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

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Listed:
  • 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
    Kinesiology Department, Seattle University, Seattle, WA 98122, USA)

  • 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)

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.

Suggested Citation

  • Claire Beale & Erica L. Rauff & Wendy J. O’Brien & Sarah P. Shultz & Philip W. Fink & Rozanne Kruger, 2020. "Are all Sedentary Behaviors Equal? An Examination of Sedentary Behavior and Associations with Indicators of Disease Risk Factors in Women," IJERPH, MDPI, vol. 17(8), pages 1-13, April.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:8:p:2643-:d:344622
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    References listed on IDEAS

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    Cited by:

    1. Wendy J. O’Brien & Erica L. Rauff & Sarah P. Shultz & McLean Sloughter & Philip W. Fink & Bernhard Breier & Rozanne Kruger, 2022. "Replacing Sedentary Time with Physically Active Behaviour Predicts Improved Body Composition and Metabolic Health Outcomes," IJERPH, MDPI, vol. 19(14), pages 1-12, July.

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