Gait Analysis In Knee Osteoarthritis

Project IDRRG3-19002 / CG001
PartnerWoodlands Health Campus
FocusMusculoskeletal, Knee Osteoarthritis
Clinical PIDr Bryan TAN Yijia
Associate Consultant, Woodlands Health Campus
Technical PIDr Cyril John William Donnelly
Principal Research Fellow, Rehabilitation Research Institute of Singapore
MentorAssoc/Prof ANG Wei Tech
Executive Director, Research Institute of Singapore
Associate Professor
School of Mechanical & Aerospace Engineering and LKC School of Medicine, Nan​yang Technological University
ResearchersAlex Lim Lek Syn, Research Assistant | Dr Amr Alhossary, Research Fellow | Dr Desmond Chong, Associate Professor, SIT | Hu Yi, LKCMedicine | Josephine Lam Pei Wen, Research Associate | Dr Lau Jun Liang, Research Fellow | Dr Phillis Teng, Assistant Professor, NIE | Dr Pua Yong Hao, SGH | Dr Ross Clark, Associate Professor, University of the Sunshine Coast | Dr Wu Tsung-Lin, Senior Research Fellow | Yong Jia Wei, Project Officer

 

The challenge

According to the Singapore Burden of Disease Study in 2014, Osteoarthritis (OA) is the 5th reason for years of life lost to disability, ill health, or early death.

While lifestyle changes such as losing weight and exercise are prescribed as a first line of defence to take weight off this load-bearing joint, increasing sedentary lifestyles that involve weekend bursts of sports activities mean that hospitals are increasingly diagnosing patients as young as 45 years of age with knee osteoarthritis.

Given that knee implants typically last 10-20 years at most and patients are living longer, such surgery is not recommended in patients below 60-70 years of age. Removing a knee implant causes devastating injury to the remaining tissue and makes further implants impractical.

There is a need, therefore, to diagnose the potential for knee damage before it happens so that preventive treatments may be applied.

 

The proposed solution

We advocate precision rehabilitation through gait analysis as a key strategy in optimising non-surgical treatment. The aim of the study is to investigate the utility of Statistical Parametric Mapping (SPM1D) to identify movement deficiencies in a patient that may lead to knee osteoarthritis in later life, and to recommend an intervention prescription that will delay or negate the need for surgical intervention.

Current status

Twenty-four healthy and twenty KOA older-aged participants of Asian ethnicity were recruited in this study. The participants went through different tasks such as static standing, 10m walk, step-down, step-up, time-up-and-go, etc., while motion data was captured by the Qualisys motion capture system and electromyography (EMG) data was captured by the PicoEMG system. Kinematic and kinetic data were calculated via the Visual 3D Professional software.

For the 10m walk task, SPM1D was used to analyse and compare joint angles, joint moments, and EMG between healthy and KOA participants. The analysis results showed that the KOA group has larger knee flexion angle and ankle dorsiflexion angle during walking as well as in static standing.

Outcome

  1. Dhruv Gupta, Cyril John Donnelly, Jeffrey A. Reinbolt. Finding emergent gait patterns may reduce progression of knee osteoarthritis in a clinically relevant time frame. Life, 12(7), 1050 (2022).
  2. Amr Alhossary, Todd Pataky, Wei Tech Ang, Karen Sui Geok Chua, Wai Hang Kwong, Cyril John Donnelly. Versatile clinical movement analysis using statistical parametric mapping in MovementRx. Scientific Reports, 13, 2414 (2023).
  3. Y. Hu, P. Teng, T.-L. Wu, R.A. Clark, Y.-H. Pua, J. Yong, A.Alhossary, L.Lim, W.Ang, B.Tan. Association between standing and walking biomechanical parameters in knee osteoarthritis patients using statistical parametric mapping. OARSI World Congress on Osteoarthritis. Denver, CO, United States. 17-20 March 2023.