基于加速度计收集的身体活动数据的统计分析综述
A Review of Statistical Analyses on Physical Activity Data Collected from Accelerometers.
作者信息
Zhang Yukun, Li Haocheng, Keadle Sarah Kozey, Matthews Charles E, Carroll Raymond J
机构信息
Department of Mathematics and Statistics, University of Calgary, Calgary, AB T2N 1N4, Canada.
Kinesiology Department, California Polytechnic State University, San Luis Obispo, CA 93407, USA.
出版信息
Stat Biosci. 2019;11(2):465-476. doi: 10.1007/s12561-019-09250-6. Epub 2019 Jun 28.
Studies for the associations between physical activity and disease risk have been supported by newly developed wearable accelerometer-based devices. These devices record raw activity/movement information in real time on a second-by-second basis and the data can be converted to a variety of summary metrics, such as energy expenditure, sedentary time and moderate-vigorous intensity physical activity. Here we review some of the methods used to analyze the accelerometer data and the R packages that can generate activity related variables from raw data. We also discuss longitudinal data and functional data approaches to perform analyses for various research purposes.
基于可穿戴加速度计的新开发设备为身体活动与疾病风险之间关联的研究提供了支持。这些设备能逐秒实时记录原始活动/运动信息，并且数据可转换为各种汇总指标，如能量消耗、久坐时间和中等至剧烈强度身体活动。在此，我们回顾一些用于分析加速度计数据的方法以及可从原始数据生成与活动相关变量的R软件包。我们还讨论用于各种研究目的的纵向数据和功能数据方法以进行分析。
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