An innovative training based on robotics for older people with subacute stroke: study protocol for a randomized controlled trial

Elvira Maranesi, Roberta Bevilacqua, Mirko Di Rosa, Giuseppe Pelliccioni, Valentina Di Donna, Riccardo Luzi, Micaela Morettini, Agnese Sbrollini, Elisa Casoni, Nadia Rinaldi, Renato Baldoni, Fabrizia Lattanzio, Laura Burattini, Giovanni R Riccardi, Elvira Maranesi, Roberta Bevilacqua, Mirko Di Rosa, Giuseppe Pelliccioni, Valentina Di Donna, Riccardo Luzi, Micaela Morettini, Agnese Sbrollini, Elisa Casoni, Nadia Rinaldi, Renato Baldoni, Fabrizia Lattanzio, Laura Burattini, Giovanni R Riccardi

Abstract

Background: Stroke is a leading cause of disability, injury, and death in elderly people and represents a major public health problem with substantial medical and economic consequences. The incidence of stroke rapidly increases with age, doubling for each decade after age 55 years. Gait impairment is one of the most important problems after stroke, and improving walking function is often a key component of any rehabilitation program. To achieve this goal, a robotic gait trainer seems to be promising. In fact, some studies underline the efficacy of robotic gait training based on end-effector technology, for different diseases, in particular in stroke patients. In this randomized controlled trial, we verify the efficacy of the robotic treatment in terms of improving the gait and reducing the risk of falling and its long-term effects.

Methods: In this single-blind randomized controlled trial, we will include 152 elderly subacute stroke patients divided in two groups to receive a traditional rehabilitation program or a robotic rehabilitation using G-EO system, an end-effector device for the gait rehabilitation, in addition to the traditional therapy. Twenty treatment sessions will be conducted, divided into 3 training sessions per week, for 7 weeks. The control group will perform traditional therapy sessions lasting 50 min. The technological intervention group, using the G-EO system, will carry out 30 min of traditional therapy and 20 min of treatment with a robotic system. The primary outcome of the study is the evaluation of the falling risk. Secondary outcomes are the assessment of the gait improvements and the fear of falling. Further evaluations, such as length and asymmetry of the step, walking and functional status, and acceptance of the technology, will be carried.

Discussion: The final goal of the present study is to propose a new approach and an innovative therapeutic plan in the post-stroke rehabilitation, focused on the use of a robotic device, in order to obtain the beneficial effects of this treatment.

Trial registration: ClinicalTrials.gov NCT04087083 . Registered on September 12, 2019.

Keywords: End-effector gait rehabilitation; Gait training; Stroke patients.

Conflict of interest statement

The authors declare that they have no competing interests and the study has not received external funding.

References

    1. World Health Organization . The World Health Report 2002: reducing risks, promoting healthy life. World Health Organization; 2002.
    1. United Nation, Department of Economic and Social Affairs . World population prospects: the 2017 revision. 2017.
    1. Truelsen T, Piechowski-Jozwiak B, Bonita R, Mathers C, Bogousslavsky J, Boysen G. Stroke incidence and prevalence in Europe: a review of available data. Eur J Neurol. 2006;13(6):581–598. doi: 10.1111/j.1468-1331.2006.01138.x.
    1. Ovbiagele B, Nguyen-Huynh MN. Stroke epidemiology: advancing our understanding of disease mechanism and therapy. Neurotherapeutics. 2011;8(3):319–329. doi: 10.1007/s13311-011-0053-1.
    1. Truelsen T, Mahonen M, Tolenen H, Asplund K, Bonita R, Vanuzzo D, et al. Trends in stroke and coronary heart diseases in the WHO MONICA Project. Stroke. 2003;34(6):1346–1352. doi: 10.1161/01.STR.0000069724.36173.4D.
    1. Taveggia G, Bordoni A, Mulé C, Villafane JH, Negrini S. Conflicting results of robot-assisted versus usual gait training during postacute rehabilitation of stroke patients: a randomized clinical trial. Int J Rehabil Res. 2016;39(1):29–35. doi: 10.1097/MRR.0000000000000137.
    1. Molteni F, Gasperini G, Cannaviello G, Guanziroli E. Exoskeleton and end-effector for upper and lower limbs rehabilitation: narrative review. Arch Phys Med Rehabil. 2018;10:S174–S188.
    1. Apte S, Plooij M, Vallery H. Influence of body weight unloading on human gait characteristics: a systematic review. J Neuroeng Rehabil. 2018;15(1):53. doi: 10.1186/s12984-018-0380-0.
    1. Van Kammen K, Boonstra A, Reinders-Messelink H, den Otter R. The combined effects of body weight support and gait speed on gait related muscle activity: a comparison between walking in the Lokomat exoskeleton and regular treadmill walking. Plos ONE. 2014;9:9.
    1. Veneman JF, Kruidhof R, Hekman EE, Ekkelenkamp R, Van Asseldonk EH, van der Kooij H. Design and evaluation of the LOPES exoskeleton robot for interactive gait rehabilitation. IEEE Trans Neural Syst Rehabil Eng. 2007;15(3):379–386. doi: 10.1109/TNSRE.2007.903919.
    1. Banala SK, Kim SH, Agrawal SK, Scholz JP. Robot assisted gait training with active leg exoskeleton (ALKEX) IEEE Trans Neural Syst Rehabil Eng. 2009;17(1):2–8. doi: 10.1109/TNSRE.2008.2008280.
    1. Hesse S, Waldner A, Tomelleri C. Innovative gait robot for the repetitive practice of floor walking and stair climbing up and down in stroke patients. J Neuroeng Rehabil. 2010;7(1):30. doi: 10.1186/1743-0003-7-30.
    1. Chen G, Chan CK, Guo Z, Yu H. A review of lower extremityassistive robotic exoskeletons in rehabilitation therapy. Crit Rev Biomed Eng. 2013;41(4-5):343–363. doi: 10.1615/CritRevBiomedEng.2014010453.
    1. Schwartz I, Meiner Z. Robot-assisted gait training in neurological patient: who may benefit? Ann Biomed Eng. 2015;43(5):1260–1269. doi: 10.1007/s10439-015-1283-x.
    1. Mazzoleni S, Focacci A, Franceschini M, Waldner A, Spagnuolo C, Battini E, Bonaiuti D. Robot-assisted end-effector-based gait training in chronic stroke patients: a multicentric uncontrolled observational retrospective clinical study. NeuroRehabil. 2017;40(4):483–492. doi: 10.3233/NRE-161435.
    1. Galli M, Cimolin V, De Pandis MF, Le Pera D, Sova I, Albertini G, Stocchi F, Franceschini M. Robot-assisted gait training versus treadmill training in patients with Parkinson’s disease: a kinematic evaluation with gait profile score. Funct Neurol. 2016;31(3):163–170. doi: 10.11138/fneur/2016.31.3.163.
    1. Sale P, Stocchi F, Galafate D, De Pandis MF, Le Pera D, Sova I, Galli M, Foti C, Franceschini M. Effects of robot assisted gait training in progressive supranuclear palsy (PSP): a preliminary report. Front Hum Neurosci. 2014;8:207. doi: 10.3389/fnhum.2014.00207.
    1. Esquenazi A, Lee S, Wikoff A, Packel A, Toczylowski T, Feeley J. A comparison of locomotor therapy interventions: partial-body weight-supported treadmill, Lokomat, and G-EO training in people with traumatic brain injury. Am J Phys Med Rehabil. 2017;9:839–846.
    1. Babaiasl M, Mahdioun SH, Jaryani P, Yazdani M. A review of technological and clinical aspects of robot-aided rehabilitation of upper-extremity after stroke. Disabil Rehabil Assist Technol. 2016;11(4):263–280. doi: 10.3109/17483107.2014.1002539.
    1. Ng MFW, Tong RKY, Li LSW. A pilot study of randomized clinical controlled trial of gait training in subacute stroke patients with partial body-weight support electromechanical gait trainer and functional electrical stimulation six-month follow-up. 2008. 10.1161/STROKEAHA.107.495705.
    1. Tramontano M, Dell'Uomo D, Cinnera AM, Luciani C, Di Lorenzo C, Marcotulli M, Vona F, Mercuro A, Abbruzzese S. Visual-spatial training in patients with sub-acute stroke without neglect: a randomized, single-blind controlled trial. Funct Neurol. 2019;34(1):7–13.
    1. Tinetti ME. Performance-oriented assessment of mobility problems in elderly patients. J Am Geriatr Soc. 1986;34(2):119–126. doi: 10.1111/j.1532-5415.1986.tb05480.x.
    1. Faber MJ, Bosscher RJ, van Wieringen PC. Clinometric properties of the performance-oriented mobility assessment. Phys Ther. 2006;86(7):944–954. doi: 10.1093/ptj/86.7.944.
    1. Meurisse GM, Bastien GJ, Schepens B. The step-to-step transition mode: a potential indicator of first-fall risk in elderly adults? PLoS One. 2019;14(8):e0220791. doi: 10.1371/journal.pone.0220791.
    1. Shulman LM, Katzel LI, Ivey FM, Sorkin JD, Favors K, Anderson KE, Smith BA, Reich SG, Weiner WJ, Macko RF. Randomized clinical trial of 3 types of physical exercise for patients with Parkinson disease. JAMA Neurol. 2013;70(2):183–190. doi: 10.1001/jamaneurol.2013.646.
    1. Cakit BD, Saracoglu M, Genc H, Erdem HR, Inan L. The effects of incremental speed-dependent treadmill training on postural instability and fear of falling in Parkinson’s disease. Clin Rehabil. 2007;21(8):698–705. doi: 10.1177/0269215507077269.
    1. Folstein MF, Folstein SE, McHugh PR. Mini-mental state. A pratical method for grading the cognitive state of patients for the clinician. J Psychiatric Res. 1975;12(3):189–198. doi: 10.1016/0022-3956(75)90026-6.
    1. Van Swieten JC, Koudstaal PJ, Visser MC, Schouten HJA, van Gijn J. Interobserver agreement for the assessment of handicap in stroke patients. Stroke. 1988;19(5):604–607. doi: 10.1161/01.STR.19.5.604.
    1. Mahoney FI, Barthel DW. Functional evaluation: the Barthel Index. Md State Med J. 1965;14:61–65.
    1. Holden MK, Gill KM, Magliozzi MR. Gait assessment for neurologically impaired. Standards for outcome assessment. Phys. Ther. 1986;66(10):1530–1539. doi: 10.1093/ptj/66.10.1530.
    1. Bohannon RW, Smith MB. Interrater reliability of a modified Ashworth scale of muscle spasticity. Phys Ther. 1987;67(2):206–207. doi: 10.1093/ptj/67.2.206.
    1. Ware JE, Kosinski M, Keller SD. SF-12: how to score the SF-12 physical and mental health summary scales. 3. Lincoln, RI: QualityMetric Incorporated; 1998.
    1. Demeurisse G, Demol O, Robaye E. Motor evaluation in bascular hemiplegia. Eur. Neurol. 1980;19(6):382–389. doi: 10.1159/000115178.
    1. Ruggiero C, Mariani T, Gugliotta R, Gasperini B, Patacchini F, Nguyen HN, Zampi E, Serra R, Dell’aquila G, Cirinei E, Cenni S, Lattanzio F, Cherubini A. Validation of the Italian version of the falls efficacy scale international (FES-I) and the SHORT FES-I in community dwelling older persons. Arch Gerontol Geriatrics. 2009;39(S):211–219. doi: 10.1016/j.archger.2009.09.031.
    1. Jutai J, Day H. Psychosocial Impact of Assistive devices Scale (PIADS) Technol Disabil. 2002;14(3):107–111. doi: 10.3233/TAD-2002-14305.
    1. Scherer MJ, Cushman LA. Measuring subjective quality of life following spinal cord injury: a validation study of the assistive technology device predisposition assessment. Disabil Rehabil. 2001;23(9):387–393. doi: 10.1080/09638280010006665.
    1. Morris JC. Clinical Dementia Rating: a reliable and valid diagnostic and staging measure for dementia of the Alzheimer type. Int Psychogeriatrics. 1997;9(S1):173–176. doi: 10.1017/S1041610297004870.
    1. Levine DF, Richards J, Whittle M. Whittle’s gait analysis. Elsevier Health Sciences; 2012.
    1. Davis RB, Ounpuu S, Tyburski D, Gage JR. A gait analysis data collection and reduction technique. Hum Movement Sci. 1991;10(5):575–587. doi: 10.1016/0167-9457(91)90046-Z.
    1. Lo K, Stephenson M, Lockwood C. Effectiveness of robotic assisted rehabilitation for mobility and functional ability in adult stroke patients: a systematic review. JBI Database System Rev Implement Rep. 2017;15(12):3049–3091. doi: 10.11124/JBISRIR-2017-003456.

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