Effects of a Machine Learning-based Lower Limb Exercise Training System for Knee Pain

The University of Hong Kong (HKU)

Status: Not yet enrolling

Conditions: Knee Osteoarthritis; Pain

Treatments: Combination Product: The AI-powered Technological Surrogate Physiotherapist Plus Face-to-face physiotherapist-supervised exercise program; Device: The AI-powered Technological Surrogate Physiotherapist; Behavioral: Face-to-face physiotherapist-supervised exercise program

Study type: Interventional

Funder types: Other

Identifiers

NCT05173064

Feedback System

Details and patient eligibility

About

The goal of the study is to confirm the idea of AI-powered Technological Surrogate Physiotherapist (TSP), by demonstrating its effectiveness and value as a new technology-based contribution to OA healthcare. Participants will be randomized to one of three groups: (1) the conventional PT group receiving the exercise program delivered through in-person sessions; (2) the AI-guided group following the program through the TSP after an initial PT session; or (3) the combined group receiving both in-person PT sessions and AI-guided home exercise. All individuals will take part in the study for 12 weeks, and data will be collected at baseline and 12 weeks after randomization.

Full description

Knee pain, often caused by osteoarthritis, is a prevalent musculoskeletal disorder among older adults and significantly reduces physical function and quality of life. Exercise therapy has been shown to be an effective form of treatment for knee pain. However, the traditional delivery of exercise therapy requires that individuals attend clinics to participate in face-to-face exercise sessions, which can be expensive and inconvenient. In recent years, information technologies have been used to support the delivery of exercise programs. The programs have also shown great benefits in improving the management of knee pain. However, it remains a concern that physical therapists are not able to provide the patients with direct and immediate supervision when exercises are taken place remotely at home or in community centers, which can be detrimental to exercise performance and the management of knee pain.

Thus, the research team has developed a machine learning-based exercise training system to provide evidence-based lower limb exercise videos, real-time movement feedback, and tracking of exercise progress for older adults with knee pain. In this study, a 12-week randomized controlled non-inferiority trial will be conducted to examine the effects of AI-powered Technological Surrogate Physiotherapist, comparing with the effects of the group receiving in-person sessions and effects of the combined group receiving both.

Enrollment: 264 estimated patients

Sex: All

Ages: 50+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

Participants will be recruited if they

• age ≥ 50 years,
• report having pain in or around the knee for more than 12 weeks and on most days of the previous month,
• report being diagnosed with knee OA by a physician, have physician-diagnosed knee OA in the medical record, or have radiographic evidence of grade 2 to 3 knee OA on the Kellgren-Lawrence scale in the posteroanterior and/or skyline view or the presence of lateral/posterior osteophytes (the X-rays will be read and classified by our orthopaedic surgeons collaborators to decide on each patient's eligibility to be included in the study),
• are willing and physically and cognitively able to perform (technology-supported) exercises required in the study protocol
• have normal or corrected to normal vision,
• are able to speak and read Chinese,
• are able to provide written informed consent.

Exclusion criteria

Individuals will be excluded if they have

• history of knee or hip replacement surgery,
• non-ambulatory status,
• systemic inflammatory arthritis (e.g., gout),
• history of trauma (e.g., fractures around the knee, dislocation, and sprains or tears of soft tissues, like ligaments) or surgical arthroscopy of either knee within the past 6 months,
• intra-articular injection to the knee within the past 6 months,
• cognitive impairment,
• involvement in a similar study in the past 6 months,
• recent or imminent surgery (within 12 weeks),
• medical co-morbidities that preclude participation in exercise.

Trial design

Primary purpose: Treatment

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Triple Blind

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264 participants in 3 patient groups

The AI-powered Technological Surrogate Physiotherapist

Experimental group

Description:

Participants will be required to perform three 30-minute exercise sessions per week for 12 weeks, with six exercises at any one time. During the exercise sessions, participants will receiveTechnological Surrogate Physiotherapist (TSP) support which will in particular leverage AI to offer innovative features and modules dedicated to enhancing exercise monitoring and supervision, real-time performance feedback, and self-assessment.

Treatment:

Device: The AI-powered Technological Surrogate Physiotherapist

Face-to-face physiotherapist-supervised exercise program

Active Comparator group

Description:

Participants will be required to visit a physiotherapist for usual face-to-face exercise therapy. Also, they will be required to perform three 30-minute home exercise sessions per week for 12 weeks. However, TSP will not be involved in this group.

Treatment:

Behavioral: Face-to-face physiotherapist-supervised exercise program

The AI-powered Technological Surrogate Physiotherapist Plus Face-to-face physiotherapist-supervised

Experimental group

Description:

Participants will receive an integrated intervention combining conventional physiotherapist-supervised sessions and TSP-supported home exercises (three 30-minute exercise sessions per week for 12 weeks).

Treatment:

Combination Product: The AI-powered Technological Surrogate Physiotherapist Plus Face-to-face physiotherapist-supervised exercise program