826, Haking Wong Building, Pokfulam, The University of Hong Kong, Hong Kong
Hong Kong, 000
Location contact
Kalun Or, Doctoral degree
CONTACT
Letong Li, Master degree
SUB_INVESTIGATOR
NCT Number: NCT05173064
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 two groups: (1) the conventional PT group receiving the exercise program delivered through in-person sessions; or (2) the AI-guided group following the program through the TSP after an initial PT session. All individuals will take part in the study for 12 weeks, and data will be collected at baseline and 12 weeks after randomization.
Trial opening soon.
Get Notified50 year and older
All sexes
Interventional
Not applicable
Hong Kong, 000
Kalun Or, Doctoral degree
CONTACT
Letong Li, Master degree
SUB_INVESTIGATOR
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 compare the effects of the AI-powered Technological Surrogate Physiotherapist with those of in-person physiotherapy sessions.
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
The AI-powered Technological Surrogate Physiotherapist will have three key features:
Physiotherapists will give usual face-to-face therapy. The assessment of participants' exercise movements will only be achieved in the traditional manner during face-to-face exercise sessions - by physiotherapists' visual inspection of and professional judgement on postural alignment and effectiveness, with verbal instructions for posture correction. The features of real-time movement feedback and tracking of exercise progress will not be provided.
Time frame: From baseline to 12 weeks
0 represents no pain and 10 represents the worst possible pain.
Time frame: From baseline to 12 weeks
The scale regards the degree of difficulty in performing usual daily activities and higher level activities that involve physical function of the knee. Each item will be rated on a 5-point Likert scale ranging from 'None' (i.e., no difficulty) to 'Extreme' (i.e., extreme difficulty), based on which a normalized total score will be calculated (0 indicating extreme symptoms and 100 indicating no symptoms).
Time frame: From baseline to 12 weeks
30-second chair stand test (30CST), which measures the number of stands the participant can complete in 30 sec, will be used to assess the general leg strength and functional performance.
Time frame: From baseline to 12 weeks
Timed up and go (TUG) test, which measures the time it takes the participant to standup from the chair, walk 3 meters, walk back to the chair, and sit down, will be used to assess functional mobility.
Time frame: From baseline to 12 weeks
The participant will be instructed to maximally extend/flex each knee for 3 trials, 3 sec each, with a 1-minute rest in between. Verbal encouragement will be given in each trial to ensure the participant makes the maximum effort. The highest force of each muscle in the three trials will be used for data analysis.
After each test trial, the severity of pain experienced by the participant during the trial will be assessed using the 11-point numerical pain rating scale.
Time frame: From baseline to 12 weeks
Exercise adherence will be indicated by the proportion of sessions and proportion of exercises performed which will be recorded by the TSP system (only for intervention group) and by participants and researchers using a log book.
Time frame: From baseline to 12 weeks
It will be rated by participants using a 7-point Likert scale with anchors of 'Extremely unsatisfied' and 'Extremely satisfied'.
Time frame: From baseline to 12 weeks
It will be assessed on an 11-point numerical rating scale, with 0 representing 'Extremely inconvenient' and 10 representing 'Extremely convenient'.
Contact information is provided by the study sponsor or research team.
The University of Hong Kong
Other
Development and Randomized Controlled Trial of an AI-powered Technological Surrogate Physiotherapist (TSP) Dedicated to Quality Enhancement and Cost Reduction in Knee Osteoarthritis Exercise Rehabilitation
OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.
View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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