Are all Sedentary Behaviors Equal? An Examination of Sedentary Behavior and Associations with Indicators of Disease Risk Factors in Women

Claire Beale, Erica L. Rauff, Wendy J. O’Brien, Sarah P. Shultz, Philip W. Fink and Rozanne Kruger
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Claire Beale: School of Sport, Exercise, and Nutrition, Massey University, 4442 Palmerston North, New Zealand
Erica L. Rauff: Kinesiology Department, Seattle University, Seattle, WA 98122, USA
Wendy J. O’Brien: School of Sport, Exercise, and Nutrition, Massey University, 4442 Palmerston North, New Zealand
Sarah P. Shultz: School of Sport, Exercise, and Nutrition, Massey University, 4442 Palmerston North, New Zealand
Philip W. Fink: School of Sport, Exercise, and Nutrition, Massey University, 4442 Palmerston North, New Zealand
Rozanne Kruger: School of Sport, Exercise, and Nutrition, Massey University, 4442 Palmerston North, New Zealand

IJERPH, 2020, vol. 17, issue 8, 1-13

Abstract: Sedentary behavior increases risk for non-communicable diseases; associations may differ within different contexts (e.g., leisure time, occupational). This study examined associations between different types of sedentary behavior and disease risk factors in women, using objectively measured accelerometer-derived sedentary data. A validation study ( n = 20 women) classified sedentary behavior into four categories: lying down; sitting (non-active); sitting (active); standing. A cross-sectional study ( n = 348 women) examined associations between these classifications and disease risk factors (body composition, metabolic, inflammatory, blood lipid variables). Participants spent an average of 7 h 42 min per day in sedentary behavior; 58% of that time was classified as non-active sitting and 26% as active sitting. Non-active sitting showed significant ( p ≤ 0.001) positive correlations with BMI (r = 0.244), body fat percent ( r = 0.216), body mass ( r = 0.236), fat mass ( r = 0.241), leptin ( r = 0.237), and negative correlations with HDL-cholesterol ( r = −0.117, p = 0.031). Conversely, active sitting was significantly ( p ≤ 0.001) negatively correlated with BMI ( r = −0.300), body fat percent ( r = −0.249), body mass ( r = −0.305), fat mass ( r = −0.320), leptin ( r = −0.259), and positively correlated with HDL-cholesterol ( r = 0.115, p = 0.035). In summary, sedentary behavior can be stratified using objectively measured accelerometer-derived activity data. Subsequently, different types of sedentary behaviors may differentially influence disease risk factors. Public health initiatives should account for sedentary classifications when developing sedentary behavior recommendations.

Keywords: sedentary behavior; accelerometry; disease risk factors (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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