The validity of the Gait Variability Index for individuals with mild to moderate Parkinson's disease

Impact of distinct cognitive domains on gait variability in individuals with mild cognitive impairment and dementia

BACKGROUND

Gait variability is a common feature in neurodegenerative diseases and has been linked to cognitive impairment. Despite this link, the influence of specific cognitive domains, such as memory, visual spatial skills, executive function, and verbal function on gait variability is not well-understood.

OBJECTIVE

To investigate the predictive value of these specific cognitive domains on gait variability in people with mild cognitive impairment (MCI) and dementia during preferred and dual task walking.

METHOD

One hundred and two participants with either MCI or dementia underwent a comprehensive cognitive assessment and completed preferred and dual-task walking trials on a pressure-sensing walkway. Gait variability was assessed using the PKMAS software. Lower extremity function was evaluated with a self-reported validated scale.

RESULTS

Our findings indicate that only visual spatial abilities had a moderate predictive value on gait variability [F (1, 78) = 17.30, p < 0.01, r = 0.43], both in preferred pace walking (70% direct effect) and dual-task walking (90% direct effect) (p's < 0.05). Additionally, lower extremity functional skills had a significant indirect effect (30%) on gait variability in preferred walking contexts.

CONCLUSION

For individuals diagnosed with MCI or dementia, increased gait variability may be driven by deficits in visual spatial processing. An increased understanding of the role of visual spatial processing in gait variability can aid in the assessment and management of individuals with MCI or dementia, potentially leading to targeted interventions to improve mobility and safety.

Characterization of Walking in Mild Parkinson's Disease: Reliability, Validity and Discriminant Ability of the Six-Minute Walk Test Instrumented with a Single Inertial Sensor

Although the 6-Minute Walk Test (6MWT) is among the recommended clinical tools to assess gait impairments in individuals with Parkinson's disease (PD), its standard clinical outcome consists only of the distance walked in 6 min. Integrating a single Inertial Measurement Unit (IMU) could provide additional quantitative and objective information about gait quality complementing standard clinical outcome. This study aims to evaluate the test-retest reliability, validity and discriminant ability of gait parameters obtained by a single IMU during the 6MWT in subjects with mild PD. Twenty-two people with mild PD and ten healthy persons performed the 6MWT wearing an IMU placed on the lower trunk. Features belonging to rhythm and pace, variability, regularity, jerkiness, intensity, dynamic instability and symmetry domains were computed. Test-retest reliability was evaluated through the Intraclass Correlation Coefficient (ICC), while concurrent validity was determined by Spearman's coefficient. Mann-Whitney U test and the Area Under the receiver operating characteristic Curve (AUC) were then applied to assess the discriminant ability of reliable and valid parameters. Results showed an overall high reliability (ICC ≥ 0.75) and multiple significant correlations with clinical scales in all domains. Several features exhibited significant alterations compared to healthy controls. Our findings suggested that the 6MWT instrumented with a single IMU can provide reliable and valid information about gait features in individuals with PD. This offers objective details about gait quality and the possibility of being integrated into clinical evaluations to better define walking rehabilitation strategies in a quick and easy way.

Functional near-infrared spectroscopy measures of frontal hemodynamic responses in Parkinson's patients and controls performing the Timed-Up-and-Go test

Using functional near-infrared spectroscopy (fNIRS), hemodynamic responses (i.e., changes in oxygenated and deoxygenated hemoglobin) were measured while participants with Parkinson's disease (PD) and healthy controls performed the Timed-Up-and-Go test (TUGT), and differences in cortical activity at baseline and three different intervals were examined between the two groups. Seventeen PD patients and twenty-two controls participated in the study, but two PD patients were excluded from statistical analysis due to the presence of freezing of gait and using walking aids during the TUGT. During the TUGT, activity in the front, left, right and total frontal cortices initially decreased significantly, then significantly increased in PD participants and low-risk faller PD participants, compared to when in a sitting position. ΔHbO (HbO change from baseline) over the front, left and total frontal cortices in the PD group was significantly lower than the control group in interval 1 (P = 0.019, P = 0.014 and P = 0.031, respectively), while significantly higher than the control group in interval 2 over the left frontal cortex (P = 0.010). No significant differences were observed between the high-risk faller and low-risk faller subgroups of PD participants in ΔHbO and ΔHbR in the three intervals (P > 0.05). In the high-risk faller subgroup, ΔHbO over the left frontal cortex was significantly higher than the right frontal cortex in interval 2 and interval 3 (P = 0.015, P = 0.030, respectively). There was a strong positive correlation between education and HbR concentration over the right frontal cortex in PD participants (rho = 0.557, P = 0.031), while there were strong negative correlations between PD duration and HbR concentration over the right and total frontal cortices in the high-risk faller subgroup of PD participants (rho = -0.854, P = 0.014 for the right; rho = -0.784, P = 0.037 for the total). The falls prediction cutoff TUGT time for PD participants was 14.2 s. These results suggest that frontal cognition training, along with exercise training, could be used as an effective training method to improve motor performance in PD patients, especially for those at high-risk for falls.

Three decades of gait index development: A comparative review of clinical and research gait indices

BACKGROUND

A wide variety of indices have been developed to quantify gait performance markers and associate them with their respective pathologies. Indices scores have enabled better decisions regarding patient treatments and allowed for optimized monitoring of the evolution of their condition. The extensive range of human gait indices presented over the last 30 years is evaluated and summarized in this narrative literature review exploring their application in clinical and research environments.

METHODS

The analysis will explore historical and modern gait indices, focusing on the clinical efficacy with respect to their proposed pathology, age range, and associated parameter limits. Features, methods, and clinically acceptable errors are discussed while simultaneously assessing indices advantages and disadvantages. This review analyses all indices published between 1994 and February 2021 identified using the Medline, PubMed, ScienceDirect, CINAHL, EMBASE, and Google Scholar databases.

FINDINGS

A total of 30 indices were identified as noteworthy for clinical and research purposes and another 137 works were included for discussion. The indices were divided in three major groups: observational (13), instrumented (16) and hybrid (1). The instrumented indices were further sub-divided in six groups, namely kinematic- (4), spatiotemporal- (5), kinetic- (2), kinematic- and kinetic- (2), electromyographic- (1) and Inertial Measurement Unit-based indices (2).

INTERPRETATION

This work is one of the first reviews to summarize observational and instrumented gait indices, exploring their applicability in research and clinical contexts. The aim of this review is to assist members of these communities with the selection of the proper index for the group in analysis.

Characterization of gait variability in multiple system atrophy and Parkinson's disease

BACKGROUND

Gait impairment is a pivotal feature of parkinsonian syndromes and increased gait variability is associated with postural instability and a higher risk of falls.

OBJECTIVES

We compared gait variability at different walking velocities between and within groups of patients with Parkinson-variant multiple system atrophy, idiopathic Parkinson's disease, and a control group of older adults.

METHODS

Gait metrics were recorded in 11 multiple system atrophy, 12 Parkinson's disease patients, and 18 controls using sensor-based gait analysis. Gait variability was analyzed for stride, swing and stance time, stride length and gait velocity. Values were compared between and within the groups at self-paced comfortable, fast and slow walking speed.

RESULTS

Multiple system atrophy patients displayed higher gait variability except for stride time at all velocities compared with controls, while Parkinson's patients did not. Compared with Parkinson's disease, multiple system atrophy patients displayed higher variability of swing time, stride length and gait velocity at comfortable speed and at slow speed for swing and stance time, stride length and gait velocity (all P < 0.05). Stride time variability was significantly higher in slow compared to comfortable walking in patients with multiple system atrophy (P = 0.014). Variability parameters significantly correlated with the postural instability/gait difficulty subscore in both disease groups. Conversely, significant correlations between variability parameters and MDS-UPDRS III score was observed only for multiple system atrophy patients.

CONCLUSION

This analysis suggests that gait variability parameters reflect the major axial impairment and postural instability displayed by multiple system atrophy patients compared with Parkinson's disease patients and controls.

The Utility of Gait Deviation Index (GDI) and Gait Variability Index (GVI) in Detecting Gait Changes in Spastic Hemiplegic Cerebral Palsy Children Using Ankle-Foot Orthoses (AFO)

Background: Cerebral palsy (CP) children present complex and heterogeneous motor disorders that cause gait deviations. Clinical gait analysis (CGA) is used to identify, understand and support the management of gait deviations in CP. Children with CP often use ankle–foot orthosis (AFO) to facilitate and optimize their walking ability. The aim of this study was to assess whether the gait deviation index (GDI) and the gait variability index (GVI) results can reflect the changes of spatio-temporal and kinematic gait parameters in spastic hemiplegic CP children wearing AFO. Method: The study group consisted of 37 CP children with hemiparesis. All had undergone a comprehensive, instrumented gait analysis while walking, both barefoot and with their AFO, during the same CGA session. Kinematic and spatio-temporal data were collected and GVI and GDI gait indexes were calculated. Results: Significant differences were found between the barefoot condition and the AFO conditions for selected spatio-temporal and kinematic gait parameters. Changes in GVI and GDI were also statistically significant. Conclusions: The use of AFO in hemiplegic CP children caused a statistically significant improvement in spatio-temporal and kinematic gait parameters. It was found that these changes were also reflected by GVI and GDI. These findings might suggest that gait indices, such as GDI and GVI, as clinical outcome measures, may reflect the effects of specific therapeutic interventions in CP children.

Agreement between the GAITRite® System and the Wearable Sensor BTS G-Walk® for measurement of gait parameters in healthy adults and Parkinson's disease patients

Background

Nowadays, the most widely used types of wearable sensors in gait analysis are inertial sensors. The aim of the study was to assess the agreement between two different systems for measuring gait parameters (inertial sensor vs. electronic walkway) on healthy control subjects (HC) and patients with Parkinson's disease (PD).

Methods

Forty healthy volunteers (26 men, 14 women, mean age 58.7 ± 7.7 years) participated in the study and 24 PD patients (19 men, five women, mean age 62.7 ± 9.8 years). Each participant walked across an electronic walkway, GAITRite, with embedded pressure sensors at their preferred walking speed. Concurrently a G-Walk sensor was attached with a semi-elastic belt to the L5 spinal segment of the subject. Walking speed, cadence, stride duration, stride length, stance, swing, single support and double support phase values were compared between both systems.

Results

The Passing-Bablock regression slope line manifested the values closest to 1.00 for cadence and stride duration (0.99 ≤ 1.00) in both groups. The slope of other parameters varied between 0.26 (double support duration in PD) and 1.74 (duration of single support for HC). The mean square error confirmed the best fit of the regression line for speed, stride duration and stride length. The y-intercepts showed higher systematic error in PD than HC for speed, stance, swing, and single support phases.

Conclusions

The final results of this study indicate that the G-Walk system can be used for evaluating the gait characteristics of the healthy subjects as well as the PD patients. However, the duration of the gait cycle phases should be used with caution due to the presence of a systematic error.

Assessing the Relationship between the Enhanced Gait Variability Index and Falls in Individuals with Parkinson's Disease

Gait impairment and increased gait variability are common among individuals with Parkinson's disease (PD) and have been associated with increased risk for falls. The development of composite scores has gained interest to aggregate multiple aspects of gait into a single metric. The Enhanced Gait Variability Index (EGVI) was developed to compare an individual's gait variability to the amount of variability in a healthy population, yet the EGVI's individual parts may also provide important information that may be lost in this conversion. We sought to contrast individual gait measures as predictors of fall frequency and the EGVI as a single predictor of fall frequency in individuals with PD. 273 patients (189M, 84F; 68 ± 10 yrs) with idiopathic PD walked over an instrumented walkway and reported fall frequency over three months (never, rarely, monthly, weekly, or daily). The predictive ability of gait velocity, step length, step time, stance time, and single support time and the EGVI was assessed using regression techniques to predict fall frequency. The EGVI explained 15.1% of the variance in fall frequency (p < 0.001, r = 0.389). Although the regression using the combined spatiotemporal measures to predict fall frequency was significant (p=0.002, r = 0.264), none of the components reached significance (gait velocity: p=0.640, step length: p=0.900, step time: p=0.525, stance time: p=0.532, single support time: p=0.480). The EGVI is a better predictor of fall frequency in persons with PD than its individual spatiotemporal components. Patients who fall more frequently have more variable gait, based on the interpretation of the EGVI. While the EGVI provides an objective measure of gait variability with some ability to predict fall frequency, full clinical interpretations and applications are currently unknown.

Application of the Gait Deviation Index in the analysis of post-stroke hemiparetic gait

Due to the complexity and volume of kinematic data from 3-dimensional gait analysis, the Gait Deviation Index (GDI) was introduced as a summary measure providing a global picture of gait kinematic data, however previously it was not validated as an outcome measure in individuals after stroke. The present study investigated the concurrent validity of the GDI as an outcome measure of gait defects at a chronic stage of recovery post-stroke, through comparisons with conventional measures of gait. Those enrolled included 65 individuals after stroke and 65 healthy individuals without gait disorders, matched for age and gender. The kinematic gait parameters were measured using a movement analysis system. Walking speed, walking distance, number of steps, self-reliant mobility, cadence, step length, and single support time were evaluated. Strong correlation was found between cadence and mGDI as well as GDI for the affected leg (0.7 ≤ |R| < 0.9; p < 0.001). Moderate correlations were found between walking speed, number of steps, step length affected leg and mGDI as well as GDI for the affected leg (0.5 ≤ |R| < 0.7; p < 0.001). Low correlations were found between walking distance, self-reliant mobility, single support time affected leg and mGDI as well as GDI for the affected leg (0.3 ≤ |R| < 0.5; p < 0.001; p < 0.005). The findings confirm the concurrent validity of the GDI, but only for the affected leg and mGDI in post-stroke patients. On the other hand, the GDI for unaffected leg may be useful in efforts to identify any compensatory mechanisms developing in post-stroke gait patterns. Trial registration: anzctr.org.au, ID:ACTRN12617000436370. Registered 24 March 2017.

The reliability and validity of the Timed Up and Go as a clinical tool in individuals with and without disabilities across a lifespan: a systematic review

PURPOSE

To summarize the available literature related to reliability and validity of the Timed Up and Go in typical adults and children, and individuals diagnosed with the following pathologies: Huntington's disease, stroke, multiple sclerosis, Parkinson's disease, spinal cord injury, Down syndrome, or cerebral palsy.

MATERIALS AND METHODS

A search was conducted using MeSH terms and keywords through a variety of databases. Data regarding reliability and validity were synthesized.

RESULTS

This review included 77 articles. Results were variable depending on the studied population. The Timed Up and Go showed excellent reliability in typical adults, in individuals with cerebral palsy, in individuals with multiple sclerosis, in individuals with Huntington's disease, individuals with a stroke, and individuals with a spinal cord injury. The TUG demonstrated strong concurrent validity for individuals with stroke and spinal cord injury. Predictive validity data was limited.

CONCLUSIONS

Based on the literature assessed, the Timed Up and Go is clinically applicable and reliable across multiple populations. The Timed Up and Go has a wide variety of clinical use making it a diverse measure that should be considered when choosing an outcome an activity based outcome measure. However, there are some limitations in the validity of the utilization of the Timed Up and Go to some populations due to a lack of data and/or poor choice of comparison outcome measures when assessing validity. Additional research is needed for young to middle aged adults.IMPLICATIONS FOR REHABILITATIONOutcome measures are a vital component of clinical practice across all populations.The Timed Up and Go is a highly studied outcome measure in the geriatric population, but lacks research of its applicability to other populations.This study was able to highlight the clinical utility of the Timed Up and Go in populations that under utilize this outcome measure.

Validity of the gait variability index for individuals after a stroke in a chronic stage of recovery

BACKGROUND

Increased variability in spatiotemporal variables has been demonstrated in individuals after stroke. Gait Variability Index (GVI) has recently been proposed, potentially to be used as a standardized tool for quantifying gait impairment due to spatiotemporal variables. The experience with the GVI in patients after stroke is unknown.

RESEARCH QUESTION

The aim of this study was to investigate the validity of the GVI as an outcome measure of gait disturbance after stroke.

METHODS

50 individuals (mean age 60.9 ± 11.2 years) after stroke at a chronic phase of recovery were included. The control group comprised 50 healthy subjects without gait disorders, matched for age and gender. Data on functional mobility and spatiotemporal gait parameters (BTS Smart system) was collected.

RESULTS

The results showed lower mean GVI (mGVI) scores (mean 78.53 ± 6.12), lower GVI for the affected leg (mean 76.32 ± 7.98) and for the unaffected leg (mean 80.74 ± 4.68) in the individuals after stroke compared to the healthy subjects (mean 98.00 ± 6.32). This was significantly different from the control group mean for both mGVI, affected and unaffected leg - p < 0.001. The GVI for the affected leg and unaffected leg as well as the mGVI were significantly correlated with all clinical measures of functional mobility (0.7≤R|<0.9, 0.5≤|R|<0.7, p < 0.001).

SIGNIFICANCE

The validity of the GVI appears to be confirmed for individuals after stroke at a chronic stage of recovery. The GVI is lower in individuals after stroke compared to healthy controls. The GVI showed moderate to strong correlations with validated clinical measures of functional mobility. Application of the GVI in the clinical practice will significantly facilitate assessment of gait in individuals after stroke, in comparison to the necessity to interpret a large number of data from 3-dimensional gait analysis.

CLINICAL TRIAL REGISTRATION

Data are parts of the following clinical trial: ACTRN12617000436370 (anzctr.org.au).

Classification of gait patterns between patients with Parkinson's disease and healthy controls using phase space reconstruction (PSR), empirical mode decomposition (EMD) and neural networks

Parkinson's disease (PD) is a common neurodegenerative disorder that affects human's quality of life, especially leading to locomotor deficits such as postural instability and gait disturbances. Gait signal is one of the best features to characterize and detect movement disorders caused by a malfunction in parts of the brain and nervous system of the patients with PD. Various classification approaches using spatiotemporal gait variables have been presented earlier to classify Parkinson's gait. In this study we propose a novel method for gait pattern classification between patients with PD and healthy controls, based upon phase space reconstruction (PSR), empirical mode decomposition (EMD) and neural networks. First, vertical ground reaction forces (GRFs) at specific positions of human feet are captured and then phase space is reconstructed. The properties associated with the gait system dynamics are preserved in the reconstructed phase space. Three-dimensional (3D) PSR together with Euclidean distance (ED) has been used. These measured parameters demonstrate significant difference in gait dynamics between the two groups and have been utilized to form a reference variable set. Second, reference variables are decomposed into Intrinsic Mode Functions (IMFs) using EMD, and the third IMFs are extracted and served as gait features. Third, neural networks are then used as the classifier to distinguish between patients with PD and healthy controls based on the difference of gait dynamics preserved in the gait features between the two groups. Finally, experiments are carried out on 93 PD patients and 73 healthy subjects to assess the effectiveness of the proposed method. By using 2-fold, 10-fold and leave-one-out cross-validation styles, the correct classification rates are reported to be 91.46%, 96.99% and 98.80%, respectively. Compared with other state-of-the-art methods, the results demonstrate superior performance and the proposed method can serve as a potential candidate for the automatic and non-invasive classification between patients with PD and healthy subjects.

Less Is More - Estimation of the Number of Strides Required to Assess Gait Variability in Spatially Confined Settings

Background: Gait variability is an established marker of gait function that can be assessed using sensor-based approaches. In clinical settings, spatial constraints and patient condition impede the execution of longer distance walks for the recording of gait parameters. Turning paradigms are often used to overcome these constraints and commercial gait analysis systems algorithmically exclude turns for gait parameters calculations. We investigated the effect of turns in sensor-based assessment of gait variability.

Methods: Continuous recordings from 31 patients with movement disorders (ataxia, essential tremor and Parkinson’s disease) and 162 healthy elderly (HE) performing level walks including 180° turns were obtained using an inertial sensor system. Accuracy of the manufacturer’s algorithm of turn-detection was verified by plotting stride time series. Strides before and after turn events were extracted and compared to respective average of all strides. Coefficient of variation (CoV) of stride length and stride time was calculated for entire set of strides, segments between turns and as cumulative values. Their variance and congruency was used to estimate the number of strides required to reliably assess the magnitude of stride variability.

Results: Non-detection of turns in 5.8% of HE lead to falsely increased CoV for these individuals. Even after exclusion of these, strides before/after turns tended to be spatially shorter and temporally longer in all groups, contributing to an increase of CoV at group level and widening of confidence margins with increasing numbers of strides. This could be attenuated by a more generous turn excision as an alternative approach. Correlation analyses revealed excellent consistency for CoVs after at most 20 strides in all groups. Respective stride counts were even lower in patients using a more generous turn excision.

Conclusion: Including turns to increase continuous walking distance in spatially confined settings does not necessarily improve the validity and reliability of gait variability measures. Specifically with gait pathology, perturbations of stride characteristics before/after algorithmically excised turns were observed that may increase gait variability with this paradigm. We conclude that shorter distance walks of around 15 strides suffice for reliable and valid recordings of gait variability in the groups studied here.

Addressing limitations of the Gait Variability Index to enhance its applicability: The enhanced GVI (EGVI)

Prior research has established the Gait Variability Index (GVI) as a composite measure of gait variability, based on spatiotemporal parameters, that is associated with functional outcomes. However, under certain circumstances the magnitude and directional specificity of the GVI is adversely affected by shortcomings in the calculation method. Here we present an enhanced gait variability index (EGVI) that addresses those shortcomings and improves the utility of the measure. The EGVI was further enhanced by removing some input spatiotemporal variables that captured overlapping/redundant information. The EGVI was used to reanalyze data from four previously published studies that used the original GVI. After removing data affected by the GVI’s prior shortcomings, the association between EGVI and GVI values was stronger for the pooled dataset (r² = 0.95) and for the individual studies (r² = 0.88–0.98). The EGVI also revealed stronger associations between the index value and functional outcomes for some studies. The EGVI successfully addresses shortcomings in the GVI calculation that affected magnitude and directional specificity of the index. We have confirmed the validity of prior published work that used the original GVI, while also demonstrating even stronger results when these prior data were re-analyzed with the EGVI. We recommend that future research should use the EGVI as a composite measure of gait variability.

Measuring Gait Quality in Parkinson's Disease through Real-Time Gait Phase Recognition

Monitoring gait quality in daily activities through wearable sensors has the potential to improve medical assessment in Parkinson’s Disease (PD). In this study, four gait partitioning methods, two based on thresholds and two based on a machine learning approach, considering the four-phase model, were compared. The methods were tested on 26 PD patients, both in OFF and ON levodopa conditions, and 11 healthy subjects, during walking tasks. All subjects were equipped with inertial sensors placed on feet. Force resistive sensors were used to assess reference time sequence of gait phases. Goodness Index (G) was evaluated to assess accuracy in gait phases estimation. A novel synthetic index called Gait Phase Quality Index (GPQI) was proposed for gait quality assessment. Results revealed optimum performance (G < 0.25) for three tested methods and good performance (0.25 < G < 0.70) for one threshold method. The GPQI resulted significantly higher in PD patients than in healthy subjects, showing a moderate correlation with clinical scales score. Furthermore, in patients with severe gait impairment, GPQI was found higher in OFF than in ON state. Our results unveil the possibility of monitoring gait quality in PD through real-time gait partitioning based on wearable sensors.