Clinical Trial Details
— Status: Recruiting
Administrative data
| NCT number |
NCT06429462 |
| Other study ID # |
339937 |
| Secondary ID |
|
| Status |
Recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
May 1, 2024 |
| Est. completion date |
May 2025 |
Study information
| Verified date |
May 2024 |
| Source |
University of Exeter |
| Contact |
Maedeh Mansoubi, PhD |
| Phone |
07866138722 |
| Email |
M.Mansoubi[@]exeter.ac.uk |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial 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 100,000 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.
Description:
1. Background
Total knee replacement (TKR) is a common procedure, with more than 100,000 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 2.5% (~2,500 patients per year in England and Wales), costing ~£14k per procedure.
Our current understanding of when stiffness develops and the timing and best
treatment(s) 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 TKR's 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 flexion/extension angles.
