Lower Knee Joint Loading by Real-time Biofeedback Stair Walking Rehabilitation for Patients With MCKOA

Not Applicable

• Conditions: Knee Osteoarthritis
• Interventions: Behavioral: Laboratory-based gait retraining (LGR); Behavioral: Sensor-based gait retraining (SGR); Behavioral: Walking exercise control (Ctrl)

• Registration Number: NCT03734380
• Lead Sponsor: The Hong Kong Polytechnic University

Brief Summary

This study will establish a machine-learning algorithm to predict KAM using IMU sensors during stair ascent and descent; and then conduct a three-arm randomized controlled trial to compare the biomechanical and clinical difference between patients receiving a course of conventional laboratory-based stair retraining, sensor-based stair retraining, and walking exercise control (i.e., walking exercise without gait retraining). The investigators hypothesise that the wearable IMUs will accurately predict KAM during stair negotiation using machine-learning algorithm, with at least 80% measurement agreement with conventional calculation of KAM. The investigators also hypothesise that patients randomized to the laboratory-based and sensor-based stair retraining conditions would evidence similar (i.e., weak and non-significant differences) reduction in KAM (primary outcome) and an improvement of symptoms (secondary outcomes), but that these subjects would evidence larger reductions in KAM than subjects assigned to the walking exercise control condition.

Detailed Description

Conventionally, gait retraining is necessarily implemented in a laboratory environment because evaluation of biomechanical markers, such as KAM, requires sophisticated motion capturing system and force plates. With advancement of wearable sensor technology, it is possible to measure gait biomechanics and provide real time biofeedback for gait retraining using inertial measurement unit (IMU), which is a lightweight and portable wireless device. In an ongoing government funded project, the investigators have developed IMU embedded footwear that measures KAM during level ground walking. The investigators have compared Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest in the prediction of KAM from IMU recordings. The investigators found that Random Forest could provide much higher KAM prediction accuracy than LASSO regression. The agreement between conventional laboratory-based and sensor-based measurement of KAM was approximately 90%. Based on investigators' previous research work, it is meaningful to extend the newly developed technology for KAM measurement during stair ascent and descent without the use of laboratory equipment. With the wearable sensors connected to the smartphones, gait retraining outside laboratory environment will become feasible but the effects of gait retraining using wearable sensors have not been directly verified.

Given these considerations, this project has two primary aims. The investigators will: (1) first establish a machine-learning algorithm to predict KAM using IMU sensors during stair ascent and descent; and then (2) conduct a three-arm randomized controlled trial to compare the biomechanical and clinical difference between patients receiving a course of conventional laboratory-based stair retraining, sensor-based stair retraining, and walking exercise control (i.e., walking exercise without gait retraining).

Primary hypothesis

Hypothesis 1: The wearable IMUs will accurately predict KAM during stair negotiation using machine-learning algorithm, with at least 80% measurement agreement with conventional calculation of KAM.

Hypothesis 2: Patients randomized to the laboratory-based and sensor-based stair retraining conditions would evidence similar (i.e., weak and non-significant differences) reduction in KAM (primary outcome) and an improvement of symptoms (secondary outcomes), but that these subjects would evidence larger reductions in KAM than subjects assigned to the walking exercise control condition.

Study Timeline

• Study First Submitted: 2018-11-06
• Study First Submitted QC: 2018-11-06
• Study First Posted: 2018-11-08
• Status Verified: 2020-02
• Last Update Submitted: 2020-02-10
• Last Update Posted: 2020-02-11
• Study Start: 2021-01-01
• Primary Completion: 2022-12-30
• Study Completion: 2023-12-30

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 57

Inclusion Criteria

1. 45-80 years of age;
2. patients with early medial compartment knee OA (Kellgren & Lawrence grade = 1 or 2);
3. self-reported knee pain at least once per week for the proceeding eight weeks;
4. patients should be able to walk unaided for at least 60 minutes.

Exclusion Criteria

1. have a body mass index >35;
2. have a known learning disability;
3. use a shoe insert or knee brace;
4. have received corticosteroid injection within the previous eight weeks;
5. have absolute contraindications for vigorous physical activities according to the American College of Sports Medicine;
6. in order to avoid floor effect of training, all subjects will undergo an initial screening and only those with KAM greater than 0.3 Nm/kg during level ground walking will be invited into the retraining study.

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Arm && Interventions

Group	Intervention	Description
Laboratory-based gait retraining (LGR)	Laboratory-based gait retraining (LGR)	Subjects will attend 6 weekly sessions of stair ascent and descent exercise over six consecutive weeks. In each session, they walk at a self-selected speed on the instrumented staircase. The training time will be progressively increased from 15 to 30 minutes over the six sessions. The auditory feedback will be gradually removed in the last three sessions.
Sensor-based gait retraining (SGR)	Sensor-based gait retraining (SGR)	Subjects will receive training similar to LGR, except the KAM measurement is based solely on inputs from IMUs embedded in the shoes. The training schedule, duration, and intensity will be identical to those of the LGR group.
Walking exercise control (Ctrl)	Walking exercise control (Ctrl)	Subjects will attend 6 weekly sessions of stair ascent and descent exercise over six consecutive weeks. In each session, they will walk on the same instrumented staircase at a self-selected pace without any guidance on gait modification. The training period and training time per session will be identical to the other two groups.

Primary Outcome Measures

Name	Time	Method
Change in knee adduction moment (KAM)	baseline and 7 weeks	The surrogate marker of the medial compartment knee joint loading (i.e. KAM) will be measured by a 10-camera motion capture system (Vicon, Oxford Metrics Group, Oxford, UK) at 100 Hz and an instrumented staircase equipped with two force plates (Bertec, Columbus, OH, USA) at 1000Hz during stair ascent and descent at baseline assessment and after 6-week stair retraining.

Secondary Outcome Measures

Name	Time	Method
Change in Chinese Knee Injury and Osteoarthritis Outcome Score (KOOS)	baseline and 7 weeks	The Chinese Knee Injury and Osteoarthritis Outcome Score (KOOS) will be used to assess knee pain, symptoms and physical function of the patients before and after stair retraining. This instrument contains 42 items addressing pain, symptoms, activities of daily living, sports and recreational function, and knee-related quality of life. The total score and sub-score for each domain (pain, symptoms, activities of daily living, sports/ recreational function, and knee-related quality of life) will be normalized from 0 to 100, with 100 indicating the worst possible state, 0 indicating no pain or loss of function.
Chnage in validated visual analogue scale (VAS)	basleline, 1 week, 2 weeks, 3 weeks, 4 weeks, 5 weeks, 6 weeks and 7 weeks	The validated visual analogue scale (VAS) of 100 mm will be used to assess overall knee pain level after each stair negotiation session, with 0 mm at the left-most end of the 100 mm scale indicating"No pain at all" and 100 mm at the right-most end indicating"Worst imaginable pain".

Trial Locations

Locations (1)

The Hong Kong Polytechnic University; 🇨🇳 Hong Kong, China