&lt;p&gt;Comparison of gait, functional activities, and patient-reported outcome measures in patients with knee osteoarthritis and healthy adults using 3D motion analysis and activity monitoring: an exploratory case-control analysis&lt;/p&gt;

Sparkes, Valerie; Whatling, Gemma Marie; Biggs, Paul; Khatib, Nidal; Al-Amri, Mohammad; Williams, David; Hemming, Rebecca; Hagen, Martina; Saleem, Ishaak; Swaminathan, Ramesh; Holt, Catherine Avril

doi:10.2147/orr.s199107

Cited by 14 publications

(9 citation statements)

References 35 publications

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“…Knee joint function, gait speed, and WOMAC scores were lower in the knee OA group than in the healthy group (Table 2). Previous studies have also reported reduced knee extensor strength and gait speed in patients with knee OA compared to those in healthy subjects [6–8, 24, 25], which means that the results of the present study support those of previous studies. Furthermore, there were significant weak positive correlations between gait cycle variability and 5MWT (r=0.46) and between gait cycle variability and WOMAC (r=0.43).…”

Section: Discussionsupporting

confidence: 91%

Evaluation of gait cycle time variability in patients with knee osteoarthritis using a triaxial accelerometer

Akimoto

Kawamura

Wada³

et al. 2021

Preprint

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Knee osteoarthritis can alter gait variability. However, few studies have compared the temporal factors of the gait cycle between patients with knee osteoarthritis and healthy subjects. Furthermore, no studies have investigated the relationship between gait variability and potential contributing factors (knee joint functions such as muscle strength) in knee osteoarthritis. The first objective of this study was to compare gait cycle variability between female patients with knee osteoarthritis and healthy elderly women to determine gait characteristics in patients with knee osteoarthritis. The second objective was to examine whether gait cycle variability in knee osteoarthritis is associated with potential contributing factors. Twenty-four female patients diagnosed with knee osteoarthritis and 12 healthy elderly women participated. Gait cycle variability (coefficient of variation of gait cycle time), knee extension range of motion, knee extension strength, 5-meter walk test, Timed Up & Go Test, and Western Ontario and McMaster Universities Osteoarthritis Index were measured. All assessment results were compared between the knee osteoarthritis and healthy groups. Gait cycle variability was significantly higher in the knee osteoarthritis group (3.2%) compared to the healthy group (2.1%). A significant positive correlation was found between the gait cycle variability and 5-meter walk test (r=0.46) and Western Ontario and McMaster Universities Osteoarthritis Index (r=0.43). The gait of patients with knee osteoarthritis may be more unstable than that of healthy individuals. In addition, unstable gait may be associated with gait speed and quality of life. Therefore, we believe that rehabilitation to improve unstable gait can enhance the quality of life of patients with knee osteoarthritis.

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Section: Discussionsupporting

confidence: 91%

Evaluation of gait cycle time variability in patients with knee osteoarthritis using a triaxial accelerometer

Akimoto

Kawamura

Wada³

et al. 2021

Preprint

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“…All the subjects who participated in this study had either end-stage osteoarthritis in the hip or knee or had recently recovered from a total joint arthroplasty. This patient population has been shown to exhibit gait adaptations, including a slower pace, shorter step length, reduced knee flexion, and increased levels of variability that may affect the generalization of the model to healthy individuals [ 28 , 48 , 49 , 50 ]. Gait measurements were taken in the laboratory environment, which may affect the subjects’ normal gait patterns.…”

Section: Discussionmentioning

confidence: 99%

The Use of Synthetic IMU Signals in the Training of Deep Learning Models Significantly Improves the Accuracy of Joint Kinematic Predictions

Renani

Eustace

Myers

et al. 2021

Sensors

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Gait analysis based on inertial sensors has become an effective method of quantifying movement mechanics, such as joint kinematics and kinetics. Machine learning techniques are used to reliably predict joint mechanics directly from streams of IMU signals for various activities. These data-driven models require comprehensive and representative training datasets to be generalizable across the movement variability seen in the population at large. Bottlenecks in model development frequently occur due to the lack of sufficient training data and the significant time and resources necessary to acquire these datasets. Reliable methods to generate synthetic biomechanical training data could streamline model development and potentially improve model performance. In this study, we developed a methodology to generate synthetic kinematics and the associated predicted IMU signals using open source musculoskeletal modeling software. These synthetic data were used to train neural networks to predict three degree-of-freedom joint rotations at the hip and knee during gait either in lieu of or along with previously measured experimental gait data. The accuracy of the models’ kinematic predictions was assessed using experimentally measured IMU signals and gait kinematics. Models trained using the synthetic data out-performed models using only the experimental data in five of the six rotational degrees of freedom at the hip and knee. On average, root mean square errors in joint angle predictions were improved by 38% at the hip (synthetic data RMSE: 2.3°, measured data RMSE: 4.5°) and 11% at the knee (synthetic data RMSE: 2.9°, measured data RMSE: 3.3°), when models trained solely on synthetic data were compared to measured data. When models were trained on both measured and synthetic data, root mean square errors were reduced by 54% at the hip (measured + synthetic data RMSE: 1.9°) and 45% at the knee (measured + synthetic data RMSE: 1.7°), compared to measured data alone. These findings enable future model development for different activities of clinical significance without the burden of generating large quantities of gait lab data for model training, streamlining model development, and ultimately improving model performance.

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“…Previous studies of gait analysis showed that walking speed and step length of patients with knee osteoarthritis are significantly reduced compared with normal people, and the single support time in the whole gait cycle is significantly longer. The above parameters of patients after TKA will be improved compared with the preoperative parameters, but there is still a certain gap compared with normal gait [33][34][35]. This study mainly compared the effects of GB and MR techniques on postoperative gait in TKA.…”

Section: Discussionmentioning

confidence: 99%

Is There an Advantage with Measured Resection for Femoral Rotation Resection in TKA? A Randomized Controlled Trial

Zhang

Sun

et al. 2019

Preprint

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BackgroundTotal knee arthroplasty (TKA) is performed by gap balance (GB) and measured resection (MR) techniques for femoral rotation resection to achieve knee flexion balance and knee stability. It is still controversial to choose which technique is more advantageous. The purpose of this study was to compare the early clinical efficacy of GB and MR in TKA.MethodsThis study was a prospective randomized, double-blind controlled trial. From March 2018 to March 2019, 99 patients (99 knees) who underwent TKA at our institution were randomly treated with GB and MR. The cutting thickness of posterior condyle, radiographic findings, medial and lateral compartment pressure, gait analysis, and patients satisfaction were recorded. ResultsA total of 110 patients were enrolled for unilateral THA. Finally, 99 patients were included for analysis and comparison at 12 months follow-up. In GB group, the angle of Cutting Block to PCA was significantly lower than in the MR group (P<0.001) and the cutting thickness of the medial and lateral condyle was significantly higher than in the MR group (P<0.05). Medial compartment pressure was significantly higher than lateral compartment at 90° flexion and full flexion in MR (P<0.05). In GB group, Sagittal max knee flexion range and transversal knee rotation range was significantly was significantly larger than that in the MR group (P<0.05). In level of satisfaction at the most recent follow-up, there was no significant difference between the two groups (p>0.05).ConclusionIn TKA, GB or MR techniques for femoral external resection has little effect on daily walking. However, the GB technique has advantages in early postoperative knee stability and knee flexion function improvement.

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<p>Comparison of gait, functional activities, and patient-reported outcome measures in patients with knee osteoarthritis and healthy adults using 3D motion analysis and activity monitoring: an exploratory case-control analysis</p>

Cited by 14 publications

References 35 publications

Evaluation of gait cycle time variability in patients with knee osteoarthritis using a triaxial accelerometer

Evaluation of gait cycle time variability in patients with knee osteoarthritis using a triaxial accelerometer

The Use of Synthetic IMU Signals in the Training of Deep Learning Models Significantly Improves the Accuracy of Joint Kinematic Predictions

Is There an Advantage with Measured Resection for Femoral Rotation Resection in TKA? A Randomized Controlled Trial

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