Department of Physical Therapy, Federal University of Rio Grande do Norte, Natal, Brazil.
Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil.
PLoS One. 2024 May 28;19(5):e0302479. doi: 10.1371/journal.pone.0302479. eCollection 2024.
Biomechanical analysis of human movement plays an essential role in understanding functional changes in people with Amyotrophic Lateral Sclerosis (ALS), providing information on muscle impairment. Studies suggest that surface electromyography (sEMG) may be able to quantify muscle activity, identify levels of fatigue, assess muscle strength, and monitor variation in limb movement. In this article, a systematic review protocol will analyze the psychometric properties of the sEMG regarding the clinical data on the skeletal muscles of people with ALS. This protocol uses the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodological tool. A specific field structure was defined to reach each phase. Nine scientific databases (PubMed, Web of Science, Embase, Elsevier, IEEE, Google Scholar, SciELO, PEDro, LILACS E CENTRAL) were searched. The framework developed will extract data (i.e. study information, sample information, sEMG information, intervention, and outcomes) from the selected studies using a rigorous approach. The data will be described quantitatively using frequency and trend analysis methods, and heterogeneity between the included studies will be assessed using the I2 test. The risk of bias will be summarized using the most recent prediction model risk of bias assessment tool. Be sure to include relevant statistics here, such as sample sizes, response rates, P values or Confidence Intervals. Be specific (by stating the value) rather than general (eg, "there were differences between the groups"). This protocol will map out the construction of a systematic review that will identify and synthesize the advances in movement analysis of people with ALS through sEMG, using data extracted from articles.
人体运动的生物力学分析在理解肌萎缩侧索硬化症(ALS)患者的功能变化方面起着至关重要的作用,为肌肉损伤提供了信息。研究表明,表面肌电图(sEMG)可能能够量化肌肉活动,识别疲劳程度,评估肌肉力量,并监测肢体运动的变化。在本文中,将通过 sEMG 分析针对 ALS 患者骨骼肌的临床数据,制定系统综述方案以评估 sEMG 的心理测量特性。该方案使用系统评价和荟萃分析的首选报告项目(PRISMA)方法工具。定义了特定的字段结构来达到每个阶段。将在九个科学数据库(PubMed、Web of Science、Embase、Elsevier、IEEE、Google Scholar、SciELO、PEDro、LILACS E CENTRAL)中进行搜索。将使用严格的方法从选定的研究中提取数据(即研究信息、样本信息、sEMG 信息、干预和结果)。将使用频率和趋势分析方法对数据进行定量描述,并使用 I2 检验评估纳入研究之间的异质性。将使用最新的预测模型风险偏倚评估工具总结风险偏倚。请务必包含相关统计信息,例如样本量、应答率、P 值或置信区间。具体说明(通过说明值),而不是一般说明(例如,“组间存在差异”)。本方案将制定系统综述的构建,通过从文章中提取的数据,通过 sEMG 识别和综合 ALS 患者运动分析的进展。