IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i23p8861-d452905.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/23/8861/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/17/23/8861/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    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. Elenice de Sousa Pereira & Mabliny Thuany & Paulo Felipe Ribeiro Bandeira & Thayse Natacha Q. F. Gomes & Fernanda Karina dos Santos, 2023. "How Do Health, Biological, Behavioral, and Cognitive Variables Interact over Time in Children of Both Sexes? A Complex Systems Approach," IJERPH, MDPI, vol. 20(3), pages 1-19, February.
    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.
    3. Dana Rad & Lavinia Denisia Cuc & Ramona Lile & Valentina E. Balas & Cornel Barna & Mioara Florina Pantea & Graziella Corina Bâtcă-Dumitru & Silviu Gabriel Szentesi & Gavril Rad, 2022. "A Cognitive Systems Engineering Approach Using Unsupervised Fuzzy C-Means Technique, Exploratory Factor Analysis and Network Analysis—A Preliminary Statistical Investigation of the Bean Counter Profil," IJERPH, MDPI, vol. 19(19), pages 1-19, October.

    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. Juliana Ribeiro Francelino Sampaio & Suely Arruda Vidal & Paulo Savio Angeiras de Goes & Paulo Felipe R. Bandeira & José Eulálio Cabral Filho, 2021. "Sociodemographic, Behavioral and Oral Health Factors in Maternal and Child Health: An Interventional and Associative Study from the Network Perspective," IJERPH, MDPI, vol. 18(8), pages 1-13, April.
    2. Elenice de Sousa Pereira & Mabliny Thuany & Paulo Felipe Ribeiro Bandeira & Thayse Natacha Q. F. Gomes & Fernanda Karina dos Santos, 2023. "How Do Health, Biological, Behavioral, and Cognitive Variables Interact over Time in Children of Both Sexes? A Complex Systems Approach," IJERPH, MDPI, vol. 20(3), pages 1-19, February.
    3. Tho Nguyen & Ladda Thiamwong & Qian Lou & Rui Xie, 2024. "Unveiling Fall Triggers in Older Adults: A Machine Learning Graphical Model Analysis," Mathematics, MDPI, vol. 12(9), pages 1-18, April.
    4. Dora Gyori & Bernadett Frida Farkas & Lili Olga Horvath & Daniel Komaromy & Gergely Meszaros & Dora Szentivanyi & Judit Balazs, 2021. "The Association of Nonsuicidal Self-Injury with Quality of Life and Mental Disorders in Clinical Adolescents—A Network Approach," IJERPH, MDPI, vol. 18(4), pages 1-21, February.
    5. Pedro Henrique Ribeiro Santiago & Gustavo Hermes Soares & Lisa Gaye Smithers & Rachel Roberts & Lisa Jamieson, 2022. "Psychological Network of Stress, Coping and Social Support in an Aboriginal Population," IJERPH, MDPI, vol. 19(22), pages 1-22, November.
    6. Li, Li & Niu, Zhimin & Griffiths, Mark D. & Wang, Wen & Chang, Chunying & Mei, Songli, 2021. "A network perspective on the relationship between gaming disorder, depression, alexithymia, boredom, and loneliness among a sample of Chinese university students," Technology in Society, Elsevier, vol. 67(C).
    7. M. Marsman & K. Huth & L. J. Waldorp & I. Ntzoufras, 2022. "Objective Bayesian Edge Screening and Structure Selection for Ising Networks," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 47-82, March.
    8. Josefina Vieta-Piferrer & Xavier Oriol & Rafael Miranda, 2024. "Longitudinal Associations Between Cyberbullying Victimization and Cognitive and Affective Components of Subjective Well-Being in Adolescents: A Network Analysis," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 19(5), pages 2967-2989, October.
    9. Conte, Federica & Costantini, Giulio & Rinaldi, Luca & Gerosa, Tiziano & Girelli, Luisa, 2020. "Intellect is not that expensive: differential association of cultural and socio-economic factors with crystallized intelligence in a sample of Italian adolescents," Intelligence, Elsevier, vol. 81(C).
    10. Georgia Mangion & Melanie Simmonds-Buckley & Stephen Kellett & Peter Taylor & Amy Degnan & Charlotte Humphrey & Kate Freshwater & Marisa Poggioli & Cristina Fiorani, 2022. "Modelling Identity Disturbance: A Network Analysis of the Personality Structure Questionnaire (PSQ)," IJERPH, MDPI, vol. 19(21), pages 1-17, October.
    11. Xiao Yang & Nilam Ram & Scott D. Gest & David M. Lydon-Staley & David E. Conroy & Aaron L. Pincus & Peter C. M. Molenaar, 2018. "Socioemotional Dynamics of Emotion Regulation and Depressive Symptoms: A Person-Specific Network Approach," Complexity, Hindawi, vol. 2018, pages 1-14, November.
    12. Frommlet, Florian & Ruhaltinger, Felix & Twaróg, Piotr & Bogdan, Małgorzata, 2012. "Modified versions of Bayesian Information Criterion for genome-wide association studies," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1038-1051.
    13. Zak-Szatkowska, Malgorzata & Bogdan, Malgorzata, 2011. "Modified versions of the Bayesian Information Criterion for sparse Generalized Linear Models," Computational Statistics & Data Analysis, Elsevier, vol. 55(11), pages 2908-2924, November.
    14. Shoo Thien Lee & Jyh Eiin Wong & Geraldine K. L. Chan & Bee Koon Poh, 2021. "Association between Compliance with Movement Behavior Guidelines and Obesity among Malaysian Preschoolers," IJERPH, MDPI, vol. 18(9), pages 1-13, April.
    15. Gaorong Li & Liugen Xue & Heng Lian, 2012. "SCAD-penalised generalised additive models with non-polynomial dimensionality," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(3), pages 681-697.
    16. Xiaotong Shen & Wei Pan & Yunzhang Zhu & Hui Zhou, 2013. "On constrained and regularized high-dimensional regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(5), pages 807-832, October.
    17. Emre Demirkaya & Yang Feng & Pallavi Basu & Jinchi Lv, 2022. "Large-scale model selection in misspecified generalized linear models [Information theory and an extension of the maximum likelihood principle]," Biometrika, Biometrika Trust, vol. 109(1), pages 123-136.
    18. Shan Luo & Zehua Chen, 2014. "Sequential Lasso Cum EBIC for Feature Selection With Ultra-High Dimensional Feature Space," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1229-1240, September.
    19. Lu Tang & Ling Zhou & Peter X. K. Song, 2019. "Fusion learning algorithm to combine partially heterogeneous Cox models," Computational Statistics, Springer, vol. 34(1), pages 395-414, March.
    20. Lian, Heng & Du, Pang & Li, YuanZhang & Liang, Hua, 2014. "Partially linear structure identification in generalized additive models with NP-dimensionality," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 197-208.

    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:23:p:8861-:d:452905. 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.