Automated Analysis of the Two-Minute Walk Test in Clinical Practice Using Accelerometer Data

Abstract

One of the core problems for people with multiple sclerosis (pwMS) is the impairment of their ability to walk, which can be severely restrictive in everyday life. Therefore, monitoring of ambulatory function is of great importance to be able to effectively counteract disease progression. An extensive gait analysis, such as the Dresden protocol for multidimensional walking assessment, covers several facets of walking impairment including a 2-min walk test, in which the distance taken by the patient in two minutes is measured by an odometer. Using this approach, it is questionable how precise the measuring methods are at recording the distance traveled. In this project, we investigate whether the current measurement can be replaced by a digital measurement method based on accelerometers (six Opal sensors from the Mobility Lab system) that are attached to the patient's body. We developed two algorithms using these data and compared the validity of these approaches using the results from 2-min walk tests from 562 pwMS that were collected with a gold-standard odometer. In 48.4% of pwMS, we detected an average relative measurement error of less than 5%, while results from 25.8% of the pwMS showed a relative measurement error of up to 10%. The algorithm had difficulties correctly calculating the walking distances in another 25.8% of pwMS; these results showed a measurement error of more than 20%. A main reason for this moderate performance was the variety of pathologically altered gait patterns in pwMS that may complicate the step detection. Overall, both algorithms achieved favorable levels of agreement (r = 0.884 and r = 0.980) with the odometer. Finally, we present suggestions for improvement of the measurement system to be implemented in the future.

Keywords: digital tools and applications; gait analysis; mobility; multiple sclerosis.

Figure 1

Demonstration of the six Mobility Lab sensors (APDM, Portland, OR, United States), both sensors applied to the forefoot serve as the basis for the calculation.

Figure 2

Flowchart for determining the distance walked; ZVU = zero velocity updates.

Figure 3

Total acceleration of one foot with detected resting periods (red).

Figure 4

(a) Velocity curve of a single foot without zero velocity updates; (b) velocity curve of a single foot with zero velocity updates.

Figure 5

Acceleration of one foot with (a) calculated distance without rest detection; (b) calculated distance with rest detection.

Figure 6

Bland–Altman and Pearson’s correlation plots. (A) Bland–Altman plot of the Digiwalk measurement; (B) Pearson correlation of the Digiwalk measurement; (C) Bland–Altman plot of the Mobility Lab algorithm; (D) Pearson correlation of the Mobility Lab algorithm. The red line shows the mean of the differences between the two respective methods, and the dashed green horizontal lines show the upper and lower 95% limits of agreement (=mean ± 1.96 × SD). R = correlation coefficient.

Figure 7

Histogram of the relative frequencies of relative measurement errors for the Digiwalk algorithm (DWA) and the Mobility Lab algorithm (MLA) compared to the current gold-standard odometer.

Figure 8

Representation of the relative measurement errors of the Digiwalk algorithm (DWA) and the Mobility Lab algorithm (MLA) in relation to the influence variables of age (A), Expanded Disability Status Scale (B), and use of aids (C).