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A Network Perspective on the Relationship between Screen Time, Executive Function, and Fundamental Motor Skills among Preschoolers

Author

Listed:
  • Clarice Maria de Lucena Martins

    (Department of Physical Education, Federal University of Paraiba, João Pessoa-PB 58000-000, Brazil)

  • Paulo Felipe Ribeiro Bandeira

    (Department of Physical Education, Universidade Regional do Cariri—URCA; Crato-CE 63105-000, Brazil)

  • Natália Batista Albuquerque Goulart Lemos

    (Department of Physical Education, Federal University of Vale do São Francisco, Petrolina-PE 56304917, Brazil)

  • Thaynã Alves Bezerra

    (Department of Physical Education, Federal University of Paraiba, João Pessoa-PB 58000-000, Brazil)

  • Cain Craig Truman Clark

    (Faculty of Health and Life Sciences, Coventry University, Priory Street, Coventry CV1 5FB, UK)

  • Jorge Mota

    (Centre of Physical Activity, Health and Leisure, Faculty of Sport Sciences, University of Porto, 4500 Porto, Portugal)

  • Michael Joseph Duncan

    (Faculty of Health and Life Sciences, Coventry University, Priory Street, Coventry CV1 5FB, UK)

Abstract

The present study aimed to analyze the dynamic and nonlinear association between screen time, executive function (EF), and fundamental motor skills (FMS) in preschoolers, considering sex and body mass index (BMI) from a network perspective. Forty-two preschoolers (24 boys, 3.91 ± 0.77 years old) provided screen time, EF, FMS, and BMI data. EF was measured using the Go/No Go task, and accuracy of Go (sustain attention), reaction time of Go, and accuracy of No Go (inhibitory control) were considered. Relationships between screen time, EF, FMS, sex, and BMI were explored using a network analysis. The emerged network highlights that screen time is intensely associated with the other variables in the network, while the accuracy of Go has the greater connectivity with other nodes in the network (2.27), being the most sensitive to potential intervention changes. Moreover, sex (1.74), screen time (0.93), and accuracy of Go (0.71) showed the greatest closeness. This study showed that in the emerged network, independent of sex, screen exposure affects the accuracy on Go task, and these components affect the variables in the network, as motor abilities and tasks involved in inhibitory control.

Suggested Citation

  • Clarice Maria de Lucena Martins & Paulo Felipe Ribeiro Bandeira & Natália Batista Albuquerque Goulart Lemos & Thaynã Alves Bezerra & Cain Craig Truman Clark & Jorge Mota & Michael Joseph Duncan, 2020. "A Network Perspective on the Relationship between Screen Time, Executive Function, and Fundamental Motor Skills among Preschoolers," IJERPH, MDPI, vol. 17(23), pages 1-12, November.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:23:p:8861-:d:452905
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    References listed on IDEAS

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    1. Epskamp, Sacha & Cramer, Angélique O.J. & Waldorp, Lourens J. & Schmittmann, Verena D. & Borsboom, Denny, 2012. "qgraph: Network Visualizations of Relationships in Psychometric Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i04).
    2. Jiahua Chen & Zehua Chen, 2008. "Extended Bayesian information criteria for model selection with large model spaces," Biometrika, Biometrika Trust, vol. 95(3), pages 759-771.
    3. Hu, Bi Ying & Johnson, Gregory Kirk & Wu, Huiping, 2018. "Screen time relationship of Chinese parents and their children," Children and Youth Services Review, Elsevier, vol. 94(C), pages 659-669.
    4. Marieke De Craemer & Duncan McGregor & Odysseas Androutsos & Yannis Manios & Greet Cardon, 2018. "Compliance with 24-h Movement Behaviour Guidelines among Belgian Pre-School Children: The ToyBox-Study," IJERPH, MDPI, vol. 15(10), pages 1-10, October.
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    2. Ze-Min Liu & Chuang-Qi Chen & Xian-Li Fan & Chen-Chen Lin & Xin-Dong Ye, 2022. "Usability and Effects of a Combined Physical and Cognitive Intervention Based on Active Video Games for Preschool Children," IJERPH, MDPI, vol. 19(12), pages 1-14, June.
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