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Demographic, psychological, behavioral, and cognitive correlates of BMI in youth: Findings from the Adolescent Brain Cognitive Development (ABCD) study

Published online by Cambridge University Press:  10 July 2019

Joshua C. Gray*
Affiliation:
Department of Medical and Clinical Psychology, Uniformed Services University, 4301 Jones Bridge Rd, Bethesda, MD20814, USA
Natasha A. Schvey
Affiliation:
Department of Medical and Clinical Psychology, Uniformed Services University, 4301 Jones Bridge Rd, Bethesda, MD20814, USA
Marian Tanofsky-Kraff
Affiliation:
Department of Medical and Clinical Psychology, Uniformed Services University, 4301 Jones Bridge Rd, Bethesda, MD20814, USA
*
Author for correspondence: Joshua C. Gray, E-mail: joshua.gray@usuhs.edu

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/m2) 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.

Type
Original Articles
Copyright
Copyright © Cambridge University Press 2019

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References

Benjamini, Y and Hochberg, Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological) 57, 289300.CrossRefGoogle Scholar
Bucksch, J, Sigmundova, D, Hamrik, Z, Troped, PJ, Melkevik, O, Ahluwalia, N, Borraccino, A, Tynjälä, J, Kalman, M and Inchley, J (2016) International trends in adolescent screen-time behaviors from 2002 to 2010. Journal of Adolescent Health 58, 417425.CrossRefGoogle ScholarPubMed
Cappuccio, FP, Taggart, FM, Kandala, N-B, Currie, A, Peile, E, Stranges, S and Miller, MA (2008) Meta-analysis of short sleep duration and obesity in children and adults. Sleep 31, 619626.CrossRefGoogle ScholarPubMed
Carroll-Scott, A, Gilstad-Hayden, K, Peters, SMRL, Joyce, RMC and Ickovics, JR (2013) Disentangling neighborhood contextual associations with child body mass index, diet, and physical activity: the role of built, socioeconomic, and social environments. Social Science & Medicine 95, 106114.CrossRefGoogle ScholarPubMed
Chandola, T, Deary, IJ, Blane, D and Batty, GD (2006) Childhood IQ in relation to obesity and weight gain in adult life: the National Child Development (1958) Study. International Journal of Obesity 30, 14221432.CrossRefGoogle ScholarPubMed
Cortese, S, Moreira-Maia, CR, St. Fleur, D, Morcillo-Peñalver, C, Rohde, LA and Faraone, SV (2016) Association between ADHD and obesity: a systematic review and meta-analysis. American Journal of Psychiatry 173, 3443.CrossRefGoogle ScholarPubMed
Dabelea, D, Mayer-Davis, EJ, Saydah, S, Imperatore, G, Linder, B, Divers, J, Bell, R, Badaru, A, Talton, JW, Crume, T and Liese, AD (2014) Prevalence of type 1 and type 2 diabetes among children and adolescents from 2001 to 2009. JAMA 311, 17781786.CrossRefGoogle ScholarPubMed
Davis, C, Patte, K, Levitan, R, Reid, C, Tweed, S and Curtis, C (2007) From motivation to behaviour: a model of reward sensitivity, overeating, and food preferences in the risk profile for obesity. Appetite 48, 1219.CrossRefGoogle Scholar
Delgado-Rico, E, Río-Valle, JS, González-Jiménez, E, Campoy, C and Verdejo-García, A (2012) BMI predicts emotion-driven impulsivity and cognitive inflexibility in adolescents with excess weight. Obesity 20, 16041610.CrossRefGoogle ScholarPubMed
Dietz, WH (1998) Health consequences of obesity in youth: childhood predictors of adult disease. Pediatrics 101, 518525.Google ScholarPubMed
Frankel, F, Sinton, M and Wilfley, D (2007) Social skills training and the treatment of pediatric overweight. In O'Donohue WT, Moore BA, and Scott BJ (ed.) Handbook of Pediatric and Adolescent Obesity Treatment. New York: Routledge, pp. 105116.Google Scholar
Freedman, DS, Butte, NF, Taveras, EM, Lundeen, EA, Blanck, HM, Goodman, AB and Ogden, CL (2017) BMI z -Scores are a poor indicator of adiposity among 2- to 19-year-olds with very high BMIs, NHANES 1999–2000 to 2013–2014. Obesity 25, 739746.CrossRefGoogle Scholar
Gao, Z, Chen, S, Pasco, D and Pope, Z (2015) A meta-analysis of active video games on health outcomes among children and adolescents. Obesity Reviews 16, 783794.CrossRefGoogle ScholarPubMed
Garavan, H, Bartsch, H, Conway, K, Decastro, A, Goldstein, RZ, Heeringa, S, Jernigan, T, Potter, A, Thompson, W and Zahs, D (2018) Recruiting the ABCD sample: design considerations and procedures. Developmental Cognitive Neuroscience 32, 1622.CrossRefGoogle ScholarPubMed
Gilmore, RO, Kennedy, JL and Adolph, KE (2018) Practical solutions for sharing data and materials from psychological research. Advances in Methods and Practices in Psychological Science 1, 121130.CrossRefGoogle ScholarPubMed
Guerrero, AD, Mao, C, Fuller, B, Bridges, M, Franke, T and Kuo, AA (2016) Racial and ethnic disparities in early childhood obesity: growth trajectories in body mass index. Journal of Racial and Ethnic Health Disparities 3, 129137.CrossRefGoogle ScholarPubMed
Hales, CM, Fryar, CD, Carroll, MD, Freedman, DS and Ogden, CL (2018) Trends in obesity and severe obesity prevalence in US youth and adults by sex and age, 2007–2008 to 2015–2016. JAMA 319, 17231725.CrossRefGoogle ScholarPubMed
Hu, G, Lindström, J, Valle, TT, Eriksson, JG, Jousilahti, P, Silventoinen, K, Qiao, Q and Tuomilehto, J (2004) Physical activity, body mass index, and risk of type 2 diabetes in patients with normal or impaired glucose regulation. Archives of Internal Medicine 164, 892896.CrossRefGoogle ScholarPubMed
James, G, Witten, D, Hastie, T and Tibshirani, R (2013) An introduction to statistical learning with applications in R. New York, NY: Springer.CrossRefGoogle Scholar
Kandula, NR, Diez-Roux, AV, Chan, C, Daviglus, ML, Jackson, SA, Ni, H and Schreiner, PJ (2008) Association of acculturation levels and prevalence of diabetes in the multi-ethnic study of atherosclerosis (MESA). Diabetes Care 31, 16211628.CrossRefGoogle Scholar
Kobes, A, Kretschmer, T, Timmerman, G and Schreuder, P (2018) Interventions aimed at preventing and reducing overweight/obesity among children and adolescents: a meta-synthesis. Obesity Reviews 19, 10651079.CrossRefGoogle ScholarPubMed
Koffarnus, MN and Bickel, W (2014) A 5-trial adjusting delay discounting task: accurate discount rates in less than one minute. Experimental and Clinical Psychopharmacology 22, 222228.CrossRefGoogle ScholarPubMed
Kopelman, P (2007) Health risks associated with overweight and obesity. Obesity Reviews 8, 1317.CrossRefGoogle ScholarPubMed
Melby-Lervåg, M, Redick, TS and Hulme, C (2016) Working memory training does not improve performance on measures of intelligence or other measures of ‘far transfer’. Perspectives on Psychological Science 11, 512534.CrossRefGoogle Scholar
Mellström, E, Forsman, C, Engh, L, Hallerbäck, MU and Wikström, S (2018) Methylphenidate and reduced overweight in children With ADHD. Journal of Attention Disorders, Available at: https://journals.sagepub.com/doi/abs/10.1177/1087054718808045.Google ScholarPubMed
Meyer, IH, Schwartz, S and Frost, DM (2008) Social patterning of stress and coping: does disadvantaged social statuses confer more stress and fewer coping resources? Social Science & Medicine 67, 368379.CrossRefGoogle ScholarPubMed
Mitchell, JA, Rodriguez, D, Schmitz, KH and Audrain-McGovern, J (2013) Greater screen time is associated with adolescent obesity: a longitudinal study of the BMI distribution from Ages 14 to 18. Obesity 21, 572575.CrossRefGoogle ScholarPubMed
Muzumdar, H and Rao, M (2006) Pulmonary dysfunction and sleep apnea in morbid obesity. Pediatric Endocrinology Reviews 3 (suppl. 4), 579583.Google ScholarPubMed
Nghiem, S, Hoang, V-N, Vu, X-B and Wilson, C (2018) The dynamic inter-relationship between obesity and school performance: new empirical evidence from Australia. Journal of Biosocial Science 50, 683705.CrossRefGoogle ScholarPubMed
Ogden, CL, Carroll, MD, Lawman, HG, Fryar, CD, Kruszon-Moran, D, Kit, BK and Flegal, KM (2016) Trends in obesity prevalence among children and adolescents in the United States, 1988–1994 through 2013–2014. JAMA 315, 22922299.CrossRefGoogle ScholarPubMed
Open Science Collaboration (2015) Estimating the reproducibility of psychological science. Science 349, aac4716.CrossRefGoogle Scholar
Pearce, AL, Leonhardt, CA and Vaidya, CJ (2018) Executive and reward-related function in pediatric obesity: a meta-analysis. Childhood Obesity 14, 265279.CrossRefGoogle ScholarPubMed
Pilitsi, E, Farr, OM, Perakakis, N, Nolen-Doerr, E and Papathanasiou, A-E (2019) Pharmacotherapy of obesity: available medications and drugs under investigation. Metabolism 92, 170192.CrossRefGoogle ScholarPubMed
Powell, LM, Wada, R, Krauss, RC and Wang, Y (2012) Ethnic disparities in adolescent body mass index in the United States: the role of parental socioeconomic status and economic contextual factors. Social Science & Medicine 75, 469476.CrossRefGoogle ScholarPubMed
Prince, SA, Adamo, KB, Hamel, M, Hardt, J, Connor Gorber, S and Tremblay, M (2008) A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review. International Journal of Behavioral Nutrition and Physical Activity 5, 56.CrossRefGoogle ScholarPubMed
Puder, JJ and Munsch, S (2010) Psychological correlates of childhood obesity. International Journal of Obesity 34, S37S43.CrossRefGoogle ScholarPubMed
Puhl, RM and King, KM (2013) Weight discrimination and bullying. Best Practice & Research Clinical Endocrinology & Metabolism 27, 117127.CrossRefGoogle ScholarPubMed
Puhl, RM and Luedicke, J (2012) Weight-based victimization among adolescents in the school setting: emotional reactions and coping behaviors. Journal of Youth and Adolescence 41, 2740.CrossRefGoogle ScholarPubMed
Sweat, V, Yates, KF, Migliaccio, R and Convit, A (2017) Obese adolescents show reduced cognitive processing speed compared with healthy weight peers. Childhood Obesity 13, 190196.CrossRefGoogle ScholarPubMed
Tackett, JL, Brandes, CM, King, KM and Markon, KE (2019) Psychology's replication crisis and clinical psychological science. Annual Review of Clinical Psychology 15, 579604.CrossRefGoogle ScholarPubMed
Taveras, EM, Gillman, MW, Kleinman, KP, Rich-Edwards, JW and Rifas-Shiman, SL (2013) Reducing racial/ethnic disparities in childhood obesity. JAMA Pediatrics 167, 731738.CrossRefGoogle ScholarPubMed
Tsukayama, E, Toomey, SL, Faith, MS and Duckworth, AL (2010) Self-control as a protective factor against overweight Status in the transition from childhood to adolescence. Archives of Pediatrics & Adolescent Medicine 164, 631635.CrossRefGoogle ScholarPubMed
van Ekris, E, Altenburg, TM, Singh, AS, Proper, KI, Heymans, MW and Chinapaw, MJM (2016) An evidence-update on the prospective relationship between childhood sedentary behaviour and biomedical health indicators: a systematic review and meta-analysis. Obesity Reviews 17, 833849.CrossRefGoogle ScholarPubMed
Wang, Y and Chen, X (2011) How much of racial/ethnic Disparities in dietary intakes, exercise, and weight status can be explained by nutrition- and health-related psychosocial factors and socioeconomic status among US adults? Journal of the American Dietetic Association 111, 19041911.CrossRefGoogle ScholarPubMed
Wang, Y and Zhang, Q (2006) Are American children and adolescents of low socioeconomic status at increased risk of obesity? Changes in the association between overweight and family income between 1971 and 2002. The American Journal of Clinical Nutrition 84, 707716.CrossRefGoogle ScholarPubMed
Wu, L, Sun, S, He, Y and Jiang, B (2016) The effect of interventions targeting screen time reduction: a systematic review and meta-analysis. Medicine 95, e4029.CrossRefGoogle ScholarPubMed
Yarkoni, T and Westfall, J (2017) Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science 12, 11001122.CrossRefGoogle ScholarPubMed
Zeller, MH, Reiter-Purtill, J, Modi, AC, Gutzwiller, J, Vannatta, K and Davies, WH (2007) Controlled study of critical parent and family factors in the obesigenic environment. Obesity 15, 126126.CrossRefGoogle ScholarPubMed
Zou, H and Hastie, T (2005) Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 67, 301320.CrossRefGoogle Scholar
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