Translation and validation of the multiple sclerosis walking scale 12 for the German population - the MSWS-12/D.

BACKGROUND: Gait impairment is a relevant problem in persons with multiple sclerosis (pwMS). The Multiple Sclerosis Walking Scale 12 (MSWS-12) is a valid Patient Reported Outcome Measure (PROM) to evaluate walking ability in pwMS. The aim of this study was to provide a linguistically valid translation of MSWS-12 into German language (MSWS-12/D) and to evaluate its psychometric properties. METHODS: The MSWS-12 was translated in a process modified from guidelines for the cross-cultural adaption of PROMs, and a pre-test was applied in a small sample of 20 pwMS to evaluate comprehensibility and acceptance. Psychometric properties (floor and ceiling effects, internal consistency, construct validity) were then assessed in 124 pwMS seen at academic MS centers. Construct validity was evaluated against Expanded Disability Status Scale (EDSS) and maximum gait speed in the Timed 25-Foot Walk (T25FW). RESULTS: Although the sample covered a wide spectrum of symptom severity, the majority had rather low levels of disability (EDSS median 2.0) and 6.5% scored EDSS of 0. In this sample, MSWS-12/D showed floor effects (36% with score 0) and for internal consistency, a Cronbach's alpha of 0.98 was calculated. MSWS-12/D score showed a relevant correlation to EDSS (ρ = 0.73) and T25FW speed (r=-0.72). CONCLUSION: We provide MSWS-12/D as a linguistically valid German version of MSWS-12. Psychometric properties (acceptance, floor and ceiling effects, internal consistency and construct validity) in pwMS were similar to those described for the original version. This indicates that MSWS-12/D can be applied as equivalent to the original version in German speaking pwMS. Results support the relevance of PROMs to capture patient perception of walking ability in addition to performance-based assessments such as maximum walking speed or maximum walking distance.

Proposal for Post Hoc Quality Control in Instrumented Motion Analysis Using Markerless Motion Capture: Development and Usability Study.

BACKGROUND: Instrumented assessment of motor symptoms has emerged as a promising extension to the clinical assessment of several movement disorders. The use of mobile and inexpensive technologies such as some markerless motion capture technologies is especially promising for large-scale application but has not transitioned into clinical routine to date. A crucial step on this path is to implement standardized, clinically applicable tools that identify and control for quality concerns. OBJECTIVE: The main goal of this study comprises the development of a systematic quality control (QC) procedure for data collected with markerless motion capture technology and its experimental implementation to identify specific quality concerns and thereby rate the usability of recordings. METHODS: We developed a post hoc QC pipeline that was evaluated using a large set of short motor task recordings of healthy controls (2010 recordings from 162 subjects) and people with multiple sclerosis (2682 recordings from 187 subjects). For each of these recordings, 2 raters independently applied the pipeline. They provided overall usability decisions and identified technical and performance-related quality concerns, which yielded respective proportions of their occurrence as a main result. RESULTS: The approach developed here has proven user-friendly and applicable on a large scale. Raters' decisions on recording usability were concordant in 71.5%-92.3% of cases, depending on the motor task. Furthermore, 39.6%-85.1% of recordings were concordantly rated as being of satisfactory quality whereas in 5.0%-26.3%, both raters agreed to discard the recording. CONCLUSIONS: We present a QC pipeline that seems feasible and useful for instant quality screening in the clinical setting. Results confirm the need of QC despite using standard test setups, testing protocols, and operator training for the employed system and by extension, for other task-based motor assessment technologies. Results of the QC process can be used to clean existing data sets, optimize quality assurance measures, as well as foster the development of automated QC approaches and therefore improve the overall reliability of kinematic data sets.