The role of robot-assisted training on rehabilitation outcomes in Parkinson’s disease: a systematic review and meta-analysis
Yanmin Tao, Jingsong Luo, Jing Tian, Sihan Peng, Hongyan Wang, Jun Cao, Zhifei Wen, Xiangeng Zhang
Abstract
Purpose The study aims to assess the efficacy of robot-assisted rehabilitation training on upper and lower limb motor function and fatigue in Parkinson’s disease (PD), and to explore the best-acting robotic rehabilitation program.Methods We searched studies in seven databases and the search period was from the build to 30 June 2023. Two researchers independently screened studies and assessed the quality of the studies for data extraction.Results A total of 21 studies were included, 18 studies related to lower limbs rehabilitation and 3 studies related to upper limbs rehabilitation, involving a total of 787 participants. The results showed that robot-assisted rehabilitation significantly improved indicators of lower limb motor function UPDRS Part III (WMD = −3.58, 95% CI = −5.91 to −1.25, p = 0.003) and BBS (WMD = 4.24, 95% CI = 2.88 to 5.54, p < 0.001), as well as non-motor symptoms of fatigue (WMD = −13.39, 95% CI = −17.92 to −8.86, p < 0.001) in PD patients. At the level of upper limb function, there was no statistically significant difference in the outcome measures of PFS (WMD = −0.25, 95% CI = −4.44 to 3.93, p = 0.9) and BBT (WMD = 1.73, 95% CI = −2.85 to 6.33, p = 0.458).Conclusion Robot-assisted rehabilitation significantly improved motor function, fatigue, and balance confidence in PD patients, but current evidence doesn’t show that intelligent rehabilitation systems improve upper limb function. In particular, robotics combined with virtual reality worked best.