Robotic Kinematic measures of the arm in chronic Stroke: part 2 - strong correlation with clinical outcome measures.
Correlation
Kinematics
Outcome measures
Robotics
Stroke
tDCS
Journal
Bioelectronic medicine
ISSN: 2332-8886
Titre abrégé: Bioelectron Med
Pays: England
ID NLM: 101660849
Informations de publication
Date de publication:
29 Dec 2021
29 Dec 2021
Historique:
received:
13
10
2021
accepted:
26
11
2021
entrez:
29
12
2021
pubmed:
30
12
2021
medline:
30
12
2021
Statut:
epublish
Résumé
A detailed sensorimotor evaluation is essential in planning effective, individualized therapy post-stroke. Robotic kinematic assay may offer better accuracy and resolution to understand stroke recovery. Here we investigate the added value of distal wrist measurement to a proximal robotic kinematic assay to improve its correlation with clinical upper extremity measures in chronic stroke. Secondly, we compare linear and nonlinear regression models. Data was sourced from a multicenter randomized controlled trial conducted from 2012 to 2016, investigating the combined effect of robotic therapy and transcranial direct current stimulation (tDCS). 24 kinematic metrics were derived from 4 shoulder-elbow tasks and 35 metrics from 3 wrist and forearm evaluation tasks. A correlation-based feature selection was performed, keeping only features substantially correlated with the target attribute (R > 0.5.) Nonlinear models took the form of a multilayer perceptron neural network: one hidden layer and one linear output. Shoulder-elbow metrics showed a significant correlation with the Fugl Meyer Assessment (upper extremity, FMA-UE), with a R = 0.82 (P < 0.001) for the linear model and R = 0.88 (P < 0.001) for the nonlinear model. Similarly, a high correlation was found for wrist kinematics and the FMA-UE (R = 0.91 (P < 0.001) and R = 0.92 (P < 0.001) for the linear and nonlinear model respectively). The combined analysis produced a correlation of R = 0.91 (P < 0.001) for the linear model and R = 0.91 (P < 0.001) for the nonlinear model. Distal wrist kinematics were highly correlated to clinical outcomes, warranting future investigation to explore our nonlinear wrist model with acute or subacute stroke populations. http://www.clinicaltrials.gov . Actual study start date September 2012. First registered on 15 November 2012. Retrospectively registered. Unique identifiers: NCT01726673 and NCT03562663 .
Sections du résumé
BACKGROUND
BACKGROUND
A detailed sensorimotor evaluation is essential in planning effective, individualized therapy post-stroke. Robotic kinematic assay may offer better accuracy and resolution to understand stroke recovery. Here we investigate the added value of distal wrist measurement to a proximal robotic kinematic assay to improve its correlation with clinical upper extremity measures in chronic stroke. Secondly, we compare linear and nonlinear regression models.
METHODS
METHODS
Data was sourced from a multicenter randomized controlled trial conducted from 2012 to 2016, investigating the combined effect of robotic therapy and transcranial direct current stimulation (tDCS). 24 kinematic metrics were derived from 4 shoulder-elbow tasks and 35 metrics from 3 wrist and forearm evaluation tasks. A correlation-based feature selection was performed, keeping only features substantially correlated with the target attribute (R > 0.5.) Nonlinear models took the form of a multilayer perceptron neural network: one hidden layer and one linear output.
RESULTS
RESULTS
Shoulder-elbow metrics showed a significant correlation with the Fugl Meyer Assessment (upper extremity, FMA-UE), with a R = 0.82 (P < 0.001) for the linear model and R = 0.88 (P < 0.001) for the nonlinear model. Similarly, a high correlation was found for wrist kinematics and the FMA-UE (R = 0.91 (P < 0.001) and R = 0.92 (P < 0.001) for the linear and nonlinear model respectively). The combined analysis produced a correlation of R = 0.91 (P < 0.001) for the linear model and R = 0.91 (P < 0.001) for the nonlinear model.
CONCLUSIONS
CONCLUSIONS
Distal wrist kinematics were highly correlated to clinical outcomes, warranting future investigation to explore our nonlinear wrist model with acute or subacute stroke populations.
TRIAL REGISTRATION
BACKGROUND
http://www.clinicaltrials.gov . Actual study start date September 2012. First registered on 15 November 2012. Retrospectively registered. Unique identifiers: NCT01726673 and NCT03562663 .
Identifiants
pubmed: 34963502
doi: 10.1186/s42234-021-00082-8
pii: 10.1186/s42234-021-00082-8
pmc: PMC8715630
doi:
Banques de données
ClinicalTrials.gov
['NCT03562663', 'NCT01726673']
Types de publication
Journal Article
Langues
eng
Pagination
21Subventions
Organisme : NICHD NIH HHS
ID : R01 HD069776
Pays : United States
Organisme : MISTI-Brazil, Massachusetts Institute of Technology
ID : 2160632
Organisme : FAPESP
ID : 2019/06551-5
Organisme : Eunice Kennedy Shriver National Institute of Child Health and Human Development
ID : R01HD069776
Informations de copyright
© 2021. The Author(s).
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