Aspects of activity behavior as a determinant of the physical activity level.
This study investigated which aspects of the individuals' activity behavior determine the physical activity level (PAL). Habitual physical activity of 20 Dutch adults (age: 26-60 years, body mass index: 24.5±2.7 kg/m2) was measured using a tri-axial accelerometer. Accelerometer output was used to id...
| Publicado en: | Scandinavian Journal of Medicine & Science in Sports (John Wiley & Sons, Inc.) Vol. 22; no. 1; pp. 139 - 146 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
John Wiley & Sons, Inc.
Feb2012
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=70250064&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 70250064 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16000838 NRNW jtl: Scandinavian Journal of Medicine & Science in Sports (John Wiley & Sons, Inc.) issn: 16000838 maglogo: N pubinfo: dt: Feb2012 vid: 22 iid: 1 pid: 52269 pub: John Wiley & Sons, Inc. artinfo: ui: 70250064 104626615 10.1111/j.1600-0838.2010.01130.x 70250064 ppf: 139 ppct: 7 formats: tig: atl: Aspects of activity behavior as a determinant of the physical activity level. aug: au: Bonomi, A. G. Plasqui, G. Goris, A. H. C. Westerterp, K. R. affil: Department of Human Biology, Maastricht University, Maastricht, The Netherlands sug: subj: Physical Activity Health Behavior Human Netherlands Funding Source Adult Middle Age Body Mass Index Evaluation Accelerometry Algorithms Energy Metabolism Evaluation Doubly Labeled Water Technique Standing Walking Sleep Multiple Linear Regression Cycling Sedentary Behavior Transportation Male Female Descriptive Statistics Regression Pearson's Correlation Coefficient T-Tests Step-Wise Multiple Regression Data Analysis Software Body Weights and Measures Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: This study investigated which aspects of the individuals' activity behavior determine the physical activity level (PAL). Habitual physical activity of 20 Dutch adults (age: 26-60 years, body mass index: 24.5±2.7 kg/m2) was measured using a tri-axial accelerometer. Accelerometer output was used to identify the engagement in different types of daily activities with a classification tree algorithm. Activity behavior was described by the daily duration of sleeping, sedentary behavior (lying, sitting, and standing), walking, running, bicycling, and generic standing activities. Simultaneously, the total energy expenditure (TEE) was measured using doubly labeled water. PAL was calculated as TEE divided by sleeping metabolic rate. PAL was significantly associated ( P<0.05) with sedentary time ( R=−0.72), and the duration of walking ( R=0.49), bicycling ( R=0.77), and active standing ( R=0.62). A negative association was observed between sedentary time and the duration of active standing ( R=−0.87; P<0.001). A multiple-linear regression analysis showed that 75% of the variance in PAL could be predicted by the duration of bicycling (Partial R2=59%; P<0.01), walking (Partial R2=9%; P<0.05) and being sedentary (Partial R2=7%; P<0.05). In conclusion, there is objective evidence that sedentary time and activities related to transportation and commuting, such as walking and bicycling, contribute significantly to the average PAL. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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