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武术锻炼对超重和肥胖人群身体形态和身体成分参数的有效性:系统评价和荟萃分析。

Effectiveness of martial arts exercise on anthropometric and body composition parameters of overweight and obese subjects: a systematic review and meta-analysis.

机构信息

Postgraduate Program in Health Sciences, University of Southern Santa Catarina, Avenida José Acácio Moreira, 787, Bairro Dehon, Tubarão, Santa Catarina, Caixa Postal 370, Brazil.

Degree in Biological Sciences, Leonardo da Vinci University Center, Capivari de Baixo, Santa Catarina, Brazil.

出版信息

BMC Public Health. 2020 Aug 17;20(1):1246. doi: 10.1186/s12889-020-09340-x.

DOI:10.1186/s12889-020-09340-x
PMID:32807119
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7433112/
Abstract

BACKGROUND

Obesity is considered a top public health concern, and its prevalence is growing every day. Thus, interventions to address this problem should be encouraged and further studied. In this regard, the aim of this review was to summarize the evidence of martial arts interventions to evaluate their effectiveness on the anthropometric and body composition parameters of overweight and obese subjects.

METHODS

A systematic literature search was conducted on January 26, 2020 using the PubMed, Medline, Lilacs, Cochrane, and Scielo databases. Reference lists of eligible articles and relevant reviews have also been examined. All randomized clinical trials on martial arts that evaluated the anthropometric and body composition parameters of overweight and obese subjects were included, and a narrative synthesis of eligible studies was conducted in accordance with PRISMA guidelines. The Downs & Black checklist was used to assess the quality of the studies. This review was registered in the International Prospective Register of Systematic Reviews (PROSPERO) (identifier CRD42018086116).

RESULTS

A total of 82 articles were identified from the initial search strategy. A further 2 articles were identified from the review of relevant bibliographies. Six studies encompassing 258 participants who were overweight or obese were included. Four studies reported Tai Chi practice, one study reported Kung Fu exercise, and another study reported martial arts exercise. The examined meta-analyses did not reveal significant benefits from martial arts practice over control groups after the experiment period for body mass index (- 1.34 kg/m; 95% CI: - 2.72, 0.05), waist circumference (1.41 cm; 95% CI: - 0.72, 3.54) and percentage of body fat (- 0.75%; 95% CI: - 5.58, 4.08).

CONCLUSION

The scarcity, heterogeneity, short intervention time, small sample size, and significant methodological limitations of the available studies do not allow to conclude whether martial arts are effective in the anthropometric and body composition parameters of overweight and obese individuals. This study highlights the need for more research to assess the benefits of martial arts for overweight and obese individuals.

摘要

背景

肥胖被认为是最主要的公共卫生问题之一,其发病率正日益增加。因此,应鼓励并进一步研究解决这一问题的干预措施。在这方面,本综述的目的是总结武术干预措施的证据,以评估其对超重和肥胖人群的人体测量和身体成分参数的有效性。

方法

于 2020 年 1 月 26 日使用 PubMed、Medline、Lilacs、Cochrane 和 Scielo 数据库进行了系统的文献检索。还查阅了合格文章和相关综述的参考文献列表。纳入了所有评估超重和肥胖人群人体测量和身体成分参数的武术随机临床试验,并根据 PRISMA 指南对合格研究进行了叙述性综合。使用 Downs 和 Black 清单评估研究质量。本综述已在国际前瞻性系统评价注册库(PROSPERO)(标识符 CRD42018086116)中注册。

结果

从最初的搜索策略中总共确定了 82 篇文章。从相关文献的综述中又确定了另外 2 篇文章。共纳入了 6 项研究,涵盖了 258 名超重或肥胖参与者。其中 4 项研究报告了太极拳练习,1 项研究报告了功夫练习,另一项研究报告了武术练习。经过实验期后,对参与者进行检查发现,武术练习与对照组相比,身体质量指数(-1.34kg/m;95%CI:-2.72,0.05)、腰围(1.41cm;95%CI:-0.72,3.54)和体脂百分比(-0.75%;95%CI:-5.58,4.08)并没有显著的获益。

结论

现有研究的稀缺性、异质性、干预时间短、样本量小以及存在严重的方法学局限性,使得我们无法确定武术是否对超重和肥胖人群的人体测量和身体成分参数有效。本研究强调了需要进行更多的研究来评估武术对超重和肥胖人群的益处。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/122d/7433112/9fcb453163b0/12889_2020_9340_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/122d/7433112/8e70ad105ec9/12889_2020_9340_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/122d/7433112/9fcb453163b0/12889_2020_9340_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/122d/7433112/8e70ad105ec9/12889_2020_9340_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/122d/7433112/9fcb453163b0/12889_2020_9340_Fig2_HTML.jpg

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