Do cell phones, iPods/MP3 players, siblings and friends matter? Predictors of child body mass in a U.S. Southern Border City Middle School

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  • Obesity Research & Clinical Practice (2012) 6, e39e53

    ORIGINAL ARTICLE

    Do cell phones, iPods/MP3 players, siblings andfriends matter? Predictors of child body mass in aU.S. Southern Border City Middle School

    MarcusHoracio

    a DepartmTX 78041,b DepartmLaredo, Tc Canseco 78041, US

    Received 1

    KEYWORBMI perceSiblings;Cell phoniPods/MP3

    CorrespoE-mail ad

    1871-403X/$

    doi:10.1016/ Antonius Ynalveza,, Ruby Ynalvezb, Marivic Torregosac, Palaciosc, John Kilburna

    ent of Behavioral Sciences, Texas A&M International University, University Boulevard, Laredo, USAent of Biology and Chemistry, Texas A&M International University, University Boulevard,X 78041, USASchool of Nursing, Texas A&M International University, University Boulevard, Laredo, TXA

    2 January 2011; received in revised form 28 March 2011; accepted 19 April 2011

    DSntile;

    es; players

    SummaryObjective: This study examines the association of childrens (i) micro-social envi-ronment, specically siblings [kin-friends] and friends from school and neighborhood[non-kin-friends], and (ii) ownership of information and communication technologies(ICT), specically cell phones and iPod/MP3 players, with body mass index percentile(BMIp).Subjects: Fifty-ve randomly selected 6th graders with a mean age of 12 years,stratied by gender (23 boys and 32 girls), from a Texas middle school located in acity along the U.S. southern border.Methods: The linear regression of BMIp on number of siblings and of non-kin-friends,and ownership of cell phone and of iPod/MP3 player was examined using two models:M1 was based on the manual selection of predictors from a pool of potential predic-tors. M2 was derived from the predictors specied in M1 using backward eliminationtechnique. Because sample size was small, the signicance of regression coefcientswas evaluated using robust standard errors to calculate t-values. Data for predictorswere obtained through a survey. Height and weight were obtained through actualanthropometric measurements. BMIp was calculated using the on-line BMI calculatorof the Center for Disease Control and Prevention.

    nding author. Tel.: +1 956 326 2621; fax: +1 956 326 2474.dress: mynalvez@tamiu.edu (M.A. Ynalvez).

    see front matter 2011 Asian Oceanian Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.

    j.orcp.2011.04.006

  • e40 M.A. Ynalvez et al.

    Results: Findings reveal that childrens social environment and ICT ownership predictBMIp; specically, number of siblings (M2: = 0.34, p-value < .001), and ownership ofiPod/MP3 players (M2: = 0.33, p-value < .001). These results underscore the impor-tance of family in conguring, and of new personal technical devices (that encouragesolitary, and oftentimes sedentary, activities) in predicting child body mass. 2011 Asian Oceanian Association for the Study of Obesity. Published by Elsevier Ltd.

    Introduc

    Child obea very serStates un[1,2]. Cheffects onprone to dmental deorthopediType 2 dilosing thebody weigaccustombody. Sincon societyto the perbody of kncontributiover the (current wtion [8], scapital [1and socia

    A dimecant inusocial envcated soci[1] reportpersonal comes. Prpeer suppactivity acal activiwhen thedecreasedwere within more weight kievidence ronment, particularmuch und

    A prevsiblings incantly asssuch nd

    e size, which may have led to oversensitivegs g 1

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    istical versus practical signicance is a topic of discussionAll rights reserved.

    tion

    sity is not just a cosmetic problem. It isious health concern that puts the Unitedder threat from life-threatening diseasesild obesity carries short- and long-term

    an individual [3,4]. Obese children areevelop cardiovascular disease, fatty liver,pression due to stigmatization, asthma,c problems, metabolic syndrome, andabetes [5,6]. Once children are obese,

    excess weight and reverting to an idealht are difcult as fat cells that have beened to storing excess energy remain in thee childhood obesity has a major impact

    in terms of health care costs in additionsonal, social, and nancial burden; a wideowledge has been built regarding factorsng towards child obesity developmentyears: inherent biological characteristicseight category) [7], genetic predisposi-

    edentariness [911], neighborhood social2], gene-environment interaction [13],l networks [1].nsion that has been cited as a signi-ence on child obesity development is theironment [1,14,15]. Using highly sophisti-al network analysis, Christakis and Fowler

    that social environment shapes not onlybehavior, but also personal health out-evious ndings indicate that parental andort is associated with increased physicalmong children [1618]. Increased physi-ty, especially among boys, was observedy were among peers [17,18]; however,

    physical activity was observed when they family [18]. Overweight children engagedintense physical activities than normal-ds when with peers [18]. While initiallinks child obesity and the social envi-the inuence of familial environment,ly siblings, on child body mass is veryerstudied.ious study indicates that the number of

    samplndinAnothrelatiorelatinmuniciPods/impacing tothose

    Thithe inment,in schpeers)iPod/Mof theamongMexicenviroties, aMexicnologylatest

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    This sway cis subdry sucold shomehas hihealthistics.94% opanic

    1 Stat a household was negatively and signi-ociated with body weight [19]. However,ings were drawn from an overly large

    among resealinger and La very small practical sigiven the sheer size of the sample [20].imension that has been investigated ino child body mass development is thato the role of new information and com-n technologies (ICT), like cell phones and

    players [15,21]. Although studies on thethe Internet are replete [21], those relat-

    inuence of cell phones are still few, andPod/MP3 player use are still very scarce.dy adds to the paucity of literature on

    nce of childrens (i) micro-social environ-cically siblings (kin-friends) and friends

    and neighborhood (non-kin-friends ord (ii) their ownership of cell phones andplayers on body mass measured in termse-gender specic BMI percentile (BMIp),

    graders in a city in Texas along the U.S.-rder a bicultural environment. Thatnt is characterized by tight-knit familye variety of food ranging from traditionaluisine to American fast-food, and a tech-ption behavior that is quick to utilize the

    CT innovation.

    was conducted in Laredo, Texas, a gate-to Mexico. The climate in this regionpical with long very warm (3840 C)ers, punctuated by mild and very shortns [22]. The southern border region isa segment of the U.S. population thatically been underrepresented in nationaldies and in proling health character-ording to the U.S. Census Bureau [23],e population identify themselves as His-which 29% is foreign born. Thirty-eightrchers in the behavioral sciences. For example, Ker-ee [20] contend that a very large sample will makedifference signicant, which may not necessarily ofnicance.

  • Predictors of child body mass e41

    percent of the population is below 18 years of age,whereas state and national gures are at 28% and24%, respectively. Under-education and poverty arehigh. Only 13% of the population has completed abachelors degree or higher compared to the stateand national averages of 23% and 24%, respectively.Approximately 27% of the residents currently livein poverty. Household median income is $36,537comparedof $50,000householdthe entiretively.

    Study de

    The majoface surveCentral M21.4% (3 of researcthe schoolUniversity6th gradewere obtaeach of other for obtained.English anconsent w

    Becauswe surve2008 and subjects uitems, thThere, wscreen, boloud and aincluded pworks, meplayer owMeasuremconductedsured usinweight weuniform bwere usedis given bheight squrate and rage-gende

    2 The targ171 girls) 6t20082009 (schools real

    Variable denition

    The outcome variable, BMIp, was derived using theCDC on-line BMI calculator [24]. Subjects gender,birth date, height, and weight were plugged intothe on-linand the nu(010) we

    enver oftherumbed aich rividudual

    straee own

    ofer oet. l iPo, an, ows wshiptorss (0spen

    cont suakfar. A

    Infe scr saand

    , wh. Soin smed

    or ionsds M

    tica

    s wchnutiovideard dcalcequmpa to the state and the national gures and $52,000, respectively. The average

    size is 3.75 while those of the state and nation stand at 2.74 and 2.59, respec-

    sign and sample

    rity of data was obtained from a face-to-y of a random sample n = 55 6th graders iniddle School.2 Non-participation rate wasgirls and 12 boys). Prior to the conducth activities, approval was obtained from

    district, and the Texas A&M Internationals Institutional Review Board. Rosters ofrs, by gender and in alphabetical order,ined from the counselors ofce. From

    those rosters one for boys and thegirls a systematic random sample was

    Using IRB-approved consent forms ind in Spanish child assent and parentalere obtained before data collection.e of the busy schedule at the school,yed in two sessions: one in Decemberthe other in April 2009. To ensure thatnderstood instructions and questionnaireey were gathered at the school library.ith the questionnaire shown on wide-th instructions and items were read-outnswered in a synchronized manner. Itemsersonal and family characteristics, net-als and drinks, cell phone and iPod/MP3nership, Internet-, sports-, and TV-hours.ents of height (m) and weight (kg) were

    by the school nurse. Height was mea-g a stadiometer, and both height andre measured with subjects wearing theirut without shoes. These measurements

    to calculate the subjects BMI, whichy an individuals weight divided by theirared [24]. Because BMI is not an accu-eliable measure of child body mass, ther specic BMI percentile (BMIp) is used.

    et population comprised of N = 298 (127 boys andh graders at Central Middle School in school yearnote: to ensure anonymity and condentiality, the

    name is not used).

    socialnumbof brothe nwe usin whof indindivirecall

    Thrused: ershipnumbInternactuaobtainHenceplayerownerpredicfriendweek

    Weor noat bresuppeif no.at thburgeJuice $1.00$0.50able consuselvesoccasing ki

    Statis

    Resultcal tedistribTo prostandwere and frTo coe calculator [24]. The number of siblings,mber of school and neighborhood friendsre used to measure the childrens micro-ironment. To solicit information about the

    siblings, subjects were asked the numbers and sisters they had. In coming up wither of school and neighborhood friends,

    name generator and name interpreter,espondents were asked to list the namesals and provide information about theses. This technique is superior to the surveytegy.rough measures of ICT utilization wereership of cell phone (1 = yes, 0 = no), own-

    iPod/MP3 player (1 = yes, 0 = no), andf hours in a typical week spent on theMeasures of actual cell phone and ofd/MP3 player usage were challenging tod were prone to very unreliable results.nership of cell phones and of iPod/MP3

    as used as reliable measures. Essentially, was used as a proxy for utilization. Other

    were number of school and neighborhood10), and number of hours in a typicalt in sports activities.trolled for other factors such as whetherbjects drink water, juice, milk, or sodast, at lunch, during snack time, and atll these were coded 1 if yes, and 0ormants told us that the typical menuhool cafeteria comprised of either andwich or pizza, both served with milk.

    bottled water can be purchased forile an additional serving of milk costda drinks were neither sold nor avail-chool; therefore, soft drinks that were

    were either brought by the kids them-by their parents/guardians. On several, parents/guardians were observed bring-cDonalds meals during lunch.

    l analysis

    ere derived using a variety of statisti-iques: descriptive statistics, frequencyns, correlation, and regression analyses.

    summary information, means, medians,eviations, minimum and maximum valuesulated for numerical variables (Table 1);encies for categorical variables (Table 2).re boys and girls on the variables in

  • e42 M.A. Ynalvez et al.

    Table 1 Descriptive statistics for numerical variablesa.

    Variables n Mean Median SD Min Max

    BMI percentileb 52 66.90 71.50 26.88 6.00 99.00Height (cm) 53 150.32 149.86 7.71 134.62 170.18Weight (kg) 53 47.89 44.91 13.22 29.48 89.36Age (years) 54 12.13 12.02 0.48 11.30 13.70No. of siblings 55 2.51 2.00 1.36 0.00 6.00No. of school and neighborhood friends 55 4.33 4.00 2.91 0.00 10.00Internet h 16.50 25.35 0.00 77.00TV hours 21.0 13.93 0.00 55.00Sports ho 12.00 12.88 0.50 52.00

    a Overa ariabb Derive

    Table 2

    Variables

    Subject iLiving witBoth pareOwns a cOwns an Eats breaDrinks waDrinks juiDrinks miDrinks soDrinks waDrinks juiDrinks miDrinks soDrinks waDrinks juiDrinks miDrinks soDrinks waDrinks juiDrinks miDrinks so

    a Each ob Overa

    Tables 1 used. To ccorrelatiowere empcontributiear regreemployed

    3 A point son correlatdichotomouours in a week 55 28.19 in a week 55 21.40 urs in a week 51 15.13

    ll sample size is n = 55. Due to missing values, sample sizes per vd using CDCs on-line child BMI calculator (www.cdc.gov).

    Frequency distribution of categorical variablesa.

    b Yesn %

    s female 32 58 h both parents 36 65 nts working 29 53 ell phone 44 80 iPOD/MP3 player 40 73 kfast 51 93 ter at breakfast 12 22ce at breakfast 32 58 lk at breakfast 31 56 da at breakfast 7 13 ter at lunch 12 22ce at lunch 11 20lk at lunch 18 33da at lunch 33 60ter at supper 14 25ce at supper 10 18 lk at supper 8...

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