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besity Research & Clinical Practice (2012) 6, e39—e53

RIGINAL ARTICLE

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

arcus Antonius Ynalveza,∗, Ruby Ynalvezb, Marivic Torregosac,oracio Palaciosc, John Kilburna

Department of Behavioral Sciences, Texas A&M International University, University Boulevard, Laredo,X 78041, USADepartment of Biology and Chemistry, Texas A&M International University, University Boulevard,aredo, TX 78041, USACanseco School of Nursing, Texas A&M International University, University Boulevard, Laredo, TX8041, USA

eceived 12 January 2011; received in revised form 28 March 2011; accepted 19 April 2011

KEYWORDSBMI percentile;Siblings;Cell phones;iPods/MP3 players

SummaryObjective: This study examines the association of children’s (i) micro-social envi-ronment, specifically siblings [kin-friends] and friends from school and neighborhood[non-kin-friends], and (ii) ownership of information and communication technologies(ICT), specifically cell phones and iPod/MP3 players, with body mass index percentile(BMIp).Subjects: Fifty-five randomly selected 6th graders with a mean age of 12 years,stratified 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 specified in M1 using backward eliminationtechnique. Because sample size was small, the significance of regression coefficientswas 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 calculator of the Center for Disease Control and Prevention.

∗ Corresponding author. Tel.: +1 956 326 2621; fax: +1 956 326 2474.E-mail address: mynalvez@tamiu.edu (M.A. Ynalvez).

871-403X/$ — see front matter © 2011 Asian Oceanian Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.

oi:10.1016/j.orcp.2011.04.006

e40 M.A. Ynalvez et al.

Results: Findings reveal that children’s social environment and ICT ownership predictBMIp; specifically, number of siblings (M2: ˇ = −0.34, p-value < .001), and ownership ofiPod/MP3 players (M2: ˇ = 0.33, p-value < .001). These results underscore the impor-tance of family in configuring, and of new personal technical devices (that encouragesolitary, and oftentimes sedentary, activities) in predicting child body mass.

ssociation for the Study of Obesity. Published by Elsevier Ltd.

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Twidchhhistics. According to the U.S. Census Bureau [23],94% of the population identify themselves as His-panic of which 29% is foreign born. Thirty-eight

1 Statistical versus practical significance is a topic of discussion

© 2011 Asian Oceanian AAll rights reserved.

Introduction

Child obesity is not just a cosmetic problem. It isa very serious health concern that puts the UnitedStates under threat from life-threatening diseases[1,2]. Child obesity carries short- and long-termeffects on an individual [3,4]. Obese children areprone to develop cardiovascular disease, fatty liver,mental depression due to stigmatization, asthma,orthopedic problems, metabolic syndrome, andType 2 diabetes [5,6]. Once children are obese,losing the excess weight and reverting to an idealbody weight are difficult as fat cells that have beenaccustomed to storing excess energy remain in thebody. Since childhood obesity has a major impacton society in terms of health care costs in additionto the personal, social, and financial burden; a widebody of knowledge has been built regarding factorscontributing towards child obesity developmentover the years: inherent biological characteristics(current weight category) [7], genetic predisposi-tion [8], sedentariness [9—11], neighborhood socialcapital [12], gene-environment interaction [13],and social networks [1].

A dimension that has been cited as a signifi-cant influence on child obesity development is thesocial environment [1,14,15]. Using highly sophisti-cated social network analysis, Christakis and Fowler[1] report that social environment shapes not onlypersonal behavior, but also personal health out-comes. Previous findings indicate that parental andpeer support is associated with increased physicalactivity among children [16—18]. Increased physi-cal activity, especially among boys, was observedwhen they were among peers [17,18]; however,decreased physical activity was observed when theywere with family [18]. Overweight children engagedin more intense physical activities than normal-weight kids when with peers [18]. While initialevidence links child obesity and the social envi-ronment, the influence of familial environment,particularly siblings, on child body mass is verymuch understudied.

A previous study indicates that the number ofsiblings in a household was negatively and signifi-cantly associated with body weight [19]. However,such findings were drawn from an overly large

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ample size, which may have led to oversensitivendings given the sheer size of the sample [20].1

nother dimension that has been investigated inelation to child body mass development is thatelating to the role of new information and com-unication technologies (ICT), like cell phones and

Pods/MP3 players [15,21]. Although studies on thempact of the Internet are replete [21], those relat-ng to the influence of cell phones are still few, andhose on iPod/MP3 player use are still very scarce.

This study adds to the paucity of literature onhe influence of children’s (i) micro-social environ-ent, specifically siblings (kin-friends) and friends

n school and neighborhood (non-kin-friends oreers), and (ii) their ownership of cell phones andPod/MP3 players on body mass measured in termsf their age-gender specific BMI percentile (BMIp),mong 6th graders in a city in Texas along the U.S.-exico border — a bicultural environment. Thatnvironment is characterized by tight-knit familyies, a wide variety of food ranging from traditionalexican cuisine to American fast-food, and a tech-ology adoption behavior that is quick to utilize theatest in ICT innovation.

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his study was conducted in Laredo, Texas, a gate-ay city to Mexico. The climate in this region

s sub-tropical with long very warm (38—40 ◦C)ry summers, punctuated by mild and very shortold seasons [22]. The southern border region isome to a segment of the U.S. population thatas historically been underrepresented in nationalealth studies and in profiling health character-

mong researchers in the behavioral sciences. For example, Ker-inger and Lee [20] contend that a very large sample will make

very small difference significant, which may not necessarily ofractical significance.

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ercent of the population is below 18 years of age,hereas state and national figures are at 28% and4%, respectively. Under-education and poverty areigh. Only 13% of the population has completed aachelor’s degree or higher compared to the statend national averages of 23% and 24%, respectively.pproximately 27% of the residents currently live

n poverty. Household median income is $36,537ompared to the state and the national figuresf $50,000 and $52,000, respectively. The averageousehold size is 3.75 while those of the state andhe entire nation stand at 2.74 and 2.59, respec-ively.

tudy design and sample

he majority of data was obtained from a face-to-ace survey of a random sample n = 55 6th graders inentral Middle School.2 Non-participation rate was1.4% (3 girls and 12 boys). Prior to the conductf research activities, approval was obtained fromhe school district, and the Texas A&M Internationalniversity’s Institutional Review Board. Rosters ofth graders, by gender and in alphabetical order,ere obtained from the counselor’s office. Fromach of those rosters — one for boys and thether for girls — a systematic random sample wasbtained. Using IRB-approved consent forms — innglish and in Spanish — child assent and parentalonsent were obtained before data collection.

Because of the busy schedule at the school,e surveyed in two sessions: one in December008 and the other in April 2009. To ensure thatubjects understood instructions and questionnairetems, they were gathered at the school library.here, with the questionnaire shown on wide-creen, both instructions and items were read-outoud and answered in a synchronized manner. Itemsncluded personal and family characteristics, net-orks, meals and drinks, cell phone and iPod/MP3layer ownership, Internet-, sports-, and TV-hours.easurements of height (m) and weight (kg) wereonducted by the school nurse. Height was mea-ured using a stadiometer, and both height andeight were measured with subjects wearing theirniform but without shoes. These measurementsere used to calculate the subjects’ BMI, which

s given by an individual’s weight divided by theireight squared [24]. Because BMI is not an accu-

ate and reliable measure of child body mass, thege-gender specific BMI percentile (BMIp) is used.

2 The target population comprised of N = 298 (127 boys and71 girls) 6th graders at Central Middle School in school year008—2009 (note: to ensure anonymity and confidentiality, thechool’s real name is not used).

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ariable definition

he outcome variable, BMIp, was derived using theDC on-line BMI calculator [24]. Subjects’ gender,irth date, height, and weight were plugged intohe on-line calculator [24]. The number of siblings,nd the number of school and neighborhood friends0—10) were used to measure the children’s micro-ocial environment. To solicit information about theumber of siblings, subjects were asked the numberf brothers and sisters they had. In coming up withhe number of school and neighborhood friends,e used a name generator and name interpreter,

n which respondents were asked to list the namesf individuals and provide information about thesendividuals. This technique is superior to the surveyecall strategy.

Three rough measures of ICT utilization weresed: ownership of cell phone (1 = yes, 0 = no), own-rship of iPod/MP3 player (1 = yes, 0 = no), andumber of hours in a typical week spent on thenternet. Measures of actual cell phone and ofctual iPod/MP3 player usage were challenging tobtain, and were prone to very unreliable results.ence, ownership of cell phones and of iPod/MP3layers was used as reliable measures. Essentially,wnership was used as a proxy for utilization. Otherredictors were number of school and neighborhoodriends (0—10), and number of hours in a typicaleek spent in sports activities.We controlled for other factors such as whether

r not subjects drink water, juice, milk, or sodat breakfast, at lunch, during snack time, and atupper. All these were coded ‘1′ if yes, and ‘0′f no. Informants told us that the typical menut the school cafeteria comprised of either aurger sandwich or pizza, both served with milk.uice and bottled water can be purchased for1.00, while an additional serving of milk cost0.50. Soda drinks were neither sold nor avail-ble in school; therefore, soft drinks that wereonsumed were either brought by the kids them-elves or by their parents/guardians. On severalccasions, parents/guardians were observed bring-ng kids McDonald’s meals during lunch.

tatistical analysis

esults were derived using a variety of statisti-al techniques: descriptive statistics, frequencyistributions, correlation, and regression analyses.o provide summary information, means, medians,

tandard deviations, minimum and maximum valuesere calculated for numerical variables (Table 1);nd frequencies for categorical variables (Table 2).o compare 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 hours in a week 55 28.19 16.50 25.35 0.00 77.00TV hours in a week 55 21.40 21.0 13.93 0.00 55.00Sports hours in a week 51 15.13 12.00 12.88 0.50 52.00

a Overall sample size is n = 55. Due to missing values, sample sizes per variable may be less than this number.b Derived using CDC’s on-line child BMI calculator (www.cdc.gov).

Table 2 Frequency distribution of categorical variablesa.

Variablesb Yes No

n % n %

Subject is female 32 58 23 42Living with both parents 36 65 19 35Both parents working 29 53 26 47Owns a cell phone 44 80 11 20Owns an iPOD/MP3 player 40 73 15 27Eats breakfast 51 93 4 7Drinks water at breakfast 12 22 43 78Drinks juice at breakfast 32 58 23 42Drinks milk at breakfast 31 56 24 44Drinks soda at breakfast 7 13 48 87Drinks water at lunch 12 22 43 78Drinks juice at lunch 11 20 44 80Drinks milk at lunch 18 33 37 67Drinks soda at lunch 33 60 22 40Drinks water at supper 14 25 41 75Drinks juice at supper 10 18 45 82Drinks milk at supper 8 15 47 85Drinks soda at supper 36 65 19 35Drinks water during snacks 28 51 27 49Drinks juice during snacks 17 31 38 69Drinks milk during snacks 4 7 50 91Drinks soda during snacks 31 56 23 42

ble be size

opredictors in Tables 1 and 2. M2 has 14 predictorsselected from M1 by called backward elimination[25—27].4 Predictors in M1 were selected based on

a Each of the variables listed represents a question answerab Overall sample size is n = 55. Due to missing values, sampl

Tables 1 and 2, independent samples t-test wasused. To compute bivariate correlations, a Pearsoncorrelation and a point-biserial correlation analysiswere employed [25].3 To examine the simultaneouscontribution of predictors on BMIp, multiple lin-

ear regression analysis with dummy variables wasemployed.

3 A point bi-serial correlation analysis is essentially a Pear-son correlation analysis between an interval-ratio level and adichotomous nominal level variable [25].

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y either a ‘yes’ or a ‘no’.s per variable may be less than this number.

Two regression models were derived: M1 consistsf 20 predictors selected from the list of potential

4 As described in Neter et al. [27] on p. 353, backward elimina-ion begins with a full model containing all potential predictorselected by the analyst, and identifies the one with the smallest-value. If that F-value is less than a predetermined thresh-ld then that predictor associated with the smallest F-values dropped. The model with the remaining predictors is againtted, and the next predictor to be dropped is identified. The

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redictors of child body mass

review of the literature and this study’s hypothe-es. In contrast, predictors in M2 were selectedased on their significance probabilities under M1.hy was it necessary to generate two models?hese models were generated to verify if the signif-

cance of the predictors was stable across varyingumber of predictors, and to identify predictorshat were significant in both models. Because ofudgetary constraints5 and refusal rates, the finalample size was small. Even with a well-designedampling plan, the risk of obtaining unstable regres-ion coefficients comes with such a small sample.ence, instead of using the usual standard errors

SE), robust standard errors (RSE) were used foroth models to calculate t-values associated withhe inferential tests for the coefficients.

Although multiple linear regression resultsxhibits a high degree of reliability even for smallample sizes [28], the authors made the extraffort of (i) using RSE-based instead of SE-basedegression results, and (ii) building two modelsM1 and M2). Finally, in checking for normalityf BMIp residuals from M1 and M2, examinationf the frequency distributions, histograms, andhe Kolmogorov—Smirnov tests (KS = 0.800 for M1,

= 0.200; KS = 0.104 for M2, p = 0.200) indicatedhat the two sets of residuals were normally dis-ributed. Values for the variance inflation factor for1 and M2 indicated no problems of multicollinear-

ty among predictors [25].

esults

esults are presented in Tables 1—7 . Table 1 con-ains the means, medians, standard deviations;inimum and maximum values for all numerical

ariables, while Table 2 presents the frequenciesnd percentages for all categorical variables. Inable 3, the means and the standard deviations foroys and for girls are presented, together with theesults of independent samples t-tests comparinghese two groups with respect to each of the numer-cal variables. Table 4 displays the frequencies andhe percentages for boys and for girls, with respecto each of the categorical variables. It also presentshe p-values associated with independent samples-tests comparing both groups. Correlation results

re shown in Table 5. In Tables 6 and 7 M1 and M2egression results are presented.

rocess goes on until there are no predictors that yield F-valueess than the threshold.5 Each subject received a $25.00-gift certificate for participat-

ng in the study.

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From Tables 1 and 2, it is clear that BMIp rangedrom 6.00 to 99.00. Its mean is 66.9 ± 26.9. Of the5 respondents, 58% (32) were girls and 42% (23)ere boys. Average age was 12.1 ± 0.5 years, with

he youngest about 11 (11.3 years) and the oldestlmost fourteen (13.7 years). Sixty-five percent (36f 55) of respondents were living with both parents,hile fifty-three percent (29 of 55) had both par-nts working. The average respondent had 2.5 ± 1.4iblings, 4.3 ± 2.9 school and neighborhood friendsith whom they played and discussed importantatters; and, spent 28.2 ± 25.4 h in a typical week

or about 4 h/day) on the Internet, 21.4 ± 13.9 hor about 3 h/day) watching TV, and 15.1 ± 12.9 ha little more than 2 h/day) engaging in sports. Inerms of ICT, 80% (44 of 55) of subjects owned aellular phone, while 73% (40 of 55) an iPod/MP3layer.

In terms of meals, all subjects reported eatingunch (not shown in Table 2), while 92.7%, 92.6%,nd 98.2% reported eating breakfast, supper, andaving snacks, respectively. As regards to fluid con-umption at breakfast, 22% (12 of 55), 58% (32), 56%21), and 13% (7) reported drinking water, juice,ilk, and soda, respectively. Clearly, juice and milkere popular choices despite that the breakfastenu only offered milk. At lunch, 22% (12 of 55),

0% (11), 33% (18), and 60% (33) reported drink-ng water, juice, milk, and soda, respectively. Thiss an interesting observation because soda is notvailable in school, and yet it is the most popularrink consumed. For snacks, 51% (28 of 55), 31%17 of 55), 7% (4 of 54), and 56% (31 of 54) reportrinking water, juice, milk, and soda, respectively.gain, soda is a popular choice. The popularity ofoda drinks at lunch and during snacks, despitehese being unavailable in school could imply thatither the kids bring soda with them to school,ave someone bring soda for them to school, oroth. At supper, 25% (14 of 55), 18% (10), 15% (8),nd 65% (36) reported drinking water, juice, milk,nd soda, respectively. Clearly, majority of childrenrink soda at lunch, during snacks, and at supper.

Table 4 indicates significant differences betweenoys and girls with respect to owning a cellularhone and drinking milk during snacks. Sixty-fiveercent (15 of 23) of boys compared to ninety-oneercent (29 of 32) of girls owned a cellular phone.eventeen percent (4 of 23) of boys compared to% (0 of 32) for girls reported drinking milk dur-ng snacks. For all other variables (Tables 3 and 4),here were no significant differences between the

wo groups. In Table 5, the most salient find-ng is that of the correlation between BMIp andumber of siblings (r = −0.458; p < 0.001). Numberf friends was positively correlated with number

e44 M.A. Ynalvez et al.

Table 3 Comparison of means between boys and girlsa.

Variables Boys Girls p-Valueb

n Mean SD n Mean SD

BMI percentilec 21 73.64 25.63 31 62.06 25.78 0.104Height (cm) 22 150.09 9.25 31 150.47 6.57 0.860Weight (kg) 22 48.89 13.35 31 47.19 13.31 0.650Age (years) 22 12.09 0.45 32 12.16 0.50 0.578No. of siblings 23 2.43 1.34 32 2.56 1.39 0.733No. of school and neighborhood friends 23 3.74 2.70 32 4.75 3.02 0.064Internet hours in a week 23 30.55 27.36 32 26.50 24.11 0.448TV hours in a week 23 20.26 10.86 32 22.23 15.90 0.494Sports hours in a week 21 16.30 13.80 28 14.28 11.43 0.560

a Overall sample sizes for boys and for girls are n = 23 and n = 32, respectively. Due to missing values, sample sizes per variablemay be less than these numbers.

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b p-Value is associated with a two-tailed independent sampc Derived using CDC’s on-line child BMI calculator (www.cdc

of Internet hours (r = +0.411; p < 0.001). Numberof Internet hours was positively correlated withsports hours (r = +0.454; p < 0.001) and ownership of

iPod/MP3 player (rpb = 0.291; p < 0.05) [25]. Despitesignificant correlations, there were no problems ofmulticollinearity in the regression analyses.

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Table 4 Comparison of percentages for categorical variab

Variables Boys

Yes No

n % n %

Living with both parents 17 73.9 6.0 26Both parents working 15 65.2 8.0 34Owns a cell phone* 15 65.2 8.0 34Owns an IPOD/MP3 Player 16 69.6 7.0 30Eats breakfast 21 91.3 2.0 8Drinks water at breakfast 5 21.7 18.0 78Drinks juice at breakfast 10 43.5 13.0 56Drinks milk at breakfast 16 69.6 7.0 30Drinks soda at breakfast 3 13.0 20.0 87Drinks water at lunch 6 26.1 17.0 73Drinks juice at lunch 6 26.1 17.0 73Drinks milk at lunch 6 26.1 17.0 73Drinks soda at lunch 16 69.6 7.0 30Drinks water at supper 7 30.4 16.0 69Drinks juice at supper 3 13.0 20.0 87Drinks milk at supper 4 17.4 19.0 82Drinks soda at supper 15 65.2 8.0 34Drinks water during snacks 9 39.1 14.0 60Drinks juice during snacks 4 17.4 19.0 82Drinks milk during snacks* 4 17.4 19.0 82Drinks soda during snacks 15 65.2 8.0 34

a Overall sample sizes for boys and for girls are n = 23 and n = 32,

may be less than these numbers.b p-Value is associated with a two-tailed independent samples t

difference at the 5%.

test..

M1 and M2 regression results are presented inables 6 and 7, respectively. M1 indicated a veryood fit between observed and predicted BMIp val-

es (R2 = 82.3%; adj-R2 = 68.8%). In other words, M1s able to account for 82.3% of the variation in BMIp.n general, results using usual standard errors (SE)

les between boys and girlsa.

Girls p-Valueb

Yes No

n % n %

.1 19.00 59.4 13.00 40.6 0.264

.8 14.00 43.8 18.00 56.3 0.120

.8 29.00 90.6 3.00 9.4 0.033

.4 24.00 75.0 8.00 25.0 0.662

.7 30.0 93.8 2.00 6.3 0.736

.3 7.00 58.3 5.00 41.7 0.991

.5 22.00 68.8 10.00 31.3 0.063

.4 15.00 46.9 17.00 53.1 0.094

.0 4.00 12.5 28.00 87.5 0.954

.9 6.00 18.8 26.00 81.3 0.525

.9 5.00 15.6 27.00 84.4 0.348

.9 12.00 37.5 20.00 62.5 0.383

.4 17.00 53.1 15.00 46.9 0.222

.6 7.00 21.9 25.00 78.1 0.481

.0 7.00 21.9 25.00 78.1 0.412

.6 4.00 12.5 28.00 87.5 0.620

.8 21.00 65.6 11.00 34.4 0.976

.9 19.00 59.4 13.00 40.6 0.144

.6 13.00 40.6 19.00 59.4 0.057

.6 0.00 0.0 31.00 100.0 0.043

.8 16.00 51.6 15.00 48.4 0.327

respectively. Due to missing values, sample sizes per variable

-test. A variable tagged by an asterisk (*) denotes significant

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Table 5 Correlation matrix.

Variables Y X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20 X21 X22 X23 X24

BMI percentile Y 1.00 — — — — — — — — — — — — — — — — — — — — — — — —No. of siblings X1 −.458** 1.00 — — — — — — — — — — — — — — — — — — — — — — —No. of school

and neigh-borhoodfriends

X2 −0.08 0.07 1.00 — — — — — — — — — — — — — — — — — — — — — —

Internet hoursin a week

X3 −0.15 0.13 .411** 1.00 — — — — — — — — — — — — — — — — — — — — —

TV hours in aweek

X4 0.10 −0.13 0.25 0.26 1.00 — — — — — — — — — — — — — — — — — — — —

Sports hours ina week

X5 −0.24 0.18 0.16 .454** 0.17 1.00 — — — — — — — — — — — — — — — — — — —

Owns a cellphone(1 = yes;0 = no)

X6 −0.07 −0.18 0.20 −0.07 0.04 0.01 1.00 — — — — — — — — — — — — — — — — — —

Owns an IPOD(1 = yes;0 = no)

X7 −0.03 0.23 0.14 .291* 0.18 0.07 0.00 1.00 — — — — — — — — — — — — — — — — —

Eats breakfast(1 = yes;0 = no)

X8 −0.20 0.16 −0.24 −0.23 −0.10 −0.21 −0.14 −0.17 1.00 — — — — — — — — — — — — — — — —

Drinks water atbreakfast(1 = yes;0 = no)

X9 −0.18 −0.04 0.17 0.05 0.25 0.11 −0.18 0.23 −0.02 1.00 — — — — — — — — — — — — — — —

Drinks juice atbreakfast(1 = yes;0 = no)

X10 −0.11 −0.01 0.14 0.14 0.06 0.01 0.13 0.14 −0.10 0.00 1.00 — — — — — — — — — — — — — —

Drinks milk atbreakfast(1 = yes;0 = no)

X11 −0.23 0.01 −0.08 −0.13 0.04 0.01 0.11 −0.05 0.04 0.02 −.300* 1.00 — — — — — — — — — — — — —

Drinks soda atbreakfast(1 = yes;0 = no)

X12 −0.26 0.10 −0.01 −0.11 −0.12 0.23 0.06 −0.01 0.11 0.06 −0.23 0.01 1.00 — — — — — — — — — — — —

Drinks water atlunch(1 = yes;0 = no)

X13 −0.17 −0.17 0.11 0.12 0.20 0.01 −0.07 0.03 −0.02 .360** .269* 0.11 0.06 1.00 — — — — — — — — — — —

e46

M.A.

Ynalvez et

al.

Table 5 (Continued)

Variables Y X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20 X21 X22 X23 X24

Drinks juice atlunch(1 = yes;0 = no)

X14 −0.25 0.01 −0.10 −0.13 −0.01 −0.10 0.14 0.00 −0.04 0.07 0.15 .348** 0.08 0.18 1.00 — — — — — — — — — —

Drinks milk atlunch(1 = yes;0 = no)

X15 0.08 0.05 −0.24 −.317* −0.18 −0.02 −0.04 −0.10 0.05 0.01 0.12 −0.09 −0.15 −0.18 −0.25 1.00 — — — — — — — — —

Drinks soda atlunch(1 = yes;0 = no)

X16 0.15 −0.08 0.18 .287* 0.14 0.21 −0.04 0.17 −0.23 −0.11 −0.02 0.11 0.09 0.07 −0.06 −.538**1.00 — — — — — — — —

Drinks water atsupper(1 = yes;0 = no)

X17 −0.03 −0.13 −0.14 0.08 0.01 0.14 −0.13 .358** 0.00 .298* 0.16 0.09 −0.10 0.20 0.02 0.13 0.14 1.00 — — — — — — —

Drinks juice atsupper(1 = yes;0 = no)

X18 −0.01 −0.04 0.00 0.10 0.03 −0.02 −0.24 0.08 0.13 0.09 .304* 0.04 −0.04 0.21 0.24 −0.03 0.10 .374** 1.00 — — — — — —

Drinks milk atsupper(1 = yes;0 = no)

X19 −0.15 0.04 −0.10 −0.05 0.07 −0.04 0.08 0.02 0.12 0.03 0.14 0.16 0.00 0.16 0.18 −0.18 −0.08 −0.12 −0.06 1.00 — — — — —

Drinks soda atsupper(1 = yes;0 = no)

X20 −0.23 0.13 0.11 0.11 −0.20 0.07 −0.08 −0.10 −0.20 −0.08 −0.15 0.06 0.05 0.01 −0.12 0.02 0.11 −.278* −0.25 −.568** 1.00 — — — —

Predictors of

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e47

Table 5 (Continued)

Variables Y X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20 X21 X22 X23 X24

Drinks waterduringsnacks(1 = yes;0 = no)

X21 −0.25 0.08 0.18 0.18 .287* −0.09 0.07 .299* 0.01 0.22 0.22 .319* −0.07 .303* 0.02 0.11 0.04 0.20 0.15 −0.02 0.00 1.00 — — —

Drinks juiceduringsnacks(1 = yes;0 = no)

X22 −0.07 −0.17 0.03 −0.14 −0.08 −0.12 0.14 −0.12 0.19 −0.16 0.09 0.19 −0.02 0.03 .354** 0.12 −0.18 0.24 .399**−0.16 −0.18 0.15 1.00 — —

Drinks milkduringsnacks(1 = yes;0 = no)

X23 0.14 −0.22 −0.20 −0.08 0.21 −0.06 −0.03 −0.14 −0.19 0.02 −0.04 0.25 −0.11 0.21 0.21 −0.04 0.23 0.16 0.05 .479** −0.24 0.00 −0.03 1.00 —

Drinks sodaduringsnacks(1 = yes;0 = no)

X24 0.06 −0.07 0.18 0.25 −0.01 .300* 0.03 0.14 −0.10 0.19 0.09 −0.09 .332* 0.16 −0.03 −0.06 .312* 0.08 0.03 −0.06 0.07 −0.11 −0.10 −0.04 1.00

* Significant rank correlation at the 0.05 level.** Significant rank correlation at the 0.01 level.

e48 M.A. Ynalvez et al.

Table 6 Multiple Linear Regression Model (M1) for BMIpa.

M1 predictors SE-based resultsb RSE-based results

ˇc S.E. ˇc R.S.E.

No. of siblings −0.35** 2.08 −0.35** 1.88No. of school and neighborhood friends 0.05 0.91 0.05 0.89Internet hours in a week 0.25 0.13 0.25 0.10Sports hours in a week −0.34** 0.23 −0.34*** 0.14Owns a cell phone (1 = yes; 0 = no) −0.26* 7.32 −0.26 8.89Owns an iPOD/MP3 player (1 = yes; 0 = no) 0.42** 7.36 0.42*** 5.82Drinks juice at breakfast (1 = yes; 0 = no) 0.13 7.06 0.13 6.50Drinks milk at breakfast (1 = yes; 0 = no) 0.34* 7.43 0.34** 5.69Drinks water at lunch (1 = yes; 0 = no) 0.12 8.19 0.12 8.00Drinks juice at lunch (1 = yes; 0 = no) −0.14 9.09 −0.14 7.00Drinks milk at lunch (1 = yes; 0 = no) 0.40* 9.85 0.40** 6.33Drinks soda at lunch (1 = yes; 0 = no) 0.35* 9.12 0.35** 6.13Drinks water at supper (1 = yes; 0 = no) −0.48** 8.01 −0.47** 9.57Drinks juice at supper (1 = yes; 0 = no) −0.23 8.20 −0.23 9.43Drinks milk at supper (1 = yes; 0 = no) −0.83*** 12.95 −0.83*** 13.63Drinks soda at supper (1 = yes; 0 = no) −0.84*** 7.85 −0.84*** 9.51Drinks water during snacks (1 = yes; 0 = no) −0.53*** 6.62 −0.53*** 5.53Drinks juice during snacks (1 = yes; 0 = no) 0.00 6.96 0.00 6.50Drinks milk during snacks (1 = yes; 0 = no) 0.31* 13.40 0.31* 11.81Drinks soda during snacks (1 = yes; 0 = no) −0.14 5.81 −0.14 5.26Coefficient of determination (R2) 82.30 — 82.30 —Adjusted coefficient of determination (adj. R2) 68.80 — 68.80 —Sample size (n) available for analysisd 47 — 47 —

a denotes the standardized regression coefficients.

b *, **, ***Denote significance at the 0.05, 0.01, and 0.001 levels, respectively.c Regression coefficients for SE- and RSE-based results are the same, but not the standard errors.d Overall sample size is n = 55 but due to missing values, effective sample size is less than this number.

Table 7 Multiple Linear Regression Model by Backward Elimination Method (M2) for BMIpa.

M2 predictors SE-based resultsb RSE-based results

ˇc S.E. ˇc R.S.E.

No. of siblings −0.34** 1.97 −0.34*** 1.73Internet hours in a week 0.29* 0.12 0.29* 0.13Sports hours in a week −0.37** 0.22 −0.37*** 0.17Owns a cell phone (1 = yes; 0 = no) −0.24* 6.39 −0.24 7.86Owns an iPOD/MP3 player (1 = yes; 0 = no) 0.33** 6.12 0.33*** 4.99Drinks milk at breakfast (1 = yes; 0 = no) 0.23* 5.81 0.23** 4.70Drinks milk at lunch (1 = yes; 0 = no) 0.44** 7.13 0.44*** 5.67Drinks soda at lunch (1 = yes; 0 = no) 0.39** 6.52 0.39*** 4.70Drinks water at supper (1 = yes; 0 = no) −0.41** 6.76 −0.41** 8.30Drinks juice at supper (1 = yes; 0 = no) −0.18 6.48 −0.18 7.02Drinks milk at supper (1 = yes; 0 = no) −0.79*** 11.17 −0.79*** 10.63Drinks soda at supper (1 = yes; 0 = no) −0.76*** 6.91 −0.76*** 7.52Drinks water during snacks (1 = yes; 0 = no) −0.46*** 5.88 −0.46** 6.58Drinks milk during snacks (1 = yes; 0 = no) 0.30* 11.31 0.30** 9.78Coefficient of determination (R2) 79.60 — 79.60 —Adjusted coefficient of determination (adj. R2) 70.70 — 70.70 —Sample size (n) available for analysisd 47 — 47 —

a denotes the standardized regression coefficients.

b *, **, ***Denote significance at the 0.05, 0.01, and 0.001 levels, respectively.c Regression coefficients for SE- and RSE-based results are the same, but not the standard errors.d Overall sample size is n = 55 but due to missing values, effective sample size is less than this number.

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nd robust standard errors (RSE) give very simi-ar pattern of significant variables, except for cellhone ownership, which was significant in the SE-,ut not in the RSE-based results. All other signifi-ant predictors in the SE- are also significant in theSE-based results, although some predictors expe-ienced shifts in their degrees of significance.

Because of the small sample size, the RSE-basedesults are appropriate. These results indicate thatumber of siblings (ˇ = −0.35; p < 0.010), sport-ours in a week (ˇ = −0.34; p < 0.001), owningn iPod/MP3 player (ˇ = +0.42; p < 0.001), drink-ng milk at breakfast (ˇ = +0.34; p < 0.010), drinkingilk at lunch (ˇ = +0.40; p < 0.001), drinking soda

t lunch (ˇ = +0.35; p < 0.01), drinking water atupper (ˇ = −0.47; p < 0.01), drinking milk at sup-er (ˇ = −0.83; p < 0.001), drinking soda at supper

= −0.84; p < 0.001), drinking water during snacks = −0.53; p < 0.001), and drinking milk during

nack (ˇ = +0.31; p < 0.05) were all significantlyssociated with BMIp.

In other words, having more siblings, more sportsours; drinking water, milk, and soda at supper; andrinking water during snacks are all associated withow BMIp kids. In contrast, owning an iPod, drink-ng soda at lunch, and drinking milk at breakfast,unch, and during snacks are associated with highMIp kids. Like Table 6, SE- and RSE-based resultsre presented in Table 7. Obviously, the pattern ofignificant variables in Tables 6 and 7 is very sim-lar, with the exception of one significant variabledded to Table 7, namely: Internet hours in a week

= −0.29; p < 0.05). In other words, RSE-basedesults in Table 6 indicated that Internet hourspent in a week is associated with high BMIp kids.

In the next section, links to the literature,ossible explanations, and hypotheses for futureesearch are forwarded. Significant predictors inoth M1 (RSE-based results) and M2 (RSE-basedesults) are considered stable predictors of BMIpnd are discussed in the following section.

iscussion

iblings and friends

n individual’s social environment shapes thatndividual’s access to material and non-materialesources; as well as that individual’s life chancesnd health status. This study focused on children’s

icro-social environment, specifically, its aspects

hat relate to their interaction with siblings, andith school and neighborhood friends. Results from

able 6 (M1 RSE) and 7 (M2 RSE) consistently

roef

e49

howed that of those two aspects, number of sib-ings was highly significantly associated with bodyass. Specifically, results indicated that greater

umber of siblings within the household was signifi-antly associated with children who have low BMIp.uch results appear to highlight the salience of kin-riends (siblings) over non-kin-friends (friends inchool and neighborhood) in predicting body mass.ence, the question: why siblings and not friends?

Similar results are reported in studies conductedn kindergarten-level age-groups in which childrenith siblings had lower BMI, and hence were less

ikely to be obese than children without siblings19,29]. It stands to reason that it is very likelyhat siblings in the same household as subjects,y their mere co-presence, may have provided theocial environment that allowed ready access tond the needed stimulus for child-to-child interac-ion, active cooperative play, and other activitieshat increase the time a child allocates to physi-al activity. All these would have been difficult, ifot impossible, to come by if a child’s social envi-onment was mainly comprised of non-kin-friendsor school and neighborhood friends) [29]. In aumber of studies [30—32], number of siblings wasne of the variables found to be significantly andositively associated with levels of physical activ-ty, with a small number of siblings serving as aisk factor for obesity. Although the results fromhis study did not show any significant correlationetween number of siblings and physical activity,his may be attributable to the fact that the lat-er only referred to number of sports hours, andhus other forms of non-structured physical activi-ies (e.g. running around the house, playing in theackyard, or leisure biking) were not taken intoonsideration.

Siblings living in the same household mayave had either (i) created an immediate socialnvironment conducive to non-structured physi-al activities (e.g. running around or rough play)hereby expending calories in the process, or (ii)rovided constant availability of playmates result-ng to almost limitless hours of a variety ofctivities. One of the challenges to increasing phys-cal activity among obese children is their easyccess to sedentary alternatives [33], which includeatching TV and playing video-games. Siblings maye providing the needed motivation to allocate timeo be physically active, which decreases the like-ihood of sedentary activities. In a study done tovaluate family-based obesity treatment, it was

eported that one of the potential advantagesf such treatment is the opportunity to influ-nce family members. With greater opportunitiesor modeling and supporting behavioral change in

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larger families, these children showed significantpositive response to family-based obesity treat-ment [33].

The results of this study also suggest that greaternumber of siblings in the household may have‘forced’ the sharing of food resources therebydecreasing the availability of food per child. Suchsharing typically occurs with siblings in the samehousehold more than it would with friends, evenneighborhood friends. This food-resource sharingamong individuals living in the same household mayin turn inhibit or limit excessive calorie in-take thatmay have contributed to lower body weight associ-ated with subjects who have many siblings. It couldalso be the case that having many siblings in thesame household not only results to the ‘forced-sharing’ of food, but could have also diminishedsubjects’ opportunities to access and use homecomputers and Internet facilities which, in contem-porary times, are the platform for games.

The literature on child obesity suggests thatyouth who are at risk of decreased physical activ-ity include children living in neighborhoods whereoutdoor physical activity is restricted by climate,safety concerns, or lack of facilities [34]. Eventhe perception of environmental factors such asneighborhood safety has been noted as barriers tophysical activity [35,36]. Hence, the need to create‘safe space’ to allow families to exercise, and to bephysically active has been suggested in Gruber andHaldeman [35] and Chatterjee et al. [37]. Thus, itcan be hypothesized that siblings living within thesame household create a readily accessible physicaland social environment that allows these childrento be physically active without reliance on environ-mental factors that promote physical activity.

Given the conservative orientation of and theclose family ties among Hispanics-Americans, itappears that the home environment is a moreimportant factor in shaping children’s physicalactivity and body mass development than their out-side environment [32]. Environmental stimuli thatdecrease sedentary behaviors and increase physi-cal activity within the home have been identifiedas important targets in preventing weight gain dur-ing the transition from childhood to adolescence[31]. Thus, in an environment (i) with limited pub-lic spaces for physical activities, (ii) situated withinan urban area located by the border with a highflow of transient migrants wherein people hardlyknow each other, and (iii) embedded in a geograph-ical region that is one of the hottest in the U.S.;

it stands to reason that the role of siblings (kin-friends who are more accessible because of theirco-presence and co-location) takes salience overschool and neighborhood friends (non-kin-friends

lato

M.A. Ynalvez et al.

ho are less accessible in the engagement of phys-cal activities because of their not being readilyvailable).

ell phones and iPods/MP3 players

CT utilization impacts many aspects of daily life:ob search, productivity in work and in school,eisure and sociability, and even personal healthtatus [38,39]. This study investigated the predic-ive role of cell phone and of iPod/MP3 playerwnership on body mass distribution. In M1, own-rship of a cell phone was not, but ownership of anPod/MP3 player was shown to be a significant pre-ictor of child BMIp. These same results are seenn M2. Based on these results, the hypothesizedredictive role of cell phone ownership was consis-ently not observed in both models. In contrast, theole of iPod/MP3 player ownership was consistentlyndicated by these two regression results. Thesendings somehow deviate from Lajunen et al. [40]ho report a weak but positive correlation betweenell phone usage and BMI.

Why is iPod/MP3 player ownership an impor-ant predictor of BMIp? Assuming that ownerships tantamount to usage, then a possible explana-ion is: although both cell phone and IPod/MP3layer ownership are linked to seemingly solitaryctivities, cell phone use is more likely to bessociated with person-to-person interaction whichay result in planning and executing organized

ctivities with peers, and decreasing tendency fornacking. Supporting evidence can be adduced fromeatherdale [41] who reports that increased cellhone communication is associated with high lev-ls of outdoor physical activities among children41]. It is also reported that higher self-esteem wasbserved among children who frequently use cellhones compared to those who use these less fre-uently [42]. In this sense, cell phone ownership,y providing enhanced opportunity for interactionsith peers, may indirectly result in increased phys-

cal activity and decreased sedentary behavior. Butiven that results from both M1 and M2 failed toetect any predictive role of cell phone ownershipn BMIp, more research is needed to establish theirection and magnitude of cell phone ownership’sssociation with body mass.

In contrast, the authors argue that iPod/MP3layer utilization is a predominantly solitary seden-ary activity, which may be conducive to eating andnacking. As mentioned earlier, one of the chal-

enges to increasing physical activity, particularlymong obese children, is how to provide easy accesso non-sedentary activities [33]. Thus, a number ofbesity treatment programs is focused on strategies

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hat combine ways to reduce sedentary behaviors,nduce physical activity to increase energy expen-iture, and decrease opportunities for unnecessarynergy in-take [33]. In refining those strategies,nalysts will need to recognize and consider theole of iPod/MP3 player usage, and its interactionith other factors that influence body mass devel-pment.

Despite the low socioeconomic situation inaredo, results indicate high rates of cell phone80%; 44 of 55) and of iPod/MP3 player (73%; 40f 55) ownership. What could be the explanationehind these seemingly paradoxical findings? Threeypotheses are in order: (i) it is possible that Cen-ral Middle School’s students are not representativef the general population. According to informants,aredo is roughly divided into north and south with.S. Highway 59 as the anecdotal boundary. Theorth is farther from the U.S.-Mexico border, moreodern, and is populated by more high-income

ouseholds than the south; (ii) it may be the casef children from a minority population in peripheralreas seeking to identify with mainstream Ameri-ans, who are typically depicted by mass media asnjoying the latest fad in new ICT; or (iii) it may behat cell phones and iPods are much more afford-ble in the U.S. that even low-income parents findhese novel devices cheaper than personal comput-rs and laptops to give kids.

A t-test revealed significantly lower cell phonewnership among boys than girls, but regressionesults indicated no association between such own-rship and BMIp. Girls’ high cell phone ownershipate might be explained by the hypothesis thathey are more sociable and into conversationalnteraction (i.e., chatting, texting). From conversa-ions with subjects (and with informants, who werehemselves students at Central), girls describedoys as ‘wasting money and time’ on video-games

a claim consistent with the findings of Park [43] while boys viewed girls as ‘busy talking.’ If girlsre more into cell phones than boys, and if much ofell phone use among girls is for chatting and tex-ing, and also if cell phone ownership has nothing too with BMIp then it is gender that might associateith BMIp.

rinks at meals and during snacks

SE-based results from M1 and M2 reveal two inter-sting patterns regarding drinks at meals and duringnacks in relation to child BMIp. First, drinking milk

t breakfast, at lunch, or during snacks are allssociated with higher BMIp; while drinking milkt supper is associated with lower BMIp. Second,rinking milk, drinking soda, and drinking water at

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e51

upper are all associated with low BMIp. The find-ng that milk intake at breakfast is associated withigher BMIp is contrary to most findings that milkonsumption specifically at breakfast contributedo lower body mass. It is possible that the schoolafeteria did not have other breakfast food offer-ngs such as a variety of grains and fruits, and thushe major source for caloric energy for subjectsas milk. In this study, the quantity of milk intakend the type of milk preferred (i.e., plain versushocolate-flavored milk; or skim versus whole milk)ere not measured. Hence, this finding is incon-lusive and requires further research. With regardo the second observation, it could be that foodst home are those (e.g. traditional Mexican foods,sh or vegetables) less palatable to children’s tastehan burger sandwiches and pizzas. As such, kidsaybe eating less of these ‘real’ foods at supper,

nd simply stuff themselves with fluids (e.g. therere kids who ‘hate’ eating vegetables, but whenorced by parents, they would ease the difficulty ofating those foods by taking them with water, orther fluids). These assertions are anecdotal, buthey provide a basis for generating hypotheses foruture studies.

BMIp was negatively associated with drinkingilk and drinking soda at supper, but was positively

ssociated with drinking milk and drinking sodat lunch. A possible explanation is that intake ofugar-sweetened beverages and milk during family-repared meals (typically at supper) may be moreimited as to intake than that of these beveragesonsumed in the presence of friends at school dur-ng lunch time. Woodruff et al. [44] found lowonsumption of sugar-sweetened beverages witheals prepared and consumed at home. They

eported that although large portion sizes are con-umed during home meals, such meals are lowestn terms of caloric count and intake of sweetenedeverages. In this sense, the presence of healthyood choices during family meal times attenuateshe energy and caloric impact of sugar-sweetenedrinks and milk on BMI.

onclusion

his study found that children’s micro-social envi-onment of siblings and ownership of iPod/MP3layers predict BMIp. These results underscore themportance of family in configuring body mass

evelopment among children, and support the the-is that having siblings induce physical activitynd reduce sedentary behavior [45]. Although theumber of siblings is not a modifiable factor in

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influencing obesity, the finding that more siblingsin the household is significantly associated withlower BMI suggests: (i) that health care providersfocus on interventions and preventive strategiesthat promote collaborative activities and siblinginteraction, (ii) that school-based efforts need toput more emphasis in promoting physical activitiesamong children who do not have siblings [29], and(iii) that proximal social networks should be takeninto consideration in developing programs and poli-cies to promote physical activity among the youth.

In consideration of the location and the popula-tion studied, this is not to say that family influencesof child body mass are exclusive to the Hispanic-American population, but perhaps these influencesmight be stronger among Hispanic-Americans thanin other U.S. ethnic groups. In addition, iPod/MP3player ownership highlights how personal techni-cal devices that encourage solitary and oftentimesnon-physical activities might strongly influencechild body mass development. Solitary non-physicalactivities may be very real threats for children whohave no siblings or for those who have siblings thatare much older or younger; and, who at the sametime are in a location with heavy migration flowssuch that these children largely depend on familymembers to assume the role of buddies and play-mates.

Finally, it is also the finding of this study that chil-dren who drink milk at supper had lower BMIp thanthose who did not. However, drinking milk at break-fast, at lunch, or during snacks was all associatedwith kids with higher BMI. Might this suggest thatthe timing of drinking milk influences whether ornot this will contribute to an increase or a decreasein BMIp? Or generally, might this point to a hypoth-esis about how the timing of and the types of fluid(i.e., juice, milk, soda or water) consumed arelinked to child weight. Ultimately, it is importantto keep in mind that this study used cross-sectionaldata. Hence, no attempts to derive causal rela-tionships should be made. Instead, these resultsmay, at best, be taken as a springboard to generatehypotheses that can guide future research.

Conflict of interest

The authors declare no conflict of interest.

Acknowledgements

This research was funded by a 2008 Texas A&MInternational University, University Research Grant

M.A. Ynalvez et al.

ward to the first author. The authors as a teamwe special thanks to the following: Cecilia Briones,osie Salazar, Roxanne Hernandez, David Canales,nd Patricia Keck for helping plan and conducthe surveys; Isabel Perez and Cristina Rosales,ho entered, validated, and performed prelimi-ary data analysis for this research project through

2009 NIH-TAMIU PUENTES Undergraduate Sum-er Research Experience; Susan Aguilar and Marcusmiel Ynalvez, who provided insightful commentso improve the manuscript; the Canseco School ofursing for granting access to its computer labora-ory, and last — but certainly not least — to the 6thraders and their parents/guardians who assented,onsented, and participated in this research.

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