Key components of mechanical work predict outcomes in robotic stroke therapy

Survivors don't fucking care about your prediction crapola. They want protocols that directly lead to recovery.   How is your distribution of protocols going to every one of the 10 million yearly stroke survivors? Since we have fucking failures of stroke associations this distribution is totally on you to figure out.   And you thought just writing this article was enough. The new sheriff in town is changing things. The new president of that great stroke association will enforce new rules. All stroke research will result in stroke rehab protocols delivered to all survivors; past, present and future.

Key components of mechanical work predict outcomes in robotic stroke therapy

• Zachary A. Wright,
• Yazan A. Majeed,
• James L. Patton &
• Felix C. Huang 

Journal of NeuroEngineering and Rehabilitation volume 17, Article number: 53 (2020) Cite this article

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Abstract

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,