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Stronger Association between High Intensity Physical Activity and Cardiometabolic Health with Improved Assessment of the Full Intensity Range Using Accelerometry.
University of Gothenburg, Sweden.
University of Gothenburg, Sweden.
Swedish School of Sport and Health Sciences, GIH, Department of Sport and Health Sciences, Åstrand Laboratory of Work Physiology.ORCID iD: 0000-0002-3901-7833
Swedish School of Sport and Health Sciences, GIH, Department of Sport and Health Sciences, Åstrand Laboratory of Work Physiology.ORCID iD: 0000-0001-6058-4982 
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2020 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 20, no 4, article id E1118Article in journal (Refereed) Published
Abstract [en]

An improved method of physical activity accelerometer data processing, involving a wider frequency filter than the most commonly used ActiGraph filter, has been shown to better capture variations in physical activity intensity in a lab setting. The aim of the study was to investigate how this improved measure of physical activity affected the relationship with markers of cardiometabolic health. Accelerometer data and markers of cardiometabolic health from 725 adults from two samples, LIV 2013 and SCAPIS pilot, were analyzed. The accelerometer data was processed using both the original ActiGraph method with a low-pass cut-off at 1.6 Hz and the improved method with a low-pass cut-off at 10 Hz. The relationship between the physical activity intensity spectrum and a cardiometabolic health composite score was investigated using partial least squares regression. The strongest association between physical activity and cardiometabolic health was shifted towards higher intensities with the 10 Hz output compared to the ActiGraph method. In addition, the total explained variance was higher with the improved method. The 10 Hz output enables correctly measuring and interpreting high intensity physical activity and shows that physical activity at this intensity is stronger related to cardiometabolic health compared to the most commonly used ActiGraph method.

Place, publisher, year, edition, pages
MDPI, 2020. Vol. 20, no 4, article id E1118
Keywords [en]
ActiGraph, LIV, SCAPIS, cardiovascular disease, frequency filtering, multivariate analysis, partial least squares regression, vigorous physical activity
National Category
Sport and Fitness Sciences
Research subject
Medicine/Technology
Identifiers
URN: urn:nbn:se:gih:diva-6078DOI: 10.3390/s20041118PubMedID: 32085652OAI: oai:DiVA.org:gih-6078DiVA, id: diva2:1414753
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Mätning av det dagliga aktivitetsmönstretAvailable from: 2020-03-16 Created: 2020-03-16 Last updated: 2020-03-16

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Ekblom Bak, ElinEkblom, Örjan

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