Contributions
Abstract: P914
Type: Poster
Abstract Category: Clinical aspects of MS - 8 Clinical assessment tools
Background: Motor impairment like gait, balance or coordination disturbances are among the symptoms with highest detrimental effect on quality-of-life in multiple sclerosis (MS). Monitoring of motor function is therefore highly relevant in clinical care and research, but challenging to implement. Instrumental motor assessment has been proposed to increase objectivity, but most current systems are time and cost intensive. We here investigate the use of low-cost gaming sensors along with customized software as a clinically applicable tool for instrumental motor assessment in Patients with Multiple Sclerosis (PwMS) and Patients with neuromyelitis optica (NMO).
Methods: The motor assessment battery PASS-MS consists of 10 simple motor tasks (finger tapping, holding arms, finger-nose test, stand up and sit down, stance with closed feet eyes open-eyes closed (POCO), stance with cognitive dual task, stepping in place (SIP), comfortable speed walk, maximum speed walk and line walk) for recording with Microsoft Kinect V2. Assessments were performed by 80 PwMS, 29 NMO patients and 70 healthy controls. Their potential for the description of motor symptoms in the above diseases is explored by effect sizes from group comparisons.
Results: Interim analysis of an ongoing test series demonstrated good applicability and protocol compliance as well as suitability of a range of kinematic parameters to describe postural imbalance, gait impairment, asymmetry and hypokinesia of limb movement. The typical patient can perform the test within 15 minutes. Preliminary analyses show significant differences between HC and PwMS in performance of e.g. SIP (e.g. knee amplitude p< .001 cohen's d = 0.56), POCO (e.g. sway speed p< .001, cohen's d = -0.66) and walking (e.g. maximum walking speed p< .001, cohen's d = 1.27). NMO showed large differences against HC in e.g. SIP (e.g. knee amplitude p< .001 cohen's d = 1.25) and walking (maximum walking speed p< .001, cohen's d = 1.28). Intraclass correlation coefficient (ICC(1,1)) for immediate repeated measurements show good to excellent repeatability in these parameters (ICC(1,1) between 0.7 and 0.99). Parameters show also correlations to established clinical scores Expanded Disability Status Scale (EDSS) and MS walking scale 12-item (MSWS-12).
Conclusion: Motor function in MS can be reliably and quantitatively assessed with low-cost consumer-grade sensors. Further research has to determine sensitivity to change.
Disclosure: Motognosis is a start-up company from the Charité's laboratory with commercial interest in the described technology. All other authors declare no potential conflict of interest.
Karen Otte: nothing to disclose
Ludwig Rasche: nothing to disclose
Sebastian Mansow-Model: owns shares in Motognosis and is named on several patent applications relevant to the work as inventor
Bastian Kayser: nothing to disclose
Elona Gusho: nothing to disclose
Judith Bellmann-Strobl: nothing to disclose
Friedemann Paul: nothing to disclose
Tanja Schmitz-Hübsch: nothing to disclose
Alexander U Brandt: owns shares in Motognosis and is named on several patent applications relevant to the work as inventor
Abstract: P914
Type: Poster
Abstract Category: Clinical aspects of MS - 8 Clinical assessment tools
Background: Motor impairment like gait, balance or coordination disturbances are among the symptoms with highest detrimental effect on quality-of-life in multiple sclerosis (MS). Monitoring of motor function is therefore highly relevant in clinical care and research, but challenging to implement. Instrumental motor assessment has been proposed to increase objectivity, but most current systems are time and cost intensive. We here investigate the use of low-cost gaming sensors along with customized software as a clinically applicable tool for instrumental motor assessment in Patients with Multiple Sclerosis (PwMS) and Patients with neuromyelitis optica (NMO).
Methods: The motor assessment battery PASS-MS consists of 10 simple motor tasks (finger tapping, holding arms, finger-nose test, stand up and sit down, stance with closed feet eyes open-eyes closed (POCO), stance with cognitive dual task, stepping in place (SIP), comfortable speed walk, maximum speed walk and line walk) for recording with Microsoft Kinect V2. Assessments were performed by 80 PwMS, 29 NMO patients and 70 healthy controls. Their potential for the description of motor symptoms in the above diseases is explored by effect sizes from group comparisons.
Results: Interim analysis of an ongoing test series demonstrated good applicability and protocol compliance as well as suitability of a range of kinematic parameters to describe postural imbalance, gait impairment, asymmetry and hypokinesia of limb movement. The typical patient can perform the test within 15 minutes. Preliminary analyses show significant differences between HC and PwMS in performance of e.g. SIP (e.g. knee amplitude p< .001 cohen's d = 0.56), POCO (e.g. sway speed p< .001, cohen's d = -0.66) and walking (e.g. maximum walking speed p< .001, cohen's d = 1.27). NMO showed large differences against HC in e.g. SIP (e.g. knee amplitude p< .001 cohen's d = 1.25) and walking (maximum walking speed p< .001, cohen's d = 1.28). Intraclass correlation coefficient (ICC(1,1)) for immediate repeated measurements show good to excellent repeatability in these parameters (ICC(1,1) between 0.7 and 0.99). Parameters show also correlations to established clinical scores Expanded Disability Status Scale (EDSS) and MS walking scale 12-item (MSWS-12).
Conclusion: Motor function in MS can be reliably and quantitatively assessed with low-cost consumer-grade sensors. Further research has to determine sensitivity to change.
Disclosure: Motognosis is a start-up company from the Charité's laboratory with commercial interest in the described technology. All other authors declare no potential conflict of interest.
Karen Otte: nothing to disclose
Ludwig Rasche: nothing to disclose
Sebastian Mansow-Model: owns shares in Motognosis and is named on several patent applications relevant to the work as inventor
Bastian Kayser: nothing to disclose
Elona Gusho: nothing to disclose
Judith Bellmann-Strobl: nothing to disclose
Friedemann Paul: nothing to disclose
Tanja Schmitz-Hübsch: nothing to disclose
Alexander U Brandt: owns shares in Motognosis and is named on several patent applications relevant to the work as inventor