Spasticity measurement based on tonic stretch reflex threshold in stroke using a portable device

Calota, Andra; Feldman, Anatol G.; Levin, Mindy F.

doi:10.1016/j.clinph.2008.07.215

Cited by 126 publications

(173 citation statements)

References 31 publications

Supporting

Mentioning

151

Contrasting

Unclassified

Order By: Relevance

“…Following this method, the level of spasticity is higher for lower TSRT values, indicating a lower freedom of limb movements. On the other hand, a higher value of TSRT indicates a superior freedom of motion, corresponding to a lower level of spasticity [10]. In the case of healthy individuals, both the regression line and the TSRT do not coincide with the biomechanical range, providing full freedom of movement [19].…”

Section: Velocity Dependent Approachmentioning

confidence: 99%

“…Currently, the modified Ashworth scale remains the reference method for clinical assessment of spasticity [10]. This scale assesses the resistance to passive muscle stretch as the expression of muscle tone.…”

Section: Methods For Spasticity Quantificationmentioning

confidence: 99%

“…The velocity of the movement derives from the digital signal recorded by the electrogoniometer. Simultaneously, the muscle activity is recorded by sEMG, with surface sensors placed at the muscle surface, in order to observe the presence of an SR [10,19]. The velocity of the muscle stretch at which an SR response is evoked defines each SRT.…”

Section: Methodsmentioning

confidence: 99%

“…In summary, the quantification of spasticity is based on the physiological process, according to the definition proposed by Lance, taking into account the physiological mechanism of motor control in healthy subjects and registering the possible changes in these mechanisms [10,19,21].…”

Section: Velocity Dependent Approachmentioning

confidence: 99%

“…The DSRT distribution is placed in a 2D orthogonal graphic with the angle in the horizontal axis versus angular velocity in the vertical axis. The regression line is then computed and the TSRT value is estimated (assumed at the point where the regression line intersects the horizontal axis, corresponding to a stretch velocity equal to zero [10,19]). In this analysis the angle is considered as the dependent variable and the angular velocity as the independent variable.…”

Section: Experimental Set-upmentioning

confidence: 99%

See 4 more Smart Citations

Development, Test and Validation of a Mechatronic Device for Spasticity Quantification

Ferreira

Moreira

Lima

et al. 2013

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

Spasticity is a common pathological phenomenon in clinical practices, frequently expressed as a stroke, multiple sclerosis or cerebral palsy. The accurate quantification of spasticity allows early action to prevent the potentially irreversible consequences. The aim of this work is to develop and implement a mechatronic device for the accurate quantification of spasticity, which is able to differentiate spasticity from other motor disorders. The proposed method is based on the quantification of the tonic stretch reflex threshold (TSRT) for the assessment of the range of motion of the limb affected by spasticity. In order to validate the developed device, experimental trials have been conducted, considering the flexor muscle of the elbow joint. The developed device was tested, first in a laboratory environment with healthy subjects and secondly, in a clinical environment with the collaboration of patients with spasticity. The evaluations in th clinical environment were performed on three different days in order to evaluate the reproducibility of the obtained results. The experimental trials confirm the sensibility, reproducibility and reliability of spasticity quantification based on the TSRT method. This project has been developed in cooperation with the Hospital of Braga and Fisimaia rehabilitation clinic, both in Portugal.

show abstract

Section: Velocity Dependent Approachmentioning

confidence: 99%

Section: Methods For Spasticity Quantificationmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Velocity Dependent Approachmentioning

confidence: 99%

Section: Experimental Set-upmentioning

confidence: 99%

See 3 more Smart Citations

Development, Test and Validation of a Mechatronic Device for Spasticity Quantification

Ferreira

Moreira

Lima

et al. 2013

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

show abstract

Increased muscle belly and tendon stiffness in patients with Parkinson's disease, as measured by myotonometry

et al. 2011

View full text Add to dashboard Cite

show abstract

Predicting post‐stroke motor recovery of upper extremity using clinical variables and performance assays: A prospective cohort study protocol

Sardesai

Arumugam

et al. 2022

Physiotherapy Res Intl

View full text Add to dashboard Cite

Background and Purpose: Measurement of movement quality is essential to distinguish motor recovery patterns and optimize rehabilitation strategies poststroke. Recently, the Stroke Recovery and Rehabilitation Roundtable Taskforce (SRRR) recommended four kinetic and kinematic performance assays to measure upper extremity (UE) movements and distinguish behavioral restitution and compensation mechanisms early post-stroke. The purpose of this study is to develop and validate a prediction model to analyze the added prognostic value of performance assays over clinical variables assessed up to 1-month post stroke for predicting recovery of UE motor impairment, capacity and quality of movement (QoM) measured at 3 months post-stroke onset. Methods:In this prospective cohort study, 120 stroke survivors will be recruited within seven days post-stroke. Candidate predictors such as baseline characteristics, demographics and performance assays as per SRRR recommendations along with tonic stretch reflex threshold will be measured up to 1-month post-stroke.Upper extremity motor recovery will be evaluated in terms of motor impairment (Fugl-Meyer assessment for UE), UE capacity measured with Action Research Arm Test (ARAT) and QoM (movement smoothness in the form of peak metrics [PM]) assessed with a reach-to-grasp-to-mouth task (mimicking a drinking task) at 3 months post-stroke. Three multivariable linear regression models will be developed to predict factors responsible for the outcomes of Fugl-Meyer assessment for upper extremity (FM-UE), ARAT and movement quality. The developed models will be internally validated using a split-sample method.Discussion: This study will provide a validated prediction model inclusive of clinical and performance assays that may assist in prediction of UE motor recovery. Predicting the amount of recovery and differentiating between behavioral restitution and compensation (as reflected by the FM-UE, QoM and ARAT) would enable us in realistic goal formation and planning rehabilitation. It would also help in encouraging patients to partake in early post-stroke rehabilitation thus improving the recovery potential.

show abstract

Spasticity measurement based on tonic stretch reflex threshold in stroke using a portable device

Cited by 126 publications

References 31 publications

Development, Test and Validation of a Mechatronic Device for Spasticity Quantification

Development, Test and Validation of a Mechatronic Device for Spasticity Quantification

Increased muscle belly and tendon stiffness in patients with Parkinson's disease, as measured by myotonometry

Predicting post‐stroke motor recovery of upper extremity using clinical variables and performance assays: A prospective cohort study protocol

Contact Info

Product

Resources

About