An Activity Index for Raw Accelerometry Data and Its Comparison with Other Activity Metrics.

David M. Buchner,Chongzhi Di,Andrea Z. LaCroix,Kelly R. Evenson,Jiawei Bai,Ciprian M. Crainiceanu,Luo Xiao

doi:10.1371/journal.pone.0160644

Abstract

Accelerometers have been widely deployed in public health studies in recent years. While they collect high-resolution acceleration signals (e.g., 10–100 Hz), research has mainly focused on summarized metrics provided by accelerometers manufactures, such as the activity count (AC) by ActiGraph or Actical. Such measures do not have a publicly available formula, lack a straightforward interpretation, and can vary by software implementation or hardware type. To address these problems, we propose the physical activity index (AI), a new metric for summarizing raw tri-axial accelerometry data. We compared this metric with the AC and another recently proposed metric for raw data, Euclidean Norm Minus One (ENMO), against energy expenditure. The comparison was conducted using data from the Objective Physical Activity and Cardiovascular Health Study, in which 194 women 60–91 years performed 9 lifestyle activities in the laboratory, wearing a tri-axial accelerometer (ActiGraph GT3X+) on the hip set to 30 Hz and an Oxycon portable calorimeter, to record both tri-axial acceleration time series (converted into AI, AC, and ENMO) and oxygen uptake during each activity (converted into metabolic equivalents (METs)) at the same time. Receiver operating characteristic analyses indicated that both AI and ENMO were more sensitive to moderate and vigorous physical activities than AC, while AI was more sensitive to sedentary and light activities than ENMO. AI had the highest coefficients of determination for METs (0.72) and was a better classifier of physical activity intensity than both AC (for all intensity levels) and ENMO (for sedentary and light intensity). The proposed AI provides a novel and transparent way to summarize densely sampled raw accelerometry data, and may serve as an alternative to AC. The AI’s largely improved sensitivity on sedentary and light activities over AC and ENMO further demonstrate its advantage in studies with older adults.

Highlights

Accelerometers are commonly used to measure physical activity, and are embedded both in research and commercial devices [1,2,3,4,5,6]
We show that activity index (AI) outperforms both activity count” (AC) and Euclidean Norm Minus One (ENMO) in terms of prediction of physical activity energy expenditure and classification of physical activity intensity
We proposed AI, a new metric of physical activity based on high-resolution raw accelerometer data

Summary

Introduction

Accelerometers are commonly used to measure physical activity, and are embedded both in research and commercial devices [1,2,3,4,5,6]. While most modern accelerometers collect high-resolution signals (e.g., 10–100 Hz), the most commonly used data output consists of summary measures over user-defined epochs (e.g., 1 minute) AC has become an umbrella term for a large number of proprietary algorithms, which leads to widespread confusion among health researchers Summary measures, such as AC, have been widely used either directly as a measure of physical activity volume or intensity, or indirectly as a predictor of energy expenditure (see analysis pathways (c), (d), (e), (f) in Fig 1) [8,9,10,11,12,13]

Methods

Results

Conclusion