Trial reportJournal of neuroengineering and rehabilitation2020
Key components of mechanical work predict outcomes in robotic stroke therapy.
Trial report in Journal of neuroengineering and rehabilitation, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02570256 (Error-enhanced Learning & Recovery in 2 & 3 Dimensions), which is not on this map. Cited by 4 papers.
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.
Error-enhanced Learning & Recovery in 2 & 3 Dimensions
Who cites it
4 citing papers in PubMed, 4 citations in OpenAlex.
- Phase-Specific Biomechanical Characterization of Upper Limb Movements in Stroke.Bioengineering (Basel, Switzerland) · 2025Article
- Phase-specific multimodal biomarkers enable explainable assessment of upper limb dysfunction in chronic stroke.Frontiers in neuroscience · 2025Article
- Integrating subject-specific workspace constraint and performance-based control strategy in robot-assisted rehabilitation.Frontiers in neuroscience · 2024Article
- Predicting Clinically Significant Improvement After Robot-Assisted Upper Limb Rehabilitation in Subacute and Chronic Stroke.Frontiers in neurology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundClinical practice typically emphasizes active involvement during therapy. However, traditional approaches can offer only general guidance on the form of involvement that would be most helpful to recovery. Beyond assisting movement, robots allow comprehensive methods for measuring practice behaviors, including the energetic input of the learner. Using data from our previous study of robot-assisted therapy, we examined how separate components of mechanical work contribute to predicting training outcomes.
methodsStroke survivors (n = 11) completed six sessions in two-weeks of upper extremity motor exploration (self-directed movement practice) training with customized forces, while a control group (n = 11) trained without assistance. We employed multiple regression analysis to predict patient outcomes with computed mechanical work as independent variables, including separate features for elbow versus shoulder joints, positive (concentric) and negative (eccentric), flexion and extension.
resultsOur analysis showed that increases in total mechanical work during therapy were positively correlated with our final outcome metric, velocity range. Further analysis revealed that greater amounts of negative work at the shoulder and positive work at the elbow as the most important predictors of recovery (using cross-validated regression, R
conclusionsThese results support robotic training for stroke survivors that increases energetic activity in eccentric shoulder and concentric elbow actions.
trial registrationClinicalTrials.gov, Identifier: NCT02570256. Registered 7 October 2015 - Retrospectively registered.
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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.