TY - JOUR
T1 - Robot-assisted rehabilitation of hand function after stroke
T2 - Development of prediction models for reference to therapy
AU - Baldan, Francesca
AU - Turolla, Andrea
AU - Rimini, Daniele
AU - Pregnolato, Giorgia
AU - Maistrello, Lorenza
AU - Agostini, Michela
AU - Jakob, Iris
N1 - Publisher Copyright:
© 2021 Elsevier Ltd
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/4
Y1 - 2021/4
N2 - Background: Recovery of hand function after stroke represents the hardest target for clinicians. Robot-assisted therapy has been proved to be effective for hand recovery. Nevertheless, studies aimed to refer patients to the best therapy are missing. Methods: With the aim to identify which clinical features are predictive for referring to robot-assisted hand therapy, 174 stroke patients were assessed with: Fugl-Meyer Assessment (FMA), Functional Independence Measure (FIM), Reaching Performance Scale (RPS), Box and Block Test (BBT), Modified Ashworth Scale (MAS), Nine Hole Pegboard Test (NHPT). Moreover, patients ability to control the robot with residual force and surface EMG (sEMG) independently, was checked. ROC curves were calculated to determine which of the measures were the predictors of the event. Results: sEMG control (AUC = 0.925) was significantly determined by FMA upper extremity (FMUE) (>24/66) and sensation (>23/24) sections, MAS at Flexor Carpi (<3/4) and total MAS (>4/20). Force control (AUC = 0.928) was correlated only with FMUE (>24/66). Conclusions: FMUE and MAS were the best predictors of preserved ability to control the device by two different modalities. This finding opens the possibility to plan specific therapies aimed at maximizing the highest functional outcome achievable after stroke.
AB - Background: Recovery of hand function after stroke represents the hardest target for clinicians. Robot-assisted therapy has been proved to be effective for hand recovery. Nevertheless, studies aimed to refer patients to the best therapy are missing. Methods: With the aim to identify which clinical features are predictive for referring to robot-assisted hand therapy, 174 stroke patients were assessed with: Fugl-Meyer Assessment (FMA), Functional Independence Measure (FIM), Reaching Performance Scale (RPS), Box and Block Test (BBT), Modified Ashworth Scale (MAS), Nine Hole Pegboard Test (NHPT). Moreover, patients ability to control the robot with residual force and surface EMG (sEMG) independently, was checked. ROC curves were calculated to determine which of the measures were the predictors of the event. Results: sEMG control (AUC = 0.925) was significantly determined by FMA upper extremity (FMUE) (>24/66) and sensation (>23/24) sections, MAS at Flexor Carpi (<3/4) and total MAS (>4/20). Force control (AUC = 0.928) was correlated only with FMUE (>24/66). Conclusions: FMUE and MAS were the best predictors of preserved ability to control the device by two different modalities. This finding opens the possibility to plan specific therapies aimed at maximizing the highest functional outcome achievable after stroke.
KW - Prediction
KW - Recovery
KW - Robotic
KW - Stroke
KW - Surface electromyography
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U2 - 10.1016/j.jelekin.2021.102534
DO - 10.1016/j.jelekin.2021.102534
M3 - Article
AN - SCOPUS:85101145310
VL - 57
JO - Journal of Electromyography and Kinesiology
JF - Journal of Electromyography and Kinesiology
SN - 1050-6411
M1 - 102534
ER -