To Örebro University

Background: Evaluation of walking capacity and risk of falls in people with multiple sclerosis often are performed in rehabilitation. The Dynamic Gait Index (DGI) evaluates walking during different tasks, but the feasibility in identifying people at risk for falls needs to be further investigated.

Objective: The objective of this study was to investigate (1) the construct validity (known groups, convergent, and discriminant) of the DGI and (2) the accuracy of predicting falls and establishing a cutoff point to identify fallers.

Design: This trial was a multicenter, cross-sectional study.

Methods: A convenience sample was composed of 81 people with multiple sclerosis with subjective gait and balance impairment who were able to walk 100 m (comparable to Expanded Disability Status Scale 1-6). Mean age of the participants was 49 years; 76% were women. The 25-Foot Timed Walk Test, Timed "Up & Go" Test, Four Square Step Test, Timed Sit-to-Stand Test, MS Walking Scale, Multiple Sclerosis Impact Scale, and self-reported falls during the previous 2 months were used for validation, to establish cutoff points for identifying fallers, and to investigate predictive values.

Results: Significantly lower DGI scores (P <=.001) were found for participants reporting falls (n=31). High sensitivity (87%) in identifying falters was found, with a cutoff score <= 19. The positive predictive value was 50%, and the negative predictive value was 87%. The positive likelihood ratio was 1.77, and the negative likelihood ratio was 0.26. The convergent validity was moderate to strong (rho=0.58-0.80), with the highest correlation coefficient found for the 25-Foot Timed Walk Test. Discriminant validity was shown with low correlation for the psychological subscale of the Multiple Sclerosis Impact Scale.

Limitations: The sample included ambulatory people participating in a randomized controlled trial investigating balance training.

Conclusions: The DGI is a valid measure of dynamic balance during walking for ambulatory people with multiple sclerosis. With the cutoff point of <= 19, sensitivity was high in identifying people at risk of falls.