Literature DB >> 30171783

Patterns of accelerometer-based sedentary behavior and their association with cardiorespiratory fitness in adults.

Antje Ullrich1,2, Sophie Baumann1,2,3, Lisa Voigt1,2, Ulrich John1,2, Neeltje van den Berg2,4, Marcus Dörr2,5, Sabina Ulbricht1,2.   

Abstract

We aimed to identify patterns of sedentary behavior (SB) and examined whether cardiorespiratory fitness differs between classes with distinct patterns of SB. One hundred and seventy participants (57% women, mean age = 56.4 years) received accelerometry monitoring for 7 days. Prior to accelerometry assessment, cardiorespiratory fitness was assessed by peak oxygen uptake (VO2peak ). VO2peak was directly measured during a symptom-limited cardiopulmonary exercise testing on a cycle ergometer. Patterns in accelerometer data were classified based on time spent in SB per day using growth mixture modeling. Model-implied class-specific VO2peak means were compared using adjusted equality test of means. Growth mixture modeling revealed four patterns of SB: "High, stable" (n = 120, M = 724.9 min/d), "Low, increase" (n = 14, M = 622.2 min/d), "Low, decrease" (n = 11, M = 540.2 min/d), and "High, decrease" (n = 25, M = 694.8 min/d). Persons in class "High, stable" had significantly lower VO2peak values (M = 25.0 mL/kg/min, SD = 0.6) compared to persons in class "Low, increase" (M = 30.5 mL/kg/min, SD = 3.6; P = 0.001), in class "Low, decrease" (M = 30.1 mL/kg/min, SD = 5.0; P = 0.009), and in class "High, decrease" (M = 29.6 mL/kg/min, SD = 5.9; P = 0.032). No differences among the other classes were found. We identified four classes of individuals with distinct patterns of SB and showed that VO2peak partially differs between classes. Especially, individuals with stable high SB levels throughout the week might be addressed in public health recommendations and interventions.
© 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Entities:  

Keywords:  fitness; latent class analysis; objective measure; sedentary patterns

Mesh:

Year:  2018        PMID: 30171783     DOI: 10.1111/sms.13289

Source DB:  PubMed          Journal:  Scand J Med Sci Sports        ISSN: 0905-7188            Impact factor:   4.221


  3 in total

1.  Do accelerometer-based physical activity patterns differentially affect cardiorespiratory fitness? A growth mixture modeling approach.

Authors:  Sophie Baumann; Diana Guertler; Franziska Weymar; Martin Bahls; Marcus Dörr; Neeltje van den Berg; Ulrich John; Sabina Ulbricht
Journal:  J Behav Med       Date:  2019-06-12

Review 2.  The physiological benefits of sitting less and moving more: Opportunities for future research.

Authors:  Chueh-Lung Hwang; Szu-Hua Chen; Chih-Hsuan Chou; Georgios Grigoriadis; Tzu-Chieh Liao; Ibra S Fancher; Ross Arena; Shane A Phillips
Journal:  Prog Cardiovasc Dis       Date:  2021-01-13       Impact factor: 11.278

3.  Movement behavior remains stable in stroke survivors within the first two months after returning home.

Authors:  Roderick Wondergem; Martijn F Pisters; Martijn W Heijmans; Eveline J M Wouters; Rob A de Bie; Cindy Veenhof; Johanna M A Visser-Meily
Journal:  PLoS One       Date:  2020-03-26       Impact factor: 3.240

  3 in total

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