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一个系统的隐性类分析的范围审查,应用于加速计评估的身体活动和久坐行为

Michael Kebede¹, Annie Green Howard^2,3, Yumeng Ren¹

¹Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina - Chapel Hill, Chapel Hill, North Carolina, United States of America.

|January 22, 2024

概括

隐性类分析 (LCA) 通过加速计数据有效地识别出不同的身体活动和久坐行为模式. 这次审查强调了LCA的重点.

科学领域:

身体活动流行病学
行为科学行为科学
生物统计学生物统计学

背景情况:

隐性类分析 (LCA) 是一种用于识别人口中未观察到的子组的统计方法.
它对使用加速度计对身体活动和久坐行为进行客观测量的应用尚未被系统审查.
本次审查通过检查LCA如何应用于加速度计数据来解决这一缺口.

研究的目的:

系统地审查和描述隐性类分析 (LCA) 对加速计评估的身体活动和久坐行为的应用.
在使用LCA与加速度计数据的研究中确定常见的方法,变量和结果.
突出了需要改进的领域,在未来的研究中使用LCA在这个领域.

主要方法:

在2021年12月31日之前,使用全面的数据库搜索 (PubMed,科学网,CINHAL,SPORTDiscus,Embase) 进行了系统范围审查.
两个独立的研究人员选了这些研究,重点关注那些将LCA应用于加速度计或加速度计/自我报告测量组合的研究.
数据提取涵盖了研究特征,加速度计方法和LCA模型规格.

主要成果:

包括来自8项独特研究的12篇论文,主要是横截面 (11/12).
隐性类变量始终包括身体活动 (100%) 和经常久坐的行为 (75%).
研究单独使用加速度计 (63%) 或与自我报告 (38%) 结合使用,采用各种分析方法来确定类和选择模型.

结论:

隐性类分析 (LCA) 作为一种有价值的数据缩小工具,用于从加速度计中识别不同的身体活动和久坐行为概况.
在单个或多个模型中,LCA可以将客观加速度计数据与自我报告测量整合起来,并将各种活动强度和久坐行为结合起来.
未来的研究应该致力于标准化的方法和报告,以提高体力活动研究中LCA发现的实用性和可比性.

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