Monitoring Arm Recovery After Stroke (MARS)

Официальное название

Developing an Accessible, Cost-Effective Motion Analysis Tool for Arm Movement After Stroke

Обзор

People who have a stroke often find it hard to do the things they did before. This can be caused by problems with arm movement. One in five people do not get any arm movement back after a stroke. Arm movements can be measured accurately in a laboratory, but it is very expensive and not easy to do in hospital. That means it is hard to tell if the arm is recovering to move like it did before the stroke or adapting to perform tasks in other ways. To tell if a treatment is working, the investigators are making a phone app to record arm movement, using the camera. The recordings will be turned into data showing movement difficulties and sent to hospital records for clinicians. Clinicians will see if movement changes, to help choose the best treatment. The investigators are looking for twelve stroke survivors to help test this app. * The session will be at King's College London, on Guy's Campus. * It will run for 2-3 hours. * Participants will wear a vest or tight-fitting clothes. * The investigators will place non-invasive markers on the participants arm. * The investigators will video simple movements such as drinking from a cup. * The investigators will also measure the same simple movements using the laboratory cameras. This will show us if our app can measure arm movement as well as laboratory tests. If they do, the investigators will know the app is accurate. In future this technology can improve recovery by correcting stroke survivors when they perform home exercises.

Подробное описание

Upper limb recovery after stroke remains poor and 20% of stroke survivors do not recover arm movement. To improve outcome and advance insights to recovery mechanisms, an international collaboration has proposed a standardised set of outcome measures, including movement kinematics. Kinematics are moresensitive to change than clinical measures and can differentiate whether recovery is achieved by compensating to impairment or true recovery. However, kinematic assessments are not performed in clinical practice as 3-D motion capture requires expensive equipment and expertise for set-up and analysis.

The investigators therefore aim to develop a low-cost tool to measure kinematics. Open-source Artificial Intelligence models can detect positions and orientations on video and are called pose estimation models. The objectives will be to deploy and test these models in stroke survivors. The investigators will invite 12 stroke survivors with mild to moderate upper limb impairment and compare the accuracy of the models to gold-standard kinematic analysis when performing a variety of upper limb tasks. The investigators will optimise the models in case of any discrepancies. The investigators will develop a front-end smartphone app to instruct, record and provide feedback of arm movement performance to clinicians and stroke survivors. The investigators will develop the software back-end performing analysis of recorded movements and integrating these findings into electronic healthcare records for longitudinal performance tracking.

This accessible technology will provide clinicians kinematic analyses. Kinematics can guide treatment modifications and progression to improve upper limb movement.

Вмешательства

• Поведенческое Biomechanical analysis of arm movement
  Marker based kinematic analysis
• Поведенческое Pose estimation of arm movement
  Marker free kinematic analysis

Первичные конечные точки

• Pose estimation model accuracy [Срок оценки: During intervention]

Вторичные конечные точки (5)

• Fugl-Meyer Upper limb Assessment [Срок оценки: baseline, before intervention]
• Motricity Index [Срок оценки: baseline, before intervention]
• Cancellation OCS [Срок оценки: baseline, before intervention]
• Box and block [Срок оценки: during the intervention]
• ARAT-2 [Срок оценки: during the intervention]

Критерии участия

Stroke survivors

Критерии включения

• History of stroke
• Arm impairment evidenced by Fugl-Meyer Upper Limb Assessment between 9-60/66.

Критерии исключения

• Severe cognitive impairment preventing ability to consent to treatment and understand and follow research protocol
• Severe language deficit preventing ability to consent to treatment and understand and follow research protocol
• Shoulder pain >3/10 on visual analog scale
• Unable to maintain independent sitting balance without a high back support.
• Wheelchair users that are unable to transfer with assistance of 1 to lab chair or whose wheelchair backrest cannot colapse.

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Да

Дизайн исследования

Модель наблюдения

Когортное

Центры проведения

Великобритания · 1 центр

• Centre for Human and Applied Physiological Sciences — London

Идентификаторы

NCT: NCT07016295 · LRS/RGO-24/25-47508

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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