The effectiveness of blood-flow restricted resistance training in the musculoskeletal rehabilitation of patients with lower limb disorders: A systematic review and meta-analysis.

Resistance exercise blood-flow restriction exercise therapy meta-analysis open kinetic chain

Journal

Clinical rehabilitation
ISSN: 1477-0873
Titre abrégé: Clin Rehabil
Pays: England
ID NLM: 8802181

Informations de publication

Date de publication:
Sep 2021
Historique:
pubmed: 23 3 2021
medline: 20 8 2021
entrez: 22 3 2021
Statut: ppublish

Résumé

This meta-analysis aimed to evaluate the effectiveness of low-load Resistance Training (RT) with or without Blood Flow Restriction (BFR) compared with conventional RT on muscle strength in open and closed kinetic chains, muscle volume and pain in individuals with orthopaedic impairments. Searches were conducted in the PubMed, Web of Science, Scopus and Cochrane databases, including the reference lists of randomised controlled trials (RCT's) up to January 2021. A total of 10 RCTs with 386 subjects (39.2 ± 17.1 years) were included in the analysis to compare low-load RT with BFR and high or low-load RT without BFR. The meta-analysis showed no significant superior effects of low-load resistance training with BFR regarding leg muscle strength in open and closed kinetic chains, muscle volume or pain compared with high or low-load RT without BFR in subjects with lower limb impairments. Low-load RT with BFR leads to changes in muscle strength, muscle volume and pain in musculoskeletal rehabilitation that are comparable to conventional RT. This appears to be independent of strength testing in open or closed kinetic chains.

Identifiants

pubmed: 33749352
doi: 10.1177/02692155211003480
doi:

Types de publication

Journal Article Meta-Analysis Systematic Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

1221-1234

Commentaires et corrections

Type : CommentIn

Auteurs

Nico Nitzsche (N)

Chemnitz University of Technology, Chemnitz, Germany.

Alexander Stäuber (A)

Chemnitz University of Technology, Chemnitz, Germany.

Samuel Tiede (S)

Chemnitz University of Technology, Chemnitz, Germany.

Henry Schulz (H)

Chemnitz University of Technology, Chemnitz, Germany.

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Classifications MeSH