Influence of normative data's walking speed on the computation of conventional gait indices

ACROMORFO study: gait analysis in a cohort of acromegalic patients

PURPOSE

In acromegaly, skeletal complications resulted to be associated with low quality of life (QoL) and high risk of falls. The aim of the present study was to perform a quantitative assessment of movement through gait analysis technique in patients with acromegaly.

STUDY POPULATION

Thirty-three acromegalic patients [9 with active disease (AD), 14 with controlled disease (CD) and 10 with disease remission (RD)] and 20 healthy subjects were enrolled for the study.

MEASUREMENTS

Kinetic and kinematic data were collected with 3D-gait analysis. Kinematic data were processed to compute the Gait Profile Score (GPS), a parameter that summarizes the overall deviation of kinematic gait data relative to unaffected population.

RESULTS

The acromegalic group showed longer stance phase duration (p < 0.0001) compared to controls. The GPS and several gait variable scores resulted to be statistically higher in the acromegalic group compared to healthy controls. GPS values were significantly higher in AD compared to CD (p < 0.05) and RD groups (p = 0.001). The AD group presented significantly higher values in terms of hip rotation and ankle dorsiflexion compared to CD and RD groups and with regard to the foot progression compared to RD. Interestingly, patients with RD exhibited a more physiological gait pattern.

CONCLUSION

Acromegalic patients showed quantitative alterations of gait pattern, suggesting instability and increased risk of falls. Arthropathy, along with its associated abnormal joint loading, proprioceptive impairment and hyperkyphosis could be contributing factors. Disease control and remission appear to improve postural balance. A better knowledge on walking performance in acromegaly would help to develop specific rehabilitation programmes to reduce falls' risk and improve QoL.

Normative dataset selection affects gait profile scores of children with cerebral palsy

INTRODUCTION

The Gait Profile Score (GPS) requires a comparative dataset, to identify altered mechanics in persons with a gait abnormality. This gait index has been shown to be useful for identifying gait pathology prior to the assessment of treatment outcomes. Though studies have shown differences in kinematic normative datasets between different testing sites, there is limited information available on the changes in GPS score based on normative dataset selection. The aim of this study was to quantify the influence of normative reference data from two institutions, on the GPS and Gait Variable Scores (GVS), calculated on the same group of patients with Cerebral Palsy.

METHODS

Seventy patients (Avg. age: 12.1 ± 2.9) diagnosed with CP underwent gait analysis during walking at a self-selected speed at Scottish Rite for Children (SRC). GPS and GVS scores were determined using normative kinematic data at a self-selected speed from, 83 typically developing children ages 4-17 from Gillette, and the same age range of children from SRC's normative dataset. Average normalized speed was compared between institutions. Signed rank tests were performed on the GPS and GVS scores using each institution's dataset. Spearman's correlations between scores using SRC and Gillette were determined within GMFCS level.

RESULTS

Normalized speed was comparable between each institution's datasets. Within each GMFCS level, significant differences when using SRC vs. Gillette were found in most scores (p < 0.05). Scores were moderately to strongly correlated within each GMFCS level (range ρ = 0.448-0.998).

CONCLUSIONS

Significant statistical differences were found in GPS and GVS scores but were within the range of previously reported variation across multiple sites. Caution and consideration may need to be taken when reporting GPS and GVS scores that are calculated utilizing different normative datasets as these scores may not be equivalent.

Single- and dual-task gait performance and their diagnostic value in early-stage Parkinson's disease

Background

Gait parameters are considered potential diagnostic markers of Parkinson's disease (PD). We aimed to 1) assess the gait impairment in early-stage PD and its related factors in the single-task (ST) and dual-task (DT) walking tests and 2) evaluate and compare the diagnostic value of gait parameters for early-stage PD under ST and DT conditions.

Methods

A total of 97 early-stage PD patients and 41 healthy controls (HC) were enrolled at Hwa Mei hospital. Gait parameters were gathered and compared between the two groups in the ST and DT walking test, controlling for covariates. Utilizing the receiver operating characteristic curve, diagnostic parameters were investigated.

Results

In the ST walking test, significantly altered gait patterns could be observed in early-stage PD patients in all domains of gait, except for asymmetry (P < 0.05). Compared to the ST walking test, the early-stage PD group performed poorly in the DT walking test in the pace, rhythm, variability and postural control domain (P < 0.05). Older, heavier subjects, as well as those with lower height, lower level of education and lower gait velocity, were found to have a poorer gait performance (P < 0.05). Stride length (AUC = 0.823, sensitivity, 68.0%; specificity, 85.4%; P < 0.001) and heel strike angle (AUC = 0.796, sensitivity, 71.1%; specificity, 80.5%; P < 0.001) could distinguish early-stage PD patients from HCs with moderate accuracy, independent of covariates. The diagnostic accuracy of gait parameters under ST conditions were statistically noninferior to those under DT conditions(P>0.05). Combining all gait parameters with diagnostic values under ST and DT walking test, the predictive power significantly increased with an AUC of 0.924 (sensitivity, 85.4%; specificity, 92.7%; P < 0.001).

Conclusion

Gait patterns altered in patients with early-stage PD but the gait symmetry remained preserved. Stride length and heel strike angle were the two most prominent gait parameters of altered gait in early-stage of PD that could serve as diagnostic markers of early-stage PD. Our findings are helpful to understand the gait pattern of early-stage PD and its related factors and can be conducive to the development of new diagnostic tools for early-stage PD.

Brain Asymmetry and Its Effects on Gait Strategies in Hemiplegic Patients: New Rehabilitative Conceptions

Brain asymmetry is connected with motor performance, suggesting that hemiparetic patients have different gait patterns depending on the side of the lesion. This retrospective cohort study aims to further investigate the difference between right and left hemiplegia in order to assess whether the injured side can influence the patient’s clinical characteristics concerning gait, thus providing insights for new personalized rehabilitation strategies. The data from 33 stroke patients (17 with left and 16 with right hemiplegia) were retrospectively compared with each other and with a control group composed of 20 unaffected age-matched individuals. The 3D gait analysis was used to assess kinematic data and spatio-temporal parameters. Compared to left hemiplegic patients, right hemiplegic patients showed worse spatio-temporal parameters (p < 0.05) and better kinematic parameters (p < 0.05). Both pathological groups were characterized by abnormal gait parameters in comparison with the control group (p < 0.05). These findings show an association between the side of the lesion—right or left—and the different stroke patients’ gait patterns: left hemiplegic patients show better spatio-temporal parameters, whereas right hemiplegic patients show better segmentary motor performances. Therefore, further studies may develop and assess new personalized rehabilitation strategies considering the injured hemisphere and brain asymmetry.

The effects of levodopa in the spatiotemporal gait parameters are mediated by self-selected gait speed in Parkinson's disease

In individuals with Parkinson's disease (PD), the medication induces different and inconsistent results in the spatiotemporal parameters of gait, making it difficult to understand its effects on gait. As spatiotemporal gait parameters have been reported to be affected by gait speed, it is essential to consider the gait speed when studying walking biomechanics to interpret the results better when comparing the gait pattern of different conditions. Since the medication alters the self-selected gait speed of individuals with PD, this study analysed whether the change in gait speed can explain the selective effects of l-DOPA on the spatiotemporal parameters of gait in individuals with PD. We analysed the spatiotemporal gait parameters at the self-selected speed of 22 individuals with PD under ON and OFF states of l-DOPA medication. Bayesian mediation analysis evaluated which gait variables were affected by the medication state and checked if those effects were mediated by speed changes induced by medication. The gait speed was significantly higher among ON compared with OFF medication. All the spatiotemporal parameters of the gait were mediated by speed, with proportions of mediation close to 1 (effect entirely explained by speed changes). Our results show that a change in gait speed better explains the changes in the spatiotemporal gait parameters than the ON-OFF phenomenon. As an implication for rehabilitation, our results suggest that it is possible to assess the effect of l-DOPA on improving motor symptoms related to gait disorders by measuring gait speed.

Gait Analysis 1 Year after Primary TKA: No Difference between Gap Balancing and Measured Resection Technique

Mechanical alignment in total knee arthroplasty (TKA) can be achieved using dependent bone cuts. The hypothesis is that patients have a better balanced TKA, as a result. The aim of this study was to determine if this technique is superior to an independent bone cut technique in terms of gait parameters, patient-reported outcome measures (PROMs), and satisfaction assessed before surgery and at 1-year follow-up. A total of 58 patients were evaluated before and 1 year following TKA, using the Press Fit Condylar (PFC) Sigma posterior stabilizer (PS) design; 39 (70 ± 8 years; 27 women) with independent bone cuts and 19 (71 ± 7 years; 12 women) with dependent bone cuts using the Specialist TRAM. Gait was evaluated with a three-dimensional motion analysis system for spatiotemporal and kinematics parameters. Pain and functional levels were assessed using the Western Ontario and McMaster Universities arthritis index (WOMAC); general health was assessed by the short form (SF)-12. Global satisfaction, as well as patient satisfaction, related to pain and functional levels were assessed using a five-point Likert's scale. No significant difference was found between both groups in terms of age, body mass index, pain, and functional levels at baseline. At 1-year follow-up, despite an overall improvement in gait, WOMAC, SF-12 physical score and pain, none of the patients showed gait parameters comparable to a healthy control group. No surgical technique effect was observed on gait, clinical outcomes, and satisfaction level. While observing an overall improvement at 1-year follow-up, we did not find any significant difference between the two surgical techniques in terms of gait parameters, patients' outcomes, and satisfaction.

An artificial neural network approach to detect presence and severity of Parkinson's disease via gait parameters

Introduction

Gait deficits are debilitating in people with Parkinson’s disease (PwPD), which inevitably deteriorate over time. Gait analysis is a valuable method to assess disease-specific gait patterns and their relationship with the clinical features and progression of the disease.

Objectives

Our study aimed to i) develop an automated diagnostic algorithm based on machine-learning techniques (artificial neural networks [ANNs]) to classify the gait deficits of PwPD according to disease progression in the Hoehn and Yahr (H-Y) staging system, and ii) identify a minimum set of gait classifiers.

Methods

We evaluated 76 PwPD (H-Y stage 1–4) and 67 healthy controls (HCs) by computerized gait analysis. We computed the time-distance parameters and the ranges of angular motion (RoMs) of the hip, knee, ankle, trunk, and pelvis. Principal component analysis was used to define a subset of features including all gait variables. An ANN approach was used to identify gait deficits according to the H-Y stage.

Results

We identified a combination of a small number of features that distinguished PwPDs from HCs (one combination of two features: knee and trunk rotation RoMs) and identified the gait patterns between different H-Y stages (two combinations of four features: walking speed and hip, knee, and ankle RoMs; walking speed and hip, knee, and trunk rotation RoMs).

Conclusion

The ANN approach enabled automated diagnosis of gait deficits in several symptomatic stages of Parkinson’s disease. These results will inspire future studies to test the utility of gait classifiers for the evaluation of treatments that could modify disease progression.

Gait strategy and body composition in patients with Prader-Willi syndrome

PURPOSE

Individuals with Prader-Willi syndrome (PWS) exhibit reduced lean body mass and increased fat-lean mass ratio when compared with individuals of normal weight and obese ones. Thus, research on the association of functional limitations during gait and body composition may be of great importance from a rehabilitative viewpoint. In particular, the aim of this study was to compare the gait profile of persons with PWS to that of unaffected individuals and to see if a relationship exists between gait profile and body composition in individuals with PWS.

METHODS

Eighteen individuals with PWS and 20 unaffected individuals (Healthy Group: HG) were assessed. Their gait pattern was quantified with 3D-Gait Analysis (3D-GA). Overall body weight, lean and fat masses were measured by dual-energy X-ray absorptiometry.

RESULTS

Individuals with PWS were found to be characterized by a significantly different (p < 0.05) gait pattern with respect to healthy controls in terms of both kinematic and kinetic parameters. No correlations were found between kinematic parameters and overall mass and lean/fat mass, while some parameters associated with ground reaction force were found to be significantly correlated with overall mass, lean mass and fat mass. Significant regression models were obtained, including impact and propulsive force and loading rate.

CONCLUSION

Our data suggest that in individuals with PWS, gait is influenced by the overall and lean body mass. Thus, therapeutic strategies should target both weight reduction and lean mass increase to optimize gait, minimize articular stress, and reduce the risk of repetitive strain on the lower limbs.

LEVEL OF EVIDENCE

Level III: Case-control analytic study.

Test of two prediction methods for minimum and maximum values of gait kinematics and kinetics data over a range of speeds

BACKGROUND

Minimum and maximum values of gait kinematics and kinetics data are commonly used to quantitatively describe a walking pattern.

RESEARCH QUESTION

The purposes of this study were to determine the effect of speed on the minimum and maximum values of gait kinematics and kinetics variables and to test two prediction methods for the estimation of these minimum and maximum values at different gait speeds.

METHODS

An open dataset with the data of 24 healthy adults (age: 27.6 ± 4.4 years, height: 171.1 ± 10.5 cm, body mass: 68.4 ± 12.2 kg) walking on a treadmill at eight gait speeds was employed in this study. The minimum and maximum angles and moments of the hip, knee, and ankle joints were extracted from speed-dependent prediction curves solely for the minimum and maximum values (PEAK method) and from speed-dependent prediction curves for the entire gait cycle (CYCLE method). The overall error, computed as the root-mean-square error (RMSE), for the minimum and maximum values predicted by these two methods were compared with the experimental true values.

RESULTS

The RMSEs for the joint angles were PEAK: 3.86 ± 1.21°, CYCLE: 3.88 ± 1.18° and for the joint moments were PEAK: 0.129 ± 0.052 Nm/kg, CYCLE: 0.131 ± 0.052 Nm/kg.

SIGNIFICANCE

The two prediction methods tested can be used to estimate the minimum and maximum values of biomechanical gait variables at a certain speed.

Lower limb sagittal gait kinematics can be predicted based on walking speed, gender, age and BMI

Clinical gait analysis attempts to provide, in a pathological context, an objective record that quantifies the magnitude of deviations from normal gait. However, the identification of deviations is highly dependent with the characteristics of the normative database used. In particular, a mismatch between patient characteristics and an asymptomatic population database in terms of walking speed, demographic and anthropometric parameters may lead to misinterpretation during the clinical process. Rather than developing a new normative data repository that may require considerable of resources and time, this study aims to assess a method for predicting lower limb sagittal kinematics using multiple regression models based on walking speed, gender, age and BMI as predictors. With this approach, we were able to predict kinematics with an error within 1 standard deviation of the mean of the original waveforms recorded on fifty-four participants. Furthermore, the proposed approach allowed us to estimate the relative contribution to angular variations of each predictor, independently from the others. It appeared that a mismatch in walking speed, but also age, sex and BMI may lead to errors higher than 5° on lower limb sagittal kinematics and should thus be taken into account before any clinical interpretation.

Individuals with knee osteoarthritis present increased gait pattern deviations as measured by a knee-specific gait deviation index

BACKGROUND

A biomechanical analysis can provide valuable information on osteoarthritis (OA) gait, but important multidimensional interactions are often ignored. The Gait Deviation Index (GDI) was designed to address the issue of data complexity in gait analyses by providing a single, encompassing, value for one's deviation from a normative reference group.

RESEARCH QUESTION

The primary aim of this study was to examine differences in a knee-specific GDI among young adults, and older individuals with and without knee OA. Secondarily, we aimed to examine these differences while controlling for gait speed.

METHOD

Sagittal and frontal plane knee joint angles and moments were used in the computation of a GDI among young adults, and older individuals with and without knee OA. The GDI was calculated such that scores ≥100% were considered typical young-healthy gait and a 10% decrease below 100 equated to 1 standard deviation from typical gait. Scores were first examined using a one-way analysis of variance, and examined again after correcting for gait speed.

RESULTS

The GDI was calculated for three groups: young-healthy adults (n = 52), older individuals without knee OA (n = 56), and individuals with knee OA (n = 191). Those with knee osteoarthritis exhibited a mean GDI of 87.2 (11.1), which was significantly lower than young adults (99.6 (10.6); p < 0.001) and older individuals without knee OA (94.3 (11.0); p < 0.001). Differences in GDI remained consistent after controlling for gait speed. Knee OA gait waveforms displayed significant variability across similar GDIs, specifically in frontal plane patterns.

CONCLUSION

Those with knee osteoarthritis exhibited lower (worse) GDIs compared to those without knee osteoarthritis and young, healthy individuals. After correcting for gait speed, these findings did not change. The GDI highlighted the significant variability in gait waveforms within individuals with knee OA, but the clinical utility of the GDI score itself remains limited.

Gait Profile Score in able-bodied and post-stroke individuals adjusted for the effect of gait speed

BACKGROUND

The Gait Profile Score (GPS) measures the quality of an individual's walking by calculating the difference between the kinematic pattern and the average walking pattern of healthy individuals.

RESEARCH QUESTIONS

The purposes of this study were to quantify the effect of speed on the GPS and to determine whether the prediction of gait patterns at a specific speed would make the GPS outcome insensitive to gait speed in the evaluation of post-stroke individuals.

METHODS

The GPS was calculated for able-bodied individuals walking at different speeds and for the comparison of post-stroke individuals with able-bodied individuals using the original experimental data (standard GPS) and the predicted gait patterns at a given speed (GPS velocity, GPS^v). We employed standard gait analysis for data collection of the subjects. Sixteen participants with a stroke history were recruited for the post-stroke group, and 15 age-matched, able-bodied participants formed the control group.

RESULTS

Gait speed significantly affects the GPS and the method to predict the gait patterns at any speed is able to mitigate the effects of gait speed on the GPS. Overall, the gap between the GPS and GPS^v values across the post-stroke individuals was small (0.5° on average, range from 0.0° to 1.4°) and not statistically significant. However, there was a significant negative linear relationship in the absolute difference between the GPS and GPS^v values for the participants of the post-stroke group with gait speed, indicating that a larger difference between the speeds of the post-stroke participant and the reference dataset resulted in a larger difference between the GPS and GPS^v.

SIGNIFICANCE

The modified version of the GPS, the GPS^v, is effective in reducing the impact of gait speed on GPS; however, the observed difference between the two methods was only around 1° for the slowest individuals in comparison to the reference dataset.

Probing Corticospinal Control During Different Locomotor Tasks Using Detailed Time-Frequency Analysis of Electromyograms

Locomotion relies on the fine-tuned coordination of different muscles which are controlled by particular neural circuits. Depending on the attendant conditions, walking patterns must be modified to optimally meet the demands of the task. Assessing neuromuscular control during dynamic conditions is methodologically highly challenging and prone to artifacts. Here we aim at assessing corticospinal involvement during different locomotor tasks using non-invasive surface electromyography. Activity in tibialis anterior (TA) and gastrocnemius medialis (GM) muscles was monitored by electromyograms (EMGs) in 27 healthy volunteers (11 female) during regular walking, walking while engaged in simultaneous cognitive dual tasks, walking with partial visual restriction, and skilled, targeted locomotion. Whereas EMG intensity of the TA and GM was considerably altered while walking with partial visual restriction and during targeted locomotion, dual-task walking induced only minor changes in total EMG intensity compared to regular walking. Targeted walking resulted in enhanced EMG intensity of GM in the frequency range associated with Piper rhythm synchronies. Likewise, targeted walking induced enhanced EMG intensity of TA at the Piper rhythm frequency around heelstrike, but not during the swing phase. Our findings indicate task- and phase-dependent modulations of neuromuscular control in distal leg muscles during various locomotor conditions in healthy subjects. Enhanced EMG intensity in the Piper rhythm frequency during targeted walking points toward enforced corticospinal drive during challenging locomotor tasks. These findings indicate that comprehensive time-frequency EMG analysis is able to gauge cortical involvement during different movement programs in a non-invasive manner and might be used as complementary diagnostic tool to assess baseline integrity of the corticospinal tract and to monitor changes in corticospinal drive as induced by neurorehabilitation interventions or during disease progression.