跳至主要内容
临床试验/NCT04577573
NCT04577573已完成不适用

Cognitive-based Rehabilitation Platform of Hand Grasp After Spinal Cord Injury Using Virtual Reality and Instrumented Wearables

VA Office of Research and Development2 个研究点 分布在 1 个国家目标入组 13 人开始时间: 2021年5月17日最近更新:
适应症

试验速览

阶段
不适用
状态
已完成
入组人数
13
试验地点
2
主要终点
Percent Change in Time to Achieve Secure Grasp (Cognition Glove Only)

研究概览

简要总结

Rehabilitation of functional movements after spinal cord injury (SCI) requires commitment and engagement to the processes of physical therapy. Outcomes may be improved by techniques that strengthen cognitive connections between users and physical therapy exercises.

The investigators will investigate combinations of virtual reality and innovative wearable technology to accelerate rehabilitation of hand grasp and reach. These devices use multi-sensory feedback to enhance the sense of agency, or feelings of control, and better train movements during physical rehabilitation exercises. The investigators will measure the effect of these devices on improving the speed, efficiency, and accuracy of performed movements in Veterans with SCI.

详细描述

Spinal cord injury (SCI) at the cervical level impairs hand function severely compromises performance of activities of daily living. The physical rehabilitation process requires commitment by the participant to achieve meaningful gains in function. Rehabilitation approaches that are cognitively engaging can facilitate greater commitment to practice and improved movement learning.

The investigators propose to develop innovative platforms that utilize virtual reality (VR) and instrumented wearables that enhance cognitive factors during motor learning of hand grasp and reach after SCI. These factors include greater sense of agency, or perception of control, and multi-sensory feedback. Sense of agency is implicated with greater movement control, and various sensory feedback modalities (visual, audio, and haptic) are proven effective in movement training. However, these factors are not well considered in traditional physical therapy approaches.

The investigators have developed two novel cognitive-based platforms for rehabilitating grasp and reach function and propose to test each platform in Veterans with chronic SCI at the cervical level.

Aim 1 will investigate how the "cognition" glove may improve functional grasp. This glove includes force and flex sensors that provide inputs to a machine learning algorithm trained to predict when secure grasp is achieved. The glove alerts the user of secure grasp through onboard sensory modules providing visual (LED), audio (beeper), and tactile (vibrator) feedback. During training, feedback is provided at gradually shorter time-intervals to progressively induce agency based on the neuroscience principle of 'intentional binding'. This principle suggests that with greater agency, one perceives their action (i.e., secure grasp) is more coupled in time to a sensory consequence (i.e., glove feedback). The glove is user-ready, and now has compatibility with customized VR applications to provide enhanced sensory feedback through engaging and customized visual and sound alerts. The investigators hypothesize that enhanced feedback in VR will produce even greater improvements in grasp performance than onboard feedback alone.

Aim 2 will investigate how Veterans with SCI may learn greater arm muscle control during virtual reaching while using a "sensory" brace that provides isometric resistance to one arm to elicit electromyography (EMG) patterns that can drive a virtual arm. The person receives visual feedback from VR and muscle tendon haptic feedback from the brace during training. Tendon stimulation can elicit movement sensations that modulate muscle activation patterns. The VR feedback will provide conscious movement training cues while vibration feedback will subconsciously elicit more distinct EMG patterns based on cluster analysis. The investigators hypothesize that the promotion of distinct EMG patterns, achieved by maximizing inter-cluster distances, will improve performance of a reach-to-touch task.

研究设计

研究类型
Interventional
分配方式
Non Randomized
干预模型
Crossover
主要目的
Device Feasibility
盲法
Single (Outcomes Assessor)

入排标准

年龄范围
18 Years 至 65 Years(Adult, Older Adult)
性别
All
接受健康志愿者

入选标准

  • SCI occurred greater than 12 months ago
  • SCI occurred between levels C1-T1
  • Hand weakness: score of 2, 3, or 4 out of 5 on manual muscle testing of finger extension, finger flexion, or finger abduction in either hand

排除标准

  • History of other serious brain or spinal cord injuries
  • History of seizures
  • Ventilator dependence; open tracheostomy
  • Use of medications that significantly lower seizure threshold
  • History of significant cognitive deficits
  • Open skin lesions over the face, neck, shoulders, arms, or hands
  • Pregnancy

结局指标

主要结局

Percent Change in Time to Achieve Secure Grasp (Cognition Glove Only)

时间窗: Baseline and Following the 6 hour lab session, assessed while performing the task

Percent change in time (seconds) to achieve secure grasp. Lower time is better.

Percent Change in Time to Complete Pick-up and Placement of Object-Cognition Glove

时间窗: Baseline and Following the 6 hour lab session, assessed while performing the task

Percent change in time (seconds) to pick up and re-place object. Lower time is better.

Percent Change in Time to Complete Trial-Sensory Brace

时间窗: Baseline and Following the 6 hour lab session, assessed while performing the task

Percent change in time (seconds) to complete target reaching trials in VR environment. Lower time is better.

次要结局

  • Percent Change in Motion Pathlength in Moving Object-Cognition Glove(Baseline and Following the 6 hour lab session, assessed while performing the task)
  • Error in Placing Object Onto Target (Cognition Glove Only)(Baseline and Following the 6 hour lab session, assessed while performing the task)
  • Percent Change in Motion Pathlength Toward Virtual Targets-Sensory Brace(Baseline and Following the 6 hour lab session, assessed while performing the task)

研究者

申办方类型
Fed
责任方
Sponsor

研究点 (2)

Loading locations...

相似试验