High calorie, low nutrient food/beverage intake and video gaming in children as potential signals for addictive behavior

Mary Ann Pentz, Donna Spruijt-Metz, Chih Ping Chou, Nathaniel R Riggs, Mary Ann Pentz, Donna Spruijt-Metz, Chih Ping Chou, Nathaniel R Riggs

Abstract

Little is known about the co-occurrence of health risk behaviors in childhood that may signal later addictive behavior. Using a survey, this study evaluated high calorie, low nutrient HCLN intake and video gaming behaviors in 964 fourth grade children over 18 months, with stress, sensation-seeking, inhibitory control, grades, perceived safety of environment, and demographic variables as predictors. SEM and growth curve analyses supported a co-occurrence model with some support for addiction specificity. Male gender, free/reduced lunch, low perceived safety and low inhibitory control independently predicted both gaming and HCLN intake. Ethnicity and low stress predicted HCLN. The findings raise questions about whether living in some impoverished neighborhoods may contribute to social isolation characterized by staying indoors, and HCLN intake and video gaming as compensatory behaviors. Future prevention programs could include skills training for inhibitory control, combined with changes in the built environment that increase safety, e.g., implementing Safe Routes to School Programs.

Trial registration: ClinicalTrials.gov NCT00787709.

Keywords: addictive behavior; children; eating; video gaming.

Figures

Figure 1
Figure 1
Co-occurrence and growth in HCLN and video gaming.
Figure 2
Figure 2
Growth curve model of predictors of HCLN intake and video gaming.

References

    1. Sussman S., Leventhal A., Bluthenthal R.N., Freimuth M., Forster M., Ames S.L. A framework for the specificity of addictions. Int. J. Environ. Res. Public Health. 2011;8:3399–3415.
    1. Diagnostic and Statistical Manual of Mental Disorders: DSM-IV-TR. 4th. American Psychiatric Association; Arlington, VA, USA: 2000. pp. 1–943.
    1. Potenza M.N. Should addictive disorders include non-substance-related conditions? Addiction. 2006;101(Suppl 1):142–151.
    1. Sussman S., Lisha N., Griffiths M. Prevalence of the addictions: A problem of the majority or the minority. Eval. Health Prof. 2011;34:3–56.
    1. Gentile D.A., Choo H., Liau A., Sim T., Li D., Fung D., Khoo A. Pathological video game use among youths: A two-year longitudinal study. Pediatrics. 2011;127:e319–e329.
    1. Petry N.M. Substance abuse, pathological gambling, and impulsiveness. Drug Alcohol Depend. 2001;63:29–38.
    1. Grüsser S.M., Thalemann R., Griffiths M.D. Excessive computer game playing: Evidence for addiction and aggression? Cyberpsychol. Behav. 2007;10:290–292.
    1. Ogden C.L., Carroll M.D., Curtin L.R., McDowell M.A., Tabak C.J., Flegal K.M. Prevalence of overweight and obesity in the United States, 1999–2004. J. Am. Med. Assoc. 2006;295:1549–1555.
    1. Pentz M.A. Understanding and preventing risks for adolescent obesity. In: Crosby R., Santelli J., editors. Adolescent Health: Understanding and Preventing Risk. John Wiley & Sons; Hoboken, NJ, USA: 2009. pp. 147–164.
    1. Pearson N., Biddle S.J. Sedentary behavior and dietary intake in children, adolescents, and adults: A systematic review. Am. J. Prev. Med. 2011;41:178–188.
    1. Ballard M.E. Video game play, behavior, and dietary health. In: Preedy V.R., Watson R.R., Martin C.R., editors. Handbook of Behavior, Food, and Nutrition. Springer; New York, NY, USA: 2011. pp. 2169–2181.
    1. Ballard M.E., Gray M.A., Reilly J., Noggle M. Correlates of video game screen time among males: Body mass, physical activity, and other media use. Eat. Behav. 2009;10:161–167.
    1. Institute of Medicine. Overview of the IOM’s Childhood Obesity Prevention Study. 2004. [(accessed on 14 April 2008)]. Available online: .
    1. Dorosty A.R., Emmett S.R.D., Cowin I.S., Reilly J.J. Factors associated with early adiposity rebound. ALSPAC Study Team. Pediatrics. 2000;105:1115–1118.
    1. Hoelscher D.M., Day R.S., Kelder S.H., Ward J.L. Reproducibility and validity of the secondary level School-Based Nutrition Monitoring (SBNM) student questionnaire. J. Am. Diet. Assoc. 2003;103:186–194.
    1. Bardo M.T., Pentz M.A. Translational research. In: Cooper H., editor. APA Handbook of Research Methods in Psychology. American Psychiatric Association; Washington, DC, USA: 2011.
    1. Stephenson M.T., Hoyle R.H., Palmgreen P., Slater M.D. Brief measures of sensation seeking for screening and large-scale surveys. Drug Alcohol Depend. 2003;72:279–286.
    1. Crawford A.M., Pentz M.A., Chou C.P., Li C., Dwyer J.H. Parallel developmental trajectories of sensation seeking and regular substance use in adolescents. Psychol. Addict. Behav. 2003;17:179–192.
    1. Kelley A.E., Schochet T., Landry C.F. Risk taking and novelty seeking in adolescence: Introduction to part I. Ann. NY Acad. Sci. 2004;1021:27–32.
    1. Belin D., Mar A.C., Dalley J.W., Robbins T.W., Everitt B.J. High impulsivity predicts the switch to compulsive cocaine-taking. Science. 2008;320:1352–1355.
    1. Verdejo-García A., Lawrence A.J., Clark L. Impulsivity as a vulnerability marker for substance-use disorders: Review of findings from high-risk research, problem gamblers and genetic association studies. Neurosci. Biobehav. Rev. 2008;32:777–810.
    1. Verdejo-Garcia A., Bechara A., Recknor E.C., Pérez-Garcia M. Executive dysfunction in substance dependent individuals during drug use and abstinence: An examination of the behavioral, cognitive and emotional correlates of addiction. J. Int. Neuropsychol. Soc. 2006;12:405–415.
    1. Bechara A. Risky business: Emotion, decision-making, and addiction. J. Gambl. Stud. 2003;19:23–51.
    1. Riggs N.R., Spruijt-Metz D., Chou C.P., Pentz M.A. Relationships between executive cognitive function and lifetime substance use, and obesity-related behaviors in fourth grade youth. Child Neuropsychol. 2011;7:1–11.
    1. Chambers R.A., Taylor J.R., Potenza M.N. Developmental neurocircuitry of motivation in adolescence: A critical period of addiction vulnerability. Am. J. Psychiatry. 2003;160:1041–1052.
    1. Fishbein D., Tarter R. Infusing neuroscience into the study and prevention of drug misuse and co-occurring aggressive behavior. Subst. Use Misuse. 2009;44:1204–1235.
    1. Riggs N.R., Greenberg M.T. Neurocognition as a moderator and mediator of adolescent substance misuse prevention. Am. J. Drug Alcohol Abuse. 2009;35:209–213.
    1. Cerqueira J.J., Mailliet F., Almeida O.F.X., Jay T.M., Sousa N. The prefrontal cortex as a key target of the maladaptive response to stress. J. Neurosci. 2007;27:2781–2787.
    1. Bickel W.K., Marsch L.A. Toward a behavioral economic understanding of drug dependence: Delay discounting processes. Addiction. 2001;96:73–86.
    1. Montague P.R., Berns G.S. Neural economics and the biological substrates of valuation. Neuron. 2002;36:265–284.
    1. Posner M.I., Rothbart M.K. Toward a physical basis of attention and self regulation. Phys. Life Rev. 2009;6:103–120.
    1. Volkow N.D., Wise R.A. How can drug addiction help us understand obesity? Nat. Neurosci. 2005;8:555–560.
    1. Marsh R., Horga G., Wang Z., Wang P., Klahr K.W., Berner L.A., Walsh B.T., Peterson B.S. An fMRI study of self-regulatory control and conflict resolution in adolescents with bulimia nervosa. Am. J. Psychiatry. 2011
    1. Guy S.C., Isquith P.K., Gioia G.A. BRIEF (The Behavior Rating Inventory of Executive-Function-Self-Report Version BRIEF-SR), Behavior Rating Inventory of Executive Function-Self-Report Version. Psychological Assessment Resources, Inc.; Odessa, FL, USA: 2004.
    1. Riggs N.R., Kobayakawa-Sakuma K.L., Pentz M.A. Preventing risk for obesity by promoting self-regulation and decision-making skills: Pilot results from the Pathways to Health Program. Eval. Rev. 2007;31:287–310.
    1. Romeo R.D., McEwen B.S. Stress and the adolescent brain. Ann. NY Acad. Sci. 2006;1094:202–214.
    1. Jasuja G.K., Chou C.P., Riggs N.R., Pentz M.A. Early cigarette use and psychological distress as predictors of obesity risk in adulthood. Nicotine Tob. Res. 2008;10:325–335.
    1. Johnston L.D., O’Malley P.M., Bachman J.G., Schulenberg J.E. Monitoring the Future National Survey Results on Drug Use, 1975–2010. Volume I: Secondary School Students. Institute for Social Research, University of Michigan; Ann Arbor, MI, USA: 2011. p. 734.
    1. Sussman S., Pentz M.A., Spruijt-Metz D., Miller T. Abuse of “study drugs”: Prevalence, consequences, and implications for therapeutic prescription and policy. Subst. Abuse Treat. Prev. Policy. 2006;1
    1. Huh H., Riggs N.R., Spruijt-Metz D., Chou C.P., Huang Z., Pentz M. Identifying patterns of eating and physical activity in children: A latent class analysis of obesity risk. Obesity. 2010;19:652–658.
    1. Nguyen-Michel S.T., Unger J.B., Metz D.S. Dietary correlates of emotional eating in adolescence. Appetite. 2007;49:494–499.
    1. Riggs N.R., Spruijt-Metz D., Sakuma K.L., Chou C.P., Pentz M.A. Executive cognitive function and food intake in children. J. Nutr. Educ. Behav. 2010;42:398–403.
    1. Rollins B.Y., Riggs N.R., Spruijt-Metz D., McClain A.D., Chou C.P., Pentz M.A. Psychometrics of the Eating in Emotional Situations Questionnaire (EESQ) among low-income Latino elementary-school children. Eat. Behav. 2011;12:156–159.
    1. Singh G.K., Slahpush M., Kogan M.D. Rising social inequalities in U.S. childhood obesity, 2003–2007. Ann. Epidemiol. 2010;20:40–52.
    1. Pentz M.A., Johnson C.A., Dwyer J.H., MacKinnon W.B., Flay B.R. A comprehensive community approach to adolescent drug abuse prevention: Effects on cardiovascular disease risk behaviors. Ann. Med. 1989;21:219–222.
    1. Greenberg M.T., Kusché C.A. Promoting Social and Emotional Development in Deaf Children: The PATHS Project. University of Washington Press; Seattle, WA, USA: 1993.
    1. SAS Institute Inc. SAS Language: Reference, Version 6. 1st. SAS Institute Inc.; Cary, NC, USA: 1990.
    1. Field A.E., Peterson K.E., Gortmaker S.L., Cheung L., Rockett H., Fox M.K., Colditz G.A. Reproducibility and validity of a food frequency questionnaire among fourth to seventh grade inner-city school children: Implications of age and day-to-day variation in dietary intake. Public Health Nutr. 1999;2:293–300.
    1. Willett W.C., Sampson L., Stampfer M.J., Rosner B., Bain C., Witschi J., Hennekens C.H., Speizer F.E. Reproducibility and validity of a semiquantitative food frequency questionnaire. Am. J. Epidemiol. 1985;122:51–65.
    1. California Healthy Kids Survey: Main Report. WestEd Health and Human Development Program for the California Department of Education; San Francisco, CA, USA: 2009.
    1. Cohen S., Kamarck T., Mermelstein R.A. Global measure of perceived stress. J. Health Soc. Behav. 1983;24:385–396.
    1. Malmberg M., Overbeek G., Monshouwer K., Lammers J., Vollebergh W.A., Engels R.C. Substance use risk profiles and associations with early substance use in adolescence. J. Behav. Med. 2010;33:474–485.
    1. Centers for Disease Control (CDC) Youth Risk Behavior Survey. CDC; Atlanta, GA, USA: 2005. [(accessed on 22 November 2011)]. Available online: .
    1. Eaton D.K., Kann L., Kinchen S., Ross J., Hawkins J., Harris W.A., Lowry R., McManus T., Chyen D., Shanklin S., et al. Youth risk behavior surveillance—United States, 2005. J. School Health. 2006;76:353–372.
    1. Paeratakul S., Lovejoy J.C., Ryan D.H., Bray G.A. The relation of gender, race and socioeconomic status to obesity and obesity comorbidities in a sample of US adults. Int. J. Obes. Relat. Metab. Disord. 2002;26:1205–1210.
    1. Muthén L.K., Muthén B.O. Mplus Users Guide. 6th. Muthén & Muthén; Los Angeles, CA, USA: 1998.
    1. Golley R.K., Hendrie G.A., McNaughton S.A. Scores on the dietary guideline index for children and adolescents are associated with nutrient intake and socio-economic position but not adiposity. J. Nutr. 2011;141:1340–1347.
    1. Reedy J., Krebs-Smith S.M. Dietary sources of energy, solid fats, and added sugars among children and adolescents in the United States. J. Am. Diet Assoc. 2010;110:1477–1484.
    1. Durand C.P., Andalib M., Dunton G.F., Wolch J., Pentz M.A. A systematic review of built environment factors related to physical activity and obesity risk: Implications for smart growth urban planning. Obes. Rev. 2011;12:e173–e182.
    1. Midei A.J., Matthews K.A. Interpersonal violence in childhood as a risk factor for obesity: A systematic review of the literature and proposed pathways. Obes. Rev. 2011;12:e159–e172.
    1. Pentz M.A. Defining Neighborhoods of Opportunity by Green Spaces, School Achievement, and Perceived Neighborhood Safety: Illustrations from Two Drug Abuse and Obesity Prevention Trials; National Hispanic Science Network on Drug Abuse; New Orleans, LA, USA. 30 September–2 October 2010.
    1. Galvan A., Hare T., Voss H., Glover G., Casey B.J. Risk-taking and the adolescent brain: Who is at risk? Dev. Sci. 2007;10:8–14.
    1. Diamond A., Lee K. Interventions shown to aid executive function development in children 4 to 12 years old. Science. 2011;333:959–964.
    1. Davis C.L., Tomporowski P.D., Boyle C.A., Waller J.L., Miller P.H., Naglieri J.A., Gregoski M. Effects of aerobic exercise on overweight children’s cognitive functioning: A randomized controlled trial. Res. Q. Exerc. Sport. 2007;78:510–519.
    1. Terry-McElrath Y.M., O’Malley P.M., Johnston L.D. Exercise and substance use among American youth, 1991–2009. Am. J. Prev. Med. 2011;40:530–540.
    1. Steffen L.M., Dai S., Fulton J.E., Labarthe D.R. Overweight in children and adolescents associated with TV viewing and parental weight: Project HeartBeat! Am. J. Prev. Med. 2009;37(Suppl 1):S50–S55.
    1. Robinson T.N., Wilde M.L., Navracruz L.C., Haydel K.F., Varady A. Effects of reducing children’s television and video game use on aggressive behavior: A randomized controlled trial. Arch. Pediatr. Adolesc. Med. 2001;155:17–23.

Source: PubMed

Спонсоры и соавторы

Заболевания

Медикаментозные вмешательства

Подписаться