Relationship of Turning Parameters in Longitudinal Change of Balance Confidence, Walking Limitation, and Disability in Multiple Sclerosis

Background: Gait velocity can assess cross-sectional and longitudinal changes in walking limitations and clinical disability in persons with multiple sclerosis (pwMS). Peak turn velocity (PTV) was previously identified as an independent predictive measure of self-reported balance confidence and walking limitation in a cross-sectional analysis.  Longitudinal change of turning parameters has not been evaluated as a measure of longitudinal changes in balance confidence, walking limitation, and clinical disability in pwMS. 

Objectives: (1) Determine whether percent change (%D) in PTV and turn number of steps (TNS) during mobility tasks adds predictive power to %D in stride velocity (SV%D) in the modeling of %D in self-perceived balance confidence and walking limitation in pwMS. (2) Determine whether TUG SV%D or 6MWT SV%D better models longitudinal changes in EDSS (DEDSS).

Methods: 24 subjects (EDSS 1.0 – 6.5) performed the TUG and 6MWT at two clinic visits 12-18 months apart. Spatiotemporal gait analysis was conducted using APDM Opal wireless body-worn sensors.  %D from the initial visit to the follow-up visit was calculated for PTV and TNS (PTV%D and TNS%D). DEDSS was calculated by subtracting the follow-up EDSS score from the initial EDSS score.  Step-wise regression analyses were conducted independently for the TUG and 6MWT to determine the addition of PTV%D and TNS%D to SV%D in the prediction of percent change in balance confidence (Activities-Specific Balance Confidence Scale (ABC%D)) and walking limitation (12-item Multiple Sclerosis Walking Scale (MSWS%D)). Linear regression analysis was performed to independently evaluate modeling power of TUG SV%D and 6MWT SV%D in the prediction of DEDSS. 

Results: ABC%D and MSWS%D were moderately correlated (r = -0.60, p < 0.01). When 6MWT TNS%D was added to 6MWT SV%D, an increase in modeling power in the prediction of ABC%D was found (R² = 0.24 increased to R² = 0.33, p < 0.05). Similarly, a significant increase in modeling power in the prediction of MSWS%D was found when 6MWT PTV%D was added to 6MWT SV%D (R² = 0.08 increased to R² = 0.27, p < 0.05). 6MWT SV%D (R² = 0.28) modeled DEDSS significantly better (p < 0.001) than TUG SV%D (R²= 0.02).

Conclusions: Changes in turn parameters add quantifiable and objective information to the changes that patients experience with ambulation and balance. Longitudinal changes in the EDSS score are better modeled by straight-line gait velocity in a longer duration test.