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

Costello, K.E.; Felson, David T.; Neogi, Tuhina; Segal, N.A.; Lewis, Cora E.; Gross, K.D.; Nevitt, M.C.; Lewis, Cara L.; Kumar, Deepak

doi:10.1016/j.joca.2021.03.009

Cited by 17 publications

(13 citation statements)

References 35 publications

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“…The non-signi cant decrease in the foot upward acceleration on the affected side, despite the decrease in walking speed, may re ect the dysfunction of shock absorption at the IC due to insu cient double knee action. A previous study using a portable force plate demonstrated a more signi cant ground reaction force in the early stance phase on the severe side of patients with KOA than the control with gait speed adjustment (22). Our ndings of the foot upward acceleration at the IC on the contralateral side in patients with KOA are consistent with the previous result.…”

Section: Discussionsupporting

confidence: 91%

The Difference 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

Yaguchi

Honda

Sekiguchi

et al. 2022

Preprint

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Background: Gait disturbance is a critical problem in patients with osteoarthritis of the knee (KOA). The inertial measurement unit (IMU) is small size and lightweight allows for the measurement of patients without physical constraints. The purpose of this study was to investigate whether an insole with a single IMU can detect differences in kinematic parameters during gait between patients with KOA and healthy controls.Methods: Twenty patients with KOA, for whom unilateral total knee arthroplasty was planned, and 13 age-matched control subjects were included in this study. Participants wore shoes with IMU-equipped insoles and were required to walk across an 8-m walkway at a self-selected speed and foot movement data were collected during gait. We compared the spatiotemporal and kinematic parameters on the right side in healthy controls and both sides in patients with KOA.Results: The self-selected walking speed and stride length in the KOA group were significantly lower than those of the controls. The foot extension angular velocity at the toe-off (TO) was significantly smaller on the affected side than on the contralateral side (p<0.001) and in the healthy controls (p<0.001). During the swing phase, foot forward acceleration was significantly smaller on the affected side of KOA compared with healthy controls (p=0.005). Furthermore, foot upward acceleration at the initial contact (IC) in patients with KOA was significantly lower on the contralateral side compared with that of healthy controls (p=0.0167) but not on the affected side (p=0.344), despite a decrease in walking speed. Conclusions:The insole with a single IMU can detect differences in foot kinematics during gait between patients with KOA and healthy controls. Our findings indicate that patients with KOA exhibit a dysfunction of the push-off at TO and shock absorption at the IC on the affected side.

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Section: Discussionsupporting

confidence: 91%

The Difference 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

Yaguchi

Honda

Sekiguchi

et al. 2022

Preprint

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“…In a cross-sectional study in the same cohort, we previously reported that limbs with radiographic OA, with or without knee pain, have higher lateral GRFs in early stance compared to limbs without radiographic OA or pain. (38) The current results suggest lateral GRF may also play a role in progression. The 5.4% increased risk of cartilage worsening for the middle versus lowest tertile of time spent lying, along with prior research showing greater sedentary time is associated with future functional decline (39) and lower quality of life (40), suggests reducing sedentary time should be investigated as a potential intervention target.…”

Section: Predictors Of Oa Progressionmentioning

confidence: 53%

“…In a cross-sectional study in the same cohort, we have previously reported that limbs with radiographic OA, with or without knee pain, have higher lateral GRFs in early stance compared to limbs free from both radiographic OA and pain. [34] The current results suggest lateral GRF may also play a role in progression.…”

Section: Discussionmentioning

confidence: 69%

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

Costello

Felson

Jafarzadeh

et al. 2022

Preprint

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Objective: To 1) develop and evaluate a machine learning model incorporating gait and physical activity to predict medial tibiofemoral cartilage worsening over two years in individuals without or with early 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 2-year follow-up from gait, physical activity, clinical and demographic data from the Multicenter Osteoarthritis Study. Model performance was evaluated with Area Under the Curve (AUC) in repeated cross-validations. The top 10 influential predictors of the outcome across 100 held-out test sets were identified by a variable importance measure statistic, and their marginal effect on the outcome was quantified by g-computation. Results: Of 947 legs included in the analysis, 14% experienced medial cartilage worsening over two years. The median (2.5th-97.5th percentile) AUC across the 100 held-out test sets was 0.73 (0.64-0.80). Presence of baseline cartilage damage, higher Kellgren-Lawrence grade, greater pain during walking, and higher lateral ground reaction force impulse were associated with greater risk of cartilage worsening. Conclusions: An ensemble machine learning approach incorporating gait, physical activity, and clinical/demographic features showed good performance for predicting cartilage worsening over two years. While identifying potential intervention targets from the machine learning model is challenging, these results suggest that addressing high lateral ground reaction force impulse should be investigated further as a potential target to reduce medial tibiofemoral cartilage worsening in persons without or with early knee osteoarthritis.

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“…Also, the ESN used in this study aims to lower the sensor computational cost and energy consumption to help its implementation in wearables. An on-board wearable algorithm will decrease the device consumption by reducing the data communications that the system must perform [ 51 ]. ESN can lower the energy consumption of the sensor by reducing the memory and the processing time needed to run, which is another indirect indicator of the algorithm’s needs with regard to energy [ 52 , 53 ].…”

Section: Discussionmentioning

confidence: 99%

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

Havashinezhadian

Chiasson-Poirier

Sylvestre

et al. 2023

IJERPH

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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.

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

Cited by 17 publications

References 35 publications

The Difference 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

The Difference 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

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

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

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