IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v21y2024i8p1073-d1457140.html
   My bibliography  Save this article

School Health Promotion, the Body Mass Index z-Score, and Psychosocial Health in Primary Schools of the Netherlands

Author

Listed:
  • Lisanne Vonk

    (Department of Health Services Research, Care and Public Health Research Institute (CAPHRI), Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands
    Academic Collaborative Center for Public Health Limburg, Public Health Service South Limburg, P.O. Box 33, 6400 AA Heerlen, The Netherlands)

  • Iris Eekhout

    (Expertise Center Child Health, Netherlands Organisation for Applied Scientific Research (TNO), P.O. Box 3005, 2301 DA Leiden, The Netherlands)

  • Tim Huijts

    (Research Centre for Education and the Labour Market (ROA), School of Business and Economics, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands)

  • Mark Levels

    (Research Centre for Education and the Labour Market (ROA), School of Business and Economics, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands)

  • Maria Jansen

    (Department of Health Services Research, Care and Public Health Research Institute (CAPHRI), Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands
    Academic Collaborative Center for Public Health Limburg, Public Health Service South Limburg, P.O. Box 33, 6400 AA Heerlen, The Netherlands)

Abstract

Childhood overweight and psychosocial issues remain significant public health concerns. Schools worldwide implement health promotion programs to address these issues and to support the physical and psychosocial health of children. However, more insight is needed into the relation between these health-promoting programs and the Body Mass Index (BMI) z-score and psychosocial health of children, while taking into account how school factors might influence this relation. Therefore, we examined whether the variation between primary schools regarding the BMI z-score and psychosocial health of students could be explained by school health promotion, operationalized as Healthy School (HS) certification, general school characteristics, and the school population; we also examined to what extent the characteristics interact. The current study had a repeated cross-sectional design. Multilevel analyses were performed to calculate the variation between schools, and to examine the association between HS certification and our outcomes. Existing data of multiple school years on 1698 schools were used for the BMI z-score and on 841 schools for psychosocial health. The school level explained 2.41% of the variation in the BMI z-score and 2.45% of the variation in psychosocial health, and differences were mostly explained by parental socioeconomic status. Additionally, HS certification was associated with slightly lower BMI z-scores, but not with psychosocial health. Therefore, obtaining HS certification might contribute to the better physical health of primary school students in general. This might indicate that HS certification also relates to healthier lifestyles in primary schools, but further research should examine this.

Suggested Citation

  • Lisanne Vonk & Iris Eekhout & Tim Huijts & Mark Levels & Maria Jansen, 2024. "School Health Promotion, the Body Mass Index z-Score, and Psychosocial Health in Primary Schools of the Netherlands," IJERPH, MDPI, vol. 21(8), pages 1-14, August.
    • Handle: RePEc:gam:jijerp:v:21:y:2024:i:8:p:1073-:d:1457140
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/21/8/1073/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/21/8/1073/
    Download Restriction: no
    ---><---

    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. Salima Meherali & Neelam Punjani & Samantha Louie-Poon & Komal Abdul Rahim & Jai K. Das & Rehana A. Salam & Zohra S. Lassi, 2021. "Mental Health of Children and Adolescents Amidst COVID-19 and Past Pandemics: A Rapid Systematic Review," IJERPH, MDPI, vol. 18(7), pages 1-16, March.
    3. Ida E. Berger & Norman O’Reilly & Milena M. Parent & Benoit Séguin & Tony Hernandez, 2008. "Determinants of Sport Participation Among Canadian Adolescents," Sport Management Review, Taylor & Francis Journals, vol. 11(3), pages 277-307, September.
    4. Lisanne Vonk & Iris Eekhout & Tim Huijts & Mark Levels & Maria Jansen, 2024. "Does School Health Promotion Have Additional Value for Educational Performance? A Repeated Cross-Sectional Multilevel Study," IJERPH, MDPI, vol. 21(6), pages 1-21, June.
    5. Nina Bartelink & Patricia van Assema & Maria Jansen & Hans Savelberg & Stef Kremers, 2019. "The Moderating Role of the School Context on the Effects of the Healthy Primary School of the Future," IJERPH, MDPI, vol. 16(13), pages 1-19, July.
    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. Lisanne Vonk & Iris Eekhout & Tim Huijts & Mark Levels & Maria Jansen, 2024. "Does School Health Promotion Have Additional Value for Educational Performance? A Repeated Cross-Sectional Multilevel Study," IJERPH, MDPI, vol. 21(6), pages 1-21, June.
    2. 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.
    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. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Margarida Frade dos Santos & Celeste Simões & Anabela Caetano Santos & Paula Lebre & Ilaria Grazzani, 2022. "Does Online Implementation Make a Difference in the Effects of a Mental Health Curriculum at Schools?," IJERPH, MDPI, vol. 19(24), pages 1-18, December.
    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. Francesco Demaria & Stefano Vicari, 2023. "Adolescent Distress: Is There a Vaccine? Social and Cultural Considerations during the COVID-19 Pandemic," IJERPH, MDPI, vol. 20(3), pages 1-11, January.
    19. 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.
    20. 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.

    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:21:y:2024:i:8:p:1073-:d:1457140. 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.