A Review of Statistical Analyses on Physical Activity Data Collected from Accelerometers

被引：5

作者：

Zhang, Yukun ^{[1
]}

Li, Haocheng ^{[1
]}

Keadle, Sarah Kozey ^{[2
]}

Matthews, Charles E. ^{[3
]}

Carroll, Raymond J. ^{[4
,5
]}

机构：

[1] Univ Calgary, Dept Math & Stat, Calgary, AB T2N 1N4, Canada

[2] Calif Polytech State Univ San Luis Obispo, Kinesiol Dept, San Luis Obispo, CA 93407 USA

[3] NCI, Bethesda, MD 20892 USA

[4] Texas A&M Univ, Dept Stat, College Stn, TX 77843 USA

[5] Univ Technol Sydney, Dept Math & Stat, Ultimo, NSW 2007, Australia

来源：

STATISTICS IN BIOSCIENCES | 2019年 / 11卷 / 02期

基金：

加拿大自然科学与工程研究理事会;

关键词：

Accelerometer; Functional data analysis; Longitudinal data analysis; Physical activity; R package; FUNCTIONAL DATA; SEDENTARY TIME; RECOMMENDATIONS; CALIBRATION; REGRESSION; PATTERNS; INSIGHTS; BEHAVIOR; METRICS;

D O I：

10.1007/s12561-019-09250-6

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页码：465 / 476

页数：12

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