Detecting prolonged sitting bouts with the ActiGraph GT3X.
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...
| Publicado en: | Scandinavian Journal of Medicine & Science in Sports (John Wiley & Sons, Inc.) Vol. 30; no. 3; pp. 572 - 583 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
John Wiley & Sons, Inc.
Mar2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=141660112&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141660112 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16000838 NRNW jtl: Scandinavian Journal of Medicine & Science in Sports (John Wiley & Sons, Inc.) issn: 16000838 maglogo: N pubinfo: dt: Mar2020 vid: 30 iid: 3 pid: 52269 pub: John Wiley & Sons, Inc. artinfo: ui: 141660112 141660112 141660112 10.1111/sms.13601 141660112 ppf: 572 ppct: 11 formats: tig: atl: Detecting prolonged sitting bouts with the ActiGraph GT3X. aug: au: Kuster, Roman P. Grooten, Wilhelmus J. A. Baumgartner, Daniel Blom, Victoria Hagströmer, Maria Ekblom, Örjan affil: Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm Sweden sug: subj: Posture Classification Sitting Sedentary Behavior Machine Learning Actigraphy Human Wrist Automation Algorithms Time Factors White Collar Workers Health Behavior Wearable Sensors ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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