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The Length and Number of Sedentary Bouts Predict Fibrinogen Levels in Postmenopausal Women

Author

Listed:
  • Pascal Izzicupo

    (Department of Medicine and Aging Sciences, University “G. D’Annunzio” of Chieti-Pescara, 66100 Chieti and Pescara, Italy)

  • Andrea Di Blasio

    (Department of Medicine and Aging Sciences, University “G. D’Annunzio” of Chieti-Pescara, 66100 Chieti and Pescara, Italy)

  • Andrea Di Credico

    (Department of Medicine and Aging Sciences, University “G. D’Annunzio” of Chieti-Pescara, 66100 Chieti and Pescara, Italy)

  • Giulia Gaggi

    (Department of Medicine and Aging Sciences, University “G. D’Annunzio” of Chieti-Pescara, 66100 Chieti and Pescara, Italy)

  • Anastasios Vamvakis

    (3rd Department of Internal Medicine, Papageorgiou Hospital, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Giorgio Napolitano

    (Department of Medicine and Aging Sciences, University “G. D’Annunzio” of Chieti-Pescara, 66100 Chieti and Pescara, Italy)

  • Fabrizio Ricci

    (Department of Neuroscience, Imaging and Clinical Sciences, University “G. D’Annunzio” of Chieti-Pescara, 66100 Chieti and Pescara, Italy
    Department of Clinical Sciences, Lund University, 21428 Malmö, Sweden)

  • Sabina Gallina

    (Department of Neuroscience, Imaging and Clinical Sciences, University “G. D’Annunzio” of Chieti-Pescara, 66100 Chieti and Pescara, Italy)

  • Barbara Ghinassi

    (Department of Medicine and Aging Sciences, University “G. D’Annunzio” of Chieti-Pescara, 66100 Chieti and Pescara, Italy)

  • Angela Di Baldassarre

    (Department of Medicine and Aging Sciences, University “G. D’Annunzio” of Chieti-Pescara, 66100 Chieti and Pescara, Italy)

Abstract

Menopause is associated with adverse changes in coagulation homeostasis. We aimed to investigate the association between objectively measured sedentary behavior (SB) and SB bouts (i.e., number and length of SB bouts) vs. fibrinogen levels in post-menopausal women. Fifty-three post-menopausal women (age 59.8 ± 6.2 years, BMI 27.3 ± 4.4) wore a multisensory device (Sensewear Mini Armband, BodyMedia, Inc., Pittsburgh, PA) for 5 days, to measure SB and physical activity (PA). Blood samples were collected to measure serum fibrinogen. Fibrinogen was directly correlated with SB (r = −0.48, p < 0.01), lying down during awake time (r = −0.50, p < 0.01), and both medium (11–30 mins) and very long bouts (>1 h) of SB (r = −0.59, p < 0.01; r = −0.51, p < 0.01, respectively), and inversely correlated with moderate to vigorous-intensity physical activity (r = −0.39, p < 0.01). Furthermore, fibrinogen was also directly correlated with BMI (r = −0.28, p < 0.05). In postmenopausal women without prevalent cardiovascular disease, the number of prolonged and uninterrupted sedentary bouts is directly correlated with increased fibrinogen levels, regardless of PA and BMI. This result suggests the importance of delivering new strategies to counteract the increase of sedentariness and inactivity of the postmenopausal population.

Suggested Citation

  • Pascal Izzicupo & Andrea Di Blasio & Andrea Di Credico & Giulia Gaggi & Anastasios Vamvakis & Giorgio Napolitano & Fabrizio Ricci & Sabina Gallina & Barbara Ghinassi & Angela Di Baldassarre, 2020. "The Length and Number of Sedentary Bouts Predict Fibrinogen Levels in Postmenopausal Women," IJERPH, MDPI, vol. 17(9), pages 1-12, April.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:9:p:3051-:d:351316
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    Citations

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    Cited by:

    1. Andrea Di Credico & Giulia Gaggi & Anastasios Vamvakis & Sofia Serafini & Barbara Ghinassi & Angela Di Baldassarre & Pascal Izzicupo, 2021. "Bioelectrical Impedance Vector Analysis of Young Elite Team Handball Players," IJERPH, MDPI, vol. 18(24), pages 1-12, December.
    2. Andrea Di Credico & David Perpetuini & Piero Chiacchiaretta & Daniela Cardone & Chiara Filippini & Giulia Gaggi & Arcangelo Merla & Barbara Ghinassi & Angela Di Baldassarre & Pascal Izzicupo, 2021. "The Prediction of Running Velocity during the 30–15 Intermittent Fitness Test Using Accelerometry-Derived Metrics and Physiological Parameters: A Machine Learning Approach," IJERPH, MDPI, vol. 18(20), pages 1-11, October.

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