School of Psychology, University of Birmingham, B15 2TT, Birmingham, UK.
School of Computer Science, University of Birmingham, B15 2TT, Birmingham, UK.
J Neuroeng Rehabil. 2019 Mar 20;16(1):42. doi: 10.1186/s12984-019-0513-0.
Intensive robot-assisted training of the upper limb after stroke can reduce motor impairment, even at the chronic stage. However, the effectiveness of practice for recovery depends on the selection of the practised movements. We hypothesized that rehabilitation can be optimized by selecting the movements to be practiced based on the trainee's performance profile.
We present a novel principle ('steepest gradients') for performance-based selection of movements. The principle is based on mapping motor performance across a workspace and then selecting movements located at regions of the steepest transition between better and worse performance. To assess the benefit of this principle we compared the effect of 15 sessions of robot-assisted reaching training on upper-limb motor impairment, between two groups of people who have moderate-to-severe chronic upper-limb hemiparesis due to stroke. The test group (N = 7) received steepest gradients-based training, iteratively selected according to the steepest gradients principle with weekly remapping, whereas the control group (N = 9) received a standard "centre-out" reaching training. Training intensity was identical.
Both groups showed improvement in Fugl-Meyer upper-extremity scores (the primary outcome measure). Moreover, the test group showed significantly greater improvement (twofold) compared to control. The score remained elevated, on average, for at least 4 weeks although the additional benefit of the steepest-gradients -based training diminished relative to control.
This study provides a proof of concept for the superior benefit of performance-based selection of practiced movements in reducing upper-limb motor impairment due to stroke. This added benefit was most evident in the short term, suggesting that performance-based steepest-gradients training may be effective in increasing the rate of initial phase of practice-based recovery; we discuss how long-term retention may also be improved.
ISRCTN, ISRCTN65226825 , registered 12 June 2018 - Retrospectively registered.
卒中后强化机器人辅助上肢训练可减轻运动障碍，即使在慢性期也是如此。然而，练习对恢复的有效性取决于所练习动作的选择。我们假设通过根据受训者的表现特征选择要练习的动作，可以优化康复。
我们提出了一种基于性能的运动选择的新原理（“最陡坡”）。该原理基于在工作空间中映射运动表现，然后选择位于表现更好和更差之间的最陡峭过渡区域的运动。为了评估该原理的益处，我们比较了两组因卒中而患有中度至重度慢性上肢偏瘫的人，在接受 15 次机器人辅助上肢训练后对上肢运动障碍的影响。实验组（N=7）接受了基于最陡坡的训练，根据最陡坡原理进行迭代选择，每周重新映射，而对照组（N=9）接受了标准的“中心到外”的上肢训练。训练强度相同。
两组的 Fugl-Meyer 上肢评分（主要的测量结果）均有所改善。此外，实验组的改善幅度（两倍）明显大于对照组。虽然基于最陡坡的训练相对于对照组的额外益处有所减弱，但平均而言，该评分仍保持升高至少 4 周。
本研究为基于性能的练习动作选择在减少卒中后上肢运动障碍方面的优越益处提供了概念验证。这种附加的益处在短期内最为明显，这表明基于性能的最陡坡训练可能会有效提高练习恢复的初始阶段的速度；我们讨论了如何也可以提高长期保留率。
ISRCTN，ISRCTN65226825，注册于 2018 年 6 月 12 日-回顾性注册。
