Evidence map›Paper›PMID 36703623›Full record

SynthesisFrontiers in neurology2022

EMG biofeedback combined with rehabilitation training may be the best physical therapy for improving upper limb motor function and relieving pain in patients with the post-stroke shoulder-hand syndrome: A Bayesian network meta-analysis.

Sisi Feng, Mingzhi Tang, Gan Huang, JuMei Wang, Sijin He, Duo Liu, LiHua Gu

2 registry-linked trialsOpen access · goldAbstract readSystematic Review
In one paragraph

Synthesis in Frontiers in neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 8 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 5 pooled it
3.7field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT07149129 naenrolling by invitationnot on this mapstarted 2025, after this paper: background citation

Use of Electromyographic Biofeedback in Upper Limb Rehabilitation After Stroke

TypeinterventionalSponsorUniversidade Federal do ParanáRan2025 to 2026Enrolled45ConditionsStrokeArmsElectromyographic Biofeedback, Sham (No Treatment)
NCT07428811 narecruitingnot on this mapstarted 2025, after this paper: background citation

Effects of Task-based Mirror Therapy on Edema, Pain and Upper Limb Motor Function for Post-stroke Shoulder-hand Syndrome

TypeinterventionalSponsorRiphah International UniversityRan2025 to 2026Enrolled34ConditionsStrokeArmsTask based mirror therapy, Conventional exercises
3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 5 syntheses or guidelines pooled it, 18 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Article
  7. Article
  8. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Sisi FengYunnan University of Traditional Chinese Medicine, Kunming, China.
Mingzhi TangYunnan University of Traditional Chinese Medicine, Kunming, China.
Gan HuangYunnan University of Traditional Chinese Medicine, Kunming, China.
JuMei WangYunnan University of Traditional Chinese Medicine, Kunming, China.
Sijin HeDepartment of Rehabilitation, Kunming Hospital of Traditional Chinese Medicine, The Third Affiliated Hospital of Yunnan University of Chinese Medicine, Kunming, China.
Duo LiuDepartment of Rehabilitation, Kunming Hospital of Traditional Chinese Medicine, The Third Affiliated Hospital of Yunnan University of Chinese Medicine, Kunming, China.
LiHua GuDepartment of Rehabilitation, Kunming Hospital of Traditional Chinese Medicine, The Third Affiliated Hospital of Yunnan University of Chinese Medicine, Kunming, China.
Yunnan University of Traditional Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Post-stroke shoulder-hand syndrome (SHS), although not a life-threatening condition, may be the most distressing and disabling problem for stroke survivors. Thus, it is essential to identify effective treatment strategies. Physical therapy is used as a first-line option for treating SHS; however, it is unclear which treatment option is preferred, which creates confusion in guiding clinical practice. Our study aims to guide clinical treatment by identifying the most effective physical therapy interventions for improving clinical symptoms in patients with post-stroke SHS using Bayesian network meta-analysis. Methods: We conducted a systematic and comprehensive search of data from randomized controlled trials using physical therapy in patients with SHS from database inception to 1 July 2022. Fugl-Meyer Upper Extremity Motor Function Scale (FMA-UE) and pain visual analog score (VAS) were used as primary and secondary outcome indicators. R (version 4.1.3) and STATA (version 16.0) software were used to analyze the data. Results: A total of 45 RCTs with 3,379 subjects were included, and the intervention efficacy of 7 physical factor therapies (PFT) combined with rehabilitation training (RT) was explored. Compared with the control group, all the PFT + RT included were of statistical benefit in improving limb motor function and pain relief. Also, our study indicated that EMG biofeedback combined with RT (BFT + RT) [the surface under the cumulative ranking curve (SUCRA) = 96.8%] might be the best choice for patients with post-stroke SHS. Conclusion: EMG biofeedback combined with rehabilitation training may be the best physical therapy for improving upper limb motor function and relieving pain in patients with post-stroke SHS according to our Bayesian network meta-analysis results. However, the above conclusions need further analysis and validation by more high-quality RCTs. Systematic review registration: www.crd.york.ac.uk/prospero/, identifier: CRD42022348743.

Indexed as

network meta analysesphysical therapyrehabilitation trainingshoulder-hand syndrome (SHS)stroke

Identifiers

PMID36703623
PMCPMC9873378
OpenAlexW4315488093

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.