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  2. School of Engineering
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  4. Publikationen School of Engineering
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dc.contributor.authorKuster, Roman-
dc.contributor.authorGrooten, Wilhelmus J. A.-
dc.contributor.authorBaumgartner, Daniel-
dc.contributor.authorBlom, Victoria-
dc.contributor.authorHagströmer, Maria-
dc.contributor.authorEkblom, Örjan-
dc.date.accessioned2020-03-19T10:11:09Z-
dc.date.available2020-03-19T10:11:09Z-
dc.date.issued2019-
dc.identifier.issn0905-7188de_CH
dc.identifier.issn1600-0838de_CH
dc.identifier.urihttp://hdl.handle.net/10616/47666de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/19792-
dc.description.abstractThe 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.de_CH
dc.language.isoende_CH
dc.publisherWileyde_CH
dc.relation.ispartofScandinavian Journal of Medicine & Science in Sportsde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectActivPALde_CH
dc.subjectAutomated feature selectionde_CH
dc.subjectBout analysisde_CH
dc.subjectMachine learningde_CH
dc.subjectPosture predictionde_CH
dc.subjectSedentary behaviorde_CH
dc.subject.ddc571: Physiologie und verwandte Themende_CH
dc.subject.ddc620: Ingenieurwesende_CH
dc.titleDetecting prolonged sitting bouts with the ActiGraph GT3Xde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Mechanische Systeme (IMES)de_CH
dc.identifier.doi10.1111/sms.13601de_CH
dc.identifier.pmid31743494de_CH
zhaw.funding.euNode_CH
zhaw.issue3de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end582de_CH
zhaw.pages.start572de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume30de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedBiomedical Simulationde_CH
zhaw.author.additionalNode_CH
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Kuster, R., Grooten, W. J. A., Baumgartner, D., Blom, V., Hagströmer, M., & Ekblom, Ö. (2019). Detecting prolonged sitting bouts with the ActiGraph GT3X. Scandinavian Journal of Medicine & Science in Sports, 30(3), 572–582. https://doi.org/10.1111/sms.13601
Kuster, R. et al. (2019) ‘Detecting prolonged sitting bouts with the ActiGraph GT3X’, Scandinavian Journal of Medicine & Science in Sports, 30(3), pp. 572–582. Available at: https://doi.org/10.1111/sms.13601.
R. Kuster, W. J. A. Grooten, D. Baumgartner, V. Blom, M. Hagströmer, and Ö. Ekblom, “Detecting prolonged sitting bouts with the ActiGraph GT3X,” Scandinavian Journal of Medicine & Science in Sports, vol. 30, no. 3, pp. 572–582, 2019, doi: 10.1111/sms.13601.
KUSTER, Roman, Wilhelmus J. A. GROOTEN, Daniel BAUMGARTNER, Victoria BLOM, Maria HAGSTRÖMER und Örjan EKBLOM, 2019. Detecting prolonged sitting bouts with the ActiGraph GT3X. Scandinavian Journal of Medicine & Science in Sports [online]. 2019. Bd. 30, Nr. 3, S. 572–582. DOI 10.1111/sms.13601. Verfügbar unter: http://hdl.handle.net/10616/47666
Kuster, Roman, Wilhelmus J. A. Grooten, Daniel Baumgartner, Victoria Blom, Maria Hagströmer, and Örjan Ekblom. 2019. “Detecting Prolonged Sitting Bouts with the ActiGraph GT3X.” Scandinavian Journal of Medicine & Science in Sports 30 (3): 572–82. https://doi.org/10.1111/sms.13601.
Kuster, Roman, et al. “Detecting Prolonged Sitting Bouts with the ActiGraph GT3X.” Scandinavian Journal of Medicine & Science in Sports, vol. 30, no. 3, 2019, pp. 572–82, https://doi.org/10.1111/sms.13601.


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