IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-36546-5.html
   My bibliography  Save this article

Associations of timing of physical activity with all-cause and cause-specific mortality in a prospective cohort study

Author

Listed:
  • Hongliang Feng

    (Guangdong Academy of Medical Sciences
    The Chinese University of Hong Kong)

  • Lulu Yang

    (Guangdong Academy of Medical Sciences)

  • Yannis Yan Liang

    (Guangdong Academy of Medical Sciences)

  • Sizhi Ai

    (The Affiliated Brain Hospital of Guangzhou Medical University
    Heart Center, The First Affiliated Hospital of Xinxiang Medical University
    The Chinese University of Hong Kong)

  • Yaping Liu

    (The Chinese University of Hong Kong
    The Chinese University of Hong Kong)

  • Yue Liu

    (Guangdong Academy of Medical Sciences)

  • Xinyi Jin

    (Guangdong Academy of Medical Sciences)

  • Binbin Lei

    (Guangdong Academy of Medical Sciences)

  • Jing Wang

    (The Affiliated Brain Hospital of Guangzhou Medical University
    The Chinese University of Hong Kong)

  • Nana Zheng

    (The Affiliated Brain Hospital of Guangzhou Medical University)

  • Xinru Chen

    (Guangdong Academy of Medical Sciences
    The Affiliated Brain Hospital of Guangzhou Medical University)

  • Joey W. Y. Chan

    (The Chinese University of Hong Kong
    The Chinese University of Hong Kong)

  • Raymond Kim Wai Sum

    (The Chinese University of Hong Kong)

  • Ngan Yin Chan

    (The Chinese University of Hong Kong
    The Chinese University of Hong Kong)

  • Xiao Tan

    (Zhejiang University School of Public Health and Sir Run Run Shaw Hospital, Zhejiang University School of Medicine
    Uppsala University)

  • Christian Benedict

    (Uppsala University)

  • Yun Kwok Wing

    (The Chinese University of Hong Kong
    The Chinese University of Hong Kong)

  • Jihui Zhang

    (The Affiliated Brain Hospital of Guangzhou Medical University
    The Chinese University of Hong Kong
    Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University
    Guangzhou Medical University)

Abstract

There is a growing interest in the role of timing of daily behaviors in improving health. However, little is known about the optimal timing of physical activity to maximize health benefits. We perform a cohort study of 92,139 UK Biobank participants with valid accelerometer data and all-cause and cause-specific mortality outcomes, comprising over 7 years of median follow-up (638,825 person-years). Moderate-to-vigorous intensity physical activity (MVPA) at any time of day is associated with lower risks for all-cause, cardiovascular disease, and cancer mortality. In addition, compared with morning group (>50% of daily MVPA during 05:00-11:00), midday-afternoon (11:00-17:00) and mixed MVPA timing groups, but not evening group (17:00-24:00), have lower risks of all-cause and cardiovascular disease mortality. These protective associations are more pronounced among the elderly, males, less physically active participants, or those with preexisting cardiovascular diseases. Here, we show that MVPA timing may have the potential to improve public health.

Suggested Citation

  • Hongliang Feng & Lulu Yang & Yannis Yan Liang & Sizhi Ai & Yaping Liu & Yue Liu & Xinyi Jin & Binbin Lei & Jing Wang & Nana Zheng & Xinru Chen & Joey W. Y. Chan & Raymond Kim Wai Sum & Ngan Yin Chan &, 2023. "Associations of timing of physical activity with all-cause and cause-specific mortality in a prospective cohort study," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-36546-5
    DOI: 10.1038/s41467-023-36546-5
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-36546-5
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-023-36546-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. Louise A C Millard & Kate Tilling & Tom R Gaunt & David Carslake & Deborah A Lawlor, 2021. "Association of physical activity intensity and bout length with mortality: An observational study of 79,503 UK Biobank participants," PLOS Medicine, Public Library of Science, vol. 18(9), pages 1-18, September.
    3. Contal, Cecile & O'Quigley, John, 1999. "An application of changepoint methods in studying the effect of age on survival in breast cancer," Computational Statistics & Data Analysis, Elsevier, vol. 30(3), pages 253-270, May.
    4. Victoria A. Acosta-Rodríguez & Filipa Rijo-Ferreira & Carla B. Green & Joseph S. Takahashi, 2021. "Importance of circadian timing for aging and longevity," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    5. Aiden Doherty & Dan Jackson & Nils Hammerla & Thomas Plötz & Patrick Olivier & Malcolm H Granat & Tom White & Vincent T van Hees & Michael I Trenell & Christoper G Owen & Stephen J Preece & Rob Gillio, 2017. "Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-14, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Noémi Kreif & Richard Grieve & Iván Díaz & David Harrison, 2015. "Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for Traumatic Brain Injury," Health Economics, John Wiley & Sons, Ltd., vol. 24(9), pages 1213-1228, September.
    2. Heinzl, Harald & Tempfer, Clemens, 2001. "A cautionary note on segmenting a cyclical covariate by minimum P-value search," Computational Statistics & Data Analysis, Elsevier, vol. 35(4), pages 451-461, February.
    3. Abhilash Bandam & Eedris Busari & Chloi Syranidou & Jochen Linssen & Detlef Stolten, 2022. "Classification of Building Types in Germany: A Data-Driven Modeling Approach," Data, MDPI, vol. 7(4), pages 1-23, April.
    4. Boonstra Philip S. & Little Roderick J.A. & West Brady T. & Andridge Rebecca R. & Alvarado-Leiton Fernanda, 2021. "A Simulation Study of Diagnostics for Selection Bias," Journal of Official Statistics, Sciendo, vol. 37(3), pages 751-769, September.
    5. Clark, Stephen & Birkin, Mark & Lomax, Nik & Morris, Michelle, 2020. "Developing a whole systems obesity classification for the UK Biobank Cohort," OSF Preprints 7nqgd, Center for Open Science.
    6. Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    7. Liangyuan Hu & Lihua Li, 2022. "Using Tree-Based Machine Learning for Health Studies: Literature Review and Case Series," IJERPH, MDPI, vol. 19(23), pages 1-13, December.
    8. Norah Alyabs & Sy Han Chiou, 2022. "The Missing Indicator Approach for Accelerated Failure Time Model with Covariates Subject to Limits of Detection," Stats, MDPI, vol. 5(2), pages 1-13, May.
    9. Feldkircher, Martin, 2014. "The determinants of vulnerability to the global financial crisis 2008 to 2009: Credit growth and other sources of risk," Journal of International Money and Finance, Elsevier, vol. 43(C), pages 19-49.
    10. Eunsil Seok & Akhgar Ghassabian & Yuyan Wang & Mengling Liu, 2024. "Statistical Methods for Modeling Exposure Variables Subject to Limit of Detection," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(2), pages 435-458, July.
    11. Ida Kubiszewski & Kenneth Mulder & Diane Jarvis & Robert Costanza, 2022. "Toward better measurement of sustainable development and wellbeing: A small number of SDG indicators reliably predict life satisfaction," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(1), pages 139-148, February.
    12. Georges Steffgen & Philipp E. Sischka & Martha Fernandez de Henestrosa, 2020. "The Quality of Work Index and the Quality of Employment Index: A Multidimensional Approach of Job Quality and Its Links to Well-Being at Work," IJERPH, MDPI, vol. 17(21), pages 1-31, October.
    13. Christopher Kath & Florian Ziel, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Papers 1811.08604, arXiv.org.
    14. Esef Hakan Toytok & Sungur Gürel, 2019. "Does Project Children’s University Increase Academic Self-Efficacy in 6th Graders? A Weak Experimental Design," Sustainability, MDPI, vol. 11(3), pages 1-12, February.
    15. J M van Niekerk & M C Vos & A Stein & L M A Braakman-Jansen & A F Voor in ‘t holt & J E W C van Gemert-Pijnen, 2020. "Risk factors for surgical site infections using a data-driven approach," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-14, October.
    16. Joost R. Ginkel, 2020. "Standardized Regression Coefficients and Newly Proposed Estimators for $${R}^{{2}}$$R2 in Multiply Imputed Data," Psychometrika, Springer;The Psychometric Society, vol. 85(1), pages 185-205, March.
    17. Lara Jehi & Xinge Ji & Alex Milinovich & Serpil Erzurum & Amy Merlino & Steve Gordon & James B Young & Michael W Kattan, 2020. "Development and validation of a model for individualized prediction of hospitalization risk in 4,536 patients with COVID-19," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-15, August.
    18. Matthew Carli & Mary H. Ward & Catherine Metayer & David C. Wheeler, 2022. "Imputation of Below Detection Limit Missing Data in Chemical Mixture Analysis with Bayesian Group Index Regression," IJERPH, MDPI, vol. 19(3), pages 1-17, January.
    19. Gerko Vink & Laurence E. Frank & Jeroen Pannekoek & Stef Buuren, 2014. "Predictive mean matching imputation of semicontinuous variables," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(1), pages 61-90, February.
    20. Simon Bussy & Mokhtar Z. Alaya & Anne‐Sophie Jannot & Agathe Guilloux, 2022. "Binacox: automatic cut‐point detection in high‐dimensional Cox model with applications in genetics," Biometrics, The International Biometric Society, vol. 78(4), pages 1414-1426, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-36546-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.