Division of Brain Sciences, Imperial College, London, United Kingdom.
Dept. of Bioengineering, Human Robotics Group, Imperial College, London, United Kingdom.
PLoS One. 2016 Oct 5;11(10):e0163413. doi: 10.1371/journal.pone.0163413. eCollection 2016.
Motor-training software on tablets or smartphones (Apps) offer a low-cost, widely-available solution to supplement arm physiotherapy after stroke. We assessed the proportions of hemiplegic stroke patients who, with their plegic hand, could meaningfully engage with mobile-gaming devices using a range of standard control-methods, as well as by using a novel wireless grip-controller, adapted for neurodisability. We screened all newly-diagnosed hemiplegic stroke patients presenting to a stroke centre over 6 months. Subjects were compared on their ability to control a tablet or smartphone cursor using: finger-swipe, tap, joystick, screen-tilt, and an adapted handgrip. Cursor control was graded as: no movement (0); less than full-range movement (1); full-range movement (2); directed movement (3). In total, we screened 345 patients, of which 87 satisfied recruitment criteria and completed testing. The commonest reason for exclusion was cognitive impairment. Using conventional controls, the proportion of patients able to direct cursor movement was 38-48%; and to move it full-range was 55-67% (controller comparison: p>0.1). By comparison, handgrip enabled directed control in 75%, and full-range movement in 93% (controller comparison: p<0.001). This difference between controllers was most apparent amongst severely-disabled subjects, with 0% achieving directed or full-range control with conventional controls, compared to 58% and 83% achieving these two levels of movement, respectively, with handgrip. In conclusion, hand, or arm, training Apps played on conventional mobile devices are likely to be accessible only to mildly-disabled stroke patients. Technological adaptations such as grip-control can enable more severely affected subjects to engage with self-training software.
平板电脑或智能手机上的运动训练软件（应用程序）为补充中风后手臂物理治疗提供了一种低成本、广泛可用的解决方案。我们评估了偏瘫中风患者用偏瘫手使用一系列标准控制方法以及使用专门为神经功能障碍设计的新型无线握持控制器来与移动游戏设备进行有意义互动的比例。我们在 6 个月内筛选了所有新诊断为偏瘫中风的患者。通过手指滑动、点击、操纵杆、屏幕倾斜和适应神经功能障碍的握持器，比较了患者控制平板电脑或智能手机光标能力。光标控制被评为：无运动（0）；小于全范围运动（1）；全范围运动（2）；定向运动（3）。总共，我们筛选了 345 名患者，其中 87 名符合入选标准并完成了测试。最常见的排除原因是认知障碍。使用传统控制，能够定向光标运动的患者比例为 38-48%；能够全范围移动的患者比例为 55-67%（控制器比较：p>0.1）。相比之下，手柄可实现 75%的定向控制和 93%的全范围运动（控制器比较：p<0.001）。控制器之间的这种差异在严重残疾患者中最为明显，使用传统控制，只有 0%的患者能够实现定向或全范围控制，而使用手柄，分别有 58%和 83%的患者能够达到这两个运动水平。总之，在传统移动设备上玩的手或手臂训练应用程序可能仅对轻度残疾的中风患者具有可访问性。技术适应，如握持控制，可以使更多受影响严重的患者能够参与自我训练软件。
