Description Module

Description Module

The Description Module contains narrative descriptions of the clinical trial, including a brief summary and detailed description. These descriptions provide important information about the study's purpose, methodology, and key details in language accessible to both researchers and the general public.

Description Module path is as follows:

Study -> Protocol Section -> Description Module

Description Module


Ignite Creation Date: 2025-12-24 @ 1:49 PM
Ignite Modification Date: 2025-12-24 @ 1:49 PM
NCT ID: NCT04737395
Brief Summary: The study assesses the feasibility of a 4-week upper extremity training program emphasizing quality of movement practiced at high intensity and dosage applied during the early subacute phase after stroke.
Detailed Description: Participants will receive 120 minutes of upper extremity training X 5 days per week for 4 weeks in addition to their daily rehabilitative routine (total therapy time 40 hours in addition to daily routine treatments of approximately 60 hours). Each session will be staffed with a 1:1 PT/OT to patient ratio. The aim of the program is to increase the time spent on tasks using customized game-based platforms that include highly immersive, challenging and rewarding virtual environments for upper limb training. The focus of these game-based platforms is to reduce upper extremity impairments through emphasizing the quality of movement execution. In one platform, the participant controls virtual dolphin movements by moving his/her paretic upper limb. The game utilizes advanced artificial intelligence (AI) analysis of the video recordings in real-time. In case the participant needs arm weight support, the practice will perform the task with a mechanical wearable exoskeleton vest, EkssoUE (Ekso Bionics) that can provide support at different weight levels. Arm weight support will be titrated as the patient progresses through each session and will be reduced/removed if no significant compensatory strategies were used. The second platform, the HandTutor system (MediTouch) consists of an ergonomic wearable glove and a dedicated software with games that allow practicing active wrist movements, grip control and finger individuation in a challenging and motivating environment. Participant progresses through simple to difficult games while adjusting the range of motion that is being practiced according to participant's abilities. Training in both platforms will be conducted in sitting. Participant will continue with their regular rehabilitative routine that includes daily physical and occupational therapy sessions and speech therapy if needed as well as group work and gym. To assess the feasibility of the proposed intervention the following data/measures will be documented: Adherence rates; Time on task (in minutes); Progression in game levels and the amount of weight support; Attendance to routine rehabilitative sessions; Visual Analogue Scale will be used to monitor the levels of pain; Rating perceived exertion (RPE), the revised category-ratio scale (0 to 10 scale) will be used to monitor and guide exercise intensity; The Pittsburgh rehabilitation participation scale (PRPS); The Intrinsic Motivation Inventory (IMI) - will be assessed at the end of intervention period; Acceptability and satisfaction of the intervention to participants will be assessed at the end of training period using a self-designed questionnaire/rating scale; Adverse events; Problems/difficulties related to intervention equipment (software, hardware, vest etc.). To evaluate the potential effectiveness of the intervention the following data/measures will be conducted at baseline (prior training), immediately post intervention and at 12 (±14) and 24 (±14) weeks post stroke: Fugl-Meyer upper extremity assessment (FM-UE); Action research arm test (ARAT); Stroke Impact Scale (SIS) hand domain, version 2.0; Upper extremity impairment and function will be assessed using a reaching and a grasping tasks that will be recorded using multiple high resolution video-cameras and analyzed using AI algorithm (OpenPose).
Study: NCT04737395
Study Brief:
Protocol Section: NCT04737395