Gait features for discriminating between mobility-limiting musculoskeletal disorders: Lumbar spinal stenosis and knee osteoarthritis

The effect of exercise-induced muscle fatigue on gait parameters among older adults: a systematic review and meta-analysis

BACKGROUND

Exercise-induced fatigue is a common consequence of physical activities. Particularly in older adults, it can affect gait performance. Due to a wide variety in fatiguing protocols and gait parameters used in experimental settings, pooled effects are not yet clear. Furthermore, specific elements of fatiguing protocols (i.e., intensity, duration, and type of activity) might lead to different changes in gait parameters. We aimed to systematically quantify to what extent exercise-induced fatigue alters gait in community-dwelling older adults, and whether specific elements of fatiguing protocols could be identified.

METHODS

This systematic review and meta-analysis was conducted in accordance with the PRISMA guidelines. In April 2023, PubMed, Web of Science, Scopus, Cochrane and CINAHL databases were searched. Two independent researchers screened and assessed articles using ASReview, Rayyan, and ROBINS-I. The extracted data related to spatio-temporal, stability, and variability gait parameters of healthy older adults (55 +) before and after a fatiguing protocol or prolonged physical exercise. Random-effects meta-analyses were performed on both absolute and non-absolute effect sizes in RStudio. Moderator analyses were performed on six clusters of gait parameters (Dynamic Balance, Lower Limb Kinematics, Regularity, Spatio-temporal Parameters, Symmetry, Velocity).

RESULTS

We included 573 effect sizes on gait parameters from 31 studies. The included studies reflected a total population of 761 older adults (57% female), with a mean age of 71 (SD 3) years. Meta-analysis indicated that exercise-induced fatigue affected gait with a standardized mean change of 0.31 (p < .001). Further analyses showed no statistical differences between the different clusters, and within clusters, the effects were non-uniform, resulting in an (indistinguishable from) zero overall effect within all clusters. Elements of fatiguing protocols like duration, (perceived) intensity, or type of activity did not moderate effects.

DISCUSSION

Due to the (mainly) low GRADE certainty ratings as a result of the heterogeneity between studies, and possible different strategies to cope with fatigue between participants, the only conclusion that can be drawn is that older adults, therapist, and researchers should be aware of the small to moderate changes in gait parameters as a result of exercise-induced fatigue.

Normative Database of Spatiotemporal Gait Metrics Across Age Groups: An Observational Case-Control Study

INTRODUCTION

Gait analysis is a vital tool in the assessment of human movement and has been widely used in clinical settings to identify potential abnormalities in individuals. However, there is a lack of consensus on the normative values for gait metrics in large populations. The primary objective of this study is to establish a normative database of spatiotemporal gait metrics across various age groups, contributing to a broader understanding of human gait dynamics. By doing so, we aim to enhance the clinical utility of gait analysis in diagnosing and managing health conditions.

METHODS

We conducted an observational case-control study involving 313 healthy participants. The MetaMotionC IMU by Mbientlab Inc., equipped with a triaxial accelerometer, gyroscope, and magnetometer, was used to capture gait data. The IMU was placed at the sternal angle of each participant to ensure optimal data capture during a 50 m walk along a flat, unobstructed pathway. Data were collected through a Bluetooth connection to a smartphone running a custom-developed application and subsequently analysed using IMUGaitPY, a specialised version of the GaitPY Python package.

RESULTS

The data showed that gait speeds decrease with ageing for males and females. The fastest gait speed is observed in the 41-50 age group at 1.35 ± 0.23 m/s. Males consistently exhibit faster gait speeds than females across all age groups. Step length and cadence do not have clear trends with ageing. Gait speed and step length increase consistently with height, with the tallest group (191-200 cm) walking at an average speed of 1.49 ± 0.12 m/s, with an average step length of 0.91 ± 0.05 m. Cadence, however, decreases with increasing height, with the tallest group taking 103.52 ± 5.04 steps/min on average.

CONCLUSIONS

This study has established a comprehensive normative database for the spatiotemporal gait metrics of gait speed, step length, and cadence, highlighting the complexities of gait dynamics across age and sex groups and the influence of height. Our findings offer valuable reference points for clinicians to distinguish between healthy and pathological gait patterns, facilitating early detection and intervention for gait-related disorders. Moreover, this database enhances the clinical utility of gait analysis, supporting more objective diagnoses and assessments of therapeutic interventions. The normative database provides a valuable reference future research and clinical practice. It enables a more nuanced understanding of how gait evolves with age, gender, and physical stature, thus informing the development of targeted interventions to maintain mobility and prevent falls in older adults. Despite potential selection bias and the cross-sectional nature of the study, the insights gained provide a solid foundation for further longitudinal studies and diverse sampling to validate and expand upon these findings.

Foot pressure transfers are altered in lumbar radiculopathy but reversible after surgery: a prospective, pedobarography study

BACKGROUND CONTEXT

Patients with lower lumbar stenosis and disc herniation report disability in standing and ambulation, despite normal neurological examination. The L5 and S1 nerve roots support the entire motor and sensory function of the foot, and their radiculopathy can affect foot loading during standing and walking. This has not been quantified before.

PURPOSE

To quantify alterations in static and dynamic foot pressure transfers in patients with lower lumbar nerve root compression, and document any beneficial effects of surgical decompression.

STUDY DESIGN

Prospective, case-control study.

PATIENT SAMPLE

Cases-Patients with unilateral radiculopathy (L5/S1) with normal neurology (n=50); Controls - Healthy volunteers (n=50).

METHODS

The volunteers and patients underwent pedobarographic analysis during standing (static) and walking (dynamic), and fifteen (12 dynamic and three static) parameters were documented. The patient's preoperative values were compared with that of the healthy volunteers. All the 50 patients underwent surgical decompression, and clinical outcome measures (VAS/ODI at 3 months) were documented. Pedobarographic analysis was repeated in the postoperative period (48 hours) and 3-month follow-up and compared with the preoperative scores.

RESULTS

In healthy controls, the mean values of all 15 parameters were comparable between the right and the left side (p>.05). When compared to controls, the patients had significantly lower maximum foot loads (p=.01) and average foot loads (p=.05) on the affected side during walking indicating lesser load transmission, in the preoperative period. Within the affected foot, the load transfer was higher on the first metatarsal/ medial arch while significantly less on the lateral metatarsals (p=.04). The percentage load on whole foot and forefoot was significantly less on standing (p=.01). Significant improvements were noted in the postoperative period, especially in the maximum foot surface area (p=.01), maximum and average foot loads, and improved weight transfers on lateral arch and forefoot (p=.02). The load on whole foot increased significantly from 46.1%±5.5% (preoperative) to 48.1%±5.5% (postoperative) and 49.9%±3.3% at follow-up (p=.01).

CONCLUSION

This is the first study using pedobarography to document altered foot pressure patterns during ambulation in patients with disc herniation and stenosis. Decreased load transfer, asymmetrical and unphysiological distribution of pressures on the affected foot were observed during weight bearing, which improved after surgical decompression.

Classification of inertial sensor-based gait patterns of orthopaedic conditions using machine learning: A pilot study

Elderly patients often have more than one disease that affects walking behavior. An objective tool to identify which disease is the main cause of functional limitations may aid clinical decision making. Therefore, we investigated whether gait patterns could be used to identify degenerative diseases using machine learning. Data were extracted from a clinical database that included sagittal joint angles and spatiotemporal parameters measured using seven inertial sensors, and anthropometric data of patients with unilateral knee or hip osteoarthritis, lumbar or cervical spinal stenosis, and healthy controls. Various classification models were explored using the MATLAB Classification Learner app, and the optimizable Support Vector Machine was chosen as the best performing model. The accuracy of discrimination between healthy and pathologic gait was 82.3%, indicating that it is possible to distinguish pathological from healthy gait. The accuracy of discrimination between the different degenerative diseases was 51.4%, indicating the similarities in gait patterns between diseases need to be further explored. Overall, the differences between pathologic and healthy gait are distinct enough to classify using a classical machine learning model; however, routinely recorded gait characteristics and anthropometric data are not sufficient for successful discrimination of the degenerative diseases.

Gait analysis using digital biomarkers including smart shoes in lumbar spinal canal stenosis: a scoping review

Lumbar spinal canal stenosis (LSS) is characterized by gait abnormalities, and objective quantitative gait analysis is useful for diagnosis and treatment. This review aimed to provide a review of objective quantitative gait analysis in LSS and note the current status and potential of smart shoes in diagnosing and treating LSS. The characteristics of gait deterioration in LSS include decreased gait velocity and asymmetry due to neuropathy (muscle weakness and pain) in the lower extremities. Previous laboratory objective and quantitative gait analyses mainly comprised marker-based three-dimensional motion analysis and ground reaction force. However, workforce, time, and costs pose some challenges. Recent developments in wearable sensor technology and markerless motion analysis systems have made gait analysis faster, easier, and less expensive outside the laboratory. Smart shoes can provide more accurate gait information than other wearable sensors. As only a few reports exist on gait disorders in patients with LSS, future studies should focus on the accuracy and cost-effectiveness of gait analysis using smart shoes.

Accelerations Recorded by Simple Inertial Measurement Units with Low Sampling Frequency Can Differentiate between Individuals with and without Knee Osteoarthritis: Implications for Remote Health Care

Determining the presence and severity of knee osteoarthritis (OA) is a valuable application of inertial measurement units (IMUs) in the remote monitoring of patients. This study aimed to employ the Fourier representation of IMU signals to differentiate between individuals with and without knee OA. We included 27 patients with unilateral knee osteoarthritis (15 females) and 18 healthy controls (11 females). Gait acceleration signals were recorded during overground walking. We obtained the frequency features of the signals using the Fourier transform. The logistic LASSO regression was employed on the frequency domain features as well as the participant's age, sex, and BMI to distinguish between the acceleration data from individuals with and without knee OA. The model's accuracy was estimated by 10-fold cross-validation. The frequency contents of the signals were different between the two groups. The average accuracy of the classification model using the frequency features was 0.91 ± 0.01. The distribution of the selected features in the final model differed between patients with different severity of knee OA. In this study, we demonstrated that using logistic LASSO regression on the Fourier representation of acceleration signals can accurately determine the presence of knee OA.

Gait Variability to Phenotype Common Orthopedic Gait Impairments Using Wearable Sensors

Mobility impairments are a common symptom of age-related degenerative diseases. Gait features can discriminate those with mobility disorders from healthy individuals, yet phenotyping specific pathologies remains challenging. This study aims to identify if gait parameters derived from two foot-mounted inertial measurement units (IMU) during the 6 min walk test (6MWT) can phenotype mobility impairment from different pathologies (Lumbar spinal stenosis (LSS)-neurogenic diseases, and knee osteoarthritis (KOA)-structural joint disease). Bilateral foot-mounted IMU data during the 6MWT were collected from patients with LSS and KOA and matched healthy controls (N = 30, 10 for each group). Eleven gait parameters representing four domains (pace, rhythm, asymmetry, variability) were derived for each minute of the 6MWT. In the entire 6MWT, gait parameters in all four domains distinguished between controls and both disease groups; however, the disease groups demonstrated no statistical differences, with a trend toward higher stride length variability in the LSS group (p = 0.057). Additional minute-by-minute comparisons identified stride length variability as a statistically significant marker between disease groups during the middle portion of 6WMT (3rd min: p ≤ 0.05; 4th min: p = 0.06). These findings demonstrate that gait variability measures are a potential biomarker to phenotype mobility impairment from different pathologies. Increased gait variability indicates loss of gait rhythmicity, a common feature in neurologic impairment of locomotor control, thus reflecting the underlying mechanism for the gait impairment in LSS. Findings from this work also identify the middle portion of the 6MWT as a potential window to detect subtle gait differences between individuals with different origins of gait impairment.

Objective gait assessment in individuals with knee osteoarthritis using inertial sensors: A systematic review and meta-analysis

BACKGROUND

Objective assessment of gait using inertial sensors has shown promising results for functional evaluations in individuals with knee osteoarthritis (OA). However, the large number of possible outcome measures calls for a systematic evaluation of most relevant parameters to be used for scientific and clinical purposes.

AIM

This systematic review and meta-analysis aimed to identify gait parameters derived from inertial sensors that reflect gait deviations in individuals with knee OA compared to healthy control subjects (HC).

METHODS

A systematic search was conducted in five electronic databases (Medline, Embase, Web of Science, CINAHL, IEEE) to identify eligible articles. Risk of bias was assessed using a modified version of the Downs and Black scale. Data regarding study population, experimental procedures, and biomechanical outcomes were extracted. When a gait parameter was reported by a sufficient number of studies, a random-effects meta-analysis was conducted using the inverse variance method.

RESULTS

Twenty-three articles comparing gait between 411 individuals with knee OA and 507 HC were included. Individuals with knee OA had a lower gait speed than HC (standardized mean difference = -1.65), driven by smaller strides with a longer duration. Stride time variability was slightly higher in individuals with knee OA than in HC. Individuals with knee OA walked with a lower range of motion of the knee during the swing phase, less lumbar motion in the coronal plane, and a lower foot strike and toe-off angle compared to HC.

SIGNIFICANCE

This review shows that inertial sensors can detect gait impairments in individuals with knee OA. Large standardized mean differences found on spatiotemporal parameters support their applicability as sensitive endpoints for mobility in individuals with knee OA. More advanced measures, including kinematics of knee and trunk, may reveal gait adaptations that are more specific to knee OA, but compelling evidence was lacking.

A machine learning approach for the identification of kinematic biomarkers of chronic neck pain during single- and dual-task gait

BACKGROUND

Changes in gait characteristics have been reported in people with chronic neck pain (CNP).

RESEARCH QUESTION

Can we classify people with and without CNP by training machine learning models with Inertial Measurement Units (IMU)-based gait kinematic data?

METHODS

Eighteen asymptomatic individuals and 21 participants with CNP were recruited for the study and performed two gait trajectories, (1) linear walking with their head straight (single-task) and (2) linear walking with continuous head-rotation (dual-task). Kinematic data were recorded from three IMU sensors attached to the forehead, upper thoracic spine (T1), and lower thoracic spine (T12). Temporal and spectral features were extracted to generate the dataset for both single- and dual-task gait. To evaluate the most significant features and simultaneously reduce the dataset size, the Neighbourhood Component Analysis (NCA) method was utilized. Three supervised models were applied, including K-Nearest Neighbour, Support Vector Machine, and Linear Discriminant Analysis to test the performance of the most important temporal and spectral features.

RESULTS

The performance of all classifiers increased after the implementation of NCA. The best performance was achieved by NCA-Support Vector Machine with an accuracy of 86.85%, specificity of 83.30%, and sensitivity of 92.85% during the dual-task gait using only nine features.

SIGNIFICANCE

The results present a data-driven approach and machine learning-based methods to identify test conditions and features from high-dimensional data obtained during gait for the classification of people with and without CNP.

Analysing gait patterns in degenerative lumbar spine diseases: a literature review

OBJECTIVES

To collate the current state of knowledge and explore differences in the spatiotemporal gait patterns of degenerative lumbar spine diseases: lumbar spinal stenosis (LSS), lumbar disc herniation (LDH) and low back pain (LBP).

BACKGROUND

LBP is common presenting complaint with degenerative lumbar spine disease being a common cause. In particular, the gait patterns of LSS, LDH and mechanical-type (facetogenic and discogenic) LBP is not established.

METHODS

A search of the literature was conducted to determine the changes in spatial and temporal gait metrics involved with each type of degenerative lumbar spine disease. A search of databases including Medline, Embase and PubMed from their date of inception to April 18th, 2021 was performed to screen, review and identify relevant studies for qualitative synthesis. Seventeen relevant studies were identified for inclusion in the present review. Of these, 5 studies investigated gait patterns in LSS, 10 studies investigated LBP and 2 studies investigated LDH. Of these, 4 studies employed wearable accelerometry in LSS (2 studies) and LBP (2 studies).

CONCLUSIONS

Previous studies suggest degenerative diseases of the lumbar spine have unique patterns of gait deterioration. LSS is characterised by asymmetry and variability. Spatiotemporal gait deterioration in gait velocity, cadence with increased double-support duration and gait variability are distinguishing features in LDH. LBP involves marginal abnormalities in temporal and spatial gait metrics. Previous studies suggest degenerative diseases of the lumbar spine have unique patterns of gait deterioration. Gait asymmetry and variability, may be relevant metrics for distinguishing between the gait profiles of lumbar spine diseases.

Examining the Association Between Self-Reported Estimates of Function and Objective Measures of Gait and Physical Capacity in Lumbar Stenosis

Objective:

To evaluate the association of self-reported physical function with subjective and objective measures as well as temporospatial gait features in lumbar spinal stenosis (LSS).

Design:

Cross-sectional pilot study.

Setting:

Outpatient multispecialty clinic.

Participants:

Participants with LSS and matched controls without LSS (n=10 per group; N=20).

Interventions:

Not applicable.

Main Outcome Measures:

Self-reported physical function (36-Item Short Form Health Survey [SF-36] physical functioning domain), Oswestry Disability Index, Swiss Spinal Stenosis Questionnaire, the Neurogenic Claudication Outcome Score, and inertia measurement unit (IMU)-derived temporospatial gait features

Results:

Higher self-reported physical function scores (SF-36 physical functioning) correlated with lower disability ratings, neurogenic claudication, and symptom severity ratings in patients with LSS (P<.05). Compared with controls without LSS, patients with LSS have lower scores on physical capacity measures (median total distance traveled on 6-minute walk test: controls 505 m vs LSS 316 m; median total distance traveled on self-paced walking test: controls 718 m vs LSS 174 m). Observed differences in IMU-derived gait features, physical capacity measures, disability ratings, and neurogenic claudication scores between populations with and without LSS were statistically significant.

Conclusions:

Further evaluation of the association of IMU-derived temporospatial gait with self-reported physical function, pain related-disability, neurogenic claudication, and spinal stenosis symptom severity score in LSS would help clarify their role in tracking LSS outcomes.

Inertial measurement units and application for remote healthcare in hip and knee osteoarthritis: a narrative review (Preprint)

Background

Measuring and modifying movement-related joint loading is integral to the management of lower extremity osteoarthritis (OA). Although traditional approaches rely on measurements made within the laboratory or clinical environments, inertial sensors provide an opportunity to quantify these outcomes in patients’ natural environments, providing greater ecological validity and opportunities to develop large data sets of movement data for the development of OA interventions.

Objective

This narrative review aimed to discuss and summarize recent developments in the use of inertial sensors for assessing movement during daily activities in individuals with hip and knee OA and to identify how this may translate to improved remote health care for this population.

Methods

A literature search was performed in November 2018 and repeated in July 2019 and March 2021 using the PubMed and Embase databases for publications on inertial sensors in hip and knee OA published in English within the previous 5 years. The search terms encompassed both OA and wearable sensors. Duplicate studies, systematic reviews, conference abstracts, and study protocols were also excluded. One reviewer screened the search result titles by removing irrelevant studies, and 2 reviewers screened study abstracts to identify studies using inertial sensors as the main sensing technology and a primary outcome related to movement quality. In addition, after the March 2021 search, 2 reviewers rescreened all previously included studies to confirm their relevance to this review.

Results

From the search process, 43 studies were determined to be relevant and subsequently included in this review. Inertial sensors have been successfully implemented for assessing the presence and severity of OA (n=11), assessing disease progression risk and providing feedback for gait retraining (n=7), and remotely monitoring intervention outcomes and identifying potential responders and nonresponders to interventions (n=14). In addition, studies have validated the use of inertial sensors for these applications (n=8) and analyzed the optimal sensor placement combinations and data input analysis for measuring different metrics of interest (n=3). These studies show promise for remote health care monitoring and intervention delivery in hip and knee OA, but many studies have focused on walking rather than a range of activities of daily living and have been performed in small samples (<100 participants) and in a laboratory rather than in a real-world environment.

Conclusions

Inertial sensors show promise for remote monitoring, risk assessment, and intervention delivery in individuals with hip and knee OA. Future opportunities remain to validate these sensors in real-world settings across a range of activities of daily living and to optimize sensor placement and data analysis approaches.

Wearable Inertial Sensors for Gait Analysis in Adults with Osteoarthritis-A Scoping Review

Our objective was to conduct a scoping review which summarizes the growing body of literature using wearable inertial sensors for gait analysis in lower limb osteoarthritis. We searched six databases using predetermined search terms which highlighted the broad areas of inertial sensors, gait, and osteoarthritis. Two authors independently conducted title and abstract reviews, followed by two authors independently completing full-text screenings. Study quality was also assessed by two independent raters and data were extracted by one reviewer in areas such as study design, osteoarthritis sample, protocols, and inertial sensor outcomes. A total of 72 articles were included, which studied the gait of 2159 adults with osteoarthritis (OA) using inertial sensors. The most common location of OA studied was the knee (n = 46), followed by the hip (n = 22), and the ankle (n = 7). The back (n = 41) and the shank (n = 40) were the most common placements for inertial sensors. The three most prevalent biomechanical outcomes studied were: mean spatiotemporal parameters (n = 45), segment or joint angles (n = 33), and linear acceleration magnitudes (n = 22). Our findings demonstrate exceptional growth in this field in the last 5 years. Nevertheless, there remains a need for more longitudinal study designs, patient-specific models, free-living assessments, and a push for "Code Reuse" to maximize the unique capabilities of these devices and ultimately improve how we diagnose and treat this debilitating disease.

Measuring Spinal Mobility Using an Inertial Measurement Unit System: A Validation Study in Axial Spondyloarthritis

Portable inertial measurement units (IMUs) are beginning to be used in human motion analysis. These devices can be useful for the evaluation of spinal mobility in individuals with axial spondyloarthritis (axSpA). The objectives of this study were to assess (a) concurrent criterion validity in individuals with axSpA by comparing spinal mobility measured by an IMU sensor-based system vs. optical motion capture as the reference standard; (b) discriminant validity comparing mobility with healthy volunteers; (c) construct validity by comparing mobility results with relevant outcome measures. A total of 70 participants with axSpA and 20 healthy controls were included. Individuals with axSpA completed function and activity questionnaires, and their mobility was measured using conventional metrology for axSpA, an optical motion capture system, and an IMU sensor-based system. The UCOASMI, a metrology index based on measures obtained by motion capture, and the IUCOASMI, the same index using IMU measures, were also calculated. Descriptive and inferential analyses were conducted to show the relationships between outcome measures. There was excellent agreement (ICC > 0.90) between both systems and a significant correlation between the IUCOASMI and conventional metrology (r = 0.91), activity (r = 0.40), function (r = 0.62), quality of life (r = 0.55) and structural change (r = 0.76). This study demonstrates the validity of an IMU system to evaluate spinal mobility in axSpA. These systems are more feasible than optical motion capture systems, and they could be useful in clinical practice.