Journal article
N. Kirlic, Janna M. Colaizzi, K. Cosgrove, Zsofia P. Cohen, H. Yeh, F. Breslin, A. Morris, Robin Aupperle, M. Paulus
2020
2020
Semantic Scholar
DOI
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APA
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Kirlic, N., Colaizzi, J. M., Cosgrove, K., Cohen, Z. P., Yeh, H., Breslin, F., … Paulus, M. (2020). Extracurricular activities, screen media activity, and sleep may be modifiable factors influencing children’s cognitive functioning: Evidence from the ABCD study.
Chicago/Turabian
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Kirlic, N., Janna M. Colaizzi, K. Cosgrove, Zsofia P. Cohen, H. Yeh, F. Breslin, A. Morris, Robin Aupperle, and M. Paulus. “Extracurricular Activities, Screen Media Activity, and Sleep May Be Modifiable Factors Influencing Children’s Cognitive Functioning: Evidence from the ABCD Study” (2020).
MLA
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Kirlic, N., et al. Extracurricular Activities, Screen Media Activity, and Sleep May Be Modifiable Factors Influencing Children’s Cognitive Functioning: Evidence from the ABCD Study. 2020.
BibTeX Click to copy
@article{n2020a,
title = {Extracurricular activities, screen media activity, and sleep may be modifiable factors influencing children’s cognitive functioning: Evidence from the ABCD study},
year = {2020},
author = {Kirlic, N. and Colaizzi, Janna M. and Cosgrove, K. and Cohen, Zsofia P. and Yeh, H. and Breslin, F. and Morris, A. and Aupperle, Robin and Paulus, M.}
}
Abstract
PurposeFluid cognitive functioning (FCF), or the capacity to learn, solve problems, and adapt to novel situations, is instrumental for academic success, psychological well-being, and adoption of healthy behaviors. Our knowledge concerning factors associated with FCF, including those that may be targeted with interventions to improve outcomes, remains limited. MethodsWe used a machine learning (ML) framework in conjunction with a large battery of measures from 9,718 youth from the Adolescent Brain Cognitive Development (ABCD) study to identify factors associated with the observed variability in FCF performance. Youth age-corrected composite FCF score was derived from the National Institutes for Health Toolbox Neurocognitive Battery. A ML pipeline using a stack ensemble of multiple ML algorithms and nested cross-validation to avoid overfitting was conducted to examine factors associated with FCF. Results The identified ML algorithm explained 14.74% of variance (95%CI: 14.53-14.88%) in FCF. Among the most important factors were those that replicated previous research (e.g., socioeconomic factors), as well as novel, potentially modifiable factors, including extracurricular involvement, screen media activity, and sleepConclusionsPragmatic and scalable interventions targeting these behaviors may not only enhance cognitive performance but may also protect against the negative impact of socioeconomic and mental health factors on cognitive performance in at-risk youth. The longitudinal data from ABCD will be able to begin to assess causality by examining how changes in these factors affect subsequent cognitive performance.