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, R
2 = 52%).
However, the work features were likely mutually correlated, suggesting a
prediction model that first removed shared variance (using PCA, R
2 = 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,
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