身体活动积累时间与健康之间的纵向关联:功能数据方法的应用
Wenyi Lin1, Jingjing Zou1, Chongzhi Di2
1Division of Biostatistics, Herbert Wertheim School of Public Health and Longevity Science, University of California, San Diego, La Jolla, CA, USA.
Statistics in biosciences
|June 29, 2023
概括
这项研究表明,身体活动 (PA) 模式的时间和变化显著影响女性与肥胖相关的健康结果. 分析详细的PA数据为减肥策略提供了超越每日总结的洞察力.
科学领域:
- 生物医学工程 生物医学工程
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 加速度计提供高分辨率的体力活动 (PA) 数据,对于理解人类运动至关重要.
- 传统分析通常依赖于一天一天的总结,可能缺少细微的时间模式.
研究的目的:
- 应用功能主要成分分析 (FPCA) 来分析超重/肥胖女性的时间PA模式.
- 为了研究这些PA模式与一年内与肥胖相关的健康结果之间的关联.
主要方法:
- 纵向功能主要成分分析 (FPCA) 用于分解高频PA数据.
- 使用混合效应回归模型将PA模式与健康结果联系起来.
- 分析了245名超重/肥胖妇女在1年内三次访问的数据.
主要成果:
- 在PA变化 (主体和访问层面) 和健康结果之间发现了显著的关联.
- 每天体育活动的时间被确定为影响健康结果变化的关键因素.
- 这些详细的时间洞察力无法通过传统的日级PA总结来实现.
结论:
- 纵向FPCA有效地阐明了高频PA数据中的时间模式.
- 了解PA模式,包括每日时间,对于制定有效的减肥指南至关重要.
- 这种方法为PA与健康结果关系提供了更全面的观点.
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