| Literature DB >> 36267617 |
Jie Zhan1,2,3, Xiaojing Wei2, Chenyang Tao2, Xiaoting Yan2, Peiming Zhang2, Rouhao Chen2, Yu Dong2, Hongxia Chen3, Jianhua Liu4, Liming Lu2.
Abstract
Background: Post-stroke shoulder pain (PSSP) is characterized by shoulder pain on the hemiplegic side, which can limit physical activity in patients with stroke. Acupuncture combined with rehabilitation training (AR) has been widely used in PSSP, but the evidence of its effectiveness is still unclear. Objective: The study aimed to evaluate the effect and safety of AR vs. rehabilitation training (RT) alone on PSSP.Entities:
Keywords: acupuncture; alternative and complementary medicine; meta-analysis; post-stroke shoulder pain; rehabilitation training
Year: 2022 PMID: 36267617 PMCID: PMC9578557 DOI: 10.3389/fmed.2022.947285
Source DB: PubMed Journal: Front Med (Lausanne) ISSN: 2296-858X
FIGURE 1Study flow diagram.
FIGURE 2Risk of bias assessments of included studies.
FIGURE 3Forest plot and meta-analysis of VAS.
FIGURE 4Forest plot and meta-analysis of FMA-U.
FIGURE 5Forest plot and meta-analysis of MBI.
FIGURE 6Forest plot and meta-analysis of ROM of internal rotation and backward extension.
FIGURE 7Forest plot and meta-analysis of ROM of anteflexion, external rotation, and abduction.
FIGURE 8Forest plot and subgroup meta-analysis of VAS (BAA, balancing acupuncture; EA, electroacupuncture; RT, rehabilitation training; TA, traditional acupuncture; VAS, visual analog scale).
Results of sensitivity analysis based on VAS.
| MD | 95%-CI | tau | tau |
| ||
| Omitting Bu L 2013 | −1.3412 | [−1.6052; −1.0771] | <0.0001 | 0.4728 | 0.6876 | 93.0% |
| Omitting Chen DM 2019 | −1.3888 | [−1.5989; −1.1786] | <0.0001 | 0.2790 | 0.5282 | 91.1% |
| Omitting Cheng G 2018 | −1.3320 | [−1.5976; −1.0665] | <0.0001 | 0.4795 | 0.6925 | 93.1% |
| Omitting Cheng YL 2006 | −1.3471 | [−1.6076; −1.0867] | <0.0001 | 0.4613 | 0.6792 | 93.1% |
| Omitting Chen HX 2011 | −1.3107 | [−1.5735; −1.0478] | <0.0001 | 0.4719 | 0.6869 | 93.1% |
| Omitting Chen J 2016 | −1.2966 | [−1.5566; −1.0366] | <0.0001 | 0.4575 | 0.6764 | 92.9% |
| Omitting Gao ZZ 2014 | −1.3268 | [−1.5899; −1.0637] | <0.0001 | 0.4753 | 0.6894 | 93.1% |
| Omitting Guo YY 2012 | −1.3113 | [−1.5764; −1.0461] | <0.0001 | 0.4768 | 0.6905 | 92.8% |
| Omitting He YY 2017 | −1.3285 | [−1.5948; −1.0621] | <0.0001 | 0.4818 | 0.6942 | 93.1% |
| Omitting Kong L 2017 | −1.3239 | [−1.5860; −1.0617] | <0.0001 | 0.4733 | 0.6880 | 93.1% |
| Omitting Liao SY 2019 | −1.3259 | [−1.5923; −1.0594] | <0.0001 | 0.4822 | 0.6944 | 93.1% |
| Omitting Li JY 2017 | −1.3427 | [−1.6064; −1.0791] | <0.0001 | 0.4711 | 0.6863 | 92.9% |
| Omitting Lin YJ 2014 | −1.2934 | [−1.5503; −1.0365] | <0.0001 | 0.4488 | 0.6699 | 93.0% |
| Omitting Li ZQ 2015 | −1.3041 | [−1.5663; −1.0420] | <0.0001 | 0.4671 | 0.6835 | 93.0% |
| Omitting Lu JH 2013 | −1.3254 | [−1.5894; −1.0613] | <0.0001 | 0.4773 | 0.6908 | 93.1% |
| Omitting Luo X 2016 | −1.3152 | [−1.5806; −1.0498] | <0.0001 | 0.4787 | 0.6919 | 93.1% |
| Omitting Qi Y 2020 | −1.2875 | [−1.5442; −1.0309] | <0.0001 | 0.4425 | 0.6652 | 89.6% |
| Omitting Wang L 2019 | −1.3057 | [−1.5681; −1.0433] | <0.0001 | 0.4685 | 0.6845 | 93.1% |
| Omitting Wen PX 2019 | −1.3245 | [−1.5897; −1.0593] | <0.0001 | 0.4796 | 0.6925 | 93.1% |
| Omitting Wen YC 2020 | −1.3358 | [−1.6014; −1.0703] | <0.0001 | 0.4783 | 0.6916 | 93.0% |
| Omitting Wu FC 2019 | −1.3336 | [−1.5988; −1.0684] | <0.0001 | 0.4784 | 0.6917 | 93.1% |
| Omitting Wu JY 2015 | −1.3018 | [−1.5635; −1.0402] | <0.0001 | 0.4646 | 0.6816 | 93.0% |
| Omitting Xiao CH 2014 | −1.2819 | [−1.5324; −1.0314] | <0.0001 | 0.4249 | 0.6519 | 92.9% |
| Omitting Xun YJ 2019 | −1.3432 | [−1.6068; −1.0796] | <0.0001 | 0.4706 | 0.6860 | 92.9% |
| Omitting Yang RC 2018 | −1.3512 | [−1.6114; −1.0909] | <0.0001 | 0.4573 | 0.6763 | 92.7% |
| Omitting Zhang B 2012 | −1.3039 | [−1.5651; −1.0426] | <0.0001 | 0.4652 | 0.6820 | 93.1% |
| Omitting Zhang JK 2022 | −1.3425 | [−1.6063; −1.0787] | <0.0001 | 0.4715 | 0.6866 | 92.9% |
| Omitting Zhang Z 2014 | −1.3404 | [−1.6049; −1.0760] | <0.0001 | 0.4740 | 0.6885 | 92.9% |
| Omitting Zhang ZX 2012 | −1.2905 | [−1.5472; −1.0337] | <0.0001 | 0.4457 | 0.6676 | 92.9% |
| Omitting Zheng LQ 2020 | −1.3051 | [−1.5674; −1.0428] | <0.0001 | 0.4680 | 0.6841 | 93.1% |
| Omitting Zhou GH 2002 | −1.3427 | [−1.6057; −1.0797] | <0.0001 | 0.4697 | 0.6853 | 93.1% |
| Pooled estimate | −1.3228 | [−1.5794; −1.0662] | <0.0001 | 0.4616 | 0.6794 | 92.90% |
MD, mean difference; CI, confidence interval. Details on meta-analytical method: Inverse variance method; Restricted maximum-likelihood estimator for tau2.
FIGURE 9Funnel plots illustrating meta-analysis of VAS, FMA-U, and MBI. (FMA-U, Fugl-Meyer Assessment Scale for upper extremity; MBI, modified Barthel Index; VAS, visual analog scale).