一个高效的二维功能混合效果模型框架,用于反复测量的功能数据.
Cheng Cao1, Jiguo Cao2, Hao Pan3
1Department of Data Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR.
可穿戴的加速度计跟踪日常身体活动,揭示复杂的模式. 一个新的二维功能混合效应模型 (2dFMM) 显示了活动和青少年心理健康之间的重要联系,有助于干预策略.
科学领域:
- 统计 统计 统计 统计
- 可穿戴技术可穿戴技术
- 公共卫生 公共卫生
背景情况:
- 可穿戴设备在几天内生成密集的,串行相关的体育活动数据.
- 现有的模型难以捕捉复杂的日内和日间活动模式.
- 对于青少年来说,了解身体活动与心理健康之间的联系至关重要.
研究的目的:
- 为分析复杂的体力活动数据提出一个高效的二维功能混合效应模型 (2dFMM).
- 在一个大群体中,研究每周体育活动模式与心理健康评估之间的关系.
- 为大规模纵向功能数据开发一个计算高效的估计程序.
主要方法:
- 开发一种新的二维功能混合效果模型 (2dFMM).
- 纳入二维固定效应和四维相关性结构.
- 实施快速的三阶段估计程序,以获得准确的推断和计算效率.
主要成果:
- 2dFMM有效地描述了体育活动数据中的纵向 (日间) 和功能 (日内) 相互作用.
- 发现了强有力的证据,证明身体活动和心理健康评估之间的时间变化的关联.
- 提出的方法证明了广泛的适用性,包括在环境数据分析中.
结论:
- 2dFMM为分析复杂的可穿戴传感器数据提供了一个强大的工具.
- 针对日常体育活动模式可能为学校青少年心理健康提供有效的干预策略.
- 该方法的效率使其适用于各种科学领域的大型数据集.
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