Key components of mechanical work predict outcomes in robotic stroke therapy
| Autor: | Yazan Abdel Majeed, Zachary A. Wright, Felix C. Huang, James L. Patton |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Male
030506 rehabilitation medicine.medical_specialty medicine.medical_treatment Movement Elbow Upper limb Health Informatics Outcomes Models Biological lcsh:RC321-571 Upper Extremity 03 medical and health sciences 0302 clinical medicine Physical medicine and rehabilitation Energetics Medicine Eccentric Humans lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry Stroke Neurorehabilitation Uncategorized Aged Rehabilitation business.industry Research Work (physics) Stroke Rehabilitation Regression analysis Robotics Middle Aged medicine.disease Prognosis medicine.anatomical_structure Treatment Outcome Regression Analysis Female Robotic therapy 0305 other medical science business Energy Metabolism 030217 neurology & neurosurgery |
| Zdroj: | Journal of NeuroEngineering and Rehabilitation Journal of NeuroEngineering and Rehabilitation, Vol 17, Iss 1, Pp 1-12 (2020) |
| ISSN: | 1743-0003 |
| Popis: | Background Clinical 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. Methods Stroke 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. Results Our 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, R2 = 52%). However, the work features were likely mutually correlated, suggesting a prediction model that first removed shared variance (using PCA, R2 = 65–85%). Conclusions These results support robotic training for stroke survivors that increases energetic activity in eccentric shoulder and concentric elbow actions. Trial registration ClinicalTrials.gov, Identifier: NCT02570256. Registered 7 October 2015 – Retrospectively registered |
| Databáze: | OpenAIRE |
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