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The Consumption of Sweets and Academic Performance among Mongolian Children

Author

Listed:
  • Noboru Nakahara

    (Department of Global Health Promotion, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo 113-8519, Japan)

  • Yusuke Matsuyama

    (Department of Global Health Promotion, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo 113-8519, Japan)

  • Shiho Kino

    (Department of Social and Behavioral Science, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA)

  • Nomin Badrakhkhuu

    (Department of Maxillofacial Orthognathics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo 113-8510, Japan)

  • Takuya Ogawa

    (Department of Maxillofacial Orthognathics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo 113-8510, Japan)

  • Keiji Moriyama

    (Department of Maxillofacial Orthognathics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo 113-8510, Japan)

  • Takeo Fujiwara

    (Department of Global Health Promotion, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo 113-8519, Japan)

  • Ichiro Kawachi

    (Department of Social and Behavioral Science, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA)

Abstract

The regular consumption of sweets has been shown to have an adverse association with the academic performance of children in developed countries; however, the situation in developing countries is less clear. Therefore, we examined the association between the consumption of sweets and academic performance among Mongolian children via a cross-sectional study employing data from 787 children aged 8–16 from two public schools in Ulaanbaatar, the capital of Mongolia. The frequency of the consumption of sweets by the children was captured using a questionnaire and then linked to their academic scores; the association between the consumption of sweets and scores in mathematics and the Mongolian language was evaluated using multiple linear regression adjusted for other covariates. It was found that out of 787 students, 58.6% ate sweets every day. After adjusting for covariates, no significant association was observed between the consumption of sweets and mathematics scores (coefficient: 0.15; 95% confidence interval (CI): −0.02–0.32), while a higher consumption of sweets was significantly associated with higher scores in the Mongolian language (coefficient: 0.25; 95% CI: 0.09–0.41). The associations established in this study are inconsistent with the reports of other studies.

Suggested Citation

  • Noboru Nakahara & Yusuke Matsuyama & Shiho Kino & Nomin Badrakhkhuu & Takuya Ogawa & Keiji Moriyama & Takeo Fujiwara & Ichiro Kawachi, 2020. "The Consumption of Sweets and Academic Performance among Mongolian Children," IJERPH, MDPI, vol. 17(23), pages 1-12, November.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:23:p:8912-:d:454048
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    References listed on IDEAS

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    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. Miyu Araki & Yuko Yasuda & Takuya Ogawa & Tsasan Tumurkhuu & Ganjargal Ganburged & Amarsaikhan Bazar & Takeo Fujiwara & Keiji Moriyama, 2017. "Associations between Malocclusion and Oral Health-Related Quality of Life among Mongolian Adolescents," IJERPH, MDPI, vol. 14(8), pages 1-10, August.
    3. Hanushek, Eric A. & Peterson, Paul E. & Talpey, Laura M. & Woessmann, Ludger, 2019. "The Unwavering SES Achievement Gap: Trends in U.S. Student Performance," Working Paper Series rwp19-012, Harvard University, John F. Kennedy School of Government.
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    Cited by:

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    2. Yuki Sagawa & Takuya Ogawa & Yusuke Matsuyama & Junka Nakagawa Kang & Miyu Yoshizawa Araki & Yuko Unnai Yasuda & Tsasan Tumurkhuu & Ganjargal Ganburged & Amarsaikhan Bazar & Toshihiro Tanaka & Takeo F, 2021. "Association between Smoking during Pregnancy and Short Root Anomaly in Offspring," IJERPH, MDPI, vol. 18(21), pages 1-11, November.

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