion capture system (BTS Bioengineering, Milano, Italy). In order to describe the characteristics of the gait, step width (mm) will be calculated. | Baseline (T0), Session 12 (4 weeks) (T1), Session 24 (8 weeks ) (T2) |
Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Irene Aprile, MD, PhD | IRCCS Fondazione Don Carlo Gnocchi | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Don Gnocchi Foundation | Rome | 00168 | Italy |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 31498139 | Background | Aprile I, Iacovelli C, Goffredo M, Cruciani A, Galli M, Simbolotti C, Pecchioli C, Padua L, Galafate D, Pournajaf S, Franceschini M. Efficacy of end-effector Robot-Assisted Gait Training in subacute stroke patients: Clinical and gait outcomes from a pilot bi-centre study. NeuroRehabilitation. 2019;45(2):201-212. doi: 10.3233/NRE-192778. | |
| Background | Goffredo M, Iacovelli C, Russo E, Pournajaf S, Di Blasi C, Galafate D, Pellicciari L, Agosti M, Filoni S, Aprile I, Franceschini M. Stroke Gait Rehabilitation: A Comparison of End-Effector, Overground Exoskeleton, and Conventional Gait Training. Applied Sciences 9, 2627, 2019. doi:10.3390/app9132627 | ||
| 28946585 |
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| ID | Term |
|---|---|
| D020521 | Stroke |
| ID | Term |
|---|---|
| D002561 | Cerebrovascular Disorders |
| D001927 | Brain Diseases |
| D002493 | Central Nervous System Diseases |
| D009422 | Nervous System Diseases |
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| GEO and HUNOVA | Device | Robot-Assisted Gait Training (RAGT) and Balance Training. The Balance Group (GHG) performs a Robot-Assisted Gait Training (RAGT) using an end-effector robotic device (G-EO system-Reha Technology-Olten, Switzerland) and a Balance training using a robotic proprioceptive platform (Hunova - Movendo Technology, Italy). |
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| Baseline (T0), Session 12 (4 weeks) (T1), Session 24 (8 weeks ) (T2) |
| Change in Modified Ashworth Scale (MAS) | The MAS is a 6 point ordinal scale used for grading hypertonia in individuals with neurological diagnoses. A score of 0 on the scale indicates no increase in tone while a score of 4 indicates rigidity. Tone is scored by passively moving the individual's limb and assessing the amount of resistance to movement felt by the examiner. | Baseline (T0), Session 12 (4 weeks) (T1), Session 24 (8 weeks ) (T2) |
| Change in Tinetti Scale Balance (TIN-B) | Scales to measure activity ICF domain. | Baseline (T0), Session 12 (4 weeks) (T1), Session 24 (8 weeks ) (T2) |
| Change in Functional Ambulation Classification (FAC) | Functional Ambulation Classification is a functional walking test that evaluates ambulation ability. This 6-point scale assesses ambulation status by determining how much human support the patient requires when walking, regardless of whether or not they use a personal assistive device. | Baseline (T0), Session 12 (4 weeks) (T1), Session 24 (8 weeks ) (T2) |
| Change in 10 Meter Walk Test (10MWT) | This test will assess the patient's speed during gait. Patients will be asked to walk at their preferred maximum and safe speed. Patients will be positioned 1 meter before the start line and instructed to walk 10 meters, and pass the end line approximately 1 meter after. The distance before and after the course are meant to minimize the effect of acceleration and deceleration. Time will be measured using a stopwatch and recorded to the one hundredth of a second (ex: 2.15 s). The test will be recorded 3 times, with adequate rests between them. The average of the 3 times should be recorded. | Baseline (T0), Session 12 (4 weeks) (T1), Session 24 (8 weeks ) (T2) |
| Change in Trunk Control Test (TCT) | The TCT assesses the motor impairment in stroke patients and it's correlated with eventual walking ability. Testing is done with the patient lying on a bed: (1) roll to weak side. (2) roll to strong side. (3) balance in sitting position on the edge of the bed with the feet off the ground for at least 30. (4) sit up from lying down. Total score: 0-100. | Baseline (T0), Session 12 (4 weeks) (T1), Session 24 (8 weeks ) (T2) |
| Change in Time Up And Go (TUG) | The Time Up And Go is a test used to assess mobility, balance, and walking in people with balance impairments. The subject must stand up from a chair (which should not be leant against a wall), walk a distance of 3 meters, turn around, walk back to the chair and sit down - all performed as quickly and as safely as possible. Time will be measured using a chronometer. | Baseline (T0), Session 12 (4 weeks) (T1), Session 24 (8 weeks ) (T2) |
| Change in Walking Handicap Scale (WHS) | The Walking Handicap Scale is a classification of 6 functional walking categories, considered as a participation category of the ICF because of its 3 items referred to community ambulation. The score ranges from 1 to 6, and do higher values represent a better outcome. | Baseline (T0), Session 12 (4 weeks) (T1), Session 24 (8 weeks ) (T2) |
| Change in Ambulation Index (AI) | The AI is a rating scale developed to assess mobility by evaluating the time and degree of assistance required to walk 25 feet. Scores range from 0 (asymptomatic and fully active) to 10 (bedridden). The patient is asked to walk a marked 25-foot course as quickly and safely as possible. The examiner records the time and type of assistance (e.g., cane, walker, crutches) needed. | Baseline (T0), Session 12 (4 weeks) (T1), Session 24 (8 weeks ) (T2) |
| Change in Numerical Rating Scale (NRS) | The Numeric Rating Scale (NRS) is the simplest and most commonly used numeric scale to rate the pain from 0 (no pain) to 10 (worst pain). | Baseline (T0), Session 12 (4 weeks) (T1), Session 24 (8 weeks ) (T2) |
| Change in Neuropathic Pain Screen (ID PAIN) | The Neuropathic Pain Screen is used to evaluate presence of neuropathic pain. Ask the older adult the questions below and score as noted. It is a numeric scale to rate the pain from -1 to 5; higher scores are more indicative of pain with a neuropathic component. A score of 3 or higher indicates likely presence of neuropathic pain and justifies a more detailed evaluation. If the older adult has more than one painful area, they are to consider the one area that is most relevant to them. Conditions that might have a neuropathic pain component include diabetic or peripheral neuropathy, back pain, post-herpetic neuralgia, complex regional pain syndrome, leg/foot pain, large joint pain, and fibromyalgia. | Baseline (T0), Session 12 (4 weeks) (T1), Session 24 (8 weeks ) (T2) |
| Change in Barthel Scale/Index (BI) | The Barthel Scale/Index (BI) is an ordinal scale used to measure performance in activities of daily living (ADL). Ten variables describing ADL and mobility are scored, a higher number being a reflection of greater ability to function independently following hospital discharge. Time taken and physical assistance required to perform each item are used in determining the assigned value of each item. The Barthel Index measures the degree of assistance required by an individual on 10 items of mobility and self care ADL. | Baseline (T0), Session 12 (4 weeks) (T1), Session 24 (8 weeks ) (T2) |
| Change in Six-Minute Walking Test (6MWT) | The 6MWT measures the distance a subject covers during an indoor gait on a flat, hard surface in 6 minutes, using assistive devices, as necessary. The test is a reliable and valid evaluation of functional exercise capacity and is used as a sub-maximal test of aerobic capacity and endurance. The minimal detectable change in distance for people with sub-acute stroke is 60.98 meters. The 6MWT is a patient self-paced walk test and assesses the level of functional capacity. Patients are allowed to stop and rest during the test. However, the timer does not stop. If the patient is unable to complete the test, the time is stopped at that moment. The missing time and the reason of the stop are recorded. This test will be administered while wearing a pulse oximeter to monitor heart rate and oxygen saturation, also integrated with Borg scale to assess dyspnea. | Baseline (T0), Session 12 (4 weeks) (T1), Session 24 (8 weeks ) (T2) |
| Gait analysis - Biomechanical data - Mediolateral distance |
Biomechanical data were collected by using the 8-camera SMART-D500 motion capture system (BTS Bioengineering, Milano, Italy). In order to describe the characteristics of the gait, mediolateral distance between the two feet during double support (mm) will be calculated. |
| Baseline (T0), Session 12 (4 weeks) (T1), Session 24 (8 weeks ) (T2) |
| Gait analysis - Biomechanical data - Longitudinal distance | Biomechanical data were collected by using the 8-camera SMART-D500 motion capture system (BTS Bioengineering, Milano, Italy). In order to describe the characteristics of the gait, longitudinal distance from one foot strike to the next one (mm) will be calculated. | Baseline (T0), Session 12 (4 weeks) (T1), Session 24 (8 weeks ) (T2) |
| Gait analysis - Biomechanical data - Stride length | Biomechanical data were collected by using the 8-camera SMART-D500 motion capture system (BTS Bioengineering, Milano, Italy). In order to describe the characteristics of the gait, stride length (mm) will be calculated. | Baseline (T0), Session 12 (4 weeks) (T1), Session 24 (8 weeks ) (T2) |
| Gait analysis - Biomechanical data - Cadence | Biomechanical data were collected by using the 8-camera SMART-D500 motion capture system (BTS Bioengineering, Milano, Italy). In order to describe the characteristics of the gait, cadence (steps/min) will be calculated. | Baseline (T0), Session 12 (4 weeks) (T1), Session 24 (8 weeks ) (T2) |
| Gait analysis - Biomechanical data - Steps | Biomechanical data were collected by using the 8-camera SMART-D500 motion capture system (BTS Bioengineering, Milano, Italy). In order to describe the characteristics of the gait, number of steps in a unit of time will be calculated. | Baseline (T0), Session 12 (4 weeks) (T1), Session 24 (8 weeks ) (T2) |
| Gait analysis - Biomechanical data - Mean velocity of progression (mean velocity) | Biomechanical data were collected by using the 8-camera SMART-D500 motion capture system (BTS Bioengineering, Milano, Italy). In order to describe the characteristics of the gait, the mean velocity of progression for each limb (m/s) will be calculated. | Baseline (T0), Session 12 (4 weeks) (T1), Session 24 (8 weeks ) (T2) |
| Gait analysis - Biomechanical data - Mean velocity of swing (swing velocity) | Biomechanical data were collected by using the 8-camera SMART-D500 motion capture system (BTS Bioengineering, Milano, Italy). In order to describe the characteristics of the gait, the mean velocity of the swing phase for each limb (m/s) will be calculated. | Baseline (T0), Session 12 (4 weeks) (T1), Session 24 (8 weeks ) (T2) |
| Gait analysis - Biomechanical data - Gait cycle | Biomechanical data were collected by using the 8-camera SMART-D500 motion capture system (BTS Bioengineering, Milano, Italy). In order to describe the characteristics of the gait, mean temporal duration of the gait cycle that begins with initial heel contact and ends with the subsequent heel contact of the same limb (m/s) will be calculated. | Baseline (T0), Session 12 (4 weeks) (T1), Session 24 (8 weeks ) (T2) |
| Gait analysis - Biomechanical data - Stance time | Biomechanical data were collected by using the 8-camera SMART-D500 motion capture system (BTS Bioengineering, Milano, Italy). In order to describe the characteristics of the gait, % of the gait cycle that begins with initial contact and ends at toe off of the same limb (as a % of the gait cycle) will be calculated. | Baseline (T0), Session 12 (4 weeks) (T1), Session 24 (8 weeks ) (T2) |
| Gait analysis - Biomechanical data - Swing time | Biomechanical data were collected by using the 8-camera SMART-D500 motion capture system (BTS Bioengineering, Milano, Italy). In order to describe the characteristics of the gait, % of the gait cycle that begins with the toe off and ends at heel strike of the same limb (as a % of the gait cycle) will be calculated. | Baseline (T0), Session 12 (4 weeks) (T1), Session 24 (8 weeks ) (T2) |
| Gait analysis - Biomechanical data - Double support | Biomechanical data were collected by using the 8-camera SMART-D500 motion capture system (BTS Bioengineering, Milano, Italy). In order to describe the characteristics of the gait, %of the gait cycle feet are on the ground (as a % of the gait cycle) will be calculated. | Baseline (T0), Session 12 (4 weeks) (T1), Session 24 (8 weeks ) (T2) |
| Gait analysis - Biomechanical data - Joints flexion and extension | To assess the lower limb joint kinematics will be also calculated hip, knee, and ankle flexion/extension (degrees) wil be defined | Baseline (T0), Session 12 (4 weeks) (T1), Session 24 (8 weeks ) (T2) |
| Gait analysis - Biomechanical data - Joints Range of Motion (RoM) | To assess the lower limb joint kinematics will be also calculated hip, knee, and ankle Range of Motion (degrees) wil be defined | Baseline (T0), Session 12 (4 weeks) (T1), Session 24 (8 weeks ) (T2) |
| Balance Analysis - Stabilometric data - Velocity antero-posterior and Velocity medio-lateral (VelocityAP and VelocityML) | Stabilometric data will be obtained from the analysis of the center of pressure (CoP) trajectories measured by robotic proprioceptive platform in standing and sitting position during static and dynamic condition. Starting from the instant positions of the CoP, the variables related to balance performance will be computed: velocity of oscillations along the antero-posterior (AP) and medio-lateral (ML) axes (mm/s). | Baseline (T0), Session 12 (4 weeks) (T1), Session 24 (8 weeks ) (T2) |
| Balance Analysis - Stabilometric data - Length | Stabilometric data will be obtained from the analysis of the center of pressure (CoP) trajectories measured by robotic proprioceptive platform in standing and sitting position during static and dynamic condition. Starting from the instant positions of the CoP, the variables related to balance performance will be computed: length of CoP trajectory (mm). | Baseline (T0), Session 12 (4 weeks) (T1), Session 24 (8 weeks ) (T2) |
| Balance Analysis - Stabilometric data - Area | Stabilometric data will be obtained from the analysis of the center of pressure (CoP) trajectories measured by robotic proprioceptive platform in standing and sitting position during static and dynamic condition. Starting from the instant positions of the CoP, the variables related to balance performance will be computed: area of the 95% confidence ellipse (mm2). | Baseline (T0), Session 12 (4 weeks) (T1), Session 24 (8 weeks ) (T2) |
| Balance Analysis - Stabilometric data - Romberg Length | Stabilometric data will be obtained from the analysis of the center of pressure (CoP) trajectories measured by robotic proprioceptive platform in standing and sitting position during static and dynamic condition. Starting from the instant positions of the CoP, the variables related to balance performance will be computed: ratio between the value of the length in the close eyes (CE) condition and the same value in the open eyes (OE) condition (mm). | Baseline (T0), Session 12 (4 weeks) (T1), Session 24 (8 weeks ) (T2) |
| Result |
| Aprile I, Iacovelli C, Padua L, Galafate D, Criscuolo S, Gabbani D, Cruciani A, Germanotta M, Di Sipio E, De Pisi F, Franceschini M. Efficacy of Robotic-Assisted Gait Training in chronic stroke patients: Preliminary results of an Italian bi-centre study. NeuroRehabilitation. 2017;41(4):775-782. doi: 10.3233/NRE-172156. |
| 26234280 | Result | Cattaneo D, Carpinella I, Aprile I, Prosperini L, Montesano A, Jonsdottir J. Comparison of upright balance in stroke, Parkinson and multiple sclerosis. Acta Neurol Scand. 2016 May;133(5):346-54. doi: 10.1111/ane.12466. Epub 2015 Aug 3. |
| 31358017 | Result | Kim HY, Shin JH, Yang SP, Shin MA, Lee SH. Robot-assisted gait training for balance and lower extremity function in patients with infratentorial stroke: a single-blinded randomized controlled trial. J Neuroeng Rehabil. 2019 Jul 29;16(1):99. doi: 10.1186/s12984-019-0553-5. |
| 24710969 | Result | Swinnen E, Beckwee D, Meeusen R, Baeyens JP, Kerckhofs E. Does robot-assisted gait rehabilitation improve balance in stroke patients? A systematic review. Top Stroke Rehabil. 2014 Mar-Apr;21(2):87-100. doi: 10.1310/tsr2102-87. |
| 17939776 | Result | Eng JJ, Tang PF. Gait training strategies to optimize walking ability in people with stroke: a synthesis of the evidence. Expert Rev Neurother. 2007 Oct;7(10):1417-36. doi: 10.1586/14737175.7.10.1417. |
| 19608100 | Result | Langhorne P, Coupar F, Pollock A. Motor recovery after stroke: a systematic review. Lancet Neurol. 2009 Aug;8(8):741-54. doi: 10.1016/S1474-4422(09)70150-4. |
| 28488268 | Result | Mehrholz J, Thomas S, Werner C, Kugler J, Pohl M, Elsner B. Electromechanical-assisted training for walking after stroke. Cochrane Database Syst Rev. 2017 May 10;5(5):CD006185. doi: 10.1002/14651858.CD006185.pub4. |
| 26649295 | Result | Mao YR, Lo WL, Lin Q, Li L, Xiao X, Raghavan P, Huang DF. The Effect of Body Weight Support Treadmill Training on Gait Recovery, Proximal Lower Limb Motor Pattern, and Balance in Patients with Subacute Stroke. Biomed Res Int. 2015;2015:175719. doi: 10.1155/2015/175719. Epub 2015 Nov 16. |
| Result | Davis RB, Ounpuu S, Tyburski D, Gage JR. A gait analysis data collection and reduction technique. Hum MovSci 1991; 10: 575-587. |
| D014652 | Vascular Diseases |
| D002318 | Cardiovascular Diseases |