IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i17p6384-d407713.html
   My bibliography  Save this article

Get Vigorous with Physical Exercise and Improve Your Well-Being at Work!

Author

Listed:
  • Ester Gil-Beltrán

    (WANT Research Team, Universitat Jaume I, Av. Vicent Sos Baynat, s/n Castellón de la Plana, 12071 Castellón, Spain)

  • Isabella Meneghel

    (Àrea de Psicologia i Salut Mental, Universitat Internacional de Catalunya, San Cugat del Vallés, 08195 Barcelona, Spain)

  • Susana Llorens

    (WANT Research Team, Universitat Jaume I, Av. Vicent Sos Baynat, s/n Castellón de la Plana, 12071 Castellón, Spain)

  • Marisa Salanova

    (WANT Research Team, Universitat Jaume I, Av. Vicent Sos Baynat, s/n Castellón de la Plana, 12071 Castellón, Spain)

Abstract

The aim of this study is to investigate whether people who exercise regularly have higher levels of psychological well-being at work. Doing physical exercise is a habit that not only has consequences for physical and mental health, but it can also have positive consequences for organizations because physical exercise makes it easier for the employee to recover from physical, mental, and emotional effort during the workday, thus showing higher levels of engagement the next day. Through the analysis of structural equation models in a sample of 485 workers from different Spanish and Latin American companies, this study shows that subjects who exercise more have higher levels of vigor in physical exercise, which is positively related to high levels of well-being at work. This means that organizations that promote activities related to physical exercise among their employees are building a process of resource recovery, which, through the vigor of these activities, makes workers feel less stressed and more satisfied, thus experiencing greater well-being at work. Therefore, at a practical level, these results suggest that the practice of physical exercise is a tool for organizations that want to promote their employees’ psychological well-being.

Suggested Citation

  • Ester Gil-Beltrán & Isabella Meneghel & Susana Llorens & Marisa Salanova, 2020. "Get Vigorous with Physical Exercise and Improve Your Well-Being at Work!," IJERPH, MDPI, vol. 17(17), pages 1-10, September.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:17:p:6384-:d:407713
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/17/6384/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/17/17/6384/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hirotugu Akaike, 1987. "Factor analysis and AIC," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 317-332, September.
    2. Severin Hornung & Jürgen Glaser, 2010. "Employee responses to relational fulfilment and work‐life benefits," International Journal of Manpower, Emerald Group Publishing Limited, vol. 31(1), pages 73-92, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Antonio Jesús Casimiro-Andújar & Ricardo Martín-Moya & María Maravé-Vivas & Pedro Jesús Ruiz-Montero, 2022. "Effects of a Personalised Physical Exercise Program on University Workers Overall Well-Being: “UAL-Activa” Program," IJERPH, MDPI, vol. 19(18), pages 1-10, September.

    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. Jiwon Lee & Midam An & Yongku Kim & Jung-In Seo, 2021. "Optimal Allocation for Electric Vehicle Charging Stations," Energies, MDPI, vol. 14(18), pages 1-10, September.
    2. Benjamin G Schultz & Catherine J Stevens & Peter E Keller & Barbara Tillmann, 2013. "A Sequence Identification Measurement Model to Investigate the Implicit Learning of Metrical Temporal Patterns," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-1, September.
    3. Daniela Andreini & Diego Rinallo & Giuseppe Pedeliento & Mara Bergamaschi, 2017. "Brands and Religion in the Secularized Marketplace and Workplace: Insights from the Case of an Italian Hospital Renamed After a Roman Catholic Pope," Journal of Business Ethics, Springer, vol. 141(3), pages 529-550, March.
    4. Andreas Wienke & Anne M. Herskind & Kaare Christensen & Axel Skytthe & Anatoli I. Yashin, 2002. "The influence of smoking and BMI on heritability in susceptibility to coronary heart disease," MPIDR Working Papers WP-2002-003, Max Planck Institute for Demographic Research, Rostock, Germany.
    5. Byrd, T. A. & Marshall, T. E., 1997. "Relating information technology investment to organizational performance: a causal model analysis," Omega, Elsevier, vol. 25(1), pages 43-56, February.
    6. Berry, Brian J.L. & Okulicz-Kozaryn, Adam, 2008. "Are there ENSO signals in the macroeconomy," Ecological Economics, Elsevier, vol. 64(3), pages 625-633, January.
    7. Nicos Nicolaou & Scott Shane, 2019. "Common genetic effects on risk-taking preferences and choices," Journal of Risk and Uncertainty, Springer, vol. 59(3), pages 261-279, December.
    8. Stephen Richards, 2010. "Author's response," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(4), pages 920-924, October.
    9. Ken B Hanscombe & Maciej Trzaskowski & Claire M A Haworth & Oliver S P Davis & Philip S Dale & Robert Plomin, 2012. "Socioeconomic Status (SES) and Children's Intelligence (IQ): In a UK-Representative Sample SES Moderates the Environmental, Not Genetic, Effect on IQ," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-16, February.
    10. Zhang, Quanzhong & Wei, Haiyan & Liu, Jing & Zhao, Zefang & Ran, Qiao & Gu, Wei, 2021. "A Bayesian network with fuzzy mathematics for species habitat suitability analysis: A case with limited Angelica sinensis (Oliv.) Diels data," Ecological Modelling, Elsevier, vol. 450(C).
    11. Oh, Man-Suk, 2014. "Bayesian comparison of models with inequality and equality constraints," Statistics & Probability Letters, Elsevier, vol. 84(C), pages 176-182.
    12. Satonori Nasu & Yu Ishibashi & Junichi Ikuta & Shingo Yamane & Ryuji Kobayashi, 2022. "Reliability and Validity of the Japanese Version of the Assessment of Readiness for Mobility Transition (ARMT-J) for Japanese Elderly," IJERPH, MDPI, vol. 19(21), pages 1-14, October.
    13. Bonaiuto, M. & Mosca, O. & Milani, A. & Ariccio, S. & Dessi, F. & Fornara, F., 2024. "Beliefs about technological and contextual features drive biofuels’ social acceptance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    14. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 345-370, September.
    15. Golob, Thomas F. & Regan, A C, 2002. "Trucking Industry Preferences for Driver Traveler Information Using Wireless Internet-enabled Devices," University of California Transportation Center, Working Papers qt40q8h6sf, University of California Transportation Center.
    16. Schreier, Alayna & Stenersen, Madeline R. & Strambler, Michael J. & Marshall, Tim & Bracey, Jeana & Kaufman, Joy S., 2023. "Needs of caregivers of youth enrolled in a statewide system of care: A latent class analysis," Children and Youth Services Review, Elsevier, vol. 147(C).
    17. Daisuke Matsumoto & Fujio Inui & Chika Honda & Rie Tomizawa & Mikio Watanabe & Karri Silventoinen & Norio Sakai, 2020. "Heritability and Environmental Correlation of Phase Angle with Anthropometric Measurements: A Twin Study," IJERPH, MDPI, vol. 17(21), pages 1-10, October.
    18. Sanjay Gupta & Kushagra Sinha, 2022. "Assessing the Factors Impacting Transport Usage of Mobility App Users in the National Capital Territory of Delhi, India," Sustainability, MDPI, vol. 14(21), pages 1-20, October.
    19. Schomaker Michael & Heumann Christian, 2011. "Model Averaging in Factor Analysis: An Analysis of Olympic Decathlon Data," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(1), pages 1-15, January.
    20. Naiara Escalante Mateos & Eider Goñi Palacios & Arantza Fernández-Zabala & Iratxe Antonio-Agirre, 2020. "Internal Structure, Reliability and Invariance across Gender Using the Multidimensional School Climate Scale PACE-33," IJERPH, MDPI, vol. 17(13), pages 1-24, July.

    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:gam:jijerp:v:17:y:2020:i:17:p:6384-:d:407713. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.