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Associations of movement behaviors and body mass index: comparison between a report-based and monitor-based method using Compositional Data Analysis
by
Kim Youngwon
, Welk, Gregory J
, Burns, Ryan D
, Duck-chul, Lee
in
Body mass index
/ Body size
/ Data analysis
/ Lifestyles
/ Luminous intensity
/ Physical activity
/ Regression analysis
/ Regression models
/ Sedentary behavior
/ Sleep
2021
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Associations of movement behaviors and body mass index: comparison between a report-based and monitor-based method using Compositional Data Analysis
by
Kim Youngwon
, Welk, Gregory J
, Burns, Ryan D
, Duck-chul, Lee
in
Body mass index
/ Body size
/ Data analysis
/ Lifestyles
/ Luminous intensity
/ Physical activity
/ Regression analysis
/ Regression models
/ Sedentary behavior
/ Sleep
2021
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Do you wish to request the book?
Associations of movement behaviors and body mass index: comparison between a report-based and monitor-based method using Compositional Data Analysis
by
Kim Youngwon
, Welk, Gregory J
, Burns, Ryan D
, Duck-chul, Lee
in
Body mass index
/ Body size
/ Data analysis
/ Lifestyles
/ Luminous intensity
/ Physical activity
/ Regression analysis
/ Regression models
/ Sedentary behavior
/ Sleep
2021
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Associations of movement behaviors and body mass index: comparison between a report-based and monitor-based method using Compositional Data Analysis
Journal Article
Associations of movement behaviors and body mass index: comparison between a report-based and monitor-based method using Compositional Data Analysis
2021
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Overview
Background/objectivesEvidence on the associations between lifestyle movement behaviors and obesity has been established without taking into account the time-constrained nature of categorized, time-based lifestyle behaviors. We examined the associations of sleep, sedentary behavior (SED), light-intensity physical activity (LPA), and moderate-to-vigorous PA (MVPA) with body mass index (BMI) using Compositional Data Analysis (CoDA), and compared the associations between a report-based method (24-h Physical Activity Recall; 24PAR) and a monitor-based method (SenseWear Armband; SWA).Subjects/methodsReplicate data from a representative sample of 1247 adults from the Physical Activity Measurement Survey (PAMS) were used in the study. Participants completed activity monitoring on two randomly selected days, each of which required wearing a SWA for a full day, and then completing a telephone-administered 24PAR the following day. Relationships among behavioral compositional parts and BMI were analyzed using CoDA via multiple linear regression models with both 24PAR and SWA data.ResultsUsing 24PAR, time spent in sleep (γ = −3.58, p = 0.011), SED (γ = 3.70, p = 0.002), and MVPA (γ = −0.53, p = 0.018) was associated with BMI. Using SWA, time spent in sleep (γ = −5.10, p < 0.001), SED (γ = 8.93, p < 0.001), LPA (γ = −3.12, p < 0.001), and MVPA (γ = −1.43, p < 0.001) was associated with BMI. The SWA models explained more variance in BMI (R2 = 0.28) compared with the 24PAR models (R2 = 0.07). The compositional isotemporal substitution models revealed reductions in BMI when replacing SED by MVPA, LPA (not with 24PAR) or sleep for both 24PAR and SWA, but the effect estimates were larger with SWA.ConclusionsFavorable levels of relative time spent in lifestyle movement behaviors were, in general, associated with decreased BMI. The observed associations were stronger using the monitor-based SWA method compared with the report-based 24PAR method.
Publisher
Nature Publishing Group
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