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Decision-Making Does not Moderate the Association between Cannabis Use and Body Mass Index among Adolescent Cannabis Users

Published online by Cambridge University Press:  15 April 2016

J. Megan Ross ,
Paulo Graziano ,
Ileana Pacheco-Colón ,
Stefany Coxe  and
Raul Gonzalez
J. Megan Ross
Affiliation:
Center for Children and Families, Florida International University, Department of Psychology, Miami, Florida
Paulo Graziano
Affiliation:
Center for Children and Families, Florida International University, Department of Psychology, Miami, Florida
Ileana Pacheco-Colón
Affiliation:
Center for Children and Families, Florida International University, Department of Psychology, Miami, Florida
Stefany Coxe
Affiliation:
Center for Children and Families, Florida International University, Department of Psychology, Miami, Florida
Raul Gonzalez*
Affiliation:
Center for Children and Families, Florida International University, Department of Psychology, Miami, Florida
*
Correspondence and reprint requests to: Raul Gonzalez, the Center for Children and Families, Florida International University, 11200 SW 8th Street, AHC4 Room 461, Miami, FL, 33199. E-mail: raul.gonzalezjr@fiu.edu.
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Abstract

Objectives: Results from research conducted on the association between cannabis use and body mass index (BMI) reveal mixed findings. It is possible that individual differences in decision-making (DM) abilities may influence these associations. Methods: This study analyzed how amount of cannabis use, DM performance, and the interaction of these variables influenced BMI and clinical classifications of weight among adolescents (ages 14 to 18 years; 56% male; 77% Hispanic). The sample consisted primarily of cannabis users (n=238) without a history of significant developmental disorders, birth complications, neurological conditions, or history of mood, thought, or attention deficit/hyperactivity disorder at screening. Furthermore, few participants engaged frequently in other drug use (except for alcohol and nicotine). Results: Analyses revealed that more lifetime cannabis use was associated with a higher BMI and greater likelihood of being overweight/obese. Interactions between DM and cannabis use on BMI were not significant, and DM was not directly associated with BMI. Discussion: Our findings suggest that among adolescents, cannabis use is associated with a greater BMI regardless of DM abilities and this association is not accounted for by other potential factors, including depression, alcohol use, nicotine use, race, ethnicity, or IQ. (JINS, 2016, 22, 944–949)

Keywords

Drug useRisk-takingAddictionAdolescenceObesity and executive abilities
Type
Brief Communications
Information
Journal of the International Neuropsychological Society , Volume 22 , Issue 9 , October 2016 , pp. 944 - 949
Copyright
Copyright © The International Neuropsychological Society 2016 

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References

Bechara, A. (2007). Iowa Gambling TaskTM Professional Manual. Lutz, FL: Psychological Assessment Resources, Inc.Google Scholar
Bechara, A., Damasio, A.R., Damasio, H., & Anderson, S.W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50(1), 7–15.Google Scholar
Brand, M., Fujiwara, E., Borsutzky, S., Kalbe, E., Kessler, J., & Markowitsch, H.J. (2005). Decision-making deficits of korsakoff patients in a new gambling task with explicit rules: Associations with executive functions. Neuropsychology, 19(3), 267.Google Scholar
Brand, M., Recknor, E.C., Grabenhorst, F., & Bechara, A. (2007). Decisions under ambiguity and decisions under risk: Correlations with executive functions and comparisons of two different gambling tasks with implicit and explicit rules. Journal of Clinical and Experimental Neuropsychology, 29, 86–99.Google Scholar
Cohen, J., Cohen, P., West, S.G., & Aiken, L.S. (2013). Applied multiple regression/correlation analysis for the behavioral sciences. New Jersey: Lawrence Erlbaum Associates, Inc.CrossRefGoogle Scholar
Dougherty, D.M., Mathias, C.W., Dawes, M.A., Furr, R.M., Charles, N.E., Liguori, A., & Acheson, A. (2013). Impulsivity, attention, memory, and decision-making among adolescent marijuana users. Psychopharmacology, 226(2), 307–319.CrossRefGoogle ScholarPubMed
Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G* Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160.Google Scholar
Gonzalez, R., Schuster, R.M., Mermelstein, R.M., & Diviak, K.R. (2015). The role of decision-making in cannabis-related problems among young adults. Drug and Alcohol Dependence, 154, 214–221.CrossRefGoogle ScholarPubMed
Gonzalez, R., Schuster, R.M., Mermelstein, R.J., Vassileva, J., Martin, E.M., & Diviak, K.R. (2012). Performance of young adult cannabis users on neurocognitive measures of impulsive behavior and their relationship to symptoms of cannabis use disorders. Journal of Clinical and Experimental Neuropsychology, 34(9), 962–976.Google Scholar
Green, B., Kavanagh, D., & Young, R. (2003). Being stoned: A review of self‐reported cannabis effects. Drug and Alcohol Review, 22(4), 453–460.CrossRefGoogle ScholarPubMed
Hedley, A.A., Ogden, C.L., Johnson, C.L., Carroll, M.D., Curtin, L.R., & Flegal, K.M. (2004). Prevalence of overweight and obesity among US children, adolescents, and adults, 1999-2002. Journal of the American Medical Association, 291(23), 2847–2850.CrossRefGoogle ScholarPubMed
Huang, D.Y., Lanza, H.I., & Anglin, M.D. (2013). Association between adolescent substance use and obesity in young adulthood: A group-based dual trajectory analysis. Addictive Behaviors, 38(11), 2653–2660.CrossRefGoogle ScholarPubMed
Johnston, L.D., O’Malley, P.M., Bachman, J.G., Schulenberg, J.E., & Miech, R.A. (2014). Monitoring the future national results on drug use: 1975-2013: Overview, key findings on adolescent drug use. Ann Arbor, MI: Institute for Social Research, The University of Michigan.Google Scholar
Kouri, E., Pope, H.G., Yurgelun-Todd, D., & Gruber, S. (1995). Attributes of heavy vs. occasional marijuana smokers in a college population. Biological Psychiatry, 38(7), 475–481.Google Scholar
Kuczmarski, R.J., Ogden, C.L., Guo, S.S., Grummer-Strawn, L.M., Flegal, K.M., Mei, Z., & Johnson, C.L. (2002). 2000 CDC Growth Charts for the United States: Methods and development. Vital and Health Statistics, 246, 1–190.Google Scholar
Lanza, H.I., Grella, C.E., & Chung, P.J. (2014). Does adolescent weight status predict problematic substance use patterns? American Journal of Health Behavior, 38(5), 708–716.CrossRefGoogle ScholarPubMed
Le Strat, Y., & Le Foll, B. (2011). Obesity and cannabis use: Results from 2 representative national surveys. American Journal of Epidemiology, 174, 929–933.Google Scholar
Levin, I.P., Hart, S.S., Weller, J.A., & Harshman, L.A. (2007). Stability of choices in a risky decision‐making task: A 3‐year longitudinal study with children and adults. Journal of Behavioral Decision Making, 20(3), 241–252.CrossRefGoogle Scholar
Lisdahl, K.M., Gilbart, E.R., Wright, N.E., & Shollenbarger, S. (2013). Dare to delay? The impacts of adolescent alcohol and marijuana use onset on cognition, brain structure, and function. Frontiers in Psychiatry, 53(4), 1–19.Google Scholar
Miech, R.A., Johnston, L., O’Malley, P.M., Bachman, J.G., Schulenberg, J., & Patrick, M.E. (2015). Trends in use of marijuana and attitudes toward marijuana among youth before and after decriminalization: The case of California 2007–2013. International Journal of Drug Policy, 26(4), 336–344.CrossRefGoogle ScholarPubMed
Pasch, K.E., Velazquez, C.E., Cance, J.D., Moe, S.G., & Lytle, L.A. (2012). Youth substance use and body composition: Does risk in one area predict risk in the other? Journal of Youth and Adolescence, 41(1), 14–26.Google Scholar
Rippeth, J.D., Heaton, R.K., Carey, C.L., Marcotte, T.D., Moore, D.J., Gonzalez, R., & Grant, I. (2004). Methamphetamine dependence increases risk of neuropsychological impairment in HIV infected persons. Journal of the International Neuropsychological Society, 10(01), 1–14.CrossRefGoogle ScholarPubMed
Schuster, R.M., Crane, N.A., Mermelstein, R., & Gonzalez, R. (2012). The influence of inhibitory control and episodic memory on the risky sexual behavior of young adult cannabis users. Journal of International Neuropsychology, 18(5), 827–833.Google ScholarPubMed
Verdejo‐García, A., Pérez‐Expósito, M., Schmidt‐Río‐Valle, J., Fernández‐Serrano, M.J., Cruz, F., Pérez‐García, M., & Marcos, A. (2010). Selective alterations within executive functions in adolescents with excess weight. Obesity, 18(8), 1572–1578.CrossRefGoogle ScholarPubMed
Volkow, N.D., Wang, G.-J., Fowler, J.S., & Telang, F. (2008). Overlapping neuronal circuits in addiction and obesity: Evidence of systems pathology. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 363(1507), 3191–3200.Google Scholar
Weller, J.A., Levin, I.P., Shiv, B., & Bechara, A. (2007). Neural correlates of adaptive decision making for risky gains and losses. Psychological Science, 18(11), 958–964.Google Scholar
Xue, G., Lu, Z., Levin, I.P., Weller, J.A., Li, X., & Bechara, A. (2009). Functional dissociations of risk and reward processing in the medial prefrontal cortex. Cerebral Cortex, 19(5), 1019–1027.CrossRefGoogle ScholarPubMed