Ignite Creation Date:
2025-12-24 @ 2:17 PM
Ignite Modification Date:
2025-12-25 @ 6:23 PM
Study NCT ID:
NCT07016295
Status:
RECRUITING
Last Update Posted:
2025-08-11
First Post:
2025-05-30
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Monitoring Arm Recovery After Stroke (MARS)
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D020521', 'term': 'Stroke'}], 'ancestors': [{'id': 'D002561', 'term': 'Cerebrovascular Disorders'}, {'id': 'D001927', 'term': 'Brain Diseases'}, {'id': 'D002493', 'term': 'Central Nervous System Diseases'}, {'id': 'D009422', 'term': 'Nervous System Diseases'}, {'id': 'D014652', 'term': 'Vascular Diseases'}, {'id': 'D002318', 'term': 'Cardiovascular Diseases'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'CROSS_SECTIONAL', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 30}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2025-07-28', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-05', 'completionDateStruct': {'date': '2027-04-30', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-08-05', 'studyFirstSubmitDate': '2025-05-30', 'studyFirstSubmitQcDate': '2025-06-10', 'lastUpdatePostDateStruct': {'date': '2025-08-11', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-06-11', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-09-30', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Pose estimation model accuracy', 'timeFrame': 'During intervention', 'description': 'Agreement between pose estimation and biomechanics measurements'}], 'secondaryOutcomes': [{'measure': 'Fugl-Meyer Upper limb Assessment', 'timeFrame': 'baseline, before intervention'}, {'measure': 'Motricity Index', 'timeFrame': 'baseline, before intervention', 'description': 'Strength'}, {'measure': 'Cancellation OCS', 'timeFrame': 'baseline, before intervention', 'description': 'neglect'}, {'measure': 'Box and block', 'timeFrame': 'during the intervention', 'description': 'dexterity'}, {'measure': 'ARAT-2', 'timeFrame': 'during the intervention'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['biomechanics', 'pose estimation models', 'upper limb'], 'conditions': ['Stroke']}, 'descriptionModule': {'briefSummary': "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.\n\nArm 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.\n\nTo 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.\n\nThe investigators are looking for twelve stroke survivors to help test this app.\n\n* The session will be at King's College London, on Guy's Campus.\n* It will run for 2-3 hours.\n* Participants will wear a vest or tight-fitting clothes.\n* The investigators will place non-invasive markers on the participants arm.\n* The investigators will video simple movements such as drinking from a cup.\n* The investigators will also measure the same simple movements using the laboratory cameras.\n\nThis 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.\n\nIn future this technology can improve recovery by correcting stroke survivors when they perform home exercises.", 'detailedDescription': '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.\n\nThe 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.\n\nThis accessible technology will provide clinicians kinematic analyses. Kinematics can guide treatment modifications and progression to improve upper limb movement.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': '18 stroke survivors 8 Healthy age-matched controls', 'healthyVolunteers': True, 'eligibilityCriteria': 'Stroke survivors\n\nInclusion Criteria:\n\n* History of stroke\n* Arm impairment evidenced by Fugl-Meyer Upper Limb Assessment between 9-60/66.\n\nExclusion Criteria:\n\n* Severe cognitive impairment preventing ability to consent to treatment and understand and follow research protocol\n* Severe language deficit preventing ability to consent to treatment and understand and follow research protocol\n* Shoulder pain \\>3/10 on visual analog scale\n* Unable to maintain independent sitting balance without a high back support.\n* Wheelchair users that are unable to transfer with assistance of 1 to lab chair or whose wheelchair backrest cannot colapse.'}, 'identificationModule': {'nctId': 'NCT07016295', 'acronym': 'MARS', 'briefTitle': 'Monitoring Arm Recovery After Stroke (MARS)', 'organization': {'class': 'OTHER', 'fullName': "King's College London"}, 'officialTitle': 'Developing an Accessible, Cost-Effective Motion Analysis Tool for Arm Movement After Stroke', 'orgStudyIdInfo': {'id': 'LRS/RGO-24/25-47508'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Stroke survivors', 'interventionNames': ['Behavioral: Biomechanical analysis of arm movement', 'Behavioral: Pose estimation of arm movement']}, {'label': 'Healthy age-matched controls', 'interventionNames': ['Behavioral: Biomechanical analysis of arm movement', 'Behavioral: Pose estimation of arm movement']}], 'interventions': [{'name': 'Biomechanical analysis of arm movement', 'type': 'BEHAVIORAL', 'description': 'Marker based kinematic analysis', 'armGroupLabels': ['Healthy age-matched controls', 'Stroke survivors']}, {'name': 'Pose estimation of arm movement', 'type': 'BEHAVIORAL', 'description': 'Marker free kinematic analysis', 'armGroupLabels': ['Healthy age-matched controls', 'Stroke survivors']}]}, 'contactsLocationsModule': {'locations': [{'zip': 'SE1 1UL', 'city': 'London', 'status': 'RECRUITING', 'country': 'United Kingdom', 'contacts': [{'name': 'Ulrike Hammerbeck, PhD', 'role': 'CONTACT', 'email': 'ulrike.hammerbeck@kcl.ac.uk', 'phone': '+44 (0) 207 848 888 x6292'}, {'name': 'Irene di Giulio, PhD', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Letizia Gionfrida, PhD', 'role': 'SUB_INVESTIGATOR'}], 'facility': 'Centre for Human and Applied Physiological Sciences', 'geoPoint': {'lat': 51.50853, 'lon': -0.12574}}], 'centralContacts': [{'name': 'Ulrike Hammerbeck, PhD', 'role': 'CONTACT', 'email': 'ulrike.hammerbeck@kcl.ac.uk', 'phone': '+44 (0) 20 7888 6292'}], 'overallOfficials': [{'name': 'Vasa Curcin, PhD', 'role': 'STUDY_CHAIR', 'affiliation': "King's College London"}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO', 'description': 'The main data includes videos of participants which are impossible to anonymise. Therefore it is not appropriate for sharing.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': "King's College London", 'class': 'OTHER'}, 'collaborators': [{'name': "King's College Hospital NHS Trust", 'class': 'OTHER'}], 'responsibleParty': {'type': 'SPONSOR'}}}}