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RESEARCH

Current Research

Continuing Education Questionnaire, page 1841Meets Learning Need Codes 3000, 3020, 4000, and 4160

Calcium and Dairy Intakes of Adolescents AreAssociated with Their Home Environment, TastePreferences, Personal Health Beliefs, and MealPatternsNICOLE I. LARSON, MPH, RD; MARY STORY, PhD, RD; MELANIE WALL, PhD; DIANNE NEUMARK-SZTAINER, PhD, MPH, RD

ABSTRACTObjective To identify correlates of calcium, dairy, and milkintakes among male and female adolescents.Design Cross-sectional study design. Adolescents self-reported measures pertaining to correlates on the ProjectEAT (Eating Among Teens) survey and completed a foodfrequency questionnaire at school.Subjects/setting Subjects were a total of 4,079 middle andhigh school students from Minneapolis/St Paul, MN, pub-lic schools.Statistical analyses performed Multiple linear regression mod-els based on social cognitive theory were examined by sex.

Results Male adolescents reported higher daily intakes ofcalcium (male: 1,217663 mg; female: 1,035588 mg;P0.001), dairy servings (male: 2.91.9; female: 2.41.7;P0.001), and milk servings (male: 2.01.5; female:1.51.4; P0.001) than female adolescents. Calcium in-takes of male adolescents were significantly and posi-tively related to availability of milk at meals, taste pref-erence for milk, eating breakfast, higher socioeconomic

status, and social support for healthful eating; intakeswere significantly and inversely related to consumption ofsoft drinks and fast food. Among female adolescents,availability of milk at meals, taste preference for milk,eating breakfast, higher socioeconomic status, personalhealth/nutrition attitudes, and self-efficacy to makehealthful food choices were significantly and positivelyrelated to intakes; intakes were significantly and inversely related to fast-food consumption. Models of cal-cium intake explained 71% of the variance in male ado-lescents and 72% of the variance in female adolescents.Conclusions Multicomponent interventions with a focus onthe family environment are likely to be most effective in

increasing calcium intakes among adolescents. J Am Diet Assoc. 2006;106:1816-1824.

Adequate dietary intake and physical activity duringadolescence are essential for the development ofoptimum peak bone mass and are, therefore, critical

to the prevention of osteoporosis in later adulthood (1).Many nutrients contribute to bone health; however, cal-cium is particularly important during the rapid growththat occurs in adolescence (2,3). In the United States,dairy foods are key sources of calcium and other bone-building nutrients such as protein, vitamin D, and mag-nesium (1). National guidelines recommend that adoles-

cents ages 9 to 18 years consume at least thr ee servingsof dairy foods and 1,300 mg calcium per day (2,4).Despite growing recognition of the benefits associated

with meeting these guidelines, national survey data suggestthat dairy and calcium intakes of adolescents have declinedover the past few decades, and several studies have foundthat adolescents intakes are well below the recommendedamounts (5-10). The most recent Centers for Disease Con-trol National Youth Risk Behavior Surveillance Survey(2003) found that only 11% of female adolescents and 23% ofmale adolescents were drinking three or more glasses ofmilk per day (11). Calcium intakes were similarly lowamong adolescents in the National Health and Nutrition

N. I. Larson is a graduate student in nutrition, Divisionof Epidemiology and Community Health, M. Story is aprofessor, Division of Epidemiology and CommunityHealth, M. Wall is an associate professor, Division of

Biostatistics, and D. Neumark-Sztainer is a professor,Division of Epidemiology and Community Health,School of Public Health, University of Minnesota, Min-neapolis.

Address correspondence to Nicole I. Larson, MPH,RD, Division of Epidemiology and Community Health,School of Public Health, University of Minnesota, 1300South 2nd Street, Suite 300, Minneapolis, MN 55454.E-mail: [email protected]

Copyright 2006 by the American DieteticAssociation.

0002-8223/06/10611-0003$32.00/0doi: 10.1016/j.jada.2006.08.018

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Examination Survey (1994-1996); only 19% of female ado-lescents and 52% of male adolescents were consuming therecommended amount (2).

Effective interventions to promote consumption ofdairy and calcium by adolescents are needed andshould address the most salient motivators and barri-ers. Empirical studies in adolescents have identifieddemographic correlates associated with low calciumand dairy consumption, including female sex, nonwhiterace, low socioeconomic status (SES), greater bodyweight, and older age (5,12,13). Quantitative and qual-itative studies among adolescents have also reported

other factors relevant to intakes of calcium-rich foods,including weight-related concerns and behaviors (5,14),family environment (5,15-18), meal patterns (12,19-23), taste preferences (12,18,24), lactose intolerance(24), substance use (5), and soft drink intake (12,25). Todetermine the most relevant of these factors associatedwith intakes of adolescents, empirical research needsto further consider comprehensive and theory-drivenmodels of dietary intake.

The goal of this study was to explain calcium anddairy intakes among adolescents using factors relatedto the three interacting domains of influence withinSocial Cognitive Theory: personal, behavioral, and so-

cioenvironmental influences (26) (Figure). It was hy-pothesized that intakes of male and female adolescentscould be explained by a combination of personal factors(taste preference for milk, lactose intolerance, health/nutrition attitudes, body satisfaction, self-efficacy tomake healthful food choices, time to eat breakfast, andweight status), behavioral factors (sport participation,breakfast intake, lunch intake, dinner intake, un-healthful weight control behaviors, fast-food intake,and soft drink intake), and socioenvironmental factors(social support for healthful eating, family SES, avail-ability of milk at meals, home availability of softdrinks, and parental presence at meals). This studybuilds on prior research by testing sex-specific, morecomprehensive models of calcium, dairy, and milk in-take based on Social Cognitive Theory in a large anddiverse sample of adolescents. Few studies have lookedat multiple outcomes (eg, calcium, dairy, and milk in-take) in a single population. Milk and dairy foods areparticularly good sources of calcium and are often tar-geted in nutritional interventions to increase calciumintake; therefore, it is important to consider whethercorrelates of intake may vary according to the source ofcalcium.

Figure. Conceptual model of factors influencing dietary intakes of calcium, dairy foods, and milk among male and female adolescents. (Data fromthis figure are available online at www.adajournal.org as part of a PowerPoint presentation featuring additional online-only content.)

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METHODS

Study Design

Data for this analysis were drawn from Project EAT(Eating Among Teens) (27,28), a study designed to inves-tigate socioenvironmental, personal, and behavioral cor-relates of dietary intake among adolescents. After ap-proval by the University of Minnesota Human Subjects

Committee and by the Research Boards of participatingschool districts, survey and anthropometric data werecollected by trained staff in health, physical education,and science classrooms during the 1998-1999 school year.Students completed surveys and had their height andweight measured within a private area. Student assess-ments included the Project EAT survey and the Youthand Adolescent Food Frequency Questionnaire, whichmeasures usual dietary intake.

Prior to use, the Project EAT survey was reviewed byan interdisciplinary team of experts and pretested among68 adolescents from schools not participating in thestudy. Additional details about survey development aredescribed in previous publications (28,29). Other research

has documented the validity and reliability of the Youthand Adolescent Food Frequency Questionnaire for use inadolescents (30,31). Acceptable correlations between es-timates quantitated from three 24-hour dietary recallsand the Youth and Adolescent Food Frequency Question-naire for individual intakes of energy (r0.35) and cal-cium (r0.46) were observed (30). For intakes of milk andsoft drinks, correlations between estimates from twoquestionnaires administered 1 year apart were similarlyadequate (r0.56 and 0.57) (31).

Study Sample

The overall study sample (n4,746 adolescents, 81.5% ofeligible students) that completed the Project EAT survey

was ethnically and socioeconomically diverse. Studentsfrom 31 public junior and senior high schools in the StPaul/Minneapolis area of Minnesota participated. Studentparticipants were equally divided by sex (50.2% male ado-lescents, 49.8% female adolescents). The mean age of thestudy sample was 14.9 years (range, 11 to 18 years); 34.3%were in middle school, and 65.7% were in high school. Theracial/ethnic backgrounds of the participants were as fol-lows: 48.5% white, 19.0% African American, 19.2% AsianAmerican, 5.8% Hispanic, 3.5% Native American, and 3.9%mixed/other.

Measures

Variables were selected from the Project EAT survey aspotential correlates of intake based on their predictiveability in prior research and the guiding framework ofSocial Cognitive Theory. Table 1 presents 18 factors se-lected from within the domains of personal, behavioral,and socioenvironmental influence along with the mea-sures that were used for each of the factors, the specificsurvey items, and properties of each item. Demographiccharacteristics including sex, grade level, and race/eth-nicity were also self-reported by adolescents on theProject EAT survey. Students reported race/ethnicity inresponse to the question: Do you think of yourselfas. . .White, Black or African American, Hispanic or

Latino, Asian American, Hawaiian or Pacific Islander, or American Indian or Native American? Subjects couldchoose more than one category; those responses indicat-ing multiple categories were coded as mixed/other.

Heights and weights were self-reported on the ProjectEAT survey and were measured by trained research staffusing standardized equipment and procedures (32). Body

mass index (BMI) was calculated as kg/m

2

. Respondentswere classified according to sex- and age-specific cutoffpoints as underweight (BMI 15th percentile), averageweight (BMI15th to 85th percentile), moderatelyoverweight (BMI85th to95th percentile), or very over-weight (BMI 95th percentile). Observed values of BMIwere used except in cases in which observed weight andheight measures were missing (n232), in which caseself-reported height and weight were substituted becauseobserved and self-reported BMI were highly correlated(r0.85, P0.001).

The Youth and Adolescent Food Frequency Question-naire was used to assess daily intakes of energy (calories),calcium (mg), and servings of dairy, milk, and soft drinks.Individual energy and calcium intakes were calculated

from reported past year average frequency of consump-tion for 149 food items. Dairy servings were based onreported consumption of 11 items (milk, chocolate milk,instant breakfast, yogurt, cottage or ricotta cheese, creamcheese, sliced cheeses, frozen yogurt, ice cream, milkshakes, and pudding). Milk servings were summed fromreported intakes of milk and chocolate milk. Soft drinkservings included soda, diet soda, punch, lemonades, andfruit drinks.

Statistical Analyses

All analyses were conducted separately for male and fe-male adolescents using SAS statistical software, version9.0 (2002-2003, SAS Institute, Cary, NC). Descriptive

statistics (means and standard deviations) were used toexamine intakes of calcium, dairy, and milk by gradelevel, ethnicity/race, SES, and weight status. Continuousdependent variables (intakes of calcium, dairy, and milk)were adjusted for positive skewness with square roottransformations. Before considering a comprehensivemodel, bivariate associations between the three trans-formed outcome variables (milligrams of calcium, dairyservings, and milk servings) and between each outcomeand predictor variable were evaluated using t tests. Sep-arate multiple linear regression models, adjusted for eth-nicity/race, grade level, weight status, and caloric intake,were run to predict each of the transformed outcomes(calcium, dairy, and milk). In addition, multiple regres-

sion models were examined in which caloric intake wasnot included to assess the predictability of the demo-graphic, personal, behavioral, and socioenvironmentalvariables on their own.

All of the independent variables identified in Table 1were entered simultaneously into the multiple regressionmodel with the exception of home availability of softdrinks. Home availability and intake of soft drinks weresignificantly correlated (r0.33, P0.0001) at a levellarge enough with respect to their individual correlationswith the outcome to cause them to have a suppressioneffect on one another (33). To avoid counterintuitive in-terpretations for the resulting coefficients when they

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are both included in the model, instead only one or theother was included in the multiple regression model atone time. Models including home availability of softdrinks produced essentially the same findings as modelswith soft drink intake.

Participants who did not complete the Youth and Ado-lescent Food Frequency Questionnaire (n344) or those

with biologically implausible responses (n258) (defineda priori as having energy intakes below 400 kcal/day orover 7,000 kcal/day) were excluded from analyses(n602). Adolescents missing only one, two, or three ofthe 18 predictor variables of interest were retained andmultiple imputation procedure of SAS statistical softwarewas used to predict the values for their missing data (34).

Table 1. Description of measures used to assess socioenvironmental, personal, and behavioral factors hypothesized to correlate with intakesof calcium, dairy, and milk among male and female adolescents in the Project EAT (Eating Among Teens) survey

Item(s)Meanstandarddeviation Range

Socioenvironmental factors

Social support for healthful eating My mother cares about eating healthy food. My mother encouragesme to eat healthy food. (Same questions for father.) Fourresponses ranging from not at all to very much (Cronbach.79).

11.6

3.2 4-16

Parental presence at meals On how many of the past 7 days was at least one of your parents inthe room with you when you ate dinner?

4.32.5 0-7

Family socioeconomic status Composite variable based primarily on parental level of education,defined by the higher level of either parent.

3.01.3 1-5

Milk served at meals Milk is served at meals at my home. Four responses ranging fromnever to always.

2.81.1 1-4

Home availability of soft drinks Soda pop is available in my home. Four responses ranging fromnever to always.

3.00.9 1-4

Personal factorsTaste preference for milk Milk tastes good to me. Four responses ranging from strongly

disagree to strongly agree.3.10.9 1-4

Lactose intolerance Are you lactose intolerant or allergic to dairy foods? (a) yes, (b) no,(c) I dont know. (Results: 4.7% yes, 82.3% no, 13.0% dont know)

Health/nutrition attitudes How much do you care about . . . (a) eating healthy foods? and (b)being healthy? Four responses ranging from not at all to verymuch. How strongly do you agree with the following statements?Three statements (eg, I am not very concerned about my health.).Four responses ranging from strongly disagree to stronglyagree. Scoring was reversed on the last 3 items (Cronbach.70).

16.12.5 5-20

Body satisfaction Body satisfaction scale including 10 items assessing satisfaction withdifferent body parts. Five responses ranging from very dissatisfiedto very satisfied.

34.49.7 10-50

Self-efficacy to make healthfulfood choices

If you wanted to, how sure are you that you could eat healthy foodwhen you are . . . ? Nine statements (eg, at a fast-food

restaurant). Six responses ranging from not at all sure to verysure (Cronbach .83).

31.39.5 9-54

Time to eat breakfast I am too rushed in the morning to eat a healthy breakfast. Fourresponses ranging from strongly disagree to strongly agree.

2.61.0 1-4

Behavioral factorsSports involvement During the past 12 months, on how many sports teams did you

play? Four responses ranging from 0 teams to 3 or moreteams.

2.21.2 1-4

Breakfast intake During the past week, how many days did you eat breakfast? 3.92.6 0-7Lunch intake During the past week, how many days did you eat lunch? 5.62.0 0-7Dinner intake During the past week, how many days did you eat dinner? 6.01.6 0-7Unhealthful weight-control

behaviorsHave you done any of the following things to lose weight or keep

from gaining weight during the past year? Nine items (eg, tookdiet pills). Number of methods used was calculated (Cronbach.70).

1.01.5 0-9

Fast-food intake In the past week, how often did you eat something from a fast-foodrestaurant (like McDonalds, Burger King, Hardees, etc)?

1.81.7 0-9

Soft-drink intake Daily servings of soda, diet soda, punch, lemonades, and fruit drinks. 1.31.1 0-6


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However, 65 adolescents who were missing four or moreof the predictor variables (20% of predictors) were ex-cluded from the sample. No significant differences inmean intakes of calcium, dairy, and milk between theexcluded sample of young people (n65) and the retainedsample of 4,079 adolescents (2,044 male adolescents and2,035 female adolescents) were found.

RESULTS

Intakes of Calcium, Dairy, and Milk

Male adolescents reported higher daily intakes of cal-cium (male: 1,217663 mg; female: 1,035588 mg;P0.001), dairy servings (male: 2.91.9; female:2.41.7; P0.001), and milk servings (male: 2.01.5;female: 1.51.4; P0.001) than female adolescents(Table 2). Male adolescents and female adolescents con-suming the greatest amounts of calcium were in juniorhigh school, of upper-middle or high SES, of averageweight, and of white or mixed/other race. The lowestintakes of calcium, milk, or dairy were reported byadolescents in senior high school, of low SES, in theoverweight group, and of Asian race.

Bivariate Associations

Strong correlations were observed between intakes ofcalcium and intakes of dairy (r0.94,P0.0001) and milk(r0.83, P0.0001). Independent variables significantlyand positively correlated with calcium intake in bivariateanalyses (data not shown) among male and female ado-lescents included caloric intake, soft drink intake, socialsupport for healthful eating, parental presence at meals,family SES, milk served at meals, home availability ofsoft drinks, taste preference for milk, body satisfaction,self-efficacy to make healthful food choices, sport involve-

ment, breakfast intake, lunch intake, dinner intake, andfast-food intake. Lack of time to eat breakfast and un-healthful weight-control behaviors were inversely corre-lated with intakes of male and female adolescents. Sim-ilar results were observed for dairy and milk. For dairy,the only difference was that lactose intolerance was alsoinversely correlated with intakes of male and female ad-olescents. Milk intake was also inversely correlated withlactose intolerance, but was not correlated with soft drinkconsumption among female adolescents or fast-food in-takes among either gender. It should be noted that thepositive, simple unadjusted associations observed forservings of soft drinks with calcium intake and dairy

Table 2. Mean (SDa) intakes of calcium, dairy, and milk among male and female adolescents according to grade level, race/ethnicity,socioeconomic status, and weight status in the Project EAT (Eating Among Teens) survey

Male Adolescents (n2,044) Female Adolescents (n2,035)

Calcium(mg/day)

Dairy(servings/day)

Milk(servings/day)

Calcium(mg/day)

Dairy(servings/day)

Milk(servings/day)

4meanSD3

Total 1,217663 2.91.9 2.01.5 1,035588 2.41.7 1.51.4Grade levelJunior high 1,245698 3.02.0 1.91.5 1,133643 2.61.9 1.61.4Senior high 1,203645 2.91.8 2.01.5 985551 2.31.6 1.51.3Race/ethnicitybc

White 1,347612 3.31.8 2.31.5 1,114507 2.81.5 1.81.3 African American 1,228762 2.72.1 1.71.6 1,046710 2.22.0 1.21.4Hispanic 1,119595 2.61.7 1.71.4 946514 2.01.4 1.01.0

Asian 840575 1.81.6 1.21.2 815595 1.71.7 1.01.2Native American 1,193671 2.92.0 1.91.5 1,112614 2.51.7 1.61.4Mixed/other 1,393793 3.22.2 2.01.6 1,210705 2.72.0 1.61.6SESd

Low 1,007684 2.31.9 1.51.4 903645 2.01.9 1.21.4

Low-middle 1,213684 2.91.9 2.01.6 966585 2.21.6 1.31.3Middle 1,188664 2.81.9 2.01.6 1,039564 2.41.6 1.51.3Upper-middle 1,293642 3.11.9 2.21.5 1,120578 2.71.7 1.71.4High 1,346600 3.21.8 2.11.5 1,175517 2.91.6 1.81.3Weight statusUnderweight 1,237666 2.92.0 1.91.5 1,125584 2.61.7 1.51.3

Average weight 1,257675 3.01.9 2.01.5 1,051600 2.51.8 1.51.4Overweight 1,178651 2.91.9 2.01.5 989549 2.31.6 1.51.3

Very overweight 1,095610 2.71.8 1.91.5 1,037596 2.41.8 1.41.4

aSDstandard deviation.bRace/ethnicity is reported as collected on survey by self-report.cSubjects could choose more than one category; responses indicating multiple categories were coded as mixed/other.dSESsocioeconomic status. Socioeconomic status was based on self-report of each parents education level and employment status and eligibility for free or reduced school lunch

or public assistance.


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servings were eliminated when an adjustment for totalenergy intake was made, indicating the importance ofadjusting the relationship for servings to account for totalintake.

Multivariate Models

Multivariate models explained more than two thirds ofthe variance in calcium intake among male adolescents(71%) and female adolescents (72%), approximately halfof the variance in dairy intake (male: R20.52, female:R20.54), and more than one third of the variance in milkintake (male: R20.37, female: R20.45). Caloric intakealone accounted for the majority of explained variance incalcium (58% in male adolescents, 53% in female adoles-cents). Models including demographic, personal, behav-ioral, and socioenvironmental factors but not total calo-

ries explained 32% of the variance in calcium intake, 30%of the variance in dairy intake, and 30% of the variance inmilk consumption among male adolescents. Among fe-male adolescents, models without total calories explained35% of the variance in calcium intake, 35% of the vari-ance in dairy intake, and 40% of the variance in milkconsumption.

Of the 18 personal, behavioral, and socioenvironmen-tal factors considered, standardized coefficients (Table3) indicated a taste preference for milk and having milkserved at meals were the strongest correlates of cal-cium intake, associated with greater intakes amongboth male and female adolescents. Eating breakfast,

SES, and social support for healthful eating were alsosignificantly and positively related to calcium intake

among male adolescents, whereas intakes of soft drinksand fast food were significantly and inversely related tocalcium intake. Among female adolescents, other vari-ables significantly and positively related to calciumintake were eating breakfast, SES, health/nutritionattitudes, and self-efficacy to make healthful foodchoices; fast-food intake was significantly and inverselyrelated to calcium.

Overall, comparable outcomes (not shown) were ob-served when models of dairy and milk intake were exam-ined. However, among female adolescents, social supportfor healthful eating was also positively related and lac-tose intolerance inversely related to daily servings ofdairy. Among male adolescents, soft drink intake, social

support for healthful eating, and SES were not significantcorrelates when dairy intake was considered as the out-come variable in place of milligrams of calcium. In modelsof milk intake among female adolescents, SES was notsignificantly related to daily servings; lunch intake andparental presence at meals were positively related andreported lactose intolerance, and unhealthful weight con-trol behaviors were inversely related to consumption.Among male adolescents, soft drink intake was also notsignificantly related to milk intake; parental presence atmeals and health/nutrition attitudes were positive pre-dictors, and lactose intolerance was a negative predictorof daily milk servings.

Table 3. Standardized coefficients from multivariate linear regression models for socioenvironmental, personal, and behavioral variablespredicting calcium intakes of male and female adolescents in the Project EAT (Eating Among Teens) surveya

Male Adolescents (n2,044) Female Adolescents (n2,035)

b (SE)c P value b (SE) P value

Socioenvironmental factors

Social support for healthful eating .029 (0.013) 0.033 .013 (0.014) 0.343Parental presence at meals .020 (0.013) 0.130 .024 (0.014) 0.077Family socioeconomic status .022 (0.014) 0.019 .057 (0.014) 0.001Milk served at meals .113 (0.015) 0.001 .136 (0.015) 0.001Personal factorsTaste preference for milk .151 (0.013) 0.001 .165 (0.013) 0.001Lactose intolerance .014 (0.012) 0.242 .021 (0.012) 0.077Health/nutrition attitudes .017 (0.013) 0.207 .027 (0.013) 0.038Body satisfaction .011 (0.013) 0.420 .012 (0.015) 0.427Self-efficacy to make healthful food choices .019 (0.013) 0.131 .038 (0.013) 0.004Time to eat breakfast .010 (0.014) 0.488 .014 (0.014) 0.310Behavioral factorsSports involvement .003 (0.013) 0.826 .020 (0.013) 0.118Breakfast intake .073 (0.014) 0.001 .073 (0.015) 0.001

Lunch intake .004 (0.013) 0.742 .018 (0.014) 0.176Dinner intake .021 (0.013) 0.108 .013 (0.013) 0.350Unhealthful weight-control behaviors .005 (0.013) 0.731 .015 (0.014) 0.288Fast-food intake .043 (0.013) 0.001 .037 (0.013) 0.004Soft-drink intake .032 (0.013) 0.020 .007 (0.014) 0.589Model R2 0.71 0.72

aAdjusted for race, grade level, weight status, and caloric intake.b coefficients are standardized and are interpreted as the amount of standard deviation (SD) change in calcium (mg) associated with a 1 SD change in predictor variable.cSEstandard error.


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DISCUSSION

This study investigated correlates of calcium, dairy, andmilk intakes among male and female adolescents. Meanintakes were less than recommended in many subgroupsof the sample and were particularly low among seniorhigh school female adolescents, students of low SES, andoverweight students. Female Hispanic students and both

male and female Asian students also reported particu-larly low intakes in comparison with their peers. For bothmale and female adolescents, milk served at meals, tastepreference for milk, breakfast consumption, family SES,and fast-food intake were identified as the most relevantfactors in predictive models of calcium intake. Soft drinkintake and social support for healthful eating were alsopredictors among male adolescents, and self-efficacy tomake healthful food choices along with health/nutritionattitudes were predictors among female adolescents. Al-though some differences were noted, predictors tended tobe similar for male and female adolescents and for thedifferent sources of calcium (ie, dairy vs milk), suggestingseveral potential strategies for families and intervention

programs to encourage calcium intake.Results of this study extend prior research findings,

which have indicated that family environment plays acritical role in influencing the dietary habits of adoles-cents (12,15-18,24). Family meals, parental modeling,family attitudes, and parental encouragement to con-sume calcium-rich foods have been related to adolescentintakes in other studies (12,19,20,24,35). Although socialsupport for healthful eating and parental presence atmeals were not predictive in every model considered bythe current study, serving milk at meals was a significantpredictor in every model of intake among male and fe-male adolescents. In addition, parental presence at mealswas a significant positive predictor of milk intake among

both sexes. This observation supports qualitative re-search among female adolescents and a previous analysisof the Project EAT data that found parental report ofserving milk at meals was associated with dairy intakesof male adolescents (17,24).

Our findings further support prior research suggestingthat parents can help their adolescents consume ade-quate calcium by preparing family meals in place of goingout for fast food (20). Fast-food intake was negativelyassociated with calcium, dairy, and milk intakes of maleand female adolescents, possibly reflecting a preferencefor consuming other beverages at quick-service restau-rants or a limited awareness of calcium-rich choices. Anassociation with calcium intake has not, however, been

observed in other research that more generally measuredthe proportion of meals consumed away from home (12).The strong association between taste preferences andcalcium intakes of male and female adolescents suggeststhat it may be especially important for parents to involveadolescents in purchasing decisions to ensure that desir-able calcium-rich choices are available at home. Tastepreferences have also been associated with intakes of softdrinks, beverages that may replace milk in the diets ofyoung people (25,36). Longitudinal research further indi-cates that taste preferences change little after the age of4 years (37). Therefore, efforts to ensure that young peo-ple consume adequate intakes of calcium as adolescents

may be most effective if calcium-rich foods are introducedduring their preschool years.

In the design of school and other youth-focused interventions to promote calcium intake, key factors associ-ated with intake, namely breakfast consumption, fast-food intake, self-efficacy to make healthful food choices,and SES should be considered. Findings from this study

and others indicate that eating breakfast may improvetotal daily calcium intakes (21,23). For example, amongadolescents in the Bogalusa Heart Study, 61.5% of thosewho ate breakfast met two thirds of the RecommendedDietary Allowance for calcium compared with only 38.5%of young people who reported no breakfast meal (22).Schools can support the nutritional health of their stu-dents by serving a breakfast that includes calcium-richfoods and allocating time for students to eat a morningmeal. Among female adolescents, observations furtherunderscored the need for interventions that address self-efficacy for making healthful food choices. Nutrition ed-ucation could help students to develop strategies tochoose calcium-rich foods at home and in social situa-tions. Finally, a priority should be placed on developinginterventions that address barriers to calcium-rich foodintake among families with limited incomes.

Multivariate models that included total caloric intake,demographic characteristics, and hypothesized SocialCognitive Theory predictors explained 71% and 72% ofthe variance in calcium intake for male and female ado-lescents. Although a large portion of the variance in cal-cium intake was explained by caloric intake alone, modelsalso provided support for the use of Social Cognitive The-ory in the development of interventions. Factors indepen-dently associated with calcium intake represented allthree interacting domains of influence within Social Cog-nitive Theory: personal (taste preferences, health/nutri-tion attitudes, and self-efficacy to make healthful food

choices), behavioral (breakfast consumption, fast-food in-take, and soft drink intake), and socioenvironmental in-fluences (family SES, milk served at meals, and socialsupport for healthful eating). Models including demo-graphic, personal, behavioral, and socioenvironmentalfactors but not total calories further explained approxi-mately one third of the variance in calcium, dairy, andmilk consumption among male and female adolescents.These results were comparable with prior research thatexplained 30% of variance in calcium intake among highschool students according to demographic and socialcharacteristics (12).

Few differences were observed between the predictorsof calcium intake and the predictors of dairy and milk

intake, suggesting that interventions designed to pro-mote dairy or milk will also more generally benefit overallcalcium intake. However, to increase milk consumption,nutrition educators will likely need to educate adoles-cents about strategies for dealing with lactose intoleranceand promote choosing milk as a beverage at fast-foodrestaurants with the help of the food industry. Soft drinkintake was inversely associated with calcium intakes,although the relationship was not statistically significantamong female adolescents in multivariate models. Amongmale and female adolescents, soft drink intake was notassociated with dairy or milk intakes. In contrast, priorresearch with 24-hour recall data has shown inverse as-


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sociations of adolescent soft drink consumption with cal-cium intakes and drinking at least 8 oz of milk daily;however, results were not stratified by sex (25). Addi-tional research using 24-hour recalls is needed to furtherclarify how the composition of meals and snacks varieswith soft drink consumption among adolescents.

In drawing conclusions from this study, strengths and

limitations of the study design should be considered.Strengths of this study include the large and diversenature of the study population; use of a validated, com-prehensive measure (Youth and Adolescent Food Fre-quency Questionnaire) to assess dietary intake; and useof a theoretical model to guide variable selection for awide range ofpotential correlates available on the ProjectEAT survey (30). However, the findings may have beeninfluenced by potential limitations of using the Youth and Adolescent Food Frequency Questionnaire in ethnicallyand socioeconomically diverse samples. Young people ofnonwhite race tend to consume less dairy and may getmore calcium from calcium-fortified foods, which are notconsidered in calculations based on the Youth and Ado-lescent Food Frequency Questionnaire (38). Also, some

variables in our model were measured with items notspecific to calcium-rich foods. For example, the ProjectEAT survey broadly assesses parental encouragement toeat healthful foods and parental presence at meals ratherthan parental encouragement to drink milk or parentalmodeling of milk intake. Other research has observedstrong associations between measures of parental model-ing and calcium intake when items specific to milk intakewere used (12). Finally, the directionality of influencecannot be inferred from a cross-sectional study. For ex-ample, it is not possible to establish whether familiesfrequently serve milk at meals because their adolescentconsumes more dairy foods or whether adolescents con-sume more dairy foods because their families frequently

serve milk at meals. Future research should considerlongitudinal models and attempt to measure predictorvariables with items more specific to calcium-rich foods.

CONCLUSIONS

Intakes of calcium, dairy, and milk are inadequateamong adolescents, and there is a need for interven-tions given the health implications of chronically lowconsumption.

Multicomponent interventions with a focus on the fam-ily environment are likely to be most effective to in-crease calcium intakes among adolescents.

Adolescents from families with limited incomes are in

particular need of interventions and nutrition education. Food and nutrition professionals should encourage par-

ents to serve milk at meals and help their adolescentfind time to eat breakfast.

Adolescents should be exposed to a variety of calcium-rich foods to help them identify many tasteful optionsand should be taught strategies for consuming calcium-rich foods in various social situations (eg, when eatingat fast-food restaurants).

Data collection was supported by grant MCJ-270834(D.N.-S., principal investigator) from the Maternal andChild Health Bureau (Title V, Social Security Act),

Health Resources and Service Administration, and theUS Department of Health and Human Services. Analyseswere supported by Grant T01-DP000112 from the Cen-ters for Disease Control and Prevention, Department ofHealth and Human Services.

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