Ground reaction force patterns in knees with and without radiographic osteoarthritis and pain: descriptive analyses of a large cohort (the Multicenter Osteoarthritis Study)

The relation of walking forces to structural damage in the knee: The Multicenter Osteoarthritis Study

OBJECTIVE

Mechanical loading is an important, modifiable risk factor for knee osteoarthritis. Identifying walking loads associated with disease worsening presents intervention opportunities. Our purpose was to evaluate the longitudinal relation of the baseline vertical ground reaction force (GRF) during walking to worsening bone marrow lesions (BMLs) and cartilage damage using cohort data from the Multicenter Osteoarthritis Study (MOST).

METHODS

MOST participants with GRF data at baseline and magnetic resonance imaging examinations at baseline and 2-year follow-up were included. Peak impact force (PIF) and average loading rate (ALR) from the vertical GRF were analyzed with respect to four joint regions (i.e., the medial and lateral portions of the tibiofemoral and patellofemoral joints). Analyses used logistic regression with generalized estimating equations and adjusted for relevant covariates.

RESULTS

Higher PIF was associated with increased odds of worsening BMLs in the lateral patellofemoral joint (odds ratio (95% confidence interval [CI]): 1.33 (1.11, 1.60)) and worsening cartilage damage in the lateral patellofemoral joint (1.48 (1.24, 1.77)), lateral tibiofemoral joint (1.24 (1.03, 1.50)), and medial tibiofemoral joint (1.25 (1.06, 1.48)). Higher ALR was associated with reduced odds of BML worsening in the lateral tibiofemoral joint (0.60 (0.41,0.87)).

CONCLUSIONS

Higher peak forces when walking were associated with worsening BMLs in the lateral patellofemoral joint and with worsening cartilage damage in regions of the knee associated with higher contact forces during walking. Higher ALRs were not associated with increased odds of structural worsening (BMLs or cartilage).

Assessment of pre- and post-operative gait dynamics in total knee arthroplasty by a wearable capture system

BACKGROUND

Walking function reconstruction is suboptimal after total knee arthroplasty. However, a comprehensive investigation of kinematic and kinetic parameters before and after total knee arthroplasty is lacking. This study aimed to quantitatively compare the differences in gait parameters before and after total knee arthroplasty with those of healthy control group.

METHODS

This study utilized a wearable capture system to obtain gait parameters from pre- operative and one-year post- operative patients, as well as from the healthy control group. The parameters included walking speed, the stance phase percentage during the gait cycle, knee flexion angle, center of pressure trajectory, vertical ground reaction force, and its moment on the coronal plane of the knee joint.

RESULTS

Post-total knee arthroplasty patients presented an averaged 12.5 % improvement in walking speed and an averaged 19.75 % increasement in the maximum knee flexion angle during the gait cycle, although both were still lower than those of the healthy control group. During the stance phase, the vertical ground reaction force exhibited a less pronounced double-hump feature, and compared to preoperative levels, the peak of the coronal plane moment of the knee was reduced by approximately half.

CONCLUSION

One-year post- total knee arthroplasty patients exhibited improved walking function compared to preoperative levels, but a gap remained compared to healthy control group. Additionally, preoperative gait abnormalities persisted postoperatively.

Individual joint contributions to forward propulsion during treadmill walking in women with hip osteoarthritis

As we age, reliance on the ankle musculature for push-off during walking reduces and increased reliance on the hip musculature is observed. It is unclear how joint pathology like osteoarthritis may affect this distal-to-proximal redistribution of propulsion. Here, we revisited a proof-of-concept study to study the effect of split-belt treadmill training, designed to reduce step length asymmetry, on forward propulsion during walking. Eleven women with hip osteoarthritis and five age-matched control participants walked on an instrumented split-belt treadmill at their preferred speed (hip osteoarthritis: 0.73 ± 0.11 m/s; controls: 0.59 ± 0.26 m/s). Women with hip osteoarthritis had less ankle power and propulsive force than controls, and greater hip contributions to forward propulsion on their involved limb. Following split-belt treadmill training, propulsive force increased on the involved limb. Five of 11 participants experienced a change in redistribution ratio that was greater than the minimal clinically meaningful difference. These "responders" had greater variability in pre-training redistribution ratio compared to non-responders. Women with hip osteoarthritis had poorer propulsive gait mechanics than controls yet split-belt treadmill training improved propulsive force. Redistribution ratio also changed in participants with high baseline variability. Our results suggest that split-belt treadmill training may be beneficial to people with hip osteoarthritis who have high variability in walking parameters. Further, the age-related shift to increased hip contributions to propulsion across populations of older adults may be due to increased variability during walking.

The association between patella alignment and morphology and knee osteoarthritis

OBJECTIVE

This study aims to quantitatively assess the relationship between the patella alignment and morphology and knee osteoarthritis (KOA), as well as the kinematics and kinetics of the knee, using gait analysis.

METHODS

Eighty age-matched patients with KOA and control subjects were evaluated. Incident radiographic osteoarthritis (iROA) was identified using a Kellgren-Lawrence (KL) grade of ≥ 2. The modified Insall-Salvati ratio (Mod-ISR), patellar tilt angle (PTA), and patella index (PI) were utilized to evaluate the sagittal and transverse alignment of the patella and its morphology, respectively. Regression analyses were conducted to explore associations between patellar measurements and KOA, iROA, kinematics, and kinetics.

RESULTS

Significant differences were observed between the control and KOA groups in terms of KL grade, patella alta, abduction angle, and reaction force to the ground (P < 0.05, respectively). Following adjustment for covariates, a significant positive association was found between patella alta and KOA (OR = 0.307, 95%CI: 0.103 to 0.918, P = 0.035). Additionally, a significant negative association was observed between PTA and abduction angle (B = -0.376, 95%CI: -0.751 to -0.002; P = 0.049). The PI exhibited a statistically significant association with log-transformed vertical ground reaction force (B = 0.002, 95%CI: 0.001 to 0.003, P = 0.002). Furthermore, adjustment for covariates did not reveal any significant correlations with other indicators (P > 0.05, respectively).

CONCLUSION

This study provides further evidence that proper alignment and morphology of the patella might be associated with maintaining normal biomechanical function. In addition, intervention measures targeting relevant patellar parameters, such as Mod-ISR, PTA, and PI, may positively impact KOA treatment outcomes.

Fourier Analysis of the Vertical Ground Reaction Force During Walking: Applications for Quantifying Differences in Gait Strategies

Time series biomechanical data inform our understanding of normal gait mechanics and pathomechanics. This study examines the utility of different quantitative methods to distinguish vertical ground reaction forces (VGRFs) from experimentally distinct gait strategies. The goals of this study are to compare measures of VGRF data-using the shape factor method and a Fourier series-based analysis-to (1) describe how these methods reflect and distinguish gait patterns and (2) determine which Fourier series coefficients discriminate normal walking, with a relatively stiff-legged gait, from compliant walking, using deep knee flexion and limited vertical oscillation. This study includes a reanalysis of previously presented VGRF data. We applied the shape factor method and fit 3- to 8-term Fourier series to zero-padded VGRF data. We compared VGRF renderings using Euclidean L2 distances and correlations stratified by gait strategy. Euclidean L2 distances improved with additional harmonics, with limited improvement after the seventh term. Euclidean L2 distances were greater in shape factor versus Fourier series renderings. In the 8 harmonic model, amplitudes of 9 Fourier coefficients-which contribute to VGRF features including peak and local minimum amplitudes and limb loading rates-were different between normal and compliant walking. The results suggest that Fourier series-based methods distinguish between gait strategies.

Effect of sensor number and location on accelerometry-based vertical ground reaction force estimation during walking

Knee osteoarthritis is a major cause of global disability and is a major cost for the healthcare system. Lower extremity loading is a determinant of knee osteoarthritis onset and progression; however, technology that assists rehabilitative clinicians in optimizing key metrics of lower extremity loading is significantly limited. The peak vertical component of the ground reaction force (vGRF) in the first 50% of stance is highly associated with biological and patient-reported outcomes linked to knee osteoarthritis symptoms. Monitoring and maintaining typical vGRF profiles may support healthy gait biomechanics and joint tissue loading to prevent the onset and progression of knee osteoarthritis. Yet, the optimal number of sensors and sensor placements for predicting accurate vGRF from accelerometry remains unknown. Our goals were to: 1) determine how many sensors and what sensor locations yielded the most accurate vGRF loading peak estimates during walking; and 2) characterize how prescribing different loading conditions affected vGRF loading peak estimates. We asked 20 young adult participants to wear 5 accelerometers on their waist, shanks, and feet and walk on a force-instrumented treadmill during control and targeted biofeedback conditions prompting 5% underloading and overloading vGRFs. We trained and tested machine learning models to estimate vGRF from the various sensor accelerometer inputs and identified which combinations were most accurate. We found that a neural network using one accelerometer at the waist yielded the most accurate loading peak vGRF estimates during walking, with average errors of 4.4% body weight. The waist-only configuration was able to distinguish between control and overloading conditions prescribed using biofeedback, matching measured vGRF outcomes. Including foot or shank acceleration signals in the model reduced accuracy, particularly for the overloading condition. Our results suggest that a system designed to monitor changes in walking vGRF or to deploy targeted biofeedback may only need a single accelerometer located at the waist for healthy participants.

Digital wearable insole-based identification of knee arthropathies and gait signatures using machine learning

Gait is impaired in musculoskeletal conditions, such as knee arthropathy. Gait analysis is used in clinical practice to inform diagnosis and monitor disease progression or intervention response. However, clinical gait analysis relies on subjective visual observation of walking as objective gait analysis has not been possible within clinical settings due to the expensive equipment, large-scale facilities, and highly trained staff required. Relatively low-cost wearable digital insoles may offer a solution to these challenges. In this work, we demonstrate how a digital insole measuring osteoarthritis-specific gait signatures yields similar results to the clinical gait-lab standard. To achieve this, we constructed a machine learning model, trained on force plate data collected in participants with knee arthropathy and controls. This model was highly predictive of force plate data from a validation set (area under the receiver operating characteristics curve [auROC] = 0.86; area under the precision-recall curve [auPR] = 0.90) and of a separate, independent digital insole dataset containing control and knee osteoarthritis subjects (auROC = 0.83; auPR = 0.86). After showing that digital insole-derived gait characteristics are comparable to traditional gait measurements, we next showed that a single stride of raw sensor time-series data could be accurately assigned to each subject, highlighting that individuals using digital insoles can be identified by their gait characteristics. This work provides a framework for a promising alternative to traditional clinical gait analysis methods, adds to the growing body of knowledge regarding wearable technology analytical pipelines, and supports clinical development of at-home gait assessments, with the potential to improve the ease, frequency, and depth of patient monitoring.

Sustained Limb-Level Loading: A Ground Reaction Force Phenotype Common to Individuals at High Risk for and Those With Knee Osteoarthritis

OBJECTIVE

The objective of this study was to compare the vertical (vGRF), anterior-posterior (apGRF), and medial-lateral (mlGRF) ground reaction force (GRF) profiles throughout the stance phase of gait (1) between individuals 6 to 12 months post-anterior cruciate ligament reconstruction (ACLR) and uninjured matched controls and (2) between ACLR and individuals with differing radiographic severities of knee osteoarthritis (KOA), defined as Kellgren and Lawrence (KL) grades KL2, KL3, and KL4.

METHODS

A total of 196 participants were included in this retrospective cross-sectional analysis. Gait biomechanics were collected from individuals 6 to 12 months post-ACLR (n = 36), uninjured controls matched to the ACLR group (n = 36), and individuals with KL2 (n = 31), KL3 (n = 67), and KL4 osteoarthritis (OA) (n = 26). Between-group differences in vGRF, apGRF, and mlGRF were assessed in reference to the ACLR group throughout each percentage of stance phase using a functional linear model.

RESULTS

The ACLR group demonstrated lower vGRF and apGRF in early and late stance compared to the uninjured controls, with large effects (Cohen's d range: 1.35-1.66). Conversely, the ACLR group exhibited greater vGRF (87%-90%; 4.88% body weight [BW]; d = 0.75) and apGRF (84%-94%; 2.41% BW; d = 0.79) than the KL2 group in a small portion of late stance. No differences in mlGRF profiles were observed between the ACLR and either the uninjured controls or the KL2 group. The magnitude of difference in GRF profiles between the ACLR and OA groups increased with OA disease severity.

CONCLUSION

Individuals 6 to 12 months post-ACLR exhibit strikingly similar GRF profiles as individuals with KL2 KOA, suggesting both patient groups may benefit from targeted interventions to address aberrant GRF profiles.

Differences in kinematic parameters during gait between the patients with knee osteoarthritis and healthy controls using an insole with a single inertial measurement unit: A case-control study

BACKGROUND

An inertial measurement unit is small and lightweight, allowing patient measurements without physical constraints. This study aimed to determine the differences in kinematic parameters during gait using an insole with a single inertial measurement unit in healthy controls and on both sides in patients with knee osteoarthritis.

METHODS

Twenty patients with knee osteoarthritis and 13 age-matched controls were included in this study. The participants walked at a self-selected speed and foot kinematics were measured during gait using an insole with a single inertial measurement unit. The right side of the healthy controls and both the affected and contralateral sides of patients with KOA were analyzed separately.

FINDINGS

The foot extension angular velocity at toe-off was significantly reduced on the affected side than on the contralateral side (P < 0.001) and in healthy controls (P < 0.001). During the swing phase, foot posterior-anterior acceleration was significantly lower on the affected side than on the healthy controls (P = 0.005). Furthermore, despite a decrease in walking speed, foot superior-inferior acceleration at initial contact in patients was significantly lower on the contralateral side than in healthy controls (P = 0.0167), but not on the affected side (P = 0.344).

INTERPRETATION

An insole with a single inertial measurement unit can detect differences in foot kinematics during gait between healthy controls and patients with knee osteoarthritis. Our findings indicate that patients with knee osteoarthritis exhibit dysfunction of push-off at toe-off and shock absorption at initial contact on the affected side.

Gait, physical activity and tibiofemoral cartilage damage: a longitudinal machine learning analysis in the Multicenter Osteoarthritis Study

OBJECTIVE

To (1) develop and evaluate a machine learning model incorporating gait and physical activity to predict medial tibiofemoral cartilage worsening over 2 years in individuals without advanced knee osteoarthritis and (2) identify influential predictors in the model and quantify their effect on cartilage worsening.

DESIGN

An ensemble machine learning model was developed to predict worsened cartilage MRI Osteoarthritis Knee Score at follow-up from gait, physical activity, clinical and demographic data from the Multicenter Osteoarthritis Study. Model performance was evaluated in repeated cross-validations. The top 10 predictors of the outcome across 100 held-out test sets were identified by a variable importance measure. Their effect on the outcome was quantified by g-computation.

RESULTS

Of 947 legs in the analysis, 14% experienced medial cartilage worsening at follow-up. The median (2.5-97.5th percentile) area under the receiver operating characteristic curve across the 100 held-out test sets was 0.73 (0.65-0.79). Baseline cartilage damage, higher Kellgren-Lawrence grade, greater pain during walking, higher lateral ground reaction force impulse, greater time spent lying and lower vertical ground reaction force unloading rate were associated with greater risk of cartilage worsening. Similar results were found for the subset of knees with baseline cartilage damage.

CONCLUSIONS

A machine learning approach incorporating gait, physical activity and clinical/demographic features showed good performance for predicting cartilage worsening over 2 years. While identifying potential intervention targets from the model is challenging, lateral ground reaction force impulse, time spent lying and vertical ground reaction force unloading rate should be investigated further as potential early intervention targets to reduce medial tibiofemoral cartilage worsening.

Changes in biomechanics, strength, physical function, and daily steps after extended-release corticosteroid injections in knee osteoarthritis: a responder analysis

INTRODUCTION/OBJECTIVE

To determine changes in gait biomechanics, quadricep strength, physical function, and daily steps after an extended-release corticosteroid knee injection at 4 and 8 weeks post-injection in individuals with knee osteoarthritis as well as between responders and non-responders based on changes in self-reported knee function.

METHOD

The single-arm, clinical trial included three study visits (baseline, 4 weeks, and 8 weeks post-injection), where participants received an extended-release corticosteroid injection following the baseline visit. Time-normalized vertical ground reaction force (vGRF), knee flexion angle (KFA), knee abduction moment (KAM), and knee extension moment (KEM) waveforms throughout stance were collected during gait biomechanical assessments. Participants also completed quadricep strength, physical function (chair-stand, stair-climb, 20-m fast-paced walk) testing, and free-living daily step assessment for 7 days following each visit.

RESULTS

All participants demonstrated increased KFA excursion (i.e., greater knee extension angle at heel strike and KFA at toe-off), increased KEM during early stance, improved physical function (all p < 0.001), and increased quadricep strength at 4 and 8 weeks. KAM increased throughout most of stance at 4 and 8 weeks post-injection (p < 0.001) but appears to be driven by gait changes in non-responders. Non-responders demonstrated lesser vGRF during late stance and lesser KEM and KFA throughout stance compared to responders at baseline.

CONCLUSIONS

Extended-release corticosteroid injections demonstrated short-term improvements in gait biomechanics, quadricep strength, and physical function for up to 4 weeks. However, non-responders demonstrated gait biomechanics associated with osteoarthritis progression prior to the corticosteroid injection, suggesting that non-responders demonstrate more deleterious gait biomechanics prior to corticosteroid injection. Key Points • Individuals with knee osteoarthritis who were treated with extended-release corticosteroid injections demonstrated improvements in gait biomechanics and physical function for 8 weeks. • Individuals with knee osteoarthritis, who walked with aberrant walking biomechanics before treatment, failed to respond to extended-release corticosteroid treatment. • Future research should determine the mechanisms contributing to the short-term changes in gait biomechanics and physical function such as reduced inflammation.

Key-Point Detection Algorithm of Deep Learning Can Predict Lower Limb Alignment with Simple Knee Radiographs

(1) Background: There have been many attempts to predict the weight-bearing line (WBL) ratio using simple knee radiographs. Using a convolutional neural network (CNN), we focused on predicting the WBL ratio quantitatively. (2) Methods: From March 2003 to December 2021, 2410 patients with 4790 knee AP radiographs were randomly selected using stratified random sampling. Our dataset was cropped by four points annotated by a specialist with a 10-pixel margin. The model predicted our interest points, which were both plateau points, i.e., starting WBL point and exit WBL point. The resulting value of the model was analyzed in two ways: pixel units and WBL error values. (3) Results: The mean accuracy (MA) was increased from around 0.5 using a 2-pixel unit to around 0.8 using 6 pixels in both the validation and the test sets. When the tibial plateau length was taken as 100%, the MA was increased from approximately 0.1, using 1%, to approximately 0.5, using 5% in both the validation and the test sets. (4) Conclusions: The DL-based key-point detection algorithm for predicting lower limb alignment through labeling using simple knee AP radiographs demonstrated comparable accuracy to that of the direct measurement using whole leg radiographs. Using this algorithm, the WBL ratio prediction with simple knee AP radiographs could be useful to diagnose lower limb alignment in osteoarthritis patients in primary care.

Inertial Sensor Location for Ground Reaction Force and Gait Event Detection Using Reservoir Computing in Gait

Inertial measurement units (IMUs) have shown promising outcomes for estimating gait event detection (GED) and ground reaction force (GRF). This study aims to determine the best sensor location for GED and GRF prediction in gait using data from IMUs for healthy and medial knee osteoarthritis (MKOA) individuals. In this study, 27 healthy and 18 MKOA individuals participated. Participants walked at different speeds on an instrumented treadmill. Five synchronized IMUs (Physilog®, 200 Hz) were placed on the lower limb (top of the shoe, heel, above medial malleolus, middle and front of tibia, and on medial of shank close to knee joint). To predict GRF and GED, an artificial neural network known as reservoir computing was trained using combinations of acceleration signals retrieved from each IMU. For GRF prediction, the best sensor location was top of the shoe for 72.2% and 41.7% of individuals in the healthy and MKOA populations, respectively, based on the minimum value of the mean absolute error (MAE). For GED, the minimum MAE value for both groups was for middle and front of tibia, then top of the shoe. This study demonstrates that top of the shoe is the best sensor location for GED and GRF prediction.

Discriminating features of ground reaction forces in overweight old and young adults during walking using functional principal component analysis

BACKGROUND

Limited attention has been paid to age- or body size-related changes in the ground reaction forces (GRF) during walking despite their strong associations with lower limb injuries and pathology.

RESEARCH QUESTION

Do the features of GRF during walking associate with age or body size?

METHODS

Fifty-four participants were subdivided into four groups according to their age and body size: overweight old (n = 12), non-overweight old (n = 13), overweight young (n = 13), and non-overweight young (n = 16). Participants were asked to walk at their self-selected speeds on level ground with force plates embedded in the center of walkway. Functional principal component analysis (FPCA) was performed to extract major modes of variation and functional principal component scores (FPCs) in three-dimensional GRFs. Analysis of variance models were employed to investigate the effect of age, body size, or their interactions on the FPCs of each component of the GRF, with the adjustment to gait speed.

RESULTS

Significant age and body size effects were observed in FPC1 across all three-dimensional GRF. Both overweight and older groups showed greater braking force after heel-strike and greater propulsive forces during pre-swing when compared to the non-overweight and younger groups, respectively. The overweight old group displayed greater medial forces during mid-stance and the overweight young group showed prominently larger medial forces during pre-swing, while non-overweight old showed a tendency of flatter medial-lateral GRF waveforms during the entire stance phase. FPC2 revealed that only body size had an effect on three-dimensional GRF with the highest FPC2 scores in the overweight old group.

SIGNIFICANCE

Three-dimensional GRF during walking could be altered by the body size and age, which were more pronounced in the overweight and older group. The more dynamic GRF pattern with greater and/or lower peaks could be contributing factors to the increased joint load and injury rates observed in overweight aged individuals.

Osteoarthritis year in review 2021: mechanics

Osteoarthritis (OA) has a complex, heterogeneous and only partly understood etiology. There is a definite role of joint cartilage pathomechanics in originating and progressing of the disease. Although it is still not identified precisely enough to design or select targeted treatments, the progress of this year's research demonstrates that this goal became much closer. On multiple scales - tissue, joint and whole body - an increasing number of studies were done, with impressive results. (1) Technology based instrument innovations, especially when combined with machine learning models, have broadened the applicability of biomechanics. (2) Combinations with imaging make biomechanics much more precise & personalized. (3) The combination of Musculoskeletal & Finite Element Models yield valid personalized cartilage loads. (4) Mechanical outcomes are becoming increasingly meaningful to inform and evaluate treatments, including predictive power from biomechanical models. Since most recent advancements in the field of biomechanics in OA are at the level of a proof op principle, future research should not only continue on this successful path of innovation, but also aim to develop clinical workflows that would facilitate including precision biomechanics in large scale studies. Eventually this will yield clinical tools for decision making and a rationale for new therapies in OA.