This article was downloaded by: [Elsa Pinto]On: 20 August 2014, At: 08:03Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

Ecology of Food and NutritionPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/gefn20

Nutrition and Physical ActivityInterventions for Childhood Obesity:Lessons LearnedElsa Pintoa, Brenda Toroa & Lizette Vicnsaa Nutrition and Dietetics Program, College of Natural Sciences,University of Puerto Rico, Ro Piedras Campus, Puerto RicoPublished online: 08 Aug 2014.

To cite this article: Elsa Pinto, Brenda Toro & Lizette Vicns (2014) Nutrition and Physical ActivityInterventions for Childhood Obesity: Lessons Learned, Ecology of Food and Nutrition, 53:5, 503-513,DOI: 10.1080/03670244.2013.873422

To link to this article: http://dx.doi.org/10.1080/03670244.2013.873422

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (theContent) contained in the publications on our platform. However, Taylor & Francis,our agents, and our licensors make no representations or warranties whatsoever as tothe accuracy, completeness, or suitability for any purpose of the Content. Any opinionsand views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor & Francis. The accuracy of the Contentshould not be relied upon and should be independently verified with primary sourcesof information. Taylor and Francis shall not be liable for any losses, actions, claims,proceedings, demands, costs, expenses, damages, and other liabilities whatsoever orhowsoever caused arising directly or indirectly in connection with, in relation to or arisingout of the use of the Content.

This article may be used for research, teaching, and private study purposes. Anysubstantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

Ecology of Food and Nutrition, 53:503513, 2014Copyright Taylor & Francis Group, LLCISSN: 0367-0244 print/1543-5237 onlineDOI: 10.1080/03670244.2013.873422

Nutrition and Physical Activity Interventionsfor Childhood Obesity: Lessons Learned

ELSA PINTO, BRENDA TORO, and LIZETTE VICNSNutrition and Dietetics Program, College of Natural Sciences, University of Puerto Rico,

Ro Piedras Campus, Puerto Rico

This article describes field experiences while implementing nutri-tion and physical activity education in a public junior high schoolin Puerto Rico (PR). Participants were classified as overweight orat risk based on body mass index (BMI). Dietary intake and weightwere collected. Changes in dietary intake assessed from baseline toend of school year did not show statistical significance. The reduc-tion in BMI Z-scores was modest at 4 months and was not observedat the end of the program. Future studies are warranted to inte-grate parents and behavioral theories and to evaluate food in theenvironment to successfully address childhood obesity.

KEYWORDS childhood obesity, nutrition, interventions

Data on the medical consequences (Bray and Bellanger 2006), reducedlongevity (Preston 2005), risk of chronic diseases (Kim and Caprio 2011),higher medical costs (Cawley and Meyerhoefer 2012), social stigma (VanderWal and Mitchell 2011), and probability of becoming an obese adult (Kimand Caprio 2011; Whitaker et al. 1997) just for being an overweight orobese youngster, is abundant. Childhood obesity is a complex problemthat includes modifiable and non-modifiable factors. Research has identifiedfactors such as inadequate fruit and vegetable intake, excessive consump-tion of fruit juice, soft drinks, total and saturated fat, being sedentary, andTV watchingall of which are modifiable factors (Singh et al. 2008). Mostrecently, mental health issues such as anxiety and depression have also beenidentified as potential risk factors or moderating factors in childhood obesity(Nieman, Leblanc, and Canadian Paediatric Society 2012).

Address correspondence to Elsa Pinto, Nutrition and Dietetics Program, College of NaturalSciences, University of Puerto Rico, Ro Piedras Campus, San Juan, Puerto Rico. E-mail:elsa.pinto@upr.edu

503

Dow

nloa

ded

by [E

lsa Pi

nto] a

t 08:0

3 20 A

ugus

t 201

4

504 E. Pinto et al.

Data from studies conducted in the United States consistently show agreater rate of overweight and obesity among adults, that may well explainhealth disparities in chronic conditions among Hispanics (Tucker et al. 2010).The Behavioral Risk Factor Surveillance Survey (BRFSS) conducted in theU.S., that includes the Puerto Rican adult population, aims to monitor theconsumption of fruits and vegetables. Data from this survey in 2009 showsthat 17.7% of the Puerto Rican population (n = 4,195) reports consumingfive or more fruits and vegetables per day (Centers for Disease and Controland Prevention 2009). It is expected that similar rates of fruit and vegetableconsumption may be observed among children and adolescents in PuertoRico (PR) since dietary habits are mostly imparted by parents and relatives ofthese children. In addition to consumption of fruits and vegetables, the mostrecent dietary guidelines recommend reduction in consumption of saturatedfats and sodium and to increase whole grain foods and physical activity(McGuire 2011).

It is our understanding that there is no published data documenting theimpact of interventions aimed to improve eating habits and weight controlamong adolescents at school in PR. Systematic reviews of the literature haveindicated that some of the factors that contribute to the success of nutri-tion education interventions delivered at the school level have a durationof at least a year and incorporate activities during the school hours (Silveira2011). A factor not frequently discussed in the literature is who delivers theseinterventions. Adolescence is characterized by specific cognitive and emo-tional processes when it is common to question authority and rely more onpeer influences (Perry 1995) than adult instruction. Therefore, we proposeimplementing interventions among adolescents delivered by undergraduatenutrition and dietetics students as the facilitators of the interventions. Thisstrategy will serve a dual purpose, provide a learning experience for nutritionand dietetics students and offer carefully tailored interventions to adolescentsdelivered by someone other than an authority figure like a teacher or nutri-tionist, that they may easily identify with and even consider a peer and apositive role model.

Studies have suggested that even though it is critical to start earlyin teaching children good eating habits, it is during adolescence that cer-tain habits are formed (Shankaran et al. 2011). This is also a periodof time when adolescents are most affected by peer influences andtheir social environment (Salvy et al. 2012). The objective of this pilotstudy is to report the experiences and impact on weight and foodconsumption, specifically fruits and vegetables, after participating in apilot project focused on nutrition education and physical-activity interven-tions among 13- to 14-year-old boys and girls in a public junior highschool.

Dow

nloa

ded

by [E

lsa Pi

nto] a

t 08:0

3 20 A

ugus

t 201

4

Interventions for Childhood Obesity 505

METHODS

Participants and Recruitment

Participants were students in a junior high school in San Juan, PR. The schoolhas approximately 280 students in grades 7 through 9. The school had begunto implement a pilot project to increase physical activity among studentsin grade 8, and reached out to academia for collaborations to include anutrition component and assistance in the implementation of the program.Thus, students that were part of a program called Estilos de Vida Activos(EVA; Active Lifestyles) were recruited to participate in the study. Studentseligible to participate: (1) were actively enrolled in school and the pro-gram, (2) obtained a low score on the Fitness Gram (Welk et al. 2011)measure of health related fitness, (3) had a BMI over the 85th percentilefor age, and (4) had signed parental consent and assent to participate inthe study. Although a comparison group of students not participating inthe program was desired to better assess the impact of the program, thiswas not feasible due to a greater number of interruptions to the regu-lar class schedule, since the program was conducted during regular schoolhours.

Study Design

A longitudinal cohort study was used to evaluate the impact of a seriesof nutrition education and physical activity interventions. After recruitment,at the beginning of the school year in August, participants received monthlyinterventions and were assessed every two months until the end of the schoolyear in May.

Interventions

Nutrition education interventions were initiated within a month of the start ofthe school year and continued on a monthly or bi-monthly basis until the endof the school year. Each intervention lasted approximately 20 to 30 minutesand was provided by undergraduate Nutrition and Dietetics students from theUniversity of Puerto Rico that visited the school prior to each interventionin order to obtain topics of interest among the students. These interven-tions were supervised by faculty from the Nutrition and Dietetics Program toensure accuracy of the information and adequacy of the teaching strategy.Some of the topics covered by these interventions included consumption offresh fruits and vegetables, healthy snacks, healthy alternatives while eat-ing away from home, water consumption and strategies on how to increasefiber in their diet. Physical activity interventions were provided monthly andwere in addition to their regular physical activity class. These lasted 50 to60 minutes and included activities such as swimming at the university pool;

Dow

nloa

ded

by [E

lsa Pi

nto] a

t 08:0

3 20 A

ugus

t 201

4

506 E. Pinto et al.

taking walks in the nearby community; playing active video games, tradi-tional games, and sport-related activities such as basketball. These activitieswere provided and supervised by physical-activity teachers at the school. TheInstitutional Review Board at the University of Puerto Rico (U.P.R.) reviewedand approved the study protocol.

Measures and Instruments

Anthropometric data (i.e., weight and height) was collected following a stan-dardized protocol that included the use of a calibrated scale and consistencyin the time of measurements. Anthropometric data was collected by thephysical activity teachers of the junior high school at baseline (beginning ofschool year), mid-semester. and end of school year. Weight and height datawas used to calculate BMI, which is considered to be a moderately sensitiveindicator of adiposity in children (Freedman and Sherry 2009) and allowsclassification of weight status as defined by the Centers for Disease Control(CDC) Growth Charts of BMI for age and sex (Kuczmarski et al. 2000).Following the guidelines set forth by the CDC, BMI values were transformedto Z-score to allow for accounting for age and gender differences in weightand height that are expected during adolescence (Kuczmarski et al. 2000).

Dietary-intake data was collected with the average of three 24-hourrecalls to obtain usual dietary intake (Dodd et al. 2006). UndergraduateNutrition and Dietetic students from the U.P.R., who volunteered to workin the project, were trained in multiple-pass dietary-recall methodology(Johnson, Driscoll, and Goran 1996) and collected dietary-intake data atbaseline and at the end of the school year. To help participants estimate theserving sizes of foods consumed, we used three-dimensional food modelsof the most common fruits, vegetables, grains, meats, and other food sta-ples. Amounts of water, fruit juices, and other drinks were estimated usingcups and glasses of different sizes with known volume. Oils or other addedfats such as salad dressings were estimated with standard cups and measur-ing spoons. Nutritionist Pro (version 5.0, Axxya systems, Sugargrove, Texas,USA) software was used to code foods and convert them into nutrients toobtain quantitative data. Fruit and vegetable consumption was measured bydetermining the number of fruit exchanges each student reported eating inthe 24-hour recalls. One fruit exchange is considered to be equivalent to onehalf a cup of fruit juice or 1 medium-size piece of fruit (American DiabetesAssociation 2008). Table 1 provides variables and instruments used for datacollection.

Statistical differences in BMI were assessed after standardization ofthe data by calculating the Z-scores. Changes in dietary behaviors andBMI Z-scores were determined using repeated-measures ANOVA. Statisticalsignificance was set at p < .05, and all data preparation and analysis wasconducted in the Statistical Package for the Social Sciences (version 21.0,SPSS Inc., Chicago, Illinois, USA).

Dow

nloa

ded

by [E

lsa Pi

nto] a

t 08:0

3 20 A

ugus

t 201

4

Interventions for Childhood Obesity 507

TABLE 1 Variables and Instruments Used for Data Collection

Variables Instrument Values

Weight Standard protocol KilogramsHeight Standard protocol CentimetersPercentile of BMI for age CDC growth charts of BMI for age PercentileBMI Z-scores CDC growth charts Z-scoresTotal calories Average of three 24-hour dietary

recallKilocalories

Total fiber consumption 24-hour dietary recall Grams of fiberFruit and vegetableconsumption

24-hour dietary recall Number of servings per day(Exchange list)

Note. BMI = Body mass index, CDC = Centers for Disease Control.

RESULTS

Participants included 31 students who were recruited to participate in theprogram. Of participants, two were lost in follow-up measures and two didnot continue with the program due to disciplinary reasons at the school.While the sample size is small, it was considered adequate for a pilot projectthat was conceptualized to allow us to determine the feasibility of interven-tions in a school environment. Therefore, a power analysis to detect statisticaldifferences was not deemed necessary, and the sample represented 30% ofthe students in grade 8. Participant characteristics are presented in table 2.The majority of participants (26 of 27) were over the 95th percentile in BMIfor their age that classifies them as overweight or obese. The remaining par-ticipant was at risk of being overweight. Dietary-intake data indicated caloricconsumption was on average 2,043 kilocalories per day.

The repeated-measures ANOVA was used to determine significant dif-ferences in BMI Z-scores from baseline. The assumption of sphericity forANOVA was not met using as indicator Mauchlys test of sphericity. Tocorrect this, the Greenhouse-Geisser adjustment in degress of freedom wasused (Salkind and Rasmussen 2007). Results show a statistically significantdifference in BMI Z-scores from baseline (2.14 .41 and 2.05 .47, F =

TABLE 2 Participant Characteristics (n = 27)Characteristics (Baseline)

Gender (male/female) 13/14Weight (kg) 84.36 17.9BMI (kg/m2) 32.1 5.5BMI Z-scores (mean) 2.14 0.4> 95th percentile (n) 26Kilocaloric intake (kcal) 2, 043 689.3Fiber consumption (g) 14.3 6.9Servings of fruits per day 2.6 1.8Servings of vegetables per day 1.2 1.0

Dow

nloa

ded

by [E

lsa Pi

nto] a

t 08:0

3 20 A

ugus

t 201

4

508 E. Pinto et al.

TABLE 3 Change in BMI Z-scores among Participants of EVA

Mean SD n F p eta squaredBaseline 2.14 0.4 27 5.905 .013 0.191Mid-semester (December) 2.08 0.43 27End of semester (May) 2.05 0.47 27Note. EVA = Estilos de Vida Saludable (Active Lifestyles).

TABLE 4 Changes in Dietary Intake among Participants of EVA

Baseline (August) Endpoint (March/May)

Mean SD F pKilocalories (kcal) 2, 043 689.3 2, 203 924.9 .518 .480Fiber (g) 14.4 6.9 12.3 6.1 1.426 .246Fruit servings per day 2.6 1 2.3 2 .320 .578Vegetable servings per day 1.2 1 1.5 1 .841 .370Note. EVA = Estilos de Vida Saludable (Active Lifestyles)

5.91, p = .013). However, post hoc analysis using Bonferroni correctionsindicate that there was no statistical difference in weight from mid-semesterto the end of the school year in the month of May. Thus, significant reductionin weight occurred from baseline to mid semester but was not maintainedto the end of the school year. Changes in dietary intake assessed from base-line to the end of the school year did not show statistical significance (table4). Neither kilocalories, nor dietary fiber consumption showed significantchanges as a result of the intervention. Mean fiber intake at baseline was14.4 6.9 grams and showed no significant difference at endpoint althoughmean intake appeared to be lower 12.3 6.1 grams. Consumption of fruitand vegetables by the number of fruit and vegetable exchanges was in someinstances higher at baseline and lower at the end of the school year althoughthese changes were not significant.

Anecdotal data collected from participants allowed us to identify spe-cific perceived needs and wants. For example, participants expressed thatthey enjoyed activities that were outdoors, and reported they would enjoyactivities scheduled during after-school hours. In fact, for some, after-schoolactivities may even be more convenient, since parents do not leave theirworkplace right after their children finish school. When asked about talkingto other non-participant friends about the activities they do in the pro-gram, participants indicated not talking about them, but stated that theywould like to. Nutrition education was well received and participating stu-dents were even proud to be more knowledgeable in certain nutritionfacts. While we did not measure nutrition knowledge, we speculate therewere gains in nutrition knowledge but these did not impact food-intakebehaviors.

Dow

nloa

ded

by [E

lsa Pi

nto] a

t 08:0

3 20 A

ugus

t 201

4

Interventions for Childhood Obesity 509

DISCUSSION

The results of our pilot project do not demonstrate the desired impact inthe long-term reduction of body weight or the desired improvement infood habits, indicated by the total kilocalories consumed, total dietary fiber,and number of fruit and vegetable exchanges consumed. These results sup-port studies that indicate there is a complex set of factors that influencehealth-related behaviors (Ding et al. 2012.; Singh et al. 2008; Velazquez et al.2011).

Pooled analysis indicates that the increased consumption of fruits andvegetables after educational interventions is modest, and thus may point tomediating or moderating factors not being taken into account within theinterventions (Howerton et al. 2007). One such factor may be that many ofthe interventions are conducted within school hours in an environment thatmay encourage much pressureboth social pressure from peers as well aspressure to perform academically. We propose that while some interventionsmay be conducted during school lunch, after-school activities may be a bettersuited milieu for this age group.

Based on anecdotal information from program participants, and giventheir age, we believe interventions should be based on models of behav-ior change such as the Social Cognitive Theory of Behavior (SCT; Bandura2001, 2004), which establishes that the manifestation of a behavior is relatedto personal and environmental factors. A recent study showed that just theact of providing and making available fresh fruits and vegetables, has beenfound to increase the number of servings adolescents consume during schoollunches (Di Noia and Contento 2010). Self-efficacy, a construct within SCT,related that adolescents perceived ability to perform a behavior can beincreased by teaching them how to cut and prepare fruits and vegetables(Gatto et al. 2012).

The lack of reduction in body weight after mid-semester can beattributed in part to a less frequent number of interventions during the sec-ond semester of the school year. Students were required to complete severalstandardized tests of academic performance, and the preparation and admin-istration of these tests partially interrupted the protocol of the study. Thus,students may have lost interest and motivation to apply what they learnedduring the interventions, and may have had less time available to dedicateto physical activity.

During the study protocol, several factors related to the school envi-ronment were identified as incongruent with the pilot projects objectives.These included high fat and simple sugar foods being sold nearby theschool grounds, and several fast food chains within walking distance fromthe school. Research in this area indicates the availability of fast foods isdirectly related to the frequency of their consumption (Forsyth et al. 2012),and the frequency of consumption is negatively related to the quality of

Dow

nloa

ded

by [E

lsa Pi

nto] a

t 08:0

3 20 A

ugus

t 201

4

510 E. Pinto et al.

the diet (Powell and Nguyen 2013). While it is possible to select healthyfoods in these establishments, other factors such as price (Khan, Powell, andWada 2012), commodity, and preference often have greater influence on theselection of foods. School officials approached fast food vendors to explainthe schools efforts and project objectives. Nevertheless, changes in this areawould require public policy changes or legislation that can help build a foodenvironment conducive of healthier food choices.

Measurements of dietary intake were done using the average of threepaper-and-pencil, multiple-pass-method dietary recalls. While other meth-ods, such as food frequency questionnaires or even food diaries may havebeen feasible, our experience in this pilot project demonstrated that studentswere not always aware of specific characteristics of foods consumed andinterviewers carried the burden to elicit this information. The importance ofdocumenting where each food was consumed during the dietary recalls facil-itated the process of coding the foods reported by participants and enabledcross referencing with the official school lunch menu. We recommend thatstudies that aim to collect detailed dietary-intake data among adolescentsconsider the use of school lunch menus or other reference data that can helpensure the integrity of the coding of foods in nutrient-composition databases.An added and important benefit of this project was the experience gainedby undergraduate Nutrition and Dietetics students that were able to practicehow to conduct a standardized multiple pass dietary recall. The inclusion ofundergraduate students in research projects fortifies their academic experi-ence and encourages their interest in graduate studies. Moreover, participantsseemed more comfortable talking to these students, as they could betteridentify with them.

One of the limitations of our program is that it did not include inter-ventions targeted to the parents, and these are considered to be essential, asshown by interventions such as CATCH (Perry et al. 1998), Gimme 5 (Daviset al. 2000), and Californias 5 a day (Foerster et al. 1995) that all includefamily and community. Another limitation was the impact of the schoolfood environment that was unable to be controlled and is considered tohave a large influence on adolescents eating and physical activity behaviors(Whitaker et al. 2013). Future intervention studies are needed that includemeasures of the neighborhood environment as being positive or negativetowards making positive changes in food and physical activity behaviors.An assessment of children and adolescents self-esteem, anxiety, depressionsymptoms and other mental health issues is also warranted to be includedin future intervention studies. Our pilot project did not assess these factorsthat prior research has shown can be a contributing factor in the develop-ment of overweight and obesity as well as in motivating behavior changes.Future projects are being considered to incorporate these factors as well asa follow up study that could tap into their gains in nutrition knowledgeand attitude towards behavior change after their participation in theproject.

Dow

nloa

ded

by [E

lsa Pi

nto] a

t 08:0

3 20 A

ugus

t 201

4

Interventions for Childhood Obesity 511

CONCLUSION

In conclusion, monthly educational interventions at a junior high school toimprove nutrition and increase physical activity among adolescents werepartially successful in reducing body weight. The reduction in body weightwas modest and was not observed at the end of the program, a findingwhich indicates that there were some limitations in the implementation ofthe study protocol. Dietary intake did not show changes, and this findingis partially attributed to limitations in the scope of the interventions thatdid not impact the nutrition environment and parent involvement. Futureintervention studies are warranted that can incorporate parents, addressfood environment, integrate behavioral theories, and conduct interventionsafter school hours to successfully address obesity among children andadolescents.

ACKNOWLEDGMENTS

The authors would like to thank and recognize the work of teachers CarmenCruz and Jos Figueroa who had the initiative to develop this project attheir school. Also, School Director, Beverly Rodrguez, is appreciated for hersupport and collaboration in the project.

REFERENCES

American Diabetes Association. 2008. Choose your foods: Exchange lists for diabetes.Chicago, IL: American Diabetes Association.

Bandura, A. 2001. Social cognitive theory: An agentic perspective. Annual Review ofPsychology 52:126.

Bandura, A. 2004. Health promotion by social cognitive means. Health EducationBehavior 31 (2): 143164.

Bray, G. A., and T. Bellanger. 2006. Epidemiology, trends, and morbidities of obesityand the metabolic syndrome. Endocrine 29 (1): 10917.

Cawley, J., and C. Meyerhoefer. 2012. The medical care costs of obesity: Aninstrumental variables approach. Journal of Health Economics 31 (1): 219230.

Davis, M., T. Baranowski, K. Resnicow, J. Baranowski, C. Doyle, M. Smith, D. T.Wang, A. Yaroch, and D. Hebert. 2000. Gimme 5 fruit and vegetables for funand health: Process evaluation. Health Education Behavior 27 (2):16776.

Ding, D., J. F. Sallis, G. J. Norman, B. E. Saelens, S. K. Harris, J. Kerr, D. Rosenberg, N.Durant, and K. Glanz. Community food environment, home food environment,and fruit and vegetable intake of children and adolescents. Journal of NutritionEducation and Behavior 44 (6): 634638.

Di Noia, J., and I. R. Contento. 2010. Fruit and vegetable availability enables ado-lescent consumption that exceeds national average. Nutrition Research 30 (6):396402.

Dow

nloa

ded

by [E

lsa Pi

nto] a

t 08:0

3 20 A

ugus

t 201

4

512 E. Pinto et al.

Dodd, K. W., P. M. Guenther, L. S. Freedman, A. F. Subar, V. Kipnis, D. Midthune, J.A. Tooze, and S. M. Krebs-Smith. 2006. Statistical methods for estimating usualintake of nutrients and foods: A review of the theory. Journal of the AmericanDietetic Association 106 (10): 16401650.

Foerster, S. B., K. W. Kizer, L. K. Disogra, D. G. Bal, B. F. Krieg, and K. L.Bunch. 1995. Californias 5 a dayfor better health! campaign: An innovativepopulation-based effort to effect large-scale dietary change. American Journalof Preventive Medicine 11 (2): 124131.

Forsyth, A., M. Wall, N. Larson, M. Story, and D. Neumark-Sztainer. 2012. Do ado-lescents who live or go to school near fast-food restaurants eat more frequentlyfrom fast-food restaurants? Health & Place 18 (6): 12611269.

Freedman, D. S., and B. Sherry. 2009. The validity of BMI as an indicator of bodyfatness and risk among children. Pediatrics 124 (Supplement 1): S23S34.

Gatto, N. M., E. E. Ventura, L. T. Cook, L. E. Gyllenhammer, and J. N. Davis. 2012. LASprouts: A garden-based mutrition intervention pilot program influences moti-vation and preferences for fruits and vegetables in Latino youth. Journal of theAcademy of Nutrition and Dietetics 112 (6) 113120.

Johnson, R. K., P. Driscoll, and M. I. Goran. 1996. Comparison of multiple-pass 24-hour recall estimates of energy intake with total energy expenditure determinedby the doubly labeled water method in young children. Journal of the AmericanDietetic Association 96 (11): 11401144.

Khan, T., L. M. Powell, and R. Wada. 2012. Fast food consumption and food prices:evidence from panel data on 5th and 8th grade children. J Obes 2012:857697.

Kim, G., and S. Caprio. 2011. Diabetes and Insulin Resistance in Pediatric Obesity.Pediatric Clinics of North America 58 (6):1355+.

Kuczmarski, R. J., C. L. Ogden, L. M. Grummer-Strawn, K. M. Flegal, S. S. Guo, R.Wei, Z. Mei, L. R. Curtin, A. F. Roche, and C. L. Johnson. 2000. CDC growthcharts: United States. Adv Data (314):127.

McGuire, S. 2011. Reports from the agencies. U.S. Department of Agriculture and U.S.Department of Health and Human Services, Dietary Guidelines for Americans,2010. 7th Edition, Washington, DC: U.S. Government Printing Office, January2011. Advances in Nutrition 2 (3): 293294.

Nieman, P., C. M. Leblanc, and Healthy Active Living and Sports Medicine CommitteeCanadian Pediatric Society. 2012. Psychosocial aspects of child and adolescentobesity. Paediatric Child Health 17 (4): 205208.

Perry, C. L., L. A. Lytle, H. Feldman, T. Nicklas, E. Stone, M. Zive, A. Garceau, andS. H. Kelder. 1998. Effects of the Child and Adolescent Trial for CardiovascularHealth (CATCH) on fruit and vegetable intake. Journal of Nutrition Education30 (6): 354360.

Powell, L. M., and B. T. Nguyen. 2013. Fast-food and full-service restaurant consump-tion among children and adolescents: Effect on energy, beverage, and nutrientintake. JAMA Pediatrics 167 (1): 1420.

Preston, S. H. 2005. Deadweight? The influence of obesity on longevity. New EnglandJournal of Medicine 352 (11): 11351137.

Salkind, N. J., and K. Rasmussen. 2007. Encyclopedia of measurement and statistics.3 vols. Thousand Oaks, CA: Sage Publications.

Dow

nloa

ded

by [E

lsa Pi

nto] a

t 08:0

3 20 A

ugus

t 201

4

Interventions for Childhood Obesity 513

Salvy, S.-J., K. de la Haye, J. C. Bowker, and R. C. J. Hermans. 2012. Influence ofpeers and friends on childrens and adolescents eating and activity behaviors.Physiology & Behavior 106 (3): 369378.

Shankaran, S., C. Bann, A. Das, B. Lester, H. Bada, C. R. Bauer, L. La Gasse, R.D. Higgins, and the Eunice Kennedy Shriver National Institute of Child Healthand Human Development Neonatal Research Network. 2011. Risk for obesity inadolescence starts in early childhood. Journal of Perinatology 31 (11): 711716.

Singh, G. K., M. D. Kogan, P. C. Van Dyck, and M. Siahpush. 2008. Racial/ethnic,socioeconomic, and behavioral determinants of childhood and adolescent obe-sity in the United States: Analyzing independent and joint associations. Annalsof Epidemiology 18 (9): 682695.

Tucker, K. L., J. Mattei, S. E. Noel, B. M. Collado, J. Mendez, J. Nelson, J. Griffith, J.M. Ordovas, and L. M. Falcon. 2010. The Boston Puerto Rican Health Study, alongitudinal cohort study on health disparities in Puerto Rican adults: Challengesand opportunities. BMC Public Health 10:107.

Vander Wal, J. S., and E. R. Mitchell. 2011. Psychological complications of pediatricobesity. Pediatric Clinics of North America 58 (6): 13931401.

Velazquez, C. E., K. E. Pasch, N. Ranjit, G. Mirchandani, and D. M. Hoelscher. 2011.Are adolescents perceptions of dietary practices associated with their dietarybehaviors? Journal of the American Dietetic Association 111 (11): 17351740.

Welk, G. J., S. B. Going, J. R. Morrow, and M. D. Meredith. 2011. Developmentof new criterion-referenced fitness standards in the FITNESSGRAM program:Rationale and conceptual overview. American Journal of Preventive Medicine41 (4 Suppl 2): S6367.

Whitaker, D., A. J. Milam, C. M. Graham, M. Cooley-Strickland, H. M. Belcher, andC. D. Furr-Holden. 2013. Neighborhood environment and urban schoolchil-drens risk for being overweight. American Journal of Health Promotion 27(6): 410416.

Whitaker, R. C., J. A. Wright, M. S. Pepe, K. D. Seidel, and W. H. Dietz. 1997.Predicting obesity in young adulthood from childhood and parental obesity.New England Journal of Medicine 337 (13): 869873.

Dow

nloa

ded

by [E

lsa Pi

nto] a

t 08:0

3 20 A

ugus

t 201

4

ABSTRACTMETHODSParticipants and RecruitmentStudy DesignInterventionsMeasures and Instruments

RESULTSDISCUSSIONCONCLUSIONACKNOWLEDGMENTSREFERENCES