Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Detecting prolonged sitting bouts with the ActiGraph GT3X
Authors : Kuster, Roman
Grooten, Wilhelmus J. A.
Baumgartner, Daniel
Blom, Victoria
Hagströmer, Maria
Ekblom, Örjan
et. al : No
DOI : 10.1111/sms.13601
Published in : Scandinavian Journal of Medicine & Science in Sports
Volume(Issue) : 30
Issue : 3
Pages : 572
Pages to: 582
Issue Date: 2019
Publisher / Ed. Institution : Wiley
ISSN: 0905-7188
1600-0838
Language : English
Subjects : ActivPAL; Automated feature selection; Bout analysis; Machine learning; Posture prediction; Sedentary behavior
Subject (DDC) : 571: Physiology and related subjects
620: Engineering
Abstract: The ActiGraph has a high ability to measure physical activity; however, it lacks an accurate posture classification to measure sedentary behavior. The aim of the present study was to develop an ActiGraph (waist-worn, 30 Hz) posture classification to detect prolonged sitting bouts, and to compare the classification to proprietary ActiGraph data. The activPAL, a highly valid posture classification device, served as reference criterion. Both sensors were worn by 38 office workers over a median duration of 9 days. An automated feature selection extracted the relevant signal information for a minute-based posture classification. The machine learning algorithm with optimal feature number to predict the time in prolonged sitting bouts (≥5 and ≥10 minutes) was searched and compared to the activPAL using Bland-Altman statistics. The comparison included optimized and frequently used cut-points (100 and 150 counts per minute (cpm), with and without low-frequency-extension (LFE) filtering). The new algorithm predicted the time in prolonged sitting bouts most accurate (bias ≤ 7 minutes/d). Of all proprietary ActiGraph methods, only 150 cpm without LFE predicted the time in prolonged sitting bouts non-significantly different from the activPAL (bias ≤ 18 minutes/d). However, the frequently used 100 cpm with LFE accurately predicted total sitting time (bias ≤ 7 minutes/d). To study the health effects of ActiGraph measured prolonged sitting, we recommend using the new algorithm. In case a cut-point is used, we recommend 150 cpm without LFE to measure prolonged sitting and 100 cpm with LFE to measure total sitting time. However, both cpm cut-points are not recommended for a detailed bout analysis.
URI: https://digitalcollection.zhaw.ch/handle/11475/19792
Fulltext version : Published version
License (according to publishing contract) : Licence according to publishing contract
Departement: School of Engineering
Organisational Unit: Institute of Mechanical Systems (IMES)
Appears in Collections:Publikationen School of Engineering

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.