Effectiveness of gait retraining interventions in individuals with hip or knee osteoarthritis: A systematic review and meta-analysis

Rynne, Rebecca; Tong, Gia Le; Cheung, Roy T.H.; Constantinou, Maria

doi:10.1016/j.gaitpost.2022.04.013

Cited by 10 publications

(6 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In-sensor computing with our MEMS device was applied to the detection of the four different gait patterns (N, TO, TL, TOTL) using a single device attached to the left foot. The automated identification of these gait patterns is relevant clinically, as it may be used for the management of certain musculoskeletal diseases, such as knee osteoarthritis, for instance, via gait retraining using real-time biofeedback 30 . While gait pattern identification can be performed in laboratories equipped with 3D motion capture systems or using multiple inertial measurement units 31 – 33 , the difficulty of the task is compounded by our use of a single accelerometer, which is motivated by our objective to develop the simplest, lowest cost, and most unintrusive wearable device.…”

Section: Resultsmentioning

confidence: 99%

In-sensor human gait analysis with machine learning in a wearable microfabricated accelerometer

Dion,

Tessier-Poirier,

Chiasson-Poirier

et al. 2024

Commun Eng

View full text Add to dashboard Cite

In-sensor computing could become a fundamentally new approach to the deployment of machine learning in small devices that must operate securely with limited energy resources, such as wearable medical devices and devices for the Internet of Things. Progress in this field has been slowed by the difficulty to find appropriate computing devices that operate using physical degrees of freedom that can be coupled directly to degrees of freedom that perform sensing. Here we leverage reservoir computing as a natural framework to do machine learning with the degrees of freedom of a physical system, to show that a micro-electromechanical system can implement computing and the sensing of accelerations by coupling the displacement of suspended microstructures. We present a complete wearable system that can be attached to the foot to identify the gait patterns of human subjects in real-time. The computing efficiency and the power consumption of this in-sensor computing system is then compared to a conventional system with a separate sensor and digital computer. For similar computing capabilities, a much better power efficiency can be expected for the highly-integrated in-sensor computing devices, thus providing a path for the ubiquitous deployment of machine learning in edge computing devices.

show abstract

Section: Resultsmentioning

confidence: 99%

In-sensor human gait analysis with machine learning in a wearable microfabricated accelerometer

Dion,

Tessier-Poirier,

Chiasson-Poirier

et al. 2024

Commun Eng

View full text Add to dashboard Cite

show abstract

“…Equally important is ensuring translation of pre-gait motion and strength gains into functional mobility tasks and gait. This includes using functional movement patterns, appropriate cueing (including use of real-time biofeedback), and gradually reducing feedback to optimize long-term retention [10,40,41,50].…”

Section: Walking Mechanics Following Thamentioning

confidence: 99%

Rehabilitation Phases, Precautions, and Mobility Goals Following Total Hip Arthroplasty

Sara

Lewis

2023

HSS Journal®: The Musculoskeletal Journal of Hospital for Speci

View full text Add to dashboard Cite

The prevalence of total hip arthroplasty (THA) for advanced hip osteoarthritis (OA) is both increasing and shifting toward a younger average age. However, THA alone does not typically normalize function in these patients. Postoperative rehabilitation is often recommended to optimize joint motion, strength, and function. To date, there are no peer-reviewed clinical practice guidelines for postoperative rehabilitation following THA. Thus, optimal postoperative rehabilitation requires consideration of the existing literature and clinical expertise. This review article summarizes current recommendations for postoperative management of THA, including phases of rehabilitation, postoperative hip precautions, the effect of rehabilitation setting and mode of delivery on postoperative outcomes, and gait mechanics.

show abstract

“…Gait retraining has the potential to alter joint loads in a non-invasive way 24 – 26 . Two different approaches have been previously proposed: (1) gait retraining with a focus on altering the gait pattern, i.e.…”

Section: Introductionmentioning

confidence: 99%

A framework based on subject-specific musculoskeletal models and Monte Carlo simulations to personalize muscle coordination retraining

Kainz,

Koller,

Wallnöfer

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Excessive loads at lower limb joints can lead to pain and degenerative diseases. Altering joint loads with muscle coordination retraining might help to treat or prevent clinical symptoms in a non-invasive way. Knowing how much muscle coordination retraining can reduce joint loads and which muscles have the biggest impact on joint loads is crucial for personalized gait retraining. We introduced a simulation framework to quantify the potential of muscle coordination retraining to reduce joint loads for an individuum. Furthermore, the proposed framework enables to pinpoint muscles, which alterations have the highest likelihood to reduce joint loads. Simulations were performed based on three-dimensional motion capture data of five healthy adolescents (femoral torsion 10°–29°, tibial torsion 19°–38°) and five patients with idiopathic torsional deformities at the femur and/or tibia (femoral torsion 18°–52°, tibial torsion 3°–50°). For each participant, a musculoskeletal model was modified to match the femoral and tibial geometry obtained from magnetic resonance images. Each participant’s model and the corresponding motion capture data were used as input for a Monte Carlo analysis to investigate how different muscle coordination strategies influence joint loads. OpenSim was used to run 10,000 simulations for each participant. Root-mean-square of muscle forces and peak joint contact forces were compared between simulations. Depending on the participant, altering muscle coordination led to a maximum reduction in hip, knee, patellofemoral and ankle joint loads between 5 and 18%, 4% and 45%, 16% and 36%, and 2% and 6%, respectively. In some but not all participants reducing joint loads at one joint increased joint loads at other joints. The required alteration in muscle forces to achieve a reduction in joint loads showed a large variability between participants. The potential of muscle coordination retraining to reduce joint loads depends on the person’s musculoskeletal geometry and gait pattern and therefore showed a large variability between participants, which highlights the usefulness and importance of the proposed framework to personalize gait retraining.

show abstract

Effectiveness of gait retraining interventions in individuals with hip or knee osteoarthritis: A systematic review and meta-analysis

Cited by 10 publications

References 45 publications

In-sensor human gait analysis with machine learning in a wearable microfabricated accelerometer

In-sensor human gait analysis with machine learning in a wearable microfabricated accelerometer

Rehabilitation Phases, Precautions, and Mobility Goals Following Total Hip Arthroplasty

A framework based on subject-specific musculoskeletal models and Monte Carlo simulations to personalize muscle coordination retraining

Contact Info

Product

Resources

About