Sökning: onr:"swepub:oai:DiVA.org:gih-5934" >
Detecting Prolonged...
Detecting Prolonged Sitting Bouts with the ActiGraph GT3X.
-
- Kuster, Roman P (författare)
- Karolinska Institutet,Karolinska Institutet, Stockholm, Sweden
-
- Grooten, Wilhelmus J A (författare)
- Karolinska Institutet,Karolinska Institutet, Stockholm, Sweden
-
- Baumgartner, Daniel (författare)
- ZHAW Zurich University of Applied Sciences, Winterthur, Switzerland.
-
visa fler...
-
- Blom, Victoria (författare)
- Gymnastik- och idrottshögskolan,Åstrandlaboratoriet,Karolinska Institutet, Stockholm, Sweden,Fysisk aktivitet och hjärnhälsa
-
- Hagströmer, Maria (författare)
- Karolinska Institutet,Sophiahemmet Högskola,Karolinska Institutet, Stockholm, Sweden
-
- Ekblom, Örjan, 1971- (författare)
- Gymnastik- och idrottshögskolan,Åstrandlaboratoriet,Fysisk aktivitet och hjärnhälsa
-
visa färre...
-
(creator_code:org_t)
-
-
visa fler...
-
-
visa färre...
- ISSN 0905-7188
- 2019-12-22
- 2020
- Engelska.
-
Ingår i: Scandinavian Journal of Medicine and Science in Sports. - Stockholm : Wiley-Blackwell. - 0905-7188 .- 1600-0838. ; 30:3, s. 572-582
- Relaterad länk:
-
http://openarchive.k...
-
visa fler...
-
http://hdl.handle.ne... (primary) (Object in context) (free)
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
https://urn.kb.se/re...
-
http://hdl.handle.ne...
-
http://kipublication...
-
visa färre...
Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Hälsovetenskap -- Arbetsmedicin och miljömedicin (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Health Sciences -- Occupational Health and Environmental Health (hsv//eng)
Nyckelord
- Automated Feature Selection
- Bout Analysis
- Machine Learning
- Posture Prediction
- Sedentary Behaviour
- activPAL
- Medicin/Teknik
- Medicine/Technology
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
Hitta via bibliotek
Till lärosätets databas