Juvenile idiopathic arthritis, gait characteristics and relation to function

Woolnough, Leandra; Pomputius, Ariel; Vincent, Heather K.

doi:10.1016/j.gaitpost.2020.12.010

Cited by 21 publications

(15 citation statements)

References 51 publications

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“…In assessing proof-of-concept, this allowed us to explore two potential applications of this technology simultaneously. Our study supported results from other studies exploring gait kinematics [14], however, no studies were identified that also explored individual knee joint analysis from single leg joint flexion-extension. We specifically chose to explore this method of data recording for several reasons.…”

Section: Strengths and Limitations Of The Studysupporting

confidence: 89%

“…Prolonged arthritis can lead to deformities and growth disturbances [11,12]. Joint inflammation can result in posture and movement modifications that disturb joint range of movement and change the normal walking gait [13,14]. JIA has an insidious onset which can affect its timely diagnosis and due to its close similarity to other diseases, there is a risk of misdiagnosis [9].…”

Section: Juvenile Idiopathic Arthritismentioning

confidence: 99%

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Proof-of-Concept Study of the Use of Accelerometry to Quantify Knee Joint Movement and Assist with the Diagnosis of Juvenile Idiopathic Arthritis

et al. 2022

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Juvenile idiopathic arthritis (JIA) is the most common rheumatic disease in childhood. Seven children and young people (CYP) with a diagnosis of JIA and suspected active arthritis of a single knee joint were recruited for this proof-of-concept study. The presence of active arthritis was confirmed by clinical examination. Four tri-axial accelerometers were integrated individually in elastic bands and placed above and below each knee. Participants performed ten periodic flexion-extensions of each knee joint while lying down, followed by walking ten meters in a straight path. The contralateral (non-inflamed) knee joint acted as a control. Accelerometry data were concordant with the results of clinical examination in six out of the seven patients recruited. There was a significant difference between the accelerometry measured range of movement (ROM, p-value = 0.032) of the knees with active arthritis and the healthy contralateral knees during flexion-extension. No statistically significant difference was identified between the ROM of the knee joints with active arthritis and healthy knee joints during the walking test. The study demonstrated that accelerometry may help in differentiating between healthy knee joints and those with active arthritis; however, further research is required to confirm these findings.

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Section: Strengths and Limitations Of The Studysupporting

confidence: 89%

Section: Juvenile Idiopathic Arthritismentioning

confidence: 99%

Proof-of-Concept Study of the Use of Accelerometry to Quantify Knee Joint Movement and Assist with the Diagnosis of Juvenile Idiopathic Arthritis

et al. 2022

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“…However, there is evidence that JIA impacts biomechanical features of gait and the motion of hip, knee and ankle joints. These changes may be alleviated by exercise or pharmaceutical interventions [51].…”

Section: Discussionmentioning

confidence: 99%

Impaired cardiorespiratory and neuromuscular fitness in children and adolescents with juvenile idiopathic arthritis: a cross-sectional case–control study in the era of biologic drug therapies

et al. 2023

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Background In recent years, biologic drug therapies have altered the course of juvenile idiopathic arthritis (JIA) possibly also improving the patients’ physical fitness. However, studies measuring both cardiorespiratory and muscular fitness in children with JIA are sparse and have failed to show consistent results. Our aim was to assess both cardiorespiratory and neuromuscular fitness and contributing factors in children and adolescents with JIA in the era of biologic drug therapies. Methods This cross-sectional study consisted of 73 JIA patients (25 boys, 48 girls) aged 6.8- 17.5 years and 73 healthy age- and sex-matched controls, investigated in 2017–2019. Cardiorespiratory fitness was assessed by maximal ergospirometry and neuromuscular fitness by speed, agility, balance, and muscle strength tests. Results Means (± SD) of maximal workload (Wmax/kg) and peak oxygen uptake (VO2peak/kg,) were lower in JIA patients than in controls (Wmax/kg: 2.80 ± 0.54 vs. 3.14 ± 0.50 Watts, p < 0.01; VO2peak/kg: 38.7 ± 7.53 vs. 45.8 ± 6.59 ml/min/kg, p < 0.01). Shuttle-run, sit-up and standing long jump test results were lower in JIA patients than in controls (p < 0.01). Mean (± SD) daily activity was lower (89.0 ± 44.7 vs. 112.7 ± 62.1 min/day, p < 0.05), and sedentary time was higher (427 ± 213 vs. 343 ± 211 min/day, p < 0.05) in JIA patients compared to controls. Physical activity and cardiorespiratory or neuromuscular fitness were not associated with disease activity. Conclusions JIA patients were physically less active and had lower cardiorespiratory and neuromuscular fitness than their same aged controls with no JIA. Therefore, JIA patients should be encouraged to engage in physical activities as a part of their multidisciplinary treatment protocols to prevent adverse health risks of low physical activity and fitness.

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“…TCN. For TCN, we use a single stacks of residual block with a kernel size 3, 128 filters in the convolutional layers, dilations of (1,2,4,8,16), dropout rate of 0.05, and weight normalization. TCN layer is followed by two dense layers and a dropout layer after each of them.…”

Section: State-of-the-art Model Descriptionmentioning

confidence: 99%

Estimation of Lower Extremity Joint Moments and 3D Ground Reaction Forces Using IMU Sensors in Multiple Walking Conditions: A Deep Learning Approach

Hossain

Guo

Choi

2023

IEEE J. Biomed. Health Inform.

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Human kinetics, specifically joint moments and ground reaction forces (GRFs) can provide important clinical information and can be used to control assistive devices. Traditionally, collection of kinetics is mostly limited to the lab environment because it relies on data that measured from a motion capture system and floor-embedded force plates to calculate the dynamics via musculoskeletal models. This spatially limited method makes it extremely challenging to measure kinetics outside the laboratory in a variety of walking conditions due to the expensive device setup and large space required. Recently, employing machine learning with IMU sensors are suggested as an alternative method for biomechanical analyses. Although these methods enable estimating human kinetic data outside the laboratory by linking IMU sensor data with kinetics dataset, they were limited to show inaccurate kinetic estimates even in highly repeatable single walking conditions due to the employment of generic deep learning algorithms. Thus, this paper proposes a novel deep learning model, Kinetics-FM-DLR-Ensemble-Net to estimate the hip, knee, and ankle joint moments in the sagittal plane and 3 dimensional ground reaction forces (GRFs) using three IMU sensors on the thigh, shank, and foot under several representative walking conditions in daily living, such as treadmill, level-ground, stair, and ramp with different walking speeds. This is the first study that implements both joint moments and GRFs in multiple walking conditions using IMU sensors via deep learning. Our deep learning model is versatile and accurate for identifying human kinetics across diverse subjects and walking conditions and our model outperforms state-of-the-art deep learning model for kinetics estimation by a large margin.

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Juvenile idiopathic arthritis, gait characteristics and relation to function

Cited by 21 publications

References 51 publications

Proof-of-Concept Study of the Use of Accelerometry to Quantify Knee Joint Movement and Assist with the Diagnosis of Juvenile Idiopathic Arthritis

Proof-of-Concept Study of the Use of Accelerometry to Quantify Knee Joint Movement and Assist with the Diagnosis of Juvenile Idiopathic Arthritis

Impaired cardiorespiratory and neuromuscular fitness in children and adolescents with juvenile idiopathic arthritis: a cross-sectional case–control study in the era of biologic drug therapies

Estimation of Lower Extremity Joint Moments and 3D Ground Reaction Forces Using IMU Sensors in Multiple Walking Conditions: A Deep Learning Approach

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