Ignite Creation Date:
2024-06-16 @ 11:48 AM
Last Modification Date:
2024-10-26 @ 3:30 PM
Study NCT ID:
NCT06429462
Status:
RECRUITING
Last Update Posted:
2024-05-28
First Post:
2024-05-02
Brief Title:
Knee4Life Project Empowering Knee Recovery After Total Knee Replacement Through Digital Health
Sponsor:
University of Exeter
Organization:
University of Exeter
Study Overview
Official Title:
Empowering Knee Recovery After Total Knee Replacement Through Digital Health Knee4life Project
Status:
RECRUITING
Status Verified Date:
2024-05
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
K4L
Brief Summary:
The research project will investigate the extent to which a smartphone camera sensor tool can help predict and measure knee stiffness and pain after Total Knee Replacement Surgery TKR and how a tool such as this could be implemented into the NHS
Total knee replacement TKR is a frequent procedure undertaken in England and Wales with more than 100000 conducted each year Although most patients have a successful outcome following their TKR approximately 10-20 of patients are dissatisfied predominantly because of pain and knee stiffness A method to detect early problems with pain and stiffness could facilitate earlier referral to non-surgical treatments which are effective in preventing the need for manipulation under anaesthetic MUA Here the investigators will validate and provide proof of concept for a smartphone camera sensor tool that measures knee range of motion alongside symptoms of pain for use in the home setting
The study will comprise of 3 stages
1 We will conduct 45 minute online interviews comprising of 1 people who have had total knee replacement surgery 2 healthcare professionals and stakeholders
2 We will invite 30 participants who are 5-9 weeks post TKR and 30 participants who have had no previous musculoskeletal injuries to attend a session at the university The lab testing will be conducted at the VSimulator a biomechanics research lab at the Exeter Science park and at the teaching labs on St Lukes Campus Exeter Here participants be asked to answer 8 questionnaires and have some of their movements measured
3 Participants will be asked to repeat the timed up and go and the sit to stand tests in their homes and record them using a mobile device
The study is funded by the NIHR Exeter Biomedical Research Centre grant and sponsored by the University of Exeter
Total knee replacement TKR is a frequent procedure undertaken in England and Wales with more than 100000 conducted each year Although most patients have a successful outcome following their TKR approximately 10-20 of patients are dissatisfied predominantly because of pain and knee stiffness A method to detect early problems with pain and stiffness could facilitate earlier referral to non-surgical treatments which are effective in preventing the need for manipulation under anaesthetic MUA Here the investigators will validate and provide proof of concept for a smartphone camera sensor tool that measures knee range of motion alongside symptoms of pain for use in the home setting
The study will comprise of 3 stages
1 We will conduct 45 minute online interviews comprising of 1 people who have had total knee replacement surgery 2 healthcare professionals and stakeholders
2 We will invite 30 participants who are 5-9 weeks post TKR and 30 participants who have had no previous musculoskeletal injuries to attend a session at the university The lab testing will be conducted at the VSimulator a biomechanics research lab at the Exeter Science park and at the teaching labs on St Lukes Campus Exeter Here participants be asked to answer 8 questionnaires and have some of their movements measured
3 Participants will be asked to repeat the timed up and go and the sit to stand tests in their homes and record them using a mobile device
The study is funded by the NIHR Exeter Biomedical Research Centre grant and sponsored by the University of Exeter
Detailed Description:
1 Background
Total knee replacement TKR is a common procedure with more than 100000 per year undertaken in England and Wales Although most patients have a successful outcome following their TKR approximately 10-20 of patients are dissatisfied chiefly because of pain and knee stiffness A method to detect early problems with pain and stiffness could facilitate earlier referral to non-surgical treatments which are effective in preventing the need for manipulation under anaesthetic MUA Currently rates of MUA are 25 2500 patients per year in England and Wales costing 14k per procedure Our current understanding of when stiffness develops and the timing and best treatments for stiffness are limited A recent James Lind Alliance Priority Setting Partnership identified stiffness after TKR as a top-10 research priority to better understand and test interventions Current measures are not accurate or suitable for use in the home The investigators need tools to accurately measure early indicators for stiffness
2 Rationale
The investigators currently have no tool to remotely and accurately detect development of early post-surgical knee stiffness This study aims to develop a cost-effective tool to measure and quantify knee stiffness before and after total knee replacement TKR surgery for use across the NHS The research seeks to understand how knee range of motion ROM recovers after TKR and detect early signs of stiffness It also aims to predict who might develop stiffness after TKR and explore the relationship between pain and stiffness
Current methods for measuring knee range of motion ROM such as hand-held tools for measuring angles have limitations in terms of accuracy and need trained healthcare staff to use them The ideal tool would be low-cost easy to use and provide rapid feedback to patients and clinical teams The study will involve the development and validation of a computer vision-based approach using cameras to assess movements to monitor knee flexion and extension and a walking pattern assessment Video-based technology or computer vision CV has recently been pioneered in Exeter to measure spine movement in patients with ankylosing spondylitis Computer vision is an emerging technology that has great potential for monitoring knee flexion in people with knee stiffness This approach involves the use of cameras and machine learning algorithms to detect and analyse knee joint angles during movement automatically By providing objective and accurate measurements of knee flexion computer vision has the potential to improve the assessment of knee stiffness and facilitate targeted treatment interventions However as with any new technology there is a need to validate the method in the context of patients with knee stiffness to ensure its accuracy and reliability Studies have highlighted the importance of developing machine learning algorithms specifically for this patient population to account for individual differences in movement patterns and limitations due to stiffness Further research is needed to assess the validity and feasibility of computer vision-based approaches for monitoring knee flexion in people with knee stiffness which could ultimately improve the diagnosis monitoring and management of this condition
Validation of the computer vision-based approach for monitoring knee flexion in people with knee stiffness is essential to ensure its reliability and accuracy This requires developing and refining machine learning algorithms that can accurately detect and measure knee joint angles in this patient population This study will evaluate the accuracy and precision of the algorithm against gold-standard measurement methods such as motion capture or goniometry Furthermore this study will examine the sensitivity of the approach to changes in knee flexion due to stiffness and pain and assess its feasibility in a clinical setting Once validated the computer vision-based approach has the potential to provide a non-invasive and objective means of monitoring knee flexion in people with knee stiffness which could inform treatment decisions and improve patient outcomes
Another tool which the investigators will use is the Gaitcapture app which takes advantage of the accelerometer and gyroscope sensor in a mobile phone and acts similarly to an inertial measurement unit IMU to provide us with acceleration and rotation data
The validation of the computer vision-based approach will involve comparing it against gold-standard measurement methods specialist physiotherapy assessment
In addition to the computer vision-based approach the study will utilise body-worn sensors and mobile apps to monitor the physical activity levels walking patterns and step counts of participants This data will provide insights into people with TKRs overall physical activity patterns and help evaluate the usability acceptability feasibility and accuracy of the tools for diagnostics and monitoring
The findings of this research project have the potential to improve the diagnosis monitoring and management of knee stiffness after total knee replacement TKR with the potential to reducing the need for MUA surgery By providing accurate measurements and early detection the tools developed in this study could enable earlier referral to non-surgical treatments and reduce the need for costly and risky procedures to improve knee range of motion ROM after total knee replacement TKR surgery like a manipulation of the knee under anaesthesia
Here the investigators will conduct a validation study of a marker-less motion capture algorithm to determine its accuracy and assess its feasibility and usability for implementation on a large scale in the home the investigators will also ascertain the test-retest reliability of algorithm outputs such as knee flexionextension angles
Total knee replacement TKR is a common procedure with more than 100000 per year undertaken in England and Wales Although most patients have a successful outcome following their TKR approximately 10-20 of patients are dissatisfied chiefly because of pain and knee stiffness A method to detect early problems with pain and stiffness could facilitate earlier referral to non-surgical treatments which are effective in preventing the need for manipulation under anaesthetic MUA Currently rates of MUA are 25 2500 patients per year in England and Wales costing 14k per procedure Our current understanding of when stiffness develops and the timing and best treatments for stiffness are limited A recent James Lind Alliance Priority Setting Partnership identified stiffness after TKR as a top-10 research priority to better understand and test interventions Current measures are not accurate or suitable for use in the home The investigators need tools to accurately measure early indicators for stiffness
2 Rationale
The investigators currently have no tool to remotely and accurately detect development of early post-surgical knee stiffness This study aims to develop a cost-effective tool to measure and quantify knee stiffness before and after total knee replacement TKR surgery for use across the NHS The research seeks to understand how knee range of motion ROM recovers after TKR and detect early signs of stiffness It also aims to predict who might develop stiffness after TKR and explore the relationship between pain and stiffness
Current methods for measuring knee range of motion ROM such as hand-held tools for measuring angles have limitations in terms of accuracy and need trained healthcare staff to use them The ideal tool would be low-cost easy to use and provide rapid feedback to patients and clinical teams The study will involve the development and validation of a computer vision-based approach using cameras to assess movements to monitor knee flexion and extension and a walking pattern assessment Video-based technology or computer vision CV has recently been pioneered in Exeter to measure spine movement in patients with ankylosing spondylitis Computer vision is an emerging technology that has great potential for monitoring knee flexion in people with knee stiffness This approach involves the use of cameras and machine learning algorithms to detect and analyse knee joint angles during movement automatically By providing objective and accurate measurements of knee flexion computer vision has the potential to improve the assessment of knee stiffness and facilitate targeted treatment interventions However as with any new technology there is a need to validate the method in the context of patients with knee stiffness to ensure its accuracy and reliability Studies have highlighted the importance of developing machine learning algorithms specifically for this patient population to account for individual differences in movement patterns and limitations due to stiffness Further research is needed to assess the validity and feasibility of computer vision-based approaches for monitoring knee flexion in people with knee stiffness which could ultimately improve the diagnosis monitoring and management of this condition
Validation of the computer vision-based approach for monitoring knee flexion in people with knee stiffness is essential to ensure its reliability and accuracy This requires developing and refining machine learning algorithms that can accurately detect and measure knee joint angles in this patient population This study will evaluate the accuracy and precision of the algorithm against gold-standard measurement methods such as motion capture or goniometry Furthermore this study will examine the sensitivity of the approach to changes in knee flexion due to stiffness and pain and assess its feasibility in a clinical setting Once validated the computer vision-based approach has the potential to provide a non-invasive and objective means of monitoring knee flexion in people with knee stiffness which could inform treatment decisions and improve patient outcomes
Another tool which the investigators will use is the Gaitcapture app which takes advantage of the accelerometer and gyroscope sensor in a mobile phone and acts similarly to an inertial measurement unit IMU to provide us with acceleration and rotation data
The validation of the computer vision-based approach will involve comparing it against gold-standard measurement methods specialist physiotherapy assessment
In addition to the computer vision-based approach the study will utilise body-worn sensors and mobile apps to monitor the physical activity levels walking patterns and step counts of participants This data will provide insights into people with TKRs overall physical activity patterns and help evaluate the usability acceptability feasibility and accuracy of the tools for diagnostics and monitoring
The findings of this research project have the potential to improve the diagnosis monitoring and management of knee stiffness after total knee replacement TKR with the potential to reducing the need for MUA surgery By providing accurate measurements and early detection the tools developed in this study could enable earlier referral to non-surgical treatments and reduce the need for costly and risky procedures to improve knee range of motion ROM after total knee replacement TKR surgery like a manipulation of the knee under anaesthesia
Here the investigators will conduct a validation study of a marker-less motion capture algorithm to determine its accuracy and assess its feasibility and usability for implementation on a large scale in the home the investigators will also ascertain the test-retest reliability of algorithm outputs such as knee flexionextension angles
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None