| 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 |
| Page(s): | 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 |
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