Prediction of robotic neurorehabilitation functional ambulatory outcome in patients with neurological disorders

Kuo, Chao-Yang; Liu, Chia-Wei; Lai, Chintu; Kang, Jiunn Horng; Tseng, Sung Hui; Su, Emily Chia-Yu

doi:10.1186/s12984-021-00965-6

Cited by 16 publications

(9 citation statements)

References 38 publications

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“…Given that the Lokomat produces extensive data outputs from each training session, and based on the developments in data analysis, it seems a logical next step to look for patterns in the data already available from the robotic gait trainers. That is precisely what Kuo et al did in their paper, recently published in the Journal of NeuroEngineering and Rehabilitation [ 33 ]. The authors used the Lokomat settings’ data (bodyweight support, gait speed, and robotic assistance) from a cohort of 91 patients from the beginning to predict the functional ambulation categories at the end of the Lokomat intervention.…”

supporting

confidence: 55%

Robot-assisted gait training: more randomized controlled trials are needed! Or maybe not?

Labruyère

2022

J NeuroEngineering Rehabil

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I was encouraged by the recent article by Kuo et al. entitled “Prediction of robotic neurorehabilitation functional ambulatory outcome in patients with neurological disorders” to write an opinion piece on the possible further development of stationary robot-assisted gait training research. Randomized clinical trials investigating stationary gait robots have not shown the superiority of these devices over comparable interventions regarding clinical effectiveness, and there are clinical practice guidelines that even recommend against their use. Nevertheless, these devices are still widely used, and our field needs to find ways to apply these devices more effectively. The authors of the article mentioned above feed different machine learning algorithms with patients’ data from the beginning of a robot-assisted gait training intervention using the robot Lokomat. The output of these algorithms allows predictions of the clinical outcome (i.e., functional ambulation categories) while the patients are still participating in the intervention. Such an analysis based on the collection of the device’s data could optimize the application of these devices. The article provides an example of how our field of research could make progress as we advance, and in this opinion piece, I would like to present my view on the prioritization of upcoming research on robot-assisted gait training. Furthermore, I briefly speculate on some drawbacks of randomized clinical trials in the field of robot-assisted gait training and how the quality and thus the effectiveness of robot-assisted gait training could potentially be improved based on the collection and analysis of clinical training data, a better patient selection and by giving greater weight to the motivational aspects for the participants.

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supporting

confidence: 55%

Robot-assisted gait training: more randomized controlled trials are needed! Or maybe not?

Labruyère

2022

J NeuroEngineering Rehabil

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“…This value is slightly lower than that the 16-18 sessions reported by the Advanced Robotic Therapy Integrated Centers network [20]. The importance of intensity in stroke gait rehabilitation has been elegantly proved by [46], probably increasing the activity of specific muscles during training [47]. Even gait speed can be considered a training parameter that can affects muscle activity [48].…”

Section: Discussionmentioning

confidence: 84%

“…velocity, guidance, body-weight support, heart rate and perceived exertion) should be considered when exploring the dose-response relationship of RAGT training. For example, body weight support seems to influence the progression in ambulatory functional categories [46], probably increasing the activity of specific muscles during training [47]. Even gait speed can be considered a training parameter that can affects muscle activity [48].…”

Section: Discussionmentioning

confidence: 99%

Is robot-assisted gait training intensity a determinant of functional recovery early after stroke? A pragmatic observational study of clinical care

Lissom

Lamberti

Lavezzi

et al. 2022

International Journal of Rehabilitation Research

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Gait rehabilitation is a critical factor in functional recovery after a stroke. The aim of this pragmatic observational study was to identify the optimal dose and timing of robot-assisted gait training (RAGT) that can lead to a favourable outcome in a sample of subacute stroke survivors. Subacute patients with stroke who underwent a RAGT within a multidisciplinary rehabilitation program were enrolled. A set of clinical (i.e. age, type of stroke and time since stroke) and rehabilitation stay outcomes (length of stay and RAGT number of sessions) were recorded to evaluate their impact on functional outcome measures by functional independence measure (FIM) or functional ambulation category (FAC). We included 236 patients (62.73 ± 11.82 year old); 38.44% were females, and 59.32% were ischaemic stroke patients. Patients that received at least 14 RAGT sessions, had 15.83% more chance to be responders compared to those that receive less sessions (P = 0.006). Similarly, younger patients (≤60 years) were more prone to be responders (+15.1%). Lastly, an early rehabilitation (<6 weeks) was found to be more efficient (+21.09%) in determining responsiveness (P < 0.001). Becoming newly independent for gait, that refers to a FAC score ≥4, was related with age and RAGT sessions (P = 0.001). In conclusion, a younger age (≤60 years), an early rehabilitation (<6 weeks since stroke) and a higher RAGT dose (at least 14 sessions) were related to a favourable outcome in patients with subacute stroke.

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“…There is some, albeit limited, evidence from clinical trials that the role of therapy parameters might be important [ 40 , 41 ]. Kuo et al, recently could show in a retrospective study that the trajectory of therapy parameters during RAGT over time correlates with the improvement of walking function [ 42 ].…”

Section: Introductionmentioning

confidence: 99%

Settings matter: a scoping review on parameters in robot-assisted gait therapy identifies the importance of reporting standards

Dellen

Labruyère

2022

J NeuroEngineering Rehabil

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Background Lokomat therapy for gait rehabilitation has become increasingly popular. Most evidence suggests that Lokomat therapy is equally effective as but not superior to standard therapy approaches. One reason might be that the Lokomat parameters to personalize therapy, such as gait speed, body weight support and Guidance Force, are not optimally used. However, there is little evidence available about the influence of Lokomat parameters on the effectiveness of the therapy. Nevertheless, an appropriate reporting of the applied therapy parameters is key to the successful clinical transfer of study results. The aim of this scoping review was therefore to evaluate how the currently available clinical studies report Lokomat parameter settings and map the current literature on Lokomat therapy parameters. Methods and results A systematic literature search was performed in three databases: Pubmed, Scopus and Embase. All primary research articles performing therapy with the Lokomat in neurologic populations in English or German were included. The quality of reporting of all clinical studies was assessed with a framework developed for this particular purpose. We identified 208 studies investigating Lokomat therapy in patients with neurologic diseases. The reporting quality was generally poor. Less than a third of the studies indicate which parameter settings have been applied. The usability of the reporting for a clinical transfer of promising results is therefore limited. Conclusion Although the currently available evidence on Lokomat parameters suggests that therapy parameters might have an influence on the effectiveness, there is currently not enough evidence available to provide detailed recommendations. Nevertheless, clinicians should pay close attention to the reported therapy parameters when translating research findings to their own clinical practice. To this end, we propose that the quality of reporting should be improved and we provide a reporting framework for authors as a quality control before submitting a Lokomat-related article.

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Prediction of robotic neurorehabilitation functional ambulatory outcome in patients with neurological disorders

Cited by 16 publications

References 38 publications

Robot-assisted gait training: more randomized controlled trials are needed! Or maybe not?

Robot-assisted gait training: more randomized controlled trials are needed! Or maybe not?

Is robot-assisted gait training intensity a determinant of functional recovery early after stroke? A pragmatic observational study of clinical care

Settings matter: a scoping review on parameters in robot-assisted gait therapy identifies the importance of reporting standards

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