Trial reportJournal of neuroengineering and rehabilitation2018
Dissociating motor learning from recovery in exoskeleton training post-stroke.
Trial report in Journal of neuroengineering and rehabilitation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 22 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 55 citations in OpenAlex.
- On the correlation between training modalities and recovery stages in poststroke robotic rehabilitation of the upper limb: a systematic review.Journal of neuroengineering and rehabilitation · 2026Pooled it
- A Systematic Review of the Learning Dynamics of Proprioception Training: Specificity, Acquisition, Retention, and Transfer.Neurorehabilitation and neural repair · 2023Pooled it
- The Efficiency, Efficacy, and Retention of Task Practice in Chronic Stroke.Neurorehabilitation and neural repair · 2020Trial
- Kinematic parameters obtained with the ArmeoSpring for upper-limb assessment after stroke: a reliability and learning effect study for guiding parameter use.Journal of neuroengineering and rehabilitation · 2020Trial
- Signal-Dependent Planning Noise Reduces Task Interference by Assisting in the Formation of Stable Motor Primitives in a Neural Network Model of Muscle Coordination Learning.Neural computation · 2026Article
- Movement Impairments May Not Preclude Visuomotor Adaptation After Stroke.Brain sciences · 2025Article
- Influence of virtual reality and task complexity on digital health metrics assessing upper limb function.Journal of neuroengineering and rehabilitation · 2024Observational
- Is the Combination of Robot-Assisted Therapy and Transcranial Direct Current Stimulation Useful for Upper Limb Motor Recovery? A Systematic Review with Meta-Analysis.Healthcare (Basel, Switzerland) · 2024Review
- Opportunities and challenges in the development of exoskeletons for locomotor assistance.Nature biomedical engineering · 2023Review
- Effect of immersive visualization technologies on cognitive load, motivation, usability, and embodiment.Virtual reality · 2023Article
- Wrist Proprioception in Adults with and without Subacute Stroke.Brain sciences · 2022Article
- Naturalistic visualization of reaching movements using head-mounted displays improves movement quality compared to conventional computer screens and proves high usability.Journal of neuroengineering and rehabilitation · 2022Article
- Assessing Impairments in Visuomotor Adaptation After Stroke.Neurorehabilitation and neural repair · 2022Article
- Towards functional robotic training: motor learning of dynamic tasks is enhanced by haptic rendering but hampered by arm weight support.Journal of neuroengineering and rehabilitation · 2022Article
- Article
- Clinical validation of kinematic assessments of post-stroke upper limb movements with a multi-joint arm exoskeleton.Journal of neuroengineering and rehabilitation · 2021Article
- Providing unloading by exoskeleton improves shoulder flexion performance after stroke.Experimental brain research · 2021Article
- Dissociating Sensorimotor Recovery and Compensation During Exoskeleton Training Following Stroke.Frontiers in human neuroscience · 2021Article
- Technology-aided assessment of functionally relevant sensorimotor impairments in arm and hand of post-stroke individuals.Journal of neuroengineering and rehabilitation · 2020Observational
- Minimizing Precision-Weighted Sensory Prediction Errors via Memory Formation and Switching in Motor Adaptation.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2019Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundA large number of robotic or gravity-supporting devices have been developed for rehabilitation of upper extremity post-stroke. Because these devices continuously monitor performance data during training, they could potentially help to develop predictive models of the effects of motor training on recovery. However, during training with such devices, patients must become adept at using the new "tool" of the exoskeleton, including learning the new forces and visuomotor transformations associated with the device. We thus hypothesized that the changes in performance during extensive training with a passive, gravity-supporting, exoskeleton device (the Armeo Spring) will follow an initial fast phase, due to learning to use the device, and a slower phase that corresponds to reduction in overall arm impairment. Of interest was whether these fast and slow processes were related.
methodsTo test the two-process hypothesis, we used mixed-effect exponential models to identify putative fast and slow changes in smoothness of arm movements during 80 arm reaching tests performed during 20 days of exoskeleton training in 53 individuals with post-acute stroke.
resultsIn line with our hypothesis, we found that double exponential models better fit the changes in smoothness of arm movements than single exponential models. In contrast, single exponential models better fit the data for a group of young healthy control subjects. In addition, in the stroke group, we showed that smoothness correlated with a measure of impairment (the upper extremity Fugl Meyer score - UEFM) at the end, but not at the beginning, of training. Furthermore, the improvement in movement smoothness due to the slow component, but not to the fast component, strongly correlated with the improvement in the UEFM between the beginning and end of training. There was no correlation between the change of peaks due to the fast process and the changes due to the slow process. Finally, the improvement in smoothness due to the slow, but not the fast, component correlated with the number of days since stroke at the onset of training - i.e. participants who started exoskeleton training sooner after stroke improved their smoothness more.
conclusionsOur results therefore demonstrate that at least two processes are involved in in performance improvements measured during mechanized training post-stroke. The fast process is consistent with learning to use the exoskeleton, while the slow process independently reflects the reduction in upper extremity impairment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.