使用非静止时间序列模型分析纵向可穿戴体育活动数据
Melina Del Angel1, Matthew Nunes2, Oliver Peacock1
1Department for Health, University of Bath, Claverton Down, Bath, BA2 7 AY, UK.
概括
这项研究引入了一种新的趋势局部静止波段 (TLSW) 模型,用于分析可穿戴设备的纵向体力活动数据. 这种新方法,包括Time in Reference Region of Variability (TIRRV),为个人和群体体体力活动的时间变化提供了强大的洞察力.
科学领域:
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
- 统计 统计 统计 统计
背景情况:
- 可穿戴设备为监控身体活动提供了新的途径.
- 传统方法往往忽略时间变化,专注于平均值或快照.
- 需要一种新的统计方法来分析纵向体力活动数据,并考虑时间结构.
研究的目的:
- 开发和验证一种新的统计方法,用于分析可穿戴设备的纵向体力活动数据.
- 为了考虑数据中的时间结构和动态.
- 为了更好地了解随着时间的推移,身体活动模式.
主要方法:
- 利用了来自多维个性化身体活动 (MIPACT) 随机对照试验的二次数据.
- 应用趋势局部静止波段 (TLSW) 模型来分析80名参与者的12周体育活动数据.
- 引入了时间在参考区域变化率 (TIRRV) 度量来评估与基线相对的个体变化.
主要成果:
- 证明了TLSW方法的有效性,用于分析每小时分辨率的体育活动数据.
- TLSW模型考虑了时间依赖性和数据结构,使得详细的趋势和信心区间分析成为可能.
- 该TIRRV指标提供了基于基线的评估,以评估个人和团体随着时间的推移所取得的进展,从而产生了强有力的见解.
结论:
- 基于TLSW的方法是一种新且有效的方法,用于分析可穿戴技术的高分辨率体育活动数据.
- TLSW趋势强烈地描述了个人和团体在长时间内的行为.
- 这种方法增强了研究人员,临床医生和患者对设备测量身体活动的时间变化的理解.
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