IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i15p8955-d869718.html
   My bibliography  Save this article

Determinants of Cognitive Performance in Children and Adolescents: A Populational Longitudinal Study

Author

Listed:
  • Rodrigo Antunes Lima

    (Research, Innovation and Teaching Unit, Parc Sanitari Sant Joan de Déu, CIBERSAM, 08830 Sant Boi de Llobregat, Spain)

  • Fernanda Cunha Soares

    (Division of Orthodontics and Pediatric Dentistry, Department of Dental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden)

  • Mireille van Poppel

    (Institute of Sport Science, University of Graz, 8010 Graz, Austria)

  • Saija Savinainen

    (Institute of Biomedicine, School of Medicine, University of Eastern Finland, 70211 Kuopio, Finland
    Department of Pediatrics, Kuopio University Hospital, 70211 Kuopio, Finland)

  • Aino Mäntyselkä

    (Department of Pediatrics, Kuopio University Hospital, 70211 Kuopio, Finland)

  • Eero A. Haapala

    (Institute of Biomedicine, School of Medicine, University of Eastern Finland, 70211 Kuopio, Finland
    Faculty of Sport and Health Sciences, University of Jyväskylä, 40014 Jyväskylä, Finland
    These authors contributed equally to this work.)

  • Timo Lakka

    (Institute of Biomedicine, School of Medicine, University of Eastern Finland, 70211 Kuopio, Finland
    Department of Clinical Physiology and Nuclear Medicine, School of Medicine, Kuopio University Hospital, University of Eastern Finland, 70211 Kuopio, Finland
    Kuopio Research Institute of Exercise Medicine, 70100 Kuopio, Finland
    These authors contributed equally to this work.)

Abstract

We evaluated the determinants of cognitive performance in children and adolescents. This is a longitudinal study, secondary analysis of the Physical Activity and Nutrition in Children (PANIC) study. We assessed 502 children (51.6% girls) at middle childhood (range: 6.6 to 9.0 years), at late childhood, 437 children (51.0% girls, range: 8.8 to 11.2 years), and in 277 adolescents (54.5% girls, range: 15.0 to 17.4 years). Raven’s progressive matrices tests estimated the participants’ cognitive performance (outcome variable) at all time points. In total, we evaluated 29 factors from various dimensions (prenatal, neonatal, child fitness, lifestyle and anthropometrics). None of the neonatal and anthropometric parameters were associated with cognitive performance. Preeclampsia (prenatal) and listening to music, writing, arts and craft and watching TV (lifestyle) were negatively associated with cognitive performance. Shuttle run and box and block tests (fitness), and playing music, reading and time at the computer (lifestyle) were positive determinants of cognitive performance in children and adolescents. Fitness and lifestyle factors during childhood and adolescence diminished the importance of prenatal factors on cognitive performance and lifestyle factors were especially relevant in regard to cognitive performance. Reading was positively associated with cognitive performance, regardless of age and time dedicated, and should be promoted.

Suggested Citation

  • Rodrigo Antunes Lima & Fernanda Cunha Soares & Mireille van Poppel & Saija Savinainen & Aino Mäntyselkä & Eero A. Haapala & Timo Lakka, 2022. "Determinants of Cognitive Performance in Children and Adolescents: A Populational Longitudinal Study," IJERPH, MDPI, vol. 19(15), pages 1-15, July.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:15:p:8955-:d:869718
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/15/8955/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/15/8955/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, 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. van de Walle, Dominique & Mu, Ren, 2007. "Fungibility and the flypaper effect of project aid: Micro-evidence for Vietnam," Journal of Development Economics, Elsevier, vol. 84(2), pages 667-685, November.
    2. de Brauw, Alan & Gilligan, Daniel O. & Hoddinott, John & Roy, Shalini, 2014. "The Impact of Bolsa Família on Women’s Decision-Making Power," World Development, Elsevier, vol. 59(C), pages 487-504.
    3. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-dimensional econometrics and regularized GMM," CeMMAP working papers CWP35/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Matias Busso & Patrick Kline, 2008. "Do Local Economic Development Programs Work? Evidence from the Federal Empowerment Zone Program," Cowles Foundation Discussion Papers 1639, Cowles Foundation for Research in Economics, Yale University.
    5. Clément de Chaisemartin & Luc Behaghel, 2020. "Estimating the Effect of Treatments Allocated by Randomized Waiting Lists," Econometrica, Econometric Society, vol. 88(4), pages 1453-1477, July.
    6. Gunther Bensch & Jörg Peters, 2013. "Alleviating Deforestation Pressures? Impacts of Improved Stove Dissemination on Charcoal Consumption in Urban Senegal," Land Economics, University of Wisconsin Press, vol. 89(4), pages 676-698.
    7. Leonardo Becchetti & Pierluigi Conzo & Alessandro Romeo, 2014. "Violence, trust, and trustworthiness: evidence from a Nairobi slum," Oxford Economic Papers, Oxford University Press, vol. 66(1), pages 283-305, January.
    8. Ruoxuan Xiong & Allison Koenecke & Michael Powell & Zhu Shen & Joshua T. Vogelstein & Susan Athey, 2021. "Federated Causal Inference in Heterogeneous Observational Data," Papers 2107.11732, arXiv.org, revised Apr 2023.
    9. Kitagawa, Toru & Muris, Chris, 2016. "Model averaging in semiparametric estimation of treatment effects," Journal of Econometrics, Elsevier, vol. 193(1), pages 271-289.
    10. Rajeev Dehejia, 2013. "The Porous Dialectic: Experimental and Non-Experimental Methods in Development Economics," WIDER Working Paper Series wp-2013-011, World Institute for Development Economic Research (UNU-WIDER).
    11. Futoshi Yamauchi & Yanyan Liu, 2013. "Impacts of an Early Stage Education Intervention on Students' Learning Achievement: Evidence from the Philippines," Journal of Development Studies, Taylor & Francis Journals, vol. 49(2), pages 208-222, February.
    12. Jessamyn Schaller & Mariana Zerpa, 2019. "Short-Run Effects of Parental Job Loss on Child Health," American Journal of Health Economics, MIT Press, vol. 5(1), pages 8-41, Winter.
    13. Susan Athey & Guido W. Imbens & Stefan Wager, 2018. "Approximate residual balancing: debiased inference of average treatment effects in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(4), pages 597-623, September.
    14. Töpfer, Marina, 2017. "Detailed RIF decomposition with selection: The gender pay gap in Italy," Hohenheim Discussion Papers in Business, Economics and Social Sciences 26-2017, University of Hohenheim, Faculty of Business, Economics and Social Sciences.
    15. Katie Meara & Francesco Pastore & Allan Webster, 2020. "The gender pay gap in the USA: a matching study," Journal of Population Economics, Springer;European Society for Population Economics, vol. 33(1), pages 271-305, January.
    16. Burt S. Barnow & Jeffrey Smith, 2015. "Employment and Training Programs," NBER Chapters, in: Economics of Means-Tested Transfer Programs in the United States, Volume 2, pages 127-234, National Bureau of Economic Research, Inc.
    17. Gernandt, Johannes & Maier, Michael & Pfeiffer, Friedhelm & Rat-Wirtzler, Julie, 2006. "Distributional effects of the high school degree in Germany," ZEW Discussion Papers 06-088, ZEW - Leibniz Centre for European Economic Research.
    18. Malerba, Daniele, 2020. "Poverty alleviation and local environmental degradation: An empirical analysis in Colombia," World Development, Elsevier, vol. 127(C).
    19. Pedro H. C. Sant'Anna & Xiaojun Song & Qi Xu, 2022. "Covariate distribution balance via propensity scores," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1093-1120, September.
    20. Victor Chernozhukov & Iván Fernández‐Val & Blaise Melly, 2013. "Inference on Counterfactual Distributions," Econometrica, Econometric Society, vol. 81(6), pages 2205-2268, November.

    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:19:y:2022:i:15:p:8955-:d:869718. 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.