Reliability of gait and dual-task measures in multiple sclerosis

Reliability of an Instrumented Pressure Walkway for Measuring Walking and Running Characteristics in Young, Athletic Individuals

Background

Spatiotemporal parameters of gait are useful for identifying pathological gait patterns and presence of impairments. Reliability of the pressure-sensitive ZenoTM Walkway has not been established in young, active individuals without impairments, and no studies to this point have included running.

Purpose

The purposes of this study were to 1) determine if up to two additional trials of walking and running on the ZenoTM Walkway are needed to produce consistent measurements of spatiotemporal variables, and 2) establish test-retest reliability and minimal detectable change (MDC) values for common spatiotemporal variables measured during walking and running.

Study Design

Cross-Sectional Laboratory Study.

Methods

Individuals (n=38) in this cross-sectional study walked and ran at self-selected comfortable speed on a pressure-sensitive ZenoTM Walkway. Twenty-one participants returned for follow-up testing between one and 14 days later. Intraclass correlation coefficients (ICCs) were used to assess reliability of spatiotemporal variable means using three, four, or five passes over the ZenoTM Walkway and to assess test-retest reliability of spatiotemporal variables across sessions.

Results

All variables showed excellent reliability (ICC > 0.995) for walking and running when measured using three, four, or five passes. Additionally, all variables demonstrated moderate to excellent test-retest reliability during walking (ICC: 0.732-0.982) and running (ICC: 0.679-0.985).

Conclusion

This study establishes a reliable measurement protocol of three one-way passes when using the ZenoTM Walkway for walking or running analysis. This is the first study to establish reliability of the ZenoTM Walkway during running and in young, active individuals without neuromusculoskeletal pathology.

Level of Evidence

3b.

How Reliable and Valid are Dual-Task Cost Metrics? A Meta-analysis of Locomotor-Cognitive Dual-Task Paradigms

OBJECTIVE

To assess the retest reliability, predictive validity, and concurrent validity of locomotor and cognitive dual-task cost (DTC) metrics derived from locomotor-cognitive dual-task paradigms.

DATA SOURCES

A literature search of electronic databases (PubMed, PsycINFO, MEDLINE, CINAHL, and Scopus) was conducted on May 29th, 2021, without time restriction.

STUDY SELECTION

For 1559 search results, titles and abstracts were screened by a single reviewer and full text of potentially eligible papers was considered by 2 independent reviewers. 25 studies that evaluated retest reliability, predictive validity, and concurrent validity of locomotor-cognitive DTC in healthy and clinical groups met inclusion criteria.

DATA EXTRACTION

Study quality was assessed using the Consensus-Based Standards for the Selection of Health Measurement Instrument checklist. Data relating to the retest reliability, predictive validity, and concurrent validity of DTC were extracted.

DATA SYNTHESIS

Meta-analysis showed that locomotor DTC metrics (intraclass correlation coefficient [ICC]=0.61, 95% confidence interval [CI; 0.53.0.70]) had better retest reliability than cognitive DTC metrics (ICC=0.27, 95% CI [0.17.0.36]). Larger retest reliability estimates were found for temporal gait outcomes (ICC=0.67-0.72) compared with spatial (ICC=0.34-0.53). Motor DTC metrics showed weak predictive validity for the incidence of future falls (r=0.14, 95% CI [-0.03.0.31]). Motor DTC metrics had weak concurrent validity with other clinical and performance assessments (r=0.11, 95% CI [0.07.0.16]), as did cognitive DTC metrics (r=0.19, 95% CI [0.08.0.30]).

CONCLUSIONS

Gait-related temporal DTC metrics achieve adequate retest reliability, while predictive and concurrent validity of DTC needs to be improved before being used widely in clinical practice and other applied settings. Future research should ensure the reliability and validity of DTC outcomes before being used to assess dual-task interference.

Machine learning corroborates subjective ratings of walking and balance difficulty in multiple sclerosis

Machine learning can discern meaningful information from large datasets. Applying machine learning techniques to raw sensor data from instrumented walkways could automatically detect subtle changes in walking and balance. Multiple sclerosis (MS) is a neurological disorder in which patients report varying degrees of walking and balance disruption. This study aimed to determine whether machine learning applied to walkway sensor data could classify severity of self-reported symptoms in MS patients. Ambulatory people with MS (n = 107) were asked to rate the severity of their walking and balance difficulties, from 1-No problems to 5-Extreme problems, using the MS-Impact Scale-29. Those who scored less than 3 (moderately) were assigned to the “mild” group (n = 35), and those scoring higher were in the “moderate” group (n = 72). Three machine learning algorithms were applied to classify the “mild” group from the “moderate” group. The classification achieved 78% accuracy, a precision of 85%, a recall of 90%, and an F1 score of 87% for distinguishing those people reporting mild from moderate walking and balance difficulty. This study demonstrates that machine learning models can reliably be applied to instrumented walkway data and distinguish severity of self-reported impairment in people with MS.

The reliability of gait parameters captured via instrumented walkways: a systematic review and meta-analysis

INTRODUCTION

Electronic pressure-sensitive walkways are commonly available solutions to quantitatively assess gait parameters for clinical and research purposes. Many studies have evaluated their measurement properties in different conditions with variable findings. In order to be informed about the current evidence of their reliability for optimal clinical and scientific decision making, this systematic review provided a quantitative synthesis of the test-retest reliability and minimal detectable change of the captured gait parameters across different test conditions (single and cognitive dual-task conditions) and population groups.

EVIDENCE ACQUISITION

A literature search was conducted in PubMed, Embase, and Scopus until November 2021 to identify articles that examined the test-retest reliability properties of the gait parameters captured by pressure-sensitive walkways (gait speed, cadence, stride length and time, double support time, base of support) in adult healthy individuals or patients. The methodological quality was rated using the Consensus-Based Standards for the Selection of Health Measurement Instruments Checklist. Data were meta-analyzed on intraclass correlation coefficient to examine the test-retest relative reliability. Quantitative synthesis was performed for absolute reliability, examined by the weighted average of minimal detectable change values.

EVIDENCE SYNTHESIS

A total of 44 studies were included in this systematic review. The methodological quality was adequate in half of the included studies. The main finding was that pressure-sensitive walkways are reliable tools for objective assessment of spatial and temporal gait parameters both in single-and cognitive dual-task conditions. Despite few exceptions, the review identified intraclass correlation coefficient higher than 0.75 and minimal detectable change lower than 30%, demonstrating satisfactory relative and absolute reliability in all examined populations (healthy adults, elderly, patients with cognitive impairment, spinocerebellar ataxia type 14, Huntington's disease, multiple sclerosis, Parkinson's disease, rheumatoid arthritis, spinal cord injury, stroke or vestibular dysfunction).

CONCLUSIONS

Current evidence suggested that, despite different populations and testing protocols used in the included studies, the test-retest reliability of the examined gait parameters was acceptable under single and cognitive dual-task conditions. Further high-quality studies with powered sample sizes are needed to examine the reliability findings of the currently understudied and unexplored pathologies and test conditions.

Machine learning classification of multiple sclerosis patients based on raw data from an instrumented walkway

Background

Using embedded sensors, instrumented walkways provide clinicians with important information regarding gait disturbances. However, because raw data are summarized into standard gait variables, there may be some salient features and patterns that are ignored. Multiple sclerosis (MS) is an inflammatory neurodegenerative disease which predominantly impacts young to middle-aged adults. People with MS may experience varying degrees of gait impairments, making it a reasonable model to test contemporary machine leaning algorithms. In this study, we employ machine learning techniques applied to raw walkway data to discern MS patients from healthy controls. We achieve this goal by constructing a range of new features which supplement standard parameters to improve machine learning model performance.

Results

Eleven variables from the standard gait feature set achieved the highest accuracy of 81%, precision of 95%, recall of 81%, and F1-score of 87%, using support vector machine (SVM). The inclusion of the novel features (toe direction, hull area, base of support area, foot length, foot width and foot area) increased classification accuracy by 7%, recall by 9%, and F1-score by 6%.

Conclusions

The use of an instrumented walkway can generate rich data that is generally unseen by clinicians and researchers. Machine learning applied to standard gait variables can discern MS patients from healthy controls with excellent accuracy. Noteworthy, classifications are made stronger by including novel gait features (toe direction, hull area, base of support area, foot length and foot area).

Dual-Task Cost and Related Clinical Features in Patients With Multiple Sclerosis

This study aimed to investigate the dual-task cost of both motor and cognitive performances in patients with multiple sclerosis (PwMS) and in healthy controls and to determine their relationships with clinical features in PwMS. The participants performed motor tasks (postural stability, walking, and manual dexterity) and cognitive tasks (mental tracking and verbal fluency) under single- and dual-task conditions. The results showed that postural stability under dual-task conditions did not change, whereas walking and manual dexterity deteriorated, regardless of the concurrent cognitive task, in PwMS (median Expanded Disability Status Scale score: 1) and the healthy controls. Verbal fluency decreased during postural stability, whereas it increased during walking, and it was maintained during manual dexterity in both groups. Mental tracking did not change during walking; it declined during manual dexterity in both groups. Mental tracking during postural stability deteriorated in PwMS, while it did not change in the healthy controls. In general, dual-task costs were associated with baseline performances of tasks rather than clinical features. Therefore, baseline performances of both tasks should be increased for improving dual-task performance in PwMS.

Foot-Worn Inertial Sensors Are Reliable to Assess Spatiotemporal Gait Parameters in Axial Spondyloarthritis under Single and Dual Task Walking in Axial Spondyloarthritis

The aim of this study was (1) to evaluate the relative and absolute reliability of gait parameters during walking in single- and dual-task conditions in patients with axial spondyloarthritis (axSpA), (2) to evaluate the absolute and relative reliability of dual task effects (DTE) parameters, and (3) to determine the number of trials required to ensure reliable gait assessment, in patients with axSpA. Twenty patients with axSpa performed a 10-m walk test in single- and dual-task conditions, three times for each condition. Spatiotemporal, symmetry, and DTE gait parameters were calculated from foot-worn inertial sensors. The relative reliability (intraclass correlation coefficients-ICC) and absolute reliability (standard error of measurement-SEM and minimum detectable change-MDC) were calculated for these parameters in each condition. Spatiotemporal gait parameters showed good to excellent reliability in both conditions (0.59 < ICC < 0.90). The reliability of symmetry and DTE parameters was low. ICC, SEM, and MDC were better when using the mean of the second and the third trials. Spatiotemporal gait parameters obtained from foot-worn inertial sensors assessed in patients with axSpA in single- and dual-task conditions are reliable. However, symmetry and DTE parameters seem less reliable and need to be interpreted with caution. Finally, better reliability of gait parameters was found when using the mean of the 2nd and the 3rd trials.

Backward Walking and Dual-Task Assessment Improve Identification of Gait Impairments and Fall Risk in Individuals with MS

Background

Individuals with multiple sclerosis (MS) experience deficits in motor and cognitive domains, resulting in impairment in dual-task walking ability. The goal of this study was to compare performance of forward walking and backward walking in single- and dual-task conditions in persons with MS to age- and sex-matched healthy controls. We also examined relationships between forward and backward walking to cognitive function, balance, and retrospective fall reports.

Methods

All measures were collected in a single session. A 2 × 2 × 2 mixed model ANOVA was used to compare differences in forward and backward walking in single- and dual-task conditions between MS and healthy controls. Spearman correlations were used to examine relationships between gait and cognitive function, falls, and balance.

Results

Eighteen individuals with relapsing-remitting MS and 14 age- and sex-matched healthy controls participated. Backward walking velocity revealed significant differences between groups for both single-task (p = 0.015) and dual-task (p = 0.014) conditions. Persons with MS demonstrated significant differences between single- and dual-task forward and backward walking velocities (p = 0.023; p = 0.004), whereas this difference was only apparent in the backward walking condition for healthy controls (p = 0.004). In persons with MS, there were significant differences in double support time between single- and dual-task conditions in both backward (p < 0.001) and forward (p = 0.001) directions. More falls at six months were significantly associated with shorter backward dual-task stride length (r = −0.490; p = 0.046) and slower velocity (r = −0.483; p = 0.050).

Conclusion

Differences in MS and age- and sex-matched healthy controls are more pronounced during backward compared to forward walking under single- and dual-task conditions. Future work with a larger sample size is needed to validate the clinical utility of backward walking and dual-task assessments and mitigate the limited sensitivity of the current dual-task assessments that primarily rely upon forward walking.