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Detecting Prolonged Sitting Bouts with the ActiGraph GT3X.
Karolinska Institutet, Stockholm, Sweden.
Karolinska Institutet, Stockholm, Sweden.
ZHAW Zurich University of Applied Sciences, Winterthur, Switzerland..
Swedish School of Sport and Health Sciences, GIH, Department of Sport and Health Sciences, Åstrand Laboratory of Work Physiology. Karolinska Institutet, Stockholm, Sweden.ORCID iD: 0000-0002-0079-124x
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2019 (English)In: Scandinavian Journal of Medicine and Science in Sports, ISSN 0905-7188, E-ISSN 1600-0838Article in journal (Refereed) Epub ahead of print
Abstract [en]

The ActiGraph has a high ability to measure physical activity, however, it lacks an accurate posture classification to measure sedentary behaviour. The aim of the present study was to develop an ActiGraph (waist-worn, 30Hz) 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.1 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 optimised 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/day). 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/day). However, the frequently used 100 cpm with LFE accurately predicted total sitting time (bias ≤7 minutes/day). 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.

Place, publisher, year, edition, pages
2019.
Keywords [en]
Automated Feature Selection, Bout Analysis, Machine Learning, Posture Prediction, Sedentary Behaviour, activPAL
National Category
Occupational Health and Environmental Health
Research subject
Medicine/Technology
Identifiers
URN: urn:nbn:se:gih:diva-5934DOI: 10.1111/sms.13601PubMedID: 31743494OAI: oai:DiVA.org:gih-5934DiVA, id: diva2:1374576
Available from: 2019-12-02 Created: 2019-12-02 Last updated: 2019-12-02Bibliographically approved

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Blom, VictoriaEkblom, Örjan

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