The Effectiveness of Overground Robot Exoskeleton Gait Training on Gait Outcomes, Balance, and Motor Function in Patients with Stroke: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Abstract
:1. Introduction
2. Methods
2.1. Research Design
2.2. Information Source
2.3. Search Strategy
2.4. Criteria for Inclusion and Exclusion of Data
2.4.1. Inclusion Criteria for the Current Study
- (1)
- Randomized controlled trials (RCTs).
- (2)
- Studies assessing participants who met the clinical diagnosis criteria for stroke or were diagnosed with stroke by MRI or CT, without comorbidities such as severe cognitive impairment, heart failure, or exercise contraindications.
- (3)
- No restrictions were placed on country, age, gender, or treatment duration.
- (4)
- Intervention using robot-assisted gait training, either alone or combined with other treatments, while control groups underwent conventional gait training, including physical therapy or other common rehabilitation approaches.
- (5)
- (Outcome measures included assessments of gait outcomes, balance, or motor function obtained through any measurement scale.
- (6)
- Studies published exclusively in English.
2.4.2. Exclusion Criteria for the Current Study
- (1)
- Single-group experimental designs without a control group.
- (2)
- Nonexperimental studies, such as observational, case studies, qualitative research, animal experiments, and duplicate publications reporting the same results.
- (3)
- Gray literature papers (abstracts and posters) without peer review.
- (4)
- Studies lacking sufficient data for effect size analysis.
- (5)
- Reported results containing errors or inaccuracies in tables or figures.
2.5. Selection Process
2.6. Data Collection Process and Data Items
2.7. Data Analysis
2.8. Risk of Bias Assessment
2.9. Reporting Bias Assessment
2.10. Synthesis Methods
3. Results
3.1. Study Selection
3.2. Characteristics of the Included Studies
3.3. Description of the Overground Robotic Exoskeleton Training
3.4. Risk of Bias
3.5. Effect Size Analysis Results
3.5.1. Gait Speed
3.5.2. Gait Endurance
3.5.3. Gait Ability
3.5.4. Balance
3.5.5. Motor Function
4. Discussion
4.1. Classification According to Dependent Variables
4.2. Interpretation of PEDro Score and Publication Bias
4.3. Study Limitations
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Babaiasl, M.; Mahdioun, S.H.; 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, 263–280. [Google Scholar] [CrossRef]
- Morone, G.; Paolucci, S.; Cherubini, A.; De Angelis, D.; Venturiero, V.; Coiro, P.; Iosa, M. Robot-assisted gait training for stroke patients: Current state of the art and perspectives of robotics. Neuropsychiatr. Dis. Treat. 2017, 13, 1303–1311. [Google Scholar] [CrossRef]
- Eng, J.J.; Pastva, A.M. Advances in remote monitoring for stroke recovery. Stroke 2022, 53, 2658–2661. [Google Scholar] [CrossRef]
- Chang, W.H.; Kim, Y.-H. Robot-assisted therapy in stroke rehabilitation. J. Stroke 2013, 15, 174. [Google Scholar] [CrossRef] [PubMed]
- Leow, X.R.G.; Ng, S.L.A.; Lau, Y. Overground Robotic Exoskeleton Training for Patients with Stroke on Walking-Related Outcomes: A Systematic Review and Meta-analysis of Randomized Controlled Trials. Arch. Phys. Med. Rehabil. 2023, 104, 1698–1710. [Google Scholar] [CrossRef] [PubMed]
- Sale, P.; Franceschini, M.; Waldner, A.; Hesse, S. Use of the robot assisted gait therapy in rehabilitation of patients with stroke and spinal cord injury. Eur. J. Phys. Rehabil. Med. 2012, 48, 111–121. [Google Scholar]
- Swinnen, E.; Beckwée, D.; Meeusen, R.; Baeyens, J.-P.; Kerckhofs, E. Does robot-assisted gait rehabilitation improve balance in stroke patients? A systematic review. Top. Stroke Rehabil. 2014, 21, 87–100. [Google Scholar] [CrossRef]
- Molteni, F.; Gasperini, G.; Cannaviello, G.; Guanziroli, E. Exoskeleton and end-effector robots for upper and lower limbs rehabilitation: Narrative review. PMR 2018, 10, S174–S188. [Google Scholar] [CrossRef] [PubMed]
- Goffredo, M.; Guanziroli, E.; Pournajaf, S.; Gaffuri, M.; Gasperini, G.; Filoni, S.; Baratta, S.; Damiani, C.; Franceschini, M.; Molteni, F.; et al. Overground wearable powered exoskeleton for gait training in subacute stroke subjects: Clinical and gait assessments. Eur. J. Phys. Rehabil. Med. 2019, 55, 710–721. [Google Scholar] [CrossRef]
- Nolan, K.J.; Karunakaran, K.K.; Chervin, K.; Monfett, M.R.; Bapineedu, R.K.; Jasey, N.N.; Oh-Park, M. Robotic exoskeleton gait training during acute stroke inpatient rehabilitation. Front. Neurorobotics 2020, 14, 581815. [Google Scholar] [CrossRef]
- Zhu, F.; Kern, M.; Fowkes, E.; Afzal, T.; Contreras-Vidal, J.-L.; Francisco, G.E.; Chang, S.-H. Effects of an exoskeleton-assisted gait training on post-stroke lower-limb muscle coordination. J. Neural Eng. 2021, 18, 046039. [Google Scholar] [CrossRef] [PubMed]
- Tamburella, F.; Lorusso, M.; Tramontano, M.; Fadlun, S.; Masciullo, M.; Scivoletto, G. Overground robotic training effects on walking and secondary health conditions in individuals with spinal cord injury: Systematic review. J. Neuroeng. Rehabil. 2022, 19, 27. [Google Scholar] [CrossRef]
- Calabrò, R.S.; Cacciola, A.; Bertè, F.; Manuli, A.; Leo, A.; Bramanti, A.; Naro, A.; Milardi, D.; Bramanti, P. Robotic gait rehabilitation and substitution devices in neurological disorders: Where are we now? Neurol. Sci. 2016, 37, 503–514. [Google Scholar] [CrossRef]
- Baronchelli, F.; Zucchella, C.; Serrao, M.; Intiso, D.; Bartolo, M. The effect of robotic assisted gait training with Lokomat® on balance control after stroke: Systematic review and meta-analysis. Front. Neurol. 2021, 12, 661815. [Google Scholar] [CrossRef]
- Bruni, M.F.; Melegari, C.; De Cola, M.C.; Bramanti, A.; Bramanti, P.; Calabrò, R.S. What does best evidence tell us about robotic gait rehabilitation in stroke patients: A systematic review and meta-analysis. J. Clin. Neurosci. 2018, 48, 11–17. [Google Scholar] [CrossRef] [PubMed]
- Calafiore, D.; Negrini, F.; Tottoli, N.; Ferraro, F.; Ozyemisci-Taskiran, O. Efficacy of robotic exoskeleton for gait rehabilitation in patients with subacute stroke: A systematic review. Eur. J. Phys. Rehabil. Med. 2022, 58, 1–8. [Google Scholar] [CrossRef]
- Hsu, C.-Y.; Cheng, Y.-H.; Lai, C.-H.; Lin, Y.-N. Clinical non-superiority of technology-assisted gait training with body weight support in patients with subacute stroke: A meta-analysis. Ann. Phys. Rehabil. Med. 2020, 63, 535–542. [Google Scholar] [CrossRef]
- Long, J.; Wang, Y.; Wang, T.; Ye, J.; Chen, G.; Liu, Q.; Wang, C. Effects of lower limb exoskeleton robot on gait parameters in hemiplegic patients. Chin. J. Rehabil. Med. 2021, 36, 1107–1110. [Google Scholar]
- Mehrholz, J.; Thomas, S.; Kugler, J.; Pohl, M.; Elsner, B. Electromechanical-assisted training for walking after stroke. Cochrane Database Syst. Rev. 2020, 10, CD006185. [Google Scholar] [CrossRef]
- Moucheboeuf, G.; Griffier, R.; Gasq, D.; Glize, B.; Bouyer, L.; Dehail, P.; Cassoudesalle, H. Effects of robotic gait training after stroke: A meta-analysis. Ann. Phys. Rehabil. Med. 2020, 63, 518–534. [Google Scholar] [CrossRef]
- Postol, N.; Marquez, J.; Spartalis, S.; Bivard, A.; Spratt, N.J. Do powered over-ground lower limb robotic exoskeletons affect outcomes in the rehabilitation of people with acquired brain injury? Disabil. Rehabil. Assist. Technol. 2019, 14, 764–775. [Google Scholar] [CrossRef]
- Tedla, J.S.; Dixit, S.; Gular, K.; Abohashrh, M. Robotic-assisted gait training effect on function and gait speed in subacute and chronic stroke population: A systematic review and meta-analysis of randomized controlled trials. Eur. Neurol. 2019, 81, 103–111. [Google Scholar] [CrossRef]
- Lu, W.; Zheng, Y.; Yini, D.; Meiling, T.; Zhang, X.; Cheng, Y.; Zhang, X.; Xiao, L. Effects of robot-assisted training on balance function in patients with stroke: A systematic review and meta-analysis. J. Rehabil. Med. 2021, 53, 2273. [Google Scholar]
- Lam, T.; Pauhl, K.; Krassioukov, A.; Eng, J.J. Using robot-applied resistance to augment body-weight–supported treadmill training in an individual with incomplete spinal cord injury. Phys. Ther. 2011, 91, 143–151. [Google Scholar] [CrossRef]
- Moher, D.; Shamseer, L.; Clarke, M.; Ghersi, D.; Liberati, A.; Petticrew, M.; Shekelle, P.; Stewart, L.A.; Group, P.-P. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst. Rev. 2015, 4, 1. [Google Scholar] [CrossRef] [PubMed]
- Higgins, J.P.; Green, S. Cochrane Handbook for Systematic Reviews of Interventions; Version 5.0.0 [updated February 2008]; The Cochrane Collaboration; Wiley: Hoboken, NJ, USA, 2008. [Google Scholar]
- Li, T.; Higgins, J.P.; Deeks, J.J. Collecting data. Cochrane Handb. Syst. Rev. Interv. 2019, 109–141. [Google Scholar]
- Yeung, L.-F.; Lau, C.C.; Lai, C.W.; Soo, Y.O.; Chan, M.-L.; Tong, R.K. Effects of wearable ankle robotics for stair and over-ground training on sub-acute stroke: A randomized controlled trial. J. NeuroEng. Rehabil. 2021, 18, 19. [Google Scholar] [CrossRef]
- Jayaraman, A.; O’brien, M.K.; Madhavan, S.; Mummidisetty, C.K.; Roth, H.R.; Hohl, K.; Tapp, A.; Brennan, K.; Kocherginsky, M.; Williams, K.J. Stride management assist exoskeleton vs functional gait training in stroke: A randomized trial. Neurology 2019, 92, e263–e273. [Google Scholar] [CrossRef]
- Bhogal, S.K.; Teasell, R.W.; Foley, N.C.; Speechley, M.R. The PEDro scale provides a more comprehensive measure of methodological quality than the Jadad scale in stroke rehabilitation literature. J. Clin. Epidemiol. 2005, 58, 668–673. [Google Scholar] [CrossRef]
- Mavridis, D.; Salanti, G. How to Assess Publication Bias: Funnel Plot, Trim-and-Fill Method and Selection Models; Royal College of Psychiatrists: London, UK, 2014; p. 30. [Google Scholar]
- Harrer, M.; Cuijpers, P.; Furukawa, T.; Ebert, D. Doing Meta-Analysis with R: A Hands-on Guide; Chapman and Hall/CRC: London, UK, 2021. [Google Scholar]
- Deeks, J.J.; Higgins, J.P.; Altman, D.G.; Group, C.S.M. Analysing data and undertaking meta-analyses. In Cochrane Handbook for Systematic Reviews of Interventions; Wiley: Hoboken, NJ, USA, 2019; pp. 241–284. [Google Scholar] [CrossRef]
- Hedges, L.V.; Olkin, I. Statistical Methods for Meta-Analysis; Academic press: Cambridge, MA, USA, 2014. [Google Scholar]
- Durlak, J.A. How to select, calculate, and interpret effect sizes. J. Pediatr. Psychol. 2009, 34, 917–928. [Google Scholar] [CrossRef]
- Bernard, R.M.; Abrami, P.C.; Borokhovski, E.; Wade, C.A.; Tamim, R.M.; Surkes, M.A.; Bethel, E.C. A meta-analysis of three types of interaction treatments in distance education. Rev. Educ. Res. 2009, 79, 1243–1289. [Google Scholar] [CrossRef]
- Sullivan, G.M.; Feinn, R. Using effect size—Or why the P value is not enough. J. Grad. Med. Educ. 2012, 4, 279–282. [Google Scholar] [CrossRef] [PubMed]
- Wright, A.; Stone, K.; Martinelli, L.; Fryer, S.; Smith, G.; Lambrick, D.; Stoner, L.; Jobson, S.; Faulkner, J. Effect of combined home-based, overground robotic-assisted gait training and usual physiotherapy on clinical functional outcomes in people with chronic stroke: A randomized controlled trial. Clin. Rehabil. 2021, 35, 882–893. [Google Scholar] [CrossRef]
- Kang, C.J.; Chun, M.H.; Lee, J.; Lee, J.Y. Effects of robot (SUBAR)-assisted gait training in patients with chronic stroke: Randomized controlled trial. Medicine 2021, 100, e27974. [Google Scholar] [CrossRef] [PubMed]
- Lee, H.-J.; Lee, S.-H.; Seo, K.; Lee, M.; Chang, W.H.; Choi, B.-O.; Ryu, G.-H.; Kim, Y.-H. Training for walking efficiency with a wearable hip-assist robot in patients with stroke: A pilot randomized controlled trial. Stroke 2019, 50, 3545–3552. [Google Scholar] [CrossRef]
- Li, Y.; Fan, T.; Qi, Q.; Wang, J.; Qiu, H.; Zhang, L.; Wu, X.; Ye, J.; Chen, G.; Long, J. Efficacy of a novel exoskeletal robot for locomotor rehabilitation in stroke patients: A multi-center, non-inferiority, randomized controlled trial. Front. Aging Neurosci. 2021, 13, 706569. [Google Scholar] [CrossRef] [PubMed]
- Louie, D.R.; Mortenson, W.B.; Durocher, M.; Schneeberg, A.; Teasell, R.; Yao, J.; Eng, J.J. Efficacy of an exoskeleton-based physical therapy program for non-ambulatory patients during subacute stroke rehabilitation: A randomized controlled trial. J. Neuroeng. Rehabil. 2021, 18, 149. [Google Scholar] [CrossRef]
- De Luca, R.; Maresca, G.; Balletta, T.; Cannavò, A.; Leonardi, S.; Latella, D.; Maggio, M.G.; Portaro, S.; Naro, A.; Calabrò, R.S. Does overground robotic gait training improve non-motor outcomes in patients with chronic stroke? Findings from a pilot study. J. Clin. Neurosci. 2020, 81, 240–245. [Google Scholar] [CrossRef]
- Molteni, F.; Guanziroli, E.; Goffredo, M.; Calabrò, R.S.; Pournajaf, S.; Gaffuri, M.; Gasperini, G.; Filoni, S.; Baratta, S.; Galafate, D.; et al. Gait recovery with an overground powered exoskeleton: A randomized controlled trial on subacute stroke subjects. Brain Sci. 2021, 11, 104. [Google Scholar] [CrossRef]
- Nam, Y.-G.; Lee, J.W.; Park, J.W.; Lee, H.J.; Nam, K.Y.; Park, J.H.; Yu, C.S.; Choi, M.R.; Kwon, B.S. Effects of electromechanical exoskeleton-assisted gait training on walking ability of stroke patients: A randomized controlled trial. Arch. Phys. Med. Rehabil. 2019, 100, 26–31. [Google Scholar] [CrossRef]
- Nam, Y.-G.; Park, J.W.; Lee, H.J.; Nam, K.Y.; Choi, M.R.; Yu, C.S.; Zhu, L.; Zhang, X.; Lee, J.W.; Kwon, B.S. Further effects of electromechanically assisted gait trainer (Exowalk?) in patients with chronic stroke: A randomized controlled trial. J. Rehabil. Med. 2020, 52, 1. [Google Scholar]
- Stein, J.; Bishop, L.; Stein, D.J.; Wong, C.K. Gait training with a robotic leg brace after stroke: A randomized controlled pilot study. Am. J. Phys. Med. Rehabil. 2014, 93, 987–994. [Google Scholar] [CrossRef]
- Tanaka, N.; Matsushita, S.; Sonoda, Y.; Maruta, Y.; Fujitaka, Y.; Sato, M.; Simomori, M.; Onaka, R.; Harada, K.; Hirata, T. Effect of stride management assist gait training for poststroke hemiplegia: A single center, open-label, randomized controlled trial. J. Stroke Cerebrovasc. Dis. 2019, 28, 477–486. [Google Scholar] [CrossRef]
- Watanabe, H.; Tanaka, N.; Inuta, T.; Saitou, H.; Yanagi, H. Locomotion improvement using a hybrid assistive limb in recovery phase stroke patients: A randomized controlled pilot study. Arch. Phys. Med. Rehabil. 2014, 95, 2006–2012. [Google Scholar] [CrossRef]
- Watanabe, H.; Goto, R.; Tanaka, N.; Matsumura, A.; Yanagi, H. Effects of gait training using the Hybrid Assistive Limb® in recovery-phase stroke patients: A 2-month follow-up, randomized, controlled study. NeuroRehabilitation 2017, 40, 363–367. [Google Scholar] [CrossRef] [PubMed]
- Watanabe, H.; Tsurushima, H.; Yanagi, H. Effect of hybrid assistive limb treatment on maximal walking speed and six-minute walking distance during stroke rehabilitation: A pilot study. J. Phys. Ther. Sci. 2021, 33, 168–174. [Google Scholar] [CrossRef] [PubMed]
- Li, D.-X.; Zha, F.-B.; Long, J.-J.; Liu, F.; Cao, J.; Wang, Y.-L. Effect of robot assisted gait training on motor and walking function in patients with subacute stroke: A random controlled study. J. Stroke Cerebrovasc. Dis. 2021, 30, 105807. [Google Scholar] [CrossRef] [PubMed]
- Yokota, C.; Yamamoto, Y.; Kamada, M.; Nakai, M.; Nishimura, K.; Ando, D.; Sato, T.; Koga, M.; Ihara, M.; Toyoda, K. Acute stroke rehabilitation for gait training with cyborg type robot Hybrid Assistive Limb: A pilot study. J. Neurol. Sci. 2019, 404, 11–15. [Google Scholar] [CrossRef]
- Yokota, C.; Tanaka, K.; Omae, K.; Kamada, M.; Nishikawa, H.; Koga, M.; Ihara, M.; Fujimoto, Y.; Sankai, Y.; Nakajima, T. Effect of cyborg-type robot Hybrid Assistive Limb on patients with severe walking disability in acute stroke: A randomized controlled study. J. Stroke Cerebrovasc. Dis. 2023, 32, 107020. [Google Scholar] [CrossRef]
- Van Dalen, J.W.; van Charante, E.P.M.; Nederkoorn, P.J.; van Gool, W.A.; Richard, E. Poststroke apathy. Stroke 2013, 44, 851–860. [Google Scholar] [CrossRef]
- Marin, R.S. Apathy: A neuropsychiatric syndrome. J. Neuropsychiatry Clin. Neurosci. 1991, 3, 243–254. [Google Scholar]
- Jw, J. Effects of Over-Ground Gait Training Using Walking Assistance Robots on Balance and Walking Ability of Stroke Patients; Korea National University of Transportation Graduate School: Chungju, Republic of Korea, 2023. [Google Scholar]
- Poli, P.; Morone, G.; Rosati, G.; Masiero, S. Robotic technologies and rehabilitation: New tools for stroke patients’ therapy. BioMed Res. Int. 2013, 2013, 153872. [Google Scholar] [CrossRef]
- Edmans, J.A.; Gladman, J.R.; Cobb, S.; Sunderland, A.; Pridmore, T.; Hilton, D.; Walker, M.F. Validity of a virtual environment for stroke rehabilitation. Stroke 2006, 37, 2770–2775. [Google Scholar] [CrossRef]
- Najafi, L.; Cowan, D. Handbook of Electronic Assistive Technology; Academic Press: Cambridge, MA, USA, 2018. [Google Scholar]
- Longatelli, V.; Pedrocchi, A.; Guanziroli, E.; Molteni, F.; Gandolla, M. Robotic exoskeleton gait training in stroke: An electromyography-based evaluation. Front. Neurorobotics 2021, 15, 733738. [Google Scholar] [CrossRef]
- Wonsetler, E.C.; Bowden, M.G. A systematic review of mechanisms of gait speed change post-stroke. Part 1: Spatiotemporal parameters and asymmetry ratios. Top. Stroke Rehabil. 2017, 24, 435–446. [Google Scholar] [CrossRef] [PubMed]
- Bortole, M.; Venkatakrishnan, A.; Zhu, F.; Moreno, J.C.; Francisco, G.E.; Pons, J.L.; Contreras-Vidal, J.L. The H2 robotic exoskeleton for gait rehabilitation after stroke: Early findings from a clinical study. J. Neuroeng. Rehabil. 2015, 12, 54. [Google Scholar] [CrossRef] [PubMed]
- Kotov, S.; Isakova, E.; Lijdvoy, V.Y.; Petrushanskaya, K.; Pismennaya, E.; Romanova, M.; Kodzokova, L. Robotic recovery of walking function in patients in the early recovery period of stroke. Zhurnal Nevrol. I Psikhiatrii Im. SS Korsakova 2020, 120, 73–80. [Google Scholar] [CrossRef] [PubMed]
- Rojek, A.; Mika, A.; Oleksy, Ł.; Stolarczyk, A.; Kielnar, R. Effects of exoskeleton gait training on balance, load distribution, and functional status in stroke: A randomized controlled trial. Front. Neurol. 2020, 10, 489493. [Google Scholar] [CrossRef]
- Gandolla, M.; Guanziroli, E.; D’Angelo, A.; Cannaviello, G.; Molteni, F.; Pedrocchi, A. Automatic setting procedure for exoskeleton-assisted overground gait: Proof of concept on stroke population. Front. Neurorobotics 2018, 12, 10. [Google Scholar] [CrossRef]
- Moore, S.A.; Boyne, P.; Fulk, G.; Verheyden, G.; Fini, N.A. Walk the talk: Current evidence for walking recovery after stroke, future pathways and a mission for research and clinical practice. Stroke 2022, 53, 3494–3505. [Google Scholar] [CrossRef]
- Mahendran, N.; Kuys, S.S.; Brauer, S.G. Which impairments, activity limitations and personal factors at hospital discharge predict walking activity across the first 6 months poststroke? Disabil. Rehabil. 2020, 42, 763–769. [Google Scholar] [CrossRef] [PubMed]
- Nolan, K.J.; Karunakaran, K.K.; Roberts, P.; Tefertiller, C.; Walter, A.M.; Zhang, J.; Leslie, D.; Jayaraman, A.; Francisco, G.E. Utilization of robotic exoskeleton for overground walking in acute and chronic stroke. Front. Neurorobotics 2021, 15, 689363. [Google Scholar] [CrossRef] [PubMed]
- Gschwind, Y.J.; Kressig, R.W.; Lacroix, A.; Muehlbauer, T.; Pfenninger, B.; Granacher, U. A best practice fall prevention exercise program to improve balance, strength/power, and psychosocial health in older adults: Study protocol for a randomized controlled trial. BMC Geriatr. 2013, 13, 105. [Google Scholar] [CrossRef]
- Downs, S.; Marquez, J.; Chiarelli, P. The Berg Balance Scale has high intra-and inter-rater reliability but absolute reliability varies across the scale: A systematic review. J. Physiother. 2013, 59, 93–99. [Google Scholar] [CrossRef]
- Gandolfi, M.; Valè, N.; Dimitrova, E.; Zanolin, M.E.; Mattiuz, N.; Battistuzzi, E.; Beccari, M.; Geroin, C.; Picelli, A.; Waldner, A. Robot-assisted stair climbing training on postural control and sensory integration processes in chronic post-stroke patients: A randomized controlled clinical trial. Front. Neurosci. 2019, 13, 1143. [Google Scholar] [CrossRef]
- Hidler, J.; Nichols, D.; Pelliccio, M.; Brady, K.; Campbell, D.D.; Kahn, J.H.; Hornby, T.G. Multicenter randomized clinical trial evaluating the effectiveness of the Lokomat in subacute stroke. Neurorehabilit. Neural Repair 2009, 23, 5–13. [Google Scholar] [CrossRef]
- Mustafaoğlu, R.; Erhan, B.; Yeldan, İ.; Hüseyinsinoğlu, B.E.; Gündüz, B.; Özdinçler, A.R. The effects of body weight-supported treadmill training on static and dynamic balance in stroke patients: A pilot, single-blind, randomized trial. Turk. J. Phys. Med. Rehabil. 2018, 64, 344. [Google Scholar] [CrossRef] [PubMed]
- Wall, A.; Borg, J.; Vreede, K.; Palmcrantz, S. A randomized controlled study incorporating an electromechanical gait machine, the Hybrid Assistive Limb, in gait training of patients with severe limitations in walking in the subacute phase after stroke. PLoS ONE 2020, 15, e0229707. [Google Scholar] [CrossRef]
- Yu, D.; Yang, Z.; Lei, L.; Chaoming, N.; Ming, W. Robot-assisted gait training plan for patients in poststroke recovery period: A single blind randomized controlled trial. BioMed Res. Int. 2021, 2021, 5820304. [Google Scholar] [CrossRef]
- Kim, H.; Park, G.; Shin, J.-H.; You, J.H. Neuroplastic effects of end-effector robotic gait training for hemiparetic stroke: A randomised controlled trial. Sci. Rep. 2020, 10, 12461. [Google Scholar] [CrossRef]
- Crozier, J.; Roig, M.; Eng, J.J.; MacKay-Lyons, M.; Fung, J.; Ploughman, M.; Bailey, D.M.; Sweet, S.N.; Giacomantonio, N.; Thiel, A.; et al. High-intensity interval training after stroke: An opportunity to promote functional recovery, cardiovascular health, and neuroplasticity. Neurorehabilit. Neural Repair 2018, 32, 543–556. [Google Scholar] [CrossRef]
- Rahayu, U.B.; Wibowo, S.; Setyopranoto, I.; Hibatullah Romli, M. Effectiveness of physiotherapy interventions in brain plasticity, balance and functional ability in stroke survivors: A randomized controlled trial. NeuroRehabilitation 2020, 47, 463–470. [Google Scholar] [CrossRef]
- Duncan, P.W.; Propst, M.; Nelson, S.G. Reliability of the Fugl-Meyer assessment of sensorimotor recovery following cerebrovascular accident. Phys. Ther. 1983, 63, 1606–1610. [Google Scholar] [CrossRef]
- Chang, W.H.; Kim, M.S.; Huh, J.P.; Lee, P.K.; Kim, Y.-H. Effects of robot-assisted gait training on cardiopulmonary fitness in subacute stroke patients: A randomized controlled study. Neurorehabilit. Neural Repair 2012, 26, 318–324. [Google Scholar] [CrossRef] [PubMed]
- Park, S.J.; Oh, S. Changes in gait performance in stroke patients after taping with scapular setting exercise. Healthcare 2020, 8, 128. [Google Scholar] [CrossRef]
- Srivastava, S.; Kao, P.-C.; Kim, S.H.; Stegall, P.; Zanotto, D.; Higginson, J.S.; Agrawal, S.K.; Scholz, J.P. Assist-as-needed robot-aided gait training improves walking function in individuals following stroke. IEEE Trans. Neural Syst. Rehabil. Eng. 2014, 23, 956–963. [Google Scholar] [CrossRef] [PubMed]
- An, C.-M.; Son, Y.-L.; Park, Y.-H.; Moon, S.-J. Relationship between dynamic balance and spatiotemporal gait symmetry in hemiplegic patients with chronic stroke. Hong Kong Physiother. J. 2017, 37, 19–24. [Google Scholar] [CrossRef] [PubMed]
- Husemann, B.; Muller, F.; Krewer, C.; Heller, S.; Koenig, E. Effects of locomotion training with assistance of a robot-driven gait orthosis in hemiparetic patients after stroke: A randomized controlled pilot study. Stroke 2007, 38, 349–354. [Google Scholar] [CrossRef] [PubMed]
- Yamato, T.P.; Arora, M.; Stevens, M.L.; Elkins, M.R.; Moseley, A.M. Quality, language, subdiscipline and promotion were associated with article accesses on Physiotherapy Evidence Database (PEDro). Physiotherapy 2018, 104, 122–128. [Google Scholar] [CrossRef]
- Kim, S.Y.; Park, J.E.; Seo, H.J.; Lee, Y.J.; Jang, B.H.; Son, H.J. NECA’s Guidance for Undertaking Systematic Reviews and Meta-Analyses for Intervention; National Evidence-based Healthcare Collaboration Agency: Seoul, Republic of Korea, 2011. [Google Scholar]
- Duval, S.; Tweedie, R. A nonparametric “trim and fill” method of accounting for publication bias in meta-analysis. J. Am. Stat. Assoc. 2000, 95, 89–98. [Google Scholar]
- Idris, N.R.N. A comparison of methods to detect publication bias for meta-analysis of continuous data. J. Appl. Sci. 2012, 12, 1413–1417. [Google Scholar] [CrossRef]
No. | Details of the Contents |
---|---|
1 | Eligibility criteria were specified. |
2 | Subjects were randomly allocated to groups. |
3 | Allocation was concealed. |
4 | Groups were similar at baseline regarding the most important prognostic indicators. |
5 | Blinding of all subjects. |
6 | Blinding of all therapists who administered the therapy. |
7 | Blinding of all assessors who measured at least one key outcome. |
8 | Measures of at least one key outcome were obtained from more than 85% of the subjects initially allocated to groups. |
9 | All subjects for whom outcome measures were available received the treatment or control condition as allocated or, where this was not the case, data for at least one key outcome was analyzed by “intention to treat”. |
10 | Results of between-group statistical comparisons are reported for at least one key outcome. |
11 | Study provides both point measures and measures of variability for at least one key outcome. |
Study /Location | Study Design | Sample Size (n, M/F) | Age (Mean) | Phase of Stroke | Robotic Device | Intervention Group | Control Group | Duration and Frequency of Study Period | Outcomes |
---|---|---|---|---|---|---|---|---|---|
Amy et al. [38] /UK | Two-center, 2-arm RCT | Total: 34 Ex 1: 16 (14/2) Con 2: 18 (14/4) | Ex 1: 59.6 ± 10.1 Con 2: 65.1 ± 10.1 | Chronic stroke | Alter G, Bionic Leg orthosis | Overground robotic-assisted gait-training program | Physical activity | 30 min per day, home based (self), 10-weeks | 6MWT 4, FAC 5, BBS 6, TUG 7 |
Jayaraman et al. [29] /USA | 2-arm RCT | Total: 50 Ex 1: 25 Con 2: 25 | Ex 1: 59.5 ± 9.7 Con 2: 61.6 ± 12.6 | Chronic stroke | Stride Management Assist (SMA)-Honda | Overground gait and functional gait training with the SMA | Functional training based on the participant’s goals | 45 min, 3 times a week for 6~8 weeks | 10MWT 3, 6MWT 4, BBS 6 |
Kang et al. [39] /Korea | 2-arm RCT | Total: 30 Ex 1: 15 (10/5) Con 2: 15 (8/7) | Ex 1: 64.3 ± 4.6 Con 2: 62.9 ± 6.0 | Chronic stroke | SUBAR exoskeleton | SUBAR-assisted gait training | Conventional physiotherapy | 30 min, 10 sessions for 3 weeks | 10MWT 3, Gait speed, FAC 5, TUG 7, BBS 6 |
Lee et al. [40] /Korea | 2-arm RCT | Total: 26 Ex 1: 14 (7/7) Con 2: 12(7/5) | Ex 1: 61.85 ± 7.87 Con 2: 62.25 ± 6.36 | Chronic stroke | Gait Enhancing and Motivating System (GEMS) | Gait training with GEMS | Gait training | 45 min, 3 times a week for 4 weeks | Gait speed |
Li et al. [41] /China | Multicenter, 2-arm RCT | Total: 130 Ex 1: 57 (48/9) Con 2: 57 (45/12) | Ex 1: 50 Con 2: 51.67 | Subacute stroke | BEAR-H1 lower limb exoskeleton robot | Locomotor training using the BEAR-H1 exoskeleton robot | Conventional gait training | 30 min, two sessions daily, 5 days a week for 4 weeks | 6MWT 4, FMA-LE 8 |
Louie et al. [42] /Canada | Multicenter, 2-arm RCT | Total: 36 Ex 1: 19 (16/3) Con 2: 17 (10/7) | Ex 1: 59.6 ± 15.8 Con 2: 55.3 ± 10.6 | Subacute stroke | EksoGT | Exoskeleton intervention | Standard physical therapy | 60 min, 3 times a week for until discharge | Gait speed, 6MWT 4, FAC 5, BBS 6, FMA-LE 8 |
Luca et al. [43] /Italy | 2-arm RCT | Total: 30 Ex 1: 15 (11/4) Con 2: 15 (11/4) | Ex 1: 54.4 ± 11.9 Con 2: 55.8 ± 13.2 | Chronic stroke | EksoGT | Robotic gait rehabilitation | Conventional gait training | 60 min, 3 times a week for 8 weeks | 10MWT 3, TUG 7 |
Molteni et al. [44] /Italy | Multicenter, 2-arm RCT | Total: 75 Ex 1: 38 (21/17) Con 2: 37 (21/19) | Ex 1: 62.13 ± 8.75 Con 2: 68.24 ± 8.58 | Subacute stroke | Ekso™ | Gait rehabilitation with Ekso™ | Conventional training | 60 min, 5 days/week for 3 weeks | 10MWT 3, 6MWT 4, FAC 5 |
Nam et al. [45] /Korea | 2-arm RCT | Total: 34 Ex 1: 18 (11/7) Con 2: 16 (6/10) | Ex 1: 48.33 ± 15.56 Con 2: 68.56 ± 17.35 | Chronic stroke | Exowalk | Electromechanically assisted gait training (Exowalk) | Physical-therapist-assisted gait training | 30 min, 5 days a week for 4 weeks | 10MWT 3, 6MWT 4, FAC 5, BBS 6 |
Nam et al. [46] /Korea | Multicenter, 2-arm RCT | Total: 38 Ex 1: 18 (8/10) Con 2: 20 (14/6) | Ex 1: 57.3 ± 8.71 Con 2: 60 ± 11.48 | Chronic stroke | Exowalk | Electromechanically assisted gait training (Exowalk) | Conventional gait training | 60 min, 5 times per week for 2 weeks | 10MWT 3, 6MWT 4, FAC 5, BBS 6 |
Stein J et al. [47] /USA | 2-arm RCT | Total: 24 Ex 1: 12 (10/2) Con 2: 12 (7/5) | Ex 1: 57.6 ± 10.7 Con 2: 56.6 ± 15.1 | Chronic stroke | Alter G, Bionic Leg orthosis | Group exercise program with robot device | Group exercise program | 1 h, 3 times per week for 6 weeks | 10MWT 3, 6MWT 4, BBS 6, TUG 7 |
Tanaka et al. [48] /Japan | 2-arm RCT | Total: 41 Ex 1: 21 (13/8) Con 2: 20 (14/6) | Ex 1: 64.9 ± 12.2 Con 2: 62.3 ± 9.3 | Subacute stroke | Stride Management Assist (SMA)-Honda | Robotic-assisted gait training | Conventional gait training | 20 min per day for 10 consecutive days | Gait speed |
Watanabe et al. [49] /Japan | 2-arm RCT | Total: 22 Ex 1: 11 (7/4) Con 2: 11 (4/7) | Ex 1: 67.0 ± 16.8 Con 2: 75.6 ± 13.9 | Subacute stroke | Robot Suit Hybrid Assistive Limb (HAL) | Gait rehabilitation with the HAL | Conventional gait training | 20 min once per day, 3 times per week for 4 weeks | 10MWT 3, 6MWT 4, FAC 5, TUG 7, FMA-LE 8 |
Watanabe et al. [50] /Japan | 2-arm RCT | Total: 24 Ex 1: 12 (8/4) Con 2: 12 (8/4) | Ex 1: 66.9 ± 16.0 Con 2: 76.8 ± 13.8 | Subacute stroke | Robot Suit Hybrid Assistive Limb (HAL) | Gait training with HAL. | Conventional gait training | 20 min once per day, 3 times a week, total of 12 sessions (4 weeks) | 10MWT 3, 6MWT 4, FAC 5, TUG 7, FMA-LE 8 |
Watanabe et al. [51] /Japan | 2-arm RCT | Total: 20 Ex 1: 9 (6/3) Con 2: 11 (4/7) | Ex 1: 60.0 ± 11.7 Con 2: 77.4 ± 14.3 | Subacute stroke | Robot Suit Hybrid Assistive Limb (HAL) | Gait treatment with HAL. | Conventional gait training | 20 min once per day, 3 times per week for 4 weeks | 10MWT 3, 6MWT 4, FAC 5, TUG 7, FMA-LE 8 |
Xia Li et al. [52] /China | 2-arm RCT | Total: 36 Ex 1: 17 (15/2) Con 2: 15 (14/1) | Ex 1: 50.13 ± 9.49 Con 2: 50.53 ± 12.26 | Subacute stroke | BEAR-H1 lower limb exoskeleton robot | Gait training assisted with the BEAR-H1 | Conventional training | 30 min, twice a day, 5 days a week for 4 weeks | Gait speed, 6MWT 4, FAC 5, FMA-LE 8 |
Yeung et al. [28] /Hong Kong | Two-center, 3-arm RCT | Total: 47 Ex 1 1: 14 (8/6) Ex 1 2: 16 (8/8) Con 2: 17(8/9) | Ex 1 1: 64.6 ± 12.6 Ex 1 2: 68.3 ± 10.3 Con 2: 63.6 ± 5.2 | Subacute stroke | Dynamixel MX-106R | Robot-assisted training | Conventional rehabilitation | 30 min, 2 session a week for 20 sessions | 10MWT 3, FAC 5, BBS 6 |
Yokota et al. [53] /Japan | 2-arm RCT | Total: 37 Ex 1: 18 (16/2)Con 2: 19 (12/7) | Ex 1: 69 Con 2: 69 | Acute stroke | Robot Suit Hybrid Assistive Limb (HAL) | Gait training with HAL | Gait training | 1–3 sessions per day (20 min per session), 3 days a week for 3 weeks | FAC 5, FMA-LE 8 |
Yokota et al. [54] /Japan | 2-arm RCT | Total: 22 Ex 1: 12 (7/5) Con 2: 10 (5/5) | Ex 1: 65.3 ± 10.1 Con 2: 62.5 ± 10.6 | Acute stroke | Robot Suit Hybrid Assistive Limb (HAL) | Gait training with HAL | Conventional physical therapy | 1–3 sessions per day (20 min per session), 3 days a week for 20 sessions | FAC 5, FMA-LE 8 |
Study (Year) | Concealed Allocation | Baseline Similarity | Subject Blinding | Therapist Blinding | Assessor Blinding | <15% Dropouts | Intention to Treat Analysis | Between-Group Difference Reported | Point Estimate, Variability Reported | Total |
---|---|---|---|---|---|---|---|---|---|---|
Amy et al. [38] | Y | Y | N | N | N | Y (8.82%) | Y | N | Y | 7 |
Jayaraman et al. [29] | N | Y | N | N | N | Y (7%) | N | N | Y | 5 |
Kang et al. [39] | N | Y | N | N | N | Y (7%) | Y | Y | Y | 7 |
Lee et al. [40] | N | Y | N | N | N | Y (0%) | Y | Y | Y | 7 |
Li et al. [41] | N | Y | N | N | N | Y (12.31%) | Y | Y | N | 6 |
Louie et al. [42] | Y | Y | N | N | Y | Y (5%) | Y | Y | Y | 9 |
Luca et al. [43] | Y | Y | N | N | N | Y (0%) | N | N | Y | 6 |
Molteni et al. [44] | N | N | N | N | N | Y (6.25%) | N | N | Y | 4 |
Nam et al. [45] | N | N | N | N | N | N (15%) | N | Y | Y | 4 |
Nam et al. [46] | N | Y | N | N | Y | Y (5%) | N | Y | N | 6 |
Stein J et al. [47] | Y | Y | N | N | Y | Y (0%) | Y | Y | Y | 9 |
Tanaka et al. [48] | N | Y | N | N | N | Y (0%) | Y | Y | Y | 7 |
Watanabe et al. [49] | N | Y | N | N | N | N (31.25%) | N | Y | Y | 5 |
Watanabe et al. [50] | N | Y | N | N | N | N (27.27%) | N | N | Y | 4 |
Watanabe et al. [51] | N | Y | N | N | N | N (39.39%) | N | Y | Y | 6 |
Xia Li et al. [52] | N | Y | N | N | Y | Y (11.11%) | N | Y | Y | 7 |
Yeung et al. [28] | N | Y | N | N | Y | Y (8.51%) | Y | Y | Y | 8 |
Yokota et al. [53] | N | Y | N | N | N | N (21.28%) | N | Y | Y | 5 |
Yokota et al. [54] | N | Y | N | N | N | Y (8.33%) | N | Y | Y | 6 |
N | p-Value | I2 | Point Estimate | 95% CI | Standard Error | Q-Value | ||
---|---|---|---|---|---|---|---|---|
Lower Limit | Upper Limit | |||||||
10MWT 1 | 10 | 0.02 | 0% | 0.28 | 0.05 | 0.21 | 1.72 | 6.14 |
Gait analysis | 5 | 0.16 | 0% | 0.22 | −0.09 | 0.53 | 3.28 | 4.16 |
Total | 15 | 0.006 | 0% | 0.26 | 0.08 | 0.44 | 1.51 | 10.39 |
Category | Subgroups | No. of Trials | Sample Size | SMD (d) | 95% CI | Heterogeneity p-Value of Chi-Square Test (I2) | Overall Effect Z Value (p-Value) |
---|---|---|---|---|---|---|---|
Region | Asian | 11 (2, 3, 4, 7, 8, 10, 11, 12, 13, 14) | 319 | 0.26 | 0.03, 0.48 | 5.50 (0%) | 2.27 (0.02 *) |
Non-Asian | 4 (1, 5, 6, 9) | 140 | 0.28 | −0.13, 0.68 | 4.25 (29%) | 1.34 (0.18) | |
Phase of stroke | Acute | 0 | 0 | ||||
Subacute | 7 (4, 5, 10, 11, 12, 13, 14) | 197 | 0.25 | −0.04. 0.53 | 1.24 (0%) | 1.70 (0.09) | |
Chronic | 8 (1, 2, 3, 6, 7, 8, 9) | 262 | 0.28 | 0.01, 0.55 | 8.50 (18%) | 2.00 (0.05) | |
Length of training session | ≤30 min/session | 9 (2, 4, 7, 10, 11, 12, 13, 14) | 255 | 0.18 | −0.07, 0.43 | 2.37 (0%) | 1.44 (0.15) |
>30 min/session | 6 (1, 3, 5, 6, 8, 9) | 204 | 0.37 | 0.05, 0.70 | 6.50 (23%) | 2.27 (0.02 *) | |
Frequency of training | ≤3 times/week | 9 (1, 3, 5, 6, 9, 11, 12, 13, 14) | 254 | 0.38 | 0.12, 0.63 | 6.62 (0%) | 2.93 (0.003 *) |
>3 times/week | 6 (2, 4, 7, 8, 10) | 205 | 0.12 | −0.15, 0.40 | 1.38 (0%) | 0.88 (0.38) | |
Duration of training | ≤4 weeks | 10 (2, 3, 4, 7, 8, 10, 11, 12, 13) | 288 | 0.25 | 0.02, 0.48 | 5.45 (0%) | 2.09 (0.04 *) |
>4 weeks | 5 (1, 5, 6, 9, 14) | 171 | 0.28 | −0.03, 0.60 | 4.28 (6%) | 1.75 (0.08) |
N | Studies Trimmed | p-Value | I2 | Point Estimate | 95% CI | Standard Error | Q-Value | ||
---|---|---|---|---|---|---|---|---|---|
Lower Limit | Upper Limit | ||||||||
Gait endurance | 12 | 0.23 | 0% | 0.11 | −0.07 | 0.28 | 0.43 | 3.87 | |
Adjusted value | 6 | −0.009 | −0.15 | 0.13 | 10.44 |
N | Studies Trimmed | p-Value | I2 | Point Estimate | 95% CI | Standard Error | Q-Value | ||
---|---|---|---|---|---|---|---|---|---|
Lower Limit | Upper Limit | ||||||||
Gait ability | 13 | 0.07 | 2% | 0.18 | −0.01 | 0.37 | 1.71 | 13.03 | |
Adjusted value | 1 | 0.14 | −0.05 | 0.34 | 15.65 |
N | χ2 | I2 | Point Estimate | 95% CI | Z | p-Value | ||
---|---|---|---|---|---|---|---|---|
Lower Limit | Upper Limit | |||||||
Berg Balance Scale | 8 | 0.25 | 23% | 0.05 | −0.23 | 0.32 | 0.34 | 0.74 |
Timed Up and Go Test | 7 | 0.43 | 0% | −0.15 | −0.42 | 0.15 | 0.96 | 0.34 |
N | χ2 | I2 | Point Estimate | 95% CI | Z | p-Value | ||
---|---|---|---|---|---|---|---|---|
Lower Limit | Upper Limit | |||||||
Fugl–Meyer Assessment—Lower Extremity | 9 | 0.20 | 28% | 0.21 | −0.05 | 0.47 | 1.56 | 0.12 |
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Lee, M.-H.; Tian, M.-Y.; Kim, M.-K. The Effectiveness of Overground Robot Exoskeleton Gait Training on Gait Outcomes, Balance, and Motor Function in Patients with Stroke: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Brain Sci. 2024, 14, 834. https://doi.org/10.3390/brainsci14080834
Lee M-H, Tian M-Y, Kim M-K. The Effectiveness of Overground Robot Exoskeleton Gait Training on Gait Outcomes, Balance, and Motor Function in Patients with Stroke: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Brain Sciences. 2024; 14(8):834. https://doi.org/10.3390/brainsci14080834
Chicago/Turabian StyleLee, Myoung-Ho, Ming-Yu Tian, and Myoung-Kwon Kim. 2024. "The Effectiveness of Overground Robot Exoskeleton Gait Training on Gait Outcomes, Balance, and Motor Function in Patients with Stroke: A Systematic Review and Meta-Analysis of Randomized Controlled Trials" Brain Sciences 14, no. 8: 834. https://doi.org/10.3390/brainsci14080834