Demographic, psychological, behavioral, and cognitive correlates of BMI in youth: Findings from the Adolescent Brain Cognitive Development (ABCD) study
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Abstract
Background: Previous research has implicated demographic, psychological, behavioral, and cognitive variables in the onset and maintenance of pediatric overweight/obesity. No adequately-powered study has simultaneously modeled these variables to assess their relative associations with body mass index (BMI; kg/m 2 ) in a nationally representative sample of youth. Methods: Multiple machine learning regression approaches were employed to estimate the relative importance of 43 demographic, psychological, behavioral, and cognitive variables previously associated with BMI in youth to elucidate the associations of both fixed (e.g. demographics) and potentially modifiable (e.g. psychological/behavioral) variables with BMI in a diverse representative sample of youth. The primary analyses consisted of 9–10 year olds divided into a training ( n = 2724) and test ( n = 1123) sets. Secondary analyses were conducted by sex, ethnicity, and race. Results: The full sample model captured 12% of the variance in both the training and test sets, suggesting good generalizability. Stimulant medications and demographic factors were most strongly associated with BMI. Lower attention problems and matrix reasoning (i.e. nonverbal abstract problem solving and inductive reasoning) and higher social problems and screen time were robust positive correlates in the primary analyses and in analyses separated by sex. Conclusions: Beyond demographics and stimulant use, this study highlights abstract reasoning as an important cognitive variable and reaffirms social problems and screen time as significant correlates of BMI and as modifiable therapeutic targets. Prospective data are needed to understand the predictive power of these variables for BMI gain.
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Total screen time was calculated based on a previously validated measure.
“Total screen time was calculated separately for weekdays and weekend days, based on a previously validated measure [93–95].”
From Screen time and mental health: a prospective analysis of the Adolescent Brain Cognitive Development (ABCD) Study (2024) · cites this paper as [94]
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Sociodemographic Correlates of Contemporary Screen Time Use among 9- and 10-Year-Old Children
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- Authors
- Gray, J. C., Schvey, N. A., & Tanofsky-Kraff, M.
- Affiliations
- Uniformed Services University of the Health Sciences (US)
- Corresponding author
- Gray, Joshua C.
- Venue
- Psychological Medicine
- Publisher
- Cambridge University Press (CUP)
- Published
- 2019-07-10
- Volume
- 50
- Issue
- 9
- Pages
- 1539-1547
- ISSN
- 0033-2917 (ISSN-L)
- 1469-8978
- OpenAlex
- W2961777555
- Cited by
- 58
- References
- 50
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 9 · 2023: 8 · 2022: 7 · 2021: 11 · 2020: 9 · 2019: 1
- FWCI
- 6.44
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Psychological Medicine (publishedVersion)
- PubMed (publishedVersion)
- PsyArXiv (OSF Preprints) (submittedVersion)
- PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
- Funders
- National Institute on Drug Abuse (U01DA041106, U01DA041120, U01DA041156, U01DA041174, U01DA041048, U01DA041148, U01DA041022, U01DA041028, U01DA041134, U01DA041089, U01DA041117)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Obesity, Physical Activity, Diet
- Social Sciences › Psychology › Clinical Psychology › Eating Disorders and Behaviors
- Social Sciences › Psychology › Clinical Psychology › Child and Adolescent Psychosocial and Emotional Development
- Keywords
- body mass index, childhood obesity, childhood overweight, Adolescent Brain Cognitive Development Study, stimulant medication, attention deficits, matrix reasoning, abstract thinking, social problems, screen time, sex differences, race and ethnicity, modifiable risk factors
- MeSH terms
- Machine Learning, Adolescent, Adolescent Behavior, Body Weight, Child, Cognition, Demography, Female, Humans, Male, Obesity, Risk Factors, Sex Factors, United States, Body Mass Index, Multivariate Analysis, Linear Models, Overweight, Health Status Disparities
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