Open AccessEffects of respiratory training on pulmonary function, bad mood, and quality of life in patients with COVID-19
Author(s) -
Jianfei Zhu,
Qing Long,
Huihui Mao,
Weirong Ran
Publication year - 2021
Publication title -
medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.59
H-Index - 148
eISSN - 1536-5964
pISSN - 0025-7974
DOI - 10.1097/md.0000000000026154
Subject(s) - medicine , medline , mood , meta analysis , quality of life (healthcare) , data extraction , randomized controlled trial , mental health , intensive care medicine , psychiatry , nursing , political science , law
Background: At present, whether respiratory training can improve the lung function, quality of life, and mental health of patients with Coronavirus Disease 2019 (COVID-19) is still controversial. Therefore, in order to provide new evidence-based medicine for clinical treatment, we conducted a systematic review and meta-analysis to evaluate the effects of respiratory training in improving lung function, quality of life, and mental health of patients with COVID-19. Methods: Relevant publications were searched from clinical trials. Computer was used to retrieve Cochrane Central Register of Controlled Trials Repositories, PubMed, Embase, and Web of Science databases. The retrieval time limit was from the establishment of the database to April 2021. Two researchers independently carried out data extraction and literature quality evaluation on the quality and meta-analysis of the included literature was performed with Revman 5.3 software. Results: The results of this meta-analysis will be submitted to a peer-reviewed journal for publication. Conclusion: This study will provide reliable evidence-based evidence on the effects of breathing training on lung function, bad mood, and quality of life in patients with COVID-19. Ethics and dissemination: Ethical approval was not required for this study. The systematic review will be published in a peer-reviewed journal, presented at conferences, and shared on social media platforms. OSF Registration number: DOI 10.17605/OSF.IO/ZQTGY.