Federico Posteraro1,2, Simona Crea3, Stefano Mazzoleni4,3, Mihai Berteanu5, Ileana Ciobanu6, Nicola Vitiello3,7, Marco Cempini3,8, Sabata Gervasio9, Natalie Mrachacz-Kersting9. 1. Department of Rehabilitation, Versilia Hospital AUSL Toscana Nord Ovest, Camaiore, Lucca, Italy - federico.posteraro@uslnordovest.toscana.it. 2. Bioengineering Rehabilitation Laboratory, Auxilium Vitae Rehabilitation Center, Volterra, Pisa, Italy - federico.posteraro@uslnordovest.toscana.it. 3. The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Pisa, Italy. 4. Bioengineering Rehabilitation Laboratory, Auxilium Vitae Rehabilitation Center, Volterra, Pisa, Italy. 5. University of Medicine and Pharmacy "Carol Davila", Bucharest, Romania. 6. University Hospital Elias, Rehabilitation Research, Bucharest, Romania. 7. Don Carlo Gnocchi Foundaton, Florence, Italy. 8. Shirley Ryan AbilityLab, Chicago, IL, USA. 9. Center for Sensory-Motor Interaction (SMI), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
Abstract
BACKGROUND: Spasticity is a muscle disorder associated with upper motor neuron syndrome occurring in neurological disorders, such as stroke, multiple sclerosis, spinal cord injury and others. It influences the patient's rehabilitation, interfering with function, limiting independence, causing pain and producing secondary impairments, such as contractures or other complications. Due to the heterogeneity of clinical signs of spasticity, there is no agreement on the most appropriate assessment and measurement modality for the evaluation of treatment outcomes. AIM: The aim of this article is to propose the use of new robotic devices for upper-limb spasticity assessment and describe the most relevant measures of spasticity which could be automatically assessed by using a technologically advanced device. DESIGN: Observational pilot study. SETTING: The treatment was provided in a Rehabilitation Centre where the device was located and the subjects were treated in an outpatients setting. POPULATION: Five post-stroke patients, age range 19-79 years (mean age 61, standard deviation [SD]±25) in their chronic phase. METHODS: A new robotic device able to automatically assess upper-limb spasticity during passive and active mobilization has been developed. The elbow spasticity of five post stroke patients has been assessed by using the new device and by means of the Modified Ashworth Scale (MAS). After the first assessment, subjects were treated with botulin toxin injections, and then underwent 10 sessions of robotic treatments. After the treatment, subjects spasticity was assessed by using the robotic device and the MAS Score. RESULTS: In four out of five patients, the botulin toxin injection and robotic treatment resulted in the improvement of the MAS Score; in three patients the robotic measures were able to detect the MAS changes. In one subject botulin toxin was not effective and the robotic device was able to detect the lack of effectiveness. CONCLUSIONS: By using the robotic device some spasticity parameters can be continuously recorded during the rehabilitation treatment in order to objectively measure the effectiveness of the interventions provided. CLINICAL REHABILITATION IMPACT: The standardized evaluation parameters recorded using robotic devices may provide several advantages: 1) the measures for spasticity assessment can be monitored during every rehabilitation session (even during each movement); 2) these measurements are able to highlight even small changes; 3) the recovery plateau can be detected early thus avoiding further rehabilitation sessions; and 4) these measurements can reduce the assessment bias in multicenter studies.
BACKGROUND:Spasticity is a muscle disorder associated with upper motor neuron syndrome occurring in neurological disorders, such as stroke, multiple sclerosis, spinal cord injury and others. It influences the patient's rehabilitation, interfering with function, limiting independence, causing pain and producing secondary impairments, such as contractures or other complications. Due to the heterogeneity of clinical signs of spasticity, there is no agreement on the most appropriate assessment and measurement modality for the evaluation of treatment outcomes. AIM: The aim of this article is to propose the use of new robotic devices for upper-limb spasticity assessment and describe the most relevant measures of spasticity which could be automatically assessed by using a technologically advanced device. DESIGN: Observational pilot study. SETTING: The treatment was provided in a Rehabilitation Centre where the device was located and the subjects were treated in an outpatients setting. POPULATION: Five post-strokepatients, age range 19-79 years (mean age 61, standard deviation [SD]±25) in their chronic phase. METHODS: A new robotic device able to automatically assess upper-limb spasticity during passive and active mobilization has been developed. The elbow spasticity of five post strokepatients has been assessed by using the new device and by means of the Modified Ashworth Scale (MAS). After the first assessment, subjects were treated with botulin toxin injections, and then underwent 10 sessions of robotic treatments. After the treatment, subjects spasticity was assessed by using the robotic device and the MAS Score. RESULTS: In four out of five patients, the botulin toxin injection and robotic treatment resulted in the improvement of the MAS Score; in three patients the robotic measures were able to detect the MAS changes. In one subject botulin toxin was not effective and the robotic device was able to detect the lack of effectiveness. CONCLUSIONS: By using the robotic device some spasticity parameters can be continuously recorded during the rehabilitation treatment in order to objectively measure the effectiveness of the interventions provided. CLINICAL REHABILITATION IMPACT: The standardized evaluation parameters recorded using robotic devices may provide several advantages: 1) the measures for spasticity assessment can be monitored during every rehabilitation session (even during each movement); 2) these measurements are able to highlight even small changes; 3) the recovery plateau can be detected early thus avoiding further rehabilitation sessions; and 4) these measurements can reduce the assessment bias in multicenter studies.