- Open Access
Rethinking the tools in the toolbox
Journal of NeuroEngineering and Rehabilitation volume 19, Article number: 61 (2022)
The commentary by Dr. Labruyere on the article by Kuo et al. (J Neuroeng Rehabil. 2021; 18:174) posits that randomized trials evaluating the comparative efficacy of robotic devices for patients with neurological injury may not be needed. The primary argument is that researchers and clinicians do not know how to optimize training parameters to maximize the benefits of this therapy, and studies vary in how they deliver robotic-assisted training. While I concur with the suggestion that additional trials using robotic devices as therapeutic tools are not warranted, an alternative hypothesis is that future studies will yield similar equivocal results regardless of the training parameters used. Attempts are made to detail arguments supporting this premise, including the notion that the original rationale for providing robotic-assisted walking training, particularly with exoskeletal devices, was flawed and that the design of some of the more commonly used devices places inherent limitations on the ability to maximize neuromuscular demands during training. While these devices arrived nearly 20 years ago amid substantial enthusiasm, we have since learned valuable lessons from robotic-assisted and other rehabilitation studies on some of the critical parameters that influence neuromuscular and cardiovascular activity during locomotor training, and different strategies are now needed to optimize rehabilitation outcomes.
The commentary by Dr. Labruyere focuses on the question of how robotic rehabilitation devices should be utilized or studied, and was instigated in part by the work of Kuo et al.  using the Lokomat, an exoskeletal robotic device introduced earlier this century. In citing the recent Cochrane review by Mehrholz et al.  on locomotor training using electrotechnical devices in individuals with stroke, Dr. Labruyere questions the conclusions that further trials are warranted, particularly for robotic-assisted training paradigms. Rather, using the Kuo article as an example, he believes that robotic training parameters such as body weight support, treadmill speed, and the amount of robotic assistance vary substantially across and within different studies or between therapists, and these differences may influence the efficacy of the training delivered. The primary argument is that manipulating these settings individually or in combination varies the neuromuscular demands of walking training, and therapists and researchers should work towards optimizing these training parameters to maximize patient outcomes. In referencing a concluding remark from the Cochrane review, Dr. Labruyere suggests that additional, larger randomized controlled trials using robotic devices are likely not needed, perhaps until we determine the best training conditions for specific patients.
Dr. Labruyere and I certainly share the opinion that, 20 years ago, robotic devices were introduced amid substantial enthusiasm that these new tools could provide greater amounts of walking practice for individuals with neurological injury, while simultaneously decreasing the physical burden on therapists. We also agree that, after two decades of evaluation of their potential efficacy, further randomized trials are not needed. However, we have very different opinions on why such studies are unwarranted.
My concern is that the results of further randomized trials using robotic-assisted training strategies will result in similar equivocal results that we have witnessed over the past 20 years. Large randomized trials are often initiated on the promise of findings from smaller trials that demonstrate a relative benefit (i.e., greater effect size) of an experimental intervention as compared to an alternative strategy. However, in published randomized trials using robotic devices, the results are consistently inconsistent and inconclusive. There are certainly some positive studies hinting towards the comparative efficacy of robotic-assisted training [3, 4], although those studies often utilize a comparison intervention that provides limited walking training (i.e., conventional therapy). That message is important; more walking practice often results in better walking outcomes [5, 6]. In many studies, however, there are no differences in primary or secondary walking outcomes. Rather, some authors often highlight small, non-significant differences that slightly favor robotic-assisted therapy [7, 8], emphasize various secondary outcomes that might be significant (gait symmetry or body composition) [9, 10], or use non-standard statistical procedures to achieve significant differences [11, 12]. That “spin” is often interpreted by clinicians and researchers as an indication of the superiority of robotic-assisted training, despite the collective evidence demonstrating limited comparative efficacy of such training (see [13, 14] for review). Conversely, in some of the early, larger randomized trials comparing robotic-assisted therapy to alternative interventions, particularly those that focus on task-specific walking with therapist assistance, the results demonstrate statistically significant differences favoring the alternative strategy; that is, robotic-assisted training is worse [15,16,17]. More recent studies still suggest no additional benefits of robotic-assisted training on mobility outcomes, even using more sophisticated training algorithms (i.e., reduced guidance or path control) [18,19,20,21]. There is very little indication that additional, larger randomized trials would yield different results. Indeed, Dr. Labruyere and I reach the same conclusion, although our rationales are very different.
Where Dr. Labruyere and I disagree is whether the training parameters that can be manipulated during robotic-assisted training need to be further evaluated to optimize the efficacy of this therapy. Dr. Labruyere cites ample data to suggest that lower limb electromyographic (EMG) activity is modulated by varying training conditions in the device used by Kuo and colleagues (i.e., Lokomat). Manipulation of specific parameters certainly can increase the neuromuscular demands of walking training, which should be the goal of therapy sessions. However, three of the important predictors in the Kuo study, including body-weight support, speed, and number of days to attend 12 sessions, are unrelated to the exoskeletal device. Importantly, the robotic exoskeletal device primarily provides compliant assistance for limb swing, with some stance-phase assistance . While critical for successful ambulation, limb swing represents a relatively minor fraction of the energetic demands of upright ambulation (10%) . Rather, propulsion, stance control, and postural stability encompass a much larger percentage of the metabolic costs of walking [24,25,26,27,28]. Unfortunately, the Lokomat, which is a stationary exoskeletal device, does not provide compliant assistance for these biomechanical demands, or in selected cases restricts them such that volitional effort may be reduced. For example, manipulating body weight support modulates the neuromuscular demands of extensor muscles for stance control [25, 29], with minor contributions from the exoskeleton, although such support is manipulated separately from the robotic device. Gait speed typically requires alterations in propulsive forces during normal treadmill walking [30, 31] and also can be manipulated separately, although the anterior-posterior pelvic restraint of the exoskeleton limits the need to generate those forces. The neuromuscular demands required to maintain lateral postural stability are also minimized in the Lokomat with the lateral restraints. The combined restrictions in movements can substantially reduce the metabolic costs observed during standing in the robotic device as compared to standing without the exoskeleton . During walking, previous data suggest that metabolic costs are reduced even more dramatically in the Lokomat as compared to treadmill walking without physical assistance or therapist assistance only as needed [6, 22, 32]. Reducing the guidance force for limb swing using more advanced training algorithms does not substantially alter the metabolic costs of walking , particularly as compared to unassisted conditions. Unfortunately, these findings are consistent during robotic-assisted stepping using an elliptical device . Given the important role of increasing energetic demands during walking training, specifically with attempts to achieve higher cardiovascular intensities [13, 34, 35], reduced metabolic costs during robotic-assisted walking is likely a major factor limiting the efficacy of this strategy, and fine-tuning the robotic training parameters will likely not influence outcomes.
Dr. Labruyere’s overarching desire to optimize training parameters is important though, as he suggest researchers should direct efforts towards identifying the variables that will increase volitional neuromuscular demands. The primary difference in our opinions is how to achieve those higher neuromuscular, and subsequently cardiovascular, demands. One strategy would be to encourage participants to work as hard as they can during robotic-assisted training, which was previously shown to result in equivalent metabolic demands early during a 10-min walking bout as compared to walking with therapist assistance-as-needed . In that study, however, abnormal muscle patterns were observed during the swing-phase of the gait cycle, and the metabolic costs gradually reduced over the 10-min bout [6, 22]; these data support the hypotheses of “slacking” [36, 37] or the “principle of laziness”  in which individuals reduce volitional effort when it is not required. Alternatively, therapists could remove patients from the robotic device but continue to focus on walking, particularly at higher cardiovascular demands. This latter strategy seems to improve walking outcomes fairly consistently across ambulatory or non-ambulatory individuals with neurological injury [13, 34, 35].
Given these limitations, the value of robotic-assisted training in the rehabilitation of patients with neurological injury is questionable, particularly when compared to walking training with therapist assistance only as needed. When attempting to mobilize patients with substantial disability, the number of therapists could certainly be reduced with robotic assistance. However, given the exorbitant purchase and maintenance fees of these devices, their value is unclear, particularly when less expensive, elastic devices could also reduce therapist’s exertion [23,24,25] A common counterargument is that robotic devices control kinematic trajectories, which was previously thought to be important for retraining ambulation . However, data from therapist-assisted training studies focused on normalizing kinematics [40, 41], or exoskeletal-assisted training studies [42, 43], suggest gains in gait function and kinematics were not superior to, or worse than, walking training strategies that do not focus on kinematics. Indeed, recent randomized trials [44, 45] and implementation efforts [35, 46, 47] in severely impaired patients post-stroke indicate that attempts to maximize the amount of stepping practice at higher cardiovascular intensities without focusing on kinematics results in significant gains in mobility outcomes and gait kinematics. If available, robotic-assisted devices may be helpful very early in the recovery process for individuals with substantial disability, although efforts should be made to remove the patients from the device as rapidly as possible to maximize volitional engagement .
Additional studies that evaluate techniques to enhance locomotor function for individuals with neurological injury are certainly needed to advance our field, but these studies should not use the same robotic tools that have not shown a clear benefit after 20 years of evaluation. We certainly learned valuable lessons from robotic-assisted training studies of what can improve walking, such as providing large amounts of task-specific (i.e., stepping) practice. We also learned valuable lessons from these tools of what not to do, including providing assistance to normalize kinematics and keeping cardiovascular intensities low. While most studies have focused on stationary robotic devices, the available data supporting the utility of mobile robotic devices are similarly inconsistent.7,12,49−53. As such, a new generation of robotic devices are needed for our field to move forward. Such devices need to be far more agile, including the ability to seamlessly don or doff these devices to allow independent ambulation and the capacity to navigate real-world environmental barriers while maintaining postural stability. Such devices would transform the landscape of mobility options used in the home and community setting, rather than utilized primarily as therapeutic tools. Unfortunately, such devices are not currently available and after two decades of evaluation of robotic-assisted training strategies, it’s time to rethink the tools in our toolbox. We need different tools.
Availability of data and materials
Kuo CY, Liu CW, Lai CH, Kang JH, Tseng SH, Su EC. Prediction of robotic neurorehabilitation functional ambulatory outcome in patients with neurological disorders. J Neuroeng Rehabil. 2021;18(1):174.
Mehrholz J, Thomas S, Kugler J, Pohl M, Elsner B. Electromechanical-assisted training for walking after stroke. Cochrane Database Syst Rev. 2020;10:CD006185.
Pohl M, Werner C, Holzgraefe M, et al. Repetitive locomotor training and physiotherapy improve walking and basic activities of daily living after stroke: a single-blind, randomized multicentre trial (DEutsche GAngtrainerStudie, DEGAS). Clin Rehabil. 2007;21(1):17–27.
Schwartz I, Sajin A, Fisher I, et al. The effectiveness of locomotor therapy using robotic-assisted gait training in subacute stroke patients: a randomized controlled trial. PMR J Injury Funct Rehabil. 2009;1(6):516–23.
Hornby TG, Moore JL, Lovell L, Roth EJ. Influence of skill and exercise training parameters on locomotor recovery during stroke rehabilitation. Curr Opin Neurol. 2016;29(6):677–83.
Hornby TG, Straube DS, Kinnaird CR, et al. Importance of specificity, amount, and intensity of locomotor training to improve ambulatory function in patients poststroke. Topics Stroke Rehabil. 2011;18(4):293–307.
Buesing C, Fisch G, O’Donnell M, et al. Effects of a wearable exoskeleton stride management assist system (SMA(R)) on spatiotemporal gait characteristics in individuals after stroke: a randomized controlled trial. J Neuroeng Rehabil. 2015;12:69.
Westlake KP, Patten C. Pilot study of Lokomat versus manual-assisted treadmill training for locomotor recovery post-stroke. J Neuroeng Rehabil. 2009;6:18.
Esquenazi A, Lee S, Packel AT, Braitman L. A randomized comparative study of manually assisted versus robotic-assisted body weight supported treadmill training in persons with a traumatic brain injury. PMR J Injury Funct Rehabil. 2013;5(4):280–90.
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 J Cereb Circul. 2007;38(2):349–54.
Edwards DJ, Forrest G, Cortes M, et al. Walking improvement in chronic incomplete spinal cord injury with exoskeleton robotic training (WISE): a randomized controlled trial. Spinal Cord. 2022.
Jayaraman A, O’Brien MK, Madhavan S, et al. Stride management assist exoskeleton vs functional gait training in stroke: A randomized trial. Neurology. 2019;92(3):e263–73.
Hornby TG, Reisman DS, Ward IG, et al. Clinical Practice Guideline to improve locomotor function following chronic stroke, incomplete spinal cord injury, and brain injury. J Neurolog Phys Ther JNPT. 2020;44(1):49–100.
Calafiore D, Negrini F, Tottoli N, Ferraro F, Ozyemisci-Taskiran O, de Sire A. Efficacy of robotic exoskeleton for gait rehabilitation in patients with subacute stroke: a systematic review. Eur J Phys Rehabil Med. 2022;58(1):1–8.
Hornby TG, Campbell DD, Kahn JH, Demott T, Moore JL, Roth HR. Enhanced gait-related improvements after therapist- versus robotic-assisted locomotor training in subjects with chronic stroke: a randomized controlled study. Stroke J Cereb Circul. 2008;39(6):1786–92.
Hidler J, Nichols D, Pelliccio M, et al. Multicenter randomized clinical trial evaluating the effectiveness of the Lokomat in subacute stroke. Neurorehabil Neural Repair. 2009;23(1):5–13.
Field-Fote EC, Roach KE. Influence of a locomotor training approach on walking speed and distance in people with chronic spinal cord injury: a randomized clinical trial. Phys Thera. 2011;91(1):48–60.
Bergmann J, Krewer C, Jahn K, Muller F. Robot-assisted gait training to reduce pusher behavior: a randomized controlled trial. Neurology. 2018;91(14):e1319–27.
Han EY, Im SH, Kim BR, Seo MJ, Kim MO. Robot-assisted gait training improves brachial-ankle pulse wave velocity and peak aerobic capacity in subacute stroke patients with totally dependent ambulation: randomized controlled trial. Medicine (Baltimore). 2016;95(41):e5078.
van Nunen MP, Gerrits KH, Konijnenbelt M, Janssen TW, de Haan A. Recovery of walking ability using a robotic device in subacute stroke patients: a randomized controlled study. Disabil Rehabil Assist Technol. 2015;10(2):141–8.
Labruyere R, van Hedel HJ. Strength training versus robot-assisted gait training after incomplete spinal cord injury: a randomized pilot study in patients depending on walking assistance. J Neuroeng Rehabil. 2014;11:4.
Israel JF, Campbell DD, Kahn JH, Hornby TG. Metabolic costs and muscle activity patterns during robotic- and therapist-assisted treadmill walking in individuals with incomplete spinal cord injury. Phys Thera. 2006;86(11):1466–78.
Gottschall J, Kram R. Energy cost and muscular activity required for leg swing during walking. J Appl Physiol. 2005;99(1):23–20.
Gottschall J, Kram R. Energy cost and muscular activity required for propulsion during walking. J Appl Physiol. 2003;94(5):1766–72.
Grabowski A, Farley CT, Kram R. Independent metabolic costs of supporting body weight and accelerating body mass during walking. J Appl Physiol. 2005;98(2):579–83.
Donelan JM, Kram R, Kuo AD. Mechanical work for step-to-step transitions is a major determinant of the metabolic cost of human walking. J Exp Biol. 2002;205(Pt 23):3717–27.
Donelan JM, Shipman DW, Kram R, Kuo AD. Mechanical and metabolic requirements for active lateral stabilization in human walking. J Biomech. 2004;37(6):827–35.
Donelan JM, Kram R, Kuo AD. Mechanical and metabolic determinants of the preferred step width in human walking. Proc Biol Sci. 2001;268(1480):1985–92.
Finch L, Barbeau H, Arsenault B. Influence of body weight support on normal human gait: development of a gait retraining strategy. Phys Thera. 1991;71(11):842–55 discussion 855 – 846.
van Hedel HJ, Tomatis L, Muller R. Modulation of leg muscle activity and gait kinematics by walking speed and bodyweight unloading. Gait Posture. 2006;24(1):35–45.
den Otter AR, Geurts AC, Mulder T, Duysens J. Speed related changes in muscle activity from normal to very slow walking speeds. Gait Posture. 2004;19(3):270–8.
Hornby TG, Kinnaird CR, Holleran CL, Rafferty MR, Rodriguez KS, Cain JB. Kinematic, muscular, and metabolic responses during exoskeletal-, elliptical-, or therapist-assisted stepping in people with incomplete spinal cord injury. Phys Thera. 2012;92(10):1278–91.
Lefeber N, De Keersmaecker E, Henderix S, Michielsen M, Kerckhofs E, Swinnen E. Physiological responses and perceived exertion during robot-assisted and body weight-supported gait after stroke. Neurorehabil Neural Repair. 2018;32(12):1043–54.
Hornby TG, Henderson CE, Holleran CL, Lovell L, Roth EJ, Jang JH. Stepwise regression and latent profile analyses of locomotor outcomes poststroke. Stroke J Cereb Circul. 2020;51(10):3074–82.
Moore JL, Nordvik JE, Erichsen A, et al. Implementation of high-intensity stepping training during inpatient stroke rehabilitation improves functional outcomes. Stroke J Cereb Circul. 2020;51(2):563–70.
Reinkensmeyer DJ, Akoner O, Ferris DP, Gordon KE. Slacking by the human motor system: computational models and implications for robotic orthoses. Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:2129–32.
Emken JL, Benitez R, Sideris A, Bobrow JE, Reinkensmeyer DJ. Motor adaptation as a greedy optimization of error and effort. J Neurophysiol. 2007;97(6):3997–4006.
Selinger JC, O’Connor SM, Wong JD, Donelan JM. Humans can continuously optimize energetic cost during walking. Curr Biol. 2015;25(18):2452–6.
Behrman A, Harkema S. Locomotor training after human spinal cord injury: a series of case studies. Phys Thera. 2000;80(7):688–700.
Duncan PW, Sullivan KJ, Behrman AL, et al. Body-weight-supported treadmill rehabilitation after stroke. N Eng J Med. 2011;364(21):2026–36.
Dobkin B, Apple D, Barbeau H, et al. Weight-supported treadmill vs over-ground training for walking after acute incomplete SCI. Neurology. 2006;66(4):484–93.
Lewek MD, Cruz TH, Moore JL, Roth HR, Dhaher YY, Hornby TG. Allowing intralimb kinematic variability during locomotor training poststroke improves kinematic consistency: a subgroup analysis from a randomized clinical trial. Phys Thera. 2009;89(8):829–39.
Nooijen CF, Ter Hoeve N, Field-Fote EC. Gait quality is improved by locomotor training in individuals with SCI regardless of training approach. J Neuroeng Rehabil. 2009;6:36.
Hornby TG, Henderson CE, Plawecki A, et al. Contributions of stepping intensity and variability to mobility in individuals poststroke. Stroke J Cereb Circul. 2019;50(9):2492–9.
Hornby TG, Holleran CL, Hennessy PW, et al. Variable intensive early walking poststroke (VIEWS): a randomized controlled trial. Neurorehabil Neural Repair. 2016;30(5):440–50.
Hornby TG, Holleran CL, Leddy AL, et al. Feasibility of focused stepping practice during inpatient rehabilitation poststroke and potential contributions to mobility outcomes. Neurorehabil Neural Repair. 2015;29(10):923–32.
Henderson CE, Fahey M, Brazg G, Moore JL, Hornby TG. Predicting discharge walking function with high-intensity stepping training during inpatient rehabilitation in nonambulatory patients poststroke. Arch Phys Med Rehabil. 2020.
Hornby T, Zemon DH, Campbell D. Robotic-assisted, body-weight-supported treadmill training in individuals following motor incomplete spinal cord injury. Phys Thera. 2005;85(1):52–66.
Louie DR, Mortenson WB, Durocher M, et al. 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(1):149.
Stein J, Bishop L, Stein DJ, Wong CK. Gait training with a robotic leg brace after stroke: a randomized controlled pilot study. Am J Phys Med Rehabil Assoc Acad Physiatr. 2014;93(11):987–94.
Kawamoto H, Kamibayashi K, Nakata Y, et al. Pilot study of locomotion improvement using hybrid assistive limb in chronic stroke patients. BMC Neurol. 2013;13:141.
Molteni F, Guanziroli E, Goffredo M, et al. Gait recovery with an overground powered exoskeleton: a randomized controlled trial on subacute stroke subjects. Brain Sci. 2021;11(1).
Calabro RS, Naro A, Russo M, et al. Shaping neuroplasticity by using powered exoskeletons in patients with stroke: a randomized clinical trial. J Neuroeng Rehabil. 2018;15(1):35.
TGH is funded by NIDILRR H133P130013, NIH/NINDS-1R01NS118009, and DOD-W81XWH-18-1-0796.
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George Hornby, T. Rethinking the tools in the toolbox. J NeuroEngineering Rehabil 19, 61 (2022). https://doi.org/10.1186/s12984-022-01041-3
- Robotic-assisted gait training