Latino Families, Primary Care, andChildhood Obesity

A Randomized Controlled Trial

Alexy D. Arauz Boudreau, MD, MPH, Daniel S. Kurowski, MPH, Wanda I. Gonzalez, MD,Melissa A. Dimond, ScM, Nicolas M. Oreskovic, MD, MPH

Background: Few successful treatment modalities exist to address childhood obesity. Given Lati-nos’ strong identity with family, a family-focused intervention may be able to control Latinochildhood obesity.

Purpose: To assess the feasibility and effectiveness of a family-centered, primary care–basedapproach to control childhood obesity through lifestyle choices.

Design: Randomizedwaitlist controlled trial inwhich control participants received the intervention6 months after the intervention group.

Setting/participants: Forty-one Latino children with BMI �85%, aged 9–12 years, and theircaregivers were recruited from an urban community health center located in a predominantlylow-income community.

Intervention: Children and their caregivers received 6 weeks of interactive group classes followedby 6 months of culturally sensitive monthly in-person or phone coaching to empower families toincorporate learned lifestyles and to address both family and social barriers to making changes.

Main outcomes measures: Caregiver report on child and child self-reported health-relatedquality of life (HRQoL); metabolic markers of obesity; BMI; and accelerometer-based physicalactivity were measured July 2010–November 2011 and compared with post-intervention assess-ments conducted at 6months and as a function of condition assignment. Datawere analyzed in 2012.

Results: Average attendance rate to each group class was 79%. Socio-environmental and familyfactors, along with knowledge, were cited as barriers to changing lifestyles to control obesity.Caregiver proxy and child self-reported HRQoL improved for both groups with a larger but notnonsignifıcant difference among intervention vs control group children (p�0.33). No differenceswere found between intervention and control children for metabolic markers of obesity, BMI, orphysical activity.

Conclusions: Latino families are willing to participate in group classes and health coaching tocontrol childhoodobesity. Itmay be necessary for primary care to partnerwith community initiativesto address childhood obesity in a more intense manner.

Trial registration: This study is registered at Clinicaltrials.partners.org 2009P001721.(Am J Prev Med 2013;44(3S3):S247–S257) © 2013 American Journal of Preventive Medicine

From the Center for Child and Adolescent Health Research and Policy(Boudreau, Oreskovic), Harvard Medical School, Massachusetts GeneralHospital, Mass General Hospital for Children (Gonzalez); the MGH Cen-ter for Community Health Improvement, Boston, Massachusetts (Di-mond); and the Center for Home Care Policy (Kurowski) and ResearchNew York, New York.

Address correspondence to: Alexy D. Arauz Boudreau, MD, MPH,Massachusetts General Hospital, Mass General Hospital for Children,Center for Child and Adolescent Health Research and Policy, 100 Cam-bridge Street, 15th Floor, BostonMA 02114. E-mail: aarauz@partners.org.

0749-3797/$36.00http://dx.doi.org/10.1016/j.amepre.2012.11.026

© 2013 American Journal of Preventive Medicine • Published by Elsevier Inc. Am J Prev Med 2013;44(3S3):S247–S257 S247

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Introduction

Obesity is a complex disorder involving biology,physical environments, societal structures, andcultural determinants.1,2 Obesity is one of the

most prevalent childhood chronic health conditions.3–5

Racial, ethnic, and socioeconomic disparities in the prev-alence of obesity are well documented.4,6 In 2010, amonghose aged 2–19 years, 21% ofHispanics were obese com-ared to 14% of non-Hispanics.7

Many Latino children face barriers to maintaining ahealthy weight, including lack of affordable healthy foodsin their neighborhoods, dangerous neighborhoods thatmake being active outdoors diffıcult, and a lack of cultur-ally relevant community activities.8–10 Families have in-dequate time for play or food preparation and use foodnd TV to encourage desired behaviors.11–13 Treatingobesity has proven diffıcult.14 Current standard medicalcare relies on primary care providers providing counsel-ing on diet and exercise, at routine visits that are monthsapart.15 Cost-effective approaches to promote healthylifestyle choices among Latino children are needed toreduce obesity rates.Children whose parents are overweight aremore likely

to be overweight16 and are at an increased risk of obesitys adults.17 Thus, addressing risk factors for childhoodbesity requires addressing family behaviors includingiet and physical activity.18 Family-centered approachesave been successful in supporting healthy eating andhysical activity in children.19–22 Family-centered ap-

proaches may be particularly relevant to Latino commu-nities, given Latinos’ sense of familismo,23 which is valu-ing the family as central to behaviors and decisions.To address challenges common to Latino communi-

ties, the authors piloted Healthy Living Today!, a family-centered, primary care–based approach to weight con-trol. Tests weremade of the feasibility and effectiveness ofthis novel pilot program, which included six interactivegroup classes focused on nutrition, physical activity, andstress management, followed by 6 months of monthlyin-person or phone health coaching. We hypothesizedthat (1) Latino families would be willing to participate inan augmented primary care approach to treat obesity;and (2) children participating in the intervention wouldhave improved quality of life, metabolic markers of obe-sity, BMI, and physical activity.

MethodsStudy Design

Participating dyads were sequentially randomized to the interven-tion or control group in a 3:2 ratio during the fırst half of the study,and then 2:2 during the second half of the study to adequately fıll the

group classes. Intervention participants began classes immediately,

ith control participants waiting 6 months prior to beginning thentervention, immediately following their 6-month assessment.oth intervention andwaitlist control participants completed eval-ation protocols at baseline and 6-months post-baseline. Care-iver consent and child assent were obtained. Participatingamilies were given a $15 gift certifıcate at the fırst visit and a $25ift certifıcate at the second visit. This study was approved by theartners IRB.

Participants

Eligible participants were Latino children ages 9–12 years whowere overweight or obese (age- and gender-specifıc BMI in the85th–94th and �95th percentile, respectively) who received pri-mary care at a single community health center. Participants wereidentifıed by their primary care pediatrician and then recruited byphone. Children with chronic diseases other than asthma wereexcluded. Given population-wide physical activity levels amongchildren aged 9–12 years,24 to have an 83% probability of detecting9% increase (12 minutes) in mean average daily physical activityn the intervention group, the current authors anticipated that 21hildren in each group would be required.

Intervention

The intervention consisted of two components: (1) Power Upclasses that educated children and caregivers about healthy behav-iors surrounding nutrition, activity, and stress management and(2) culturally sensitive coaching to empower families to incorpo-rate learned behaviors and address both family and social barriersto lifestyle changes. Classes were conducted in fıve consecutiveweekly sessions, with a sixth 3 months later. Coaching began con-currently with the group classes and continued for a total of6 months; meetings were in-person at the health center, at thefamilies’ home, or by phone. Ideal contact occurred monthly, al-though frequency and modality varied according to family prefer-ence. Classes occurred at the health center but outside of thepediatric practice in order to avoid pre-exposure for the waitlistcontrol group.

Power Up. Families attended interactive classes aimed at educa-tion and support in choosing healthy behaviors with the goal ofreducing childhood obesity. Power Up is a 1.5-hour interactivecurriculum for overweight/obese children and their caregivers of-fered at an urban community health center during early eveninghours. It is based on the 2007 American Association of Pediatri-cians (AAP) Obesity Guidelines15 and the 2008 DHHS PhysicalActivity Guidelines for Children and Adolescents.25,26

Children and their caregivers were grouped separately, and par-ticipated in interactive games and activities (i.e., nutrition jeop-ardy, in-door jump-rope, and food pyramid bingo) that modelbehavior. Classes were led by a team of a health educator, physicaltherapist, nutritionist, and primary care pediatrician. Topics in-cluded portion control, healthy snacking, the dangers of liquidcalories, label reading, goal setting, TV viewing, making changes asa family, the relationship between stress and overeating, stressreduction, and fıtting physical activity into daily life. Classes al-lowed for open discussion between the leader and the participants,who were encouraged to offer suggestions to the group.Sessions ended with a physical activity component, which in-

cluded instruction in the importance of warming up, stretching,

andmaintaining adequate hydration during exercise. Lessons were

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reinforced at home via the use of journals, assignments, and give-away items to promote physical activity at home (i.e., balls forchildren and pedometers for caregivers). At a reunion session3 months later, participants reviewed prior topics, and discussedtheir progress with lifestyle changes and self-image.

Health coaching. Coaching was modeled on a successful La-ino adult diabetes program (ESFT Model)27 and tailored to in-clude the child and family, focusing on opportunities and solutionsto behaviors affecting weight. ESFT emphasizes cultural sensitivityand relationship building to reveal patients’ Explanatory model,Social barriers, Fears and understanding of Treatment.27 Thecoach supported families to overcome barriers, and provided ac-countability for family goal setting andprogress toward these goals.The coach also empowered families to access community resourcesfor healthy food choices and physical activity.All families met with the coach in person at least once, followed

by periodic in-person or phonemeetings during the 6months afterenrollment. Home visits were offered and families were encour-aged to have contact with the coach at leastmonthly; however, eachfamily’s preferences and needs dictated the setting and timing ofmeetings. Family cases were documented in fıeld notes, whichwereinductively coded to cite barriers to achieving lifestyle changes orparticipation.

Data Collection

Data were collected between July 2010 and November 2011 andanalyzed in 2012.

Self-reported measures. Health-related quality of life(HRQoL) was assessed using the PedsQL™ child self-report andcaregiver proxy report generic core scales.28,29 The PedsQL™ pro-ides information on the physical, emotional, social, and school-elated aspects of HRQoL using a 5-point rating scale and askingbout the previous month period. Scales range from 0 to 100, withigher scores indicating better HRQoL30 Psychometric alphas of

0.92 and 0.90 for measuring HRQoL among healthy populationsand children with chronic conditions are reported.28,31 ThePedsQL™ has been validated among Spanish- andEnglish-speakingHispanic groups.32

Nutrition knowledge and intake were assessed by the SchoolPhysical Activity and Nutrition (SPAN) questionnaire, a validatedsurvey that assesses dietary and physical activity behavior, atti-tudes, and knowledge.33–37 SPAN includes 24-hour recall ques-tions, with responses ranging from 0 to �3, and tests nutritionknowledge and attitudes. Agreement for food intake questions isreported at 70%–98%, with � statistics ranging from 0.54 to 0.93nd correlations between 0.66 and 0.97.34 The initial validationpopulation included 41% Hispanics. This questionnaire was pi-loted and collected only on a subset of participants (n�19, 46%).

Body composition and metabolic measures. Anthropo-morphic data were collected by trained Harvard Catalyst ClinicalTranslational Science Center (CTSC) research dietitians. The dataincluded height and weight measured in duplicate with a stadiom-eter and electronic scale, used to calculate each participant’s BMIusing age- and gender-specifıc CDC growth curves, then trans-formed to BMI z-scores using SAS for CDC Growth Charts

(www.cdc.gov/nccdphp/dnpao/growthcharts/resources/sas.htm). e

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Metabolic markers. Lipids; glucose; insulin; hemoglobin A1c

(HbA1c); alanine aminotransferase–aspartate aminotransferaseratio (AST/ALT); C-reactive protein; interleukin 6 (IL-6); and tu-mor necrosis factor � (TNF-�) and other markers were collectedy venipuncture by CTSC nurses using standard protocols. Sam-leswere processed byCTSC laboratories using published industryssays. IL-6 and insulin were analyzed by using Access Chemilu-inescent Immunoassay kit (Beckman Coulter). TNF-� was ana-

lyzed with Enzyme-linked Immunosorbent Assay kit (R & D Sys-tems Inc). Lipids and glucose were analyzed by enzymatic testingwith CHOD-PAP and Gluco-quant Glucose/HK kits (Roche) re-spectively. HbA1cwas done using the Tina-quantHbA1cGen.2 kit(Roche). C-reactive protein was tested using the Cobras IntegraCardiac C-Reactive Protein (Latex) High sensitivity kit (Roche).

Physical activity measure. Physical activity data were col-ected over 5 weekdays and 2 weekend days using GT1M Acti-raph accelerometers, worn around the hip, a validated tool forssessing objective physical activity in children.38 Acceleration inthe vertical fıeld was used to calculate physical activity counts at30-second intervals. Data were then reintegrated into 1-minutetime periods for analyses.A valid day was defıned as �8 hours of accelerometer wear with

a minimum of 10% nonzero epochs per hour to be a valid hour;participants who had at least 1 valid day at both baseline andfollow-up visits were included in analyses. Although 4 or morevalid days is commonly used to estimate children’s physical activ-ity,39 applying this standardwould have decreased this pilot study’sample of subjects providing physical activity data by more than3% and would not have allowed for physical activity assessment.alid accelerometer data were available for participants on at least(94.4%); 2 (88.8%); 3 (77.7%); or �4 (72.2%) days at Visit 1, andt least 1 (88.8%); 2 (85.2%); 3 (80.5%); or �4 (70.5%) days atVisit 2.

Data Processing

Physical activity measure. Accelerometer data were down-oaded to a computer using ActiLife software from which eachhild’s total dailyminutes ofmoderate- to vigorous-intensity phys-cal activity (MVPA) were calculated using age-appropriateounts-per-minute cutpoints.40 For all valid data, each partici-ant’s total daily minutes of MVPA and number of valid days werexported to Microsoft Excel where total minutes in moderate andigorous activity were summed and divided by the number of validays of accelerometer wear to obtain each participant’s mean dailyVPA minutes.

Data Analysis

A difference-in-difference analysis was used, which measures thechange in each outcome within the group of intervention childrenand also accounts for any trend not attributable to the interventionby controlling for the measured change in the control group.PedsQL™ items were reverse-scored and linearly transformed to a0–100 scale and the total score, psychosocial health summaryscore, and physical health summary score were calculated per sur-vey specifıcations.30 Matched case differences in the change in theedsQL™ scores, metabolic biomarkers, and BMI z-scores werealculated.Mixed-effects linear models were used tomeasure differences in

ach of the outcome variables. Backward selection of seven control

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variables was used; variable were chosen based on prior research,including child’s age and gender; caregiver BMI; caregiver educa-tion; primary language spoken at home (English vs non-English);and average daily temperature. Model variables were chosen basedon fıt statistics at a p�0.10 signifıcance level due to the small samplesize, except average temperature, which was forced into the modelfor physical activity outcome. Correlation analysis was done toensure that control variables were not highly correlated with oneanother. Analysis was done using SAS, version 9.2.

ResultsA total of 41 participants were enrolled, 18 of whomwerein the control group.All 23 interventionparticipants tookpart in group classes. Attendance rate to each group classwas 79%. Nine percent (2/23) dropped out of coachingand 26% (6/23) missed one mutually scheduled coachingappointment. Reasons given for inability to continueclasses and coaching were work schedules, other extra-curricular activities, transportation, and child care (al-though siblings were allowed to participate). Fifteen parti-cipants (37%) did not return for Visit 2, either becausethey were lost to follow-up or withdrew from the study. Atotal of 67% (12/18) control and 61% (14/23) interventionparticipants took part in fırst and second visits (Figure 1).The fınal sample included 26 (63%) participants (14

intervention, 12 control). Sensitivity analysis comparingthe children’s gender, primary language spoken in thehome, and caregiver BMI, education, and chronic condi-tion between dyads with incomplete and complete datasuggests that those who did not complete both data col-lections were more likely to have a caregiver with achronic condition compared to those who completeddata collection (p�0.08). Children’s age, gender, andBMI z-score, and caregiver education were not differentetween groups at baseline (Table 1).A majority of control and intervention group partici-ants did not speak English as their primary language at

23 given immediate intervention

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18 waitlist control

21 completed coaching sessions

12 (67%) completed Visit 2

23 completed education sessions

14 (61%) completed Visit 2

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ome. The average reportedmaternal BMIwas higher forhe control group than the intervention group (32.4 vs6.7, p�0.06). No differences were observed betweenroups in accelerometer wear times or counts at baseliner follow-up. However, average valid days among controlnd intervention participants at Visit 1 were 12.4 and3.0 hours/day (p�0.68), respectively, and 10.1 and1.8 hours/day (p�0.09) at Visit 2. Mean total daily ac-elerometer counts among control and intervention par-icipants at Visit 1 were 1,039,551 and 1,327,725p�0.46), respectively, and 1,137,541 and 1,088,754p�0.90) at Visit 2.During coaching sessions, caregiversmost often cited a

ack of knowledge about healthy eating as a barrier toddressing their child’s weight. This was followed by lackf ability to be physically active. Cited barriers to physicalctivity were as follows: inability to fınd places where theamily could exercise together, the cost of programs,ransportation, competing non-active extracurricular ac-ivities, and safety concerns. Safety concerns includedhysical safety and the potential of meeting others whoould encourage risky behaviors in their children. Lackf resources to buy groceries was cited by 26% (6) ofamilies in coaching sessions. Other barriers cited in-luded the inability to control school meals, discordanceetween caregivers about a child’s weight leading to de-reased family support, and tensions due to pre-adoles-ents’ emerging independence.

Self-Reported Measures

Health-related quality-of-life measures. There was anoverall improvement in the total scale for the control andintervention groups for caregiver proxy and child self-reports (Figure 2). The physical health and psychosocialhealth subdomains also improved for all participants,with the exception of the control group’s child self-reporton the psychosocial health subdomain (�2.6 points). Inthe fınal models, caregiver scores were controlled formaternal BMI and caregiver education, and child scoreswere controlled for primary household language andchild’s gender. There was no signifıcant improvement inPedsQLTM scores in the intervention group (�5.6) or theontrol group (�0.4; p�0.48).

utrition knowledge. Pilot nutritional survey datahowed a gap between nutrition knowledge and actualutrition based on a 24-hour food recall. Knowledge onuggested servings averaged 2.6 for both fruits and vege-ables (compared to the correct answer of 5); 29.4% ofhildren reported not knowing the answer. Twenty-four-our recall of servings of fruit was 1.25, and 0.70 foregetables. Many children reported eating meals at

chool always or often (88.2% for lunches and 33.3% for

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breakfast). Most children (82.4%) reported having a neg-ative perception of school lunches (almost never or neverhealthy vs always/almost always/sometimes).

Body Composition and Metabolic Measures

Anthropomorphic measures. BMI z-scores did notchange signifıcantly for the control group (�0.05) or theintervention group (�0.03). After controlling for care-giver education and maternal BMI, the difference in thechange in BMI z-scores between the control and inter-vention group was not signifıcantly different (p�0.31;Table 2).

Metabolic biomarkers. Over the 6-month period therewas no difference in changes between the control and

Table 1. Summary of demographic characteristics for con% or M (SD)

Controln�12

Gender

Male 41.7

Female 58.3

Age (years) 10.4 (1.2)

Child percent BMI 97.8 (3.1)

Male, n�10 99.3 (0.5)

Female, n�16 96.8 (3.8)

Child BMI z-score 2.2 (0.4)

Male, n�10 2.5 (0.2)

Female, n�16 2.0 (0.4)

Primary household language

English 25.0

Non-English 75.0

Highest caregiver education

Below high school 25.0

High school or higher 75.0

Caregiver BMI 32.4 (6.5)

Caregiver chronic conditionsb

Yes 41.7

No 58.3

Immigrant generation

1st 41.7

�2nd 58.3

aChi-squared analysis or Fisher’s exact test and t-tests were used tbIncludes obesity, hypertension, and diabetes mellitus

intervention groups. Although not reaching a priori–

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designated signifıcancelevels, the lipid panelshowed an average de-crease in cholesterol forthe intervention group(�8.4 mg/dL) com-pared to an increase incholesterol for the con-trol group (0.9 mg/dL)in fınal models adjust-ing for household lan-guage (p�0.08). Partic-ipant non-fasting andlaboratory processingproblems contributedto variations in thesample size for each test(Table 2).

Physical activity. Com-plete data for the fırstand second visits wereavailable for four con-trol and 12 interven-tion participants. Themost common reasonfor an incomplete datapair was loss of the ac-celerometer device. Theaverage daily MVPAwas higher for the in-tervention group (27.9minutes) than for thecontrol group (16.8minutes) at baseline(p�0.03). On average,control group partici-pants had a 1.6-minute(SD�3.2) decrease indaily MVPA compared

to a 7.2-minute (SD�19.5) decrease among interventiongroup participants (p�0.62), corresponding to a 4.8%decrease and 0.3% increase in daily MVPA, respectively.After controlling for age, gender, primary household lan-guage, and change in average temperature, difference-in-difference analysis showed no difference in MPVAbetween the two groups (p�0.88; Table 2).

DiscussionObesity is a multifactorial condition that is influenced bysocial environments, social networks, and individual de-cisions that are affected by context. Addressing the child-hood obesity epidemic requires interventions at eachlevel of influence, from national policy to individual

and intervention groups,

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and benefıts of treating obesity through a family-centeredlifestyle approach delivered in a primary care setting.Family-centered approaches to improving food andphysical activity behaviors are increasingly recognized ascentral components to combating obesity, such as theFirst Lady’s recently launched Let’s Move (www.etsmove.gov/.) initiative.In a low-income community, Latino families withbese children are willing to participate in early eveningessions that allow for siblings to participate. They arelso willing to work with a health coach. Social factorsuch as work hours, extracurricular activities, transporta-ion, and child care hindered participation. Many care-ivers cited factors that were out of their control as chal-enges to adopting healthy behaviors. These included thenability to fınd family encompassing physical activitiesnd to control what their child chooses to eat, emergingndependence, and social stressors such as family discord,ınancial stress, and time pressures. However, because ofack of statistical power and families missing return visitseeded solely for data collection, it was not possible tohow that combining child and caregiver interactive ed-cation with health coaching may provide benefıts overoutine medical care.The low quality-of-life scores observed among all par-

icipants suggest that obesity has a substantial impact onatino children’s quality of life. Latino children and theiraregiver proxies reported PedsQL scores that are belowanges of children with cancer41 or diabetes42 and evenlower than scores previously reported among obese chil-dren in general.43,44 Possible explanations include directealth consequences such as joint pain and asthma, andmpacts of obesity such as bullying, self-esteem, or body

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Child self-report Parent-proxy reportIntervention Control ControlIntervention

(p=0.25) (p=0.94) (p=0.94)(p=0.25)

Adjusteda difference-in-difference (p=0.33)

Adjusteda difference-in-difference (p=0.16)

Visit 1 Visit 2

Figure 2. Summary of PedsQL total scores for caregivernd child surveys: intervention and control groups

aAdjusted for primary household language and child’s genderPedsQL, Pediatric Quality of Life Inventory

mage. However, given that the present study was t

onducted in a low-income urban community, the influ-nces that fınancial stress, racism, and bias have on thisopulation cannot be discounted. Although changes inRQoL scores over a 6-month period among the inter-ention group appeared larger than in the control group,here was no signifıcant difference between the tworoups’ changes.Overall, the study found no differences among those

eceiving educational classes and coaching compared toontrols. No differences in changes in BMI or metabolicarkers were found between intervention and controlroup. No differences were noted in physical activityevels between the groups, with activity declining amongoth groups. These fındings may be due to families re-uiring a more-intensive intervention that includescheduled coaching and/or changes to the environmentalontext. The lack of signifıcant fındings may also be dueo the small sample size.The current fındings suggest that improving schooleal programs may offer a key potential community

ntervention. More than 80% of children in the piloteported receiving meals at school consistent with stateata.45 Although school meals followed U.S. Departmentf Agriculture (USDA) regulations,46 more than two

thirds of children reported that their school meals wereunhealthy. This points to the wide range of possible foodoptions, some healthy, other less so, that qualify forschool meal programs under USDA regulations.Newly issued regulations will begin to go into effect to

improve dietary balance in 2013–2014.47 Nationally, inıscal year 2010, more than 31.7 million and more than1.6 million children participated each day in the Na-ional School Lunch48 and National School Breakfastrograms,48 respectively. Given the reach of school mealrograms, community initiatives that aim to ensure im-lementation of regulations and maximize the nutri-ional quality of foods served in school could succeedoth as population-level prevention intervention andopulation-level therapeutic intervention for obesehildren.

LimitationsThe scope of the current results is limited because ofpower and generalizability. The sample size was smalland fairly homogenous as it was drawn exclusively from alow-income, predominantly Latino community. Thus,fındings are limited to populations that share these char-acteristics. However, this is a population that has tradi-tionally not participated in research studies49,50; thus, theurrent study provides needed insight into how to involveatinos in research.Although participants were randomized, because of

he waitlist study design, neither participants nor study

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Table 2. Pre- and post-intervention BMI z-scoresa and metabolic markersb with unadjusted and adjusted difference-in-difference models

Control Intervention Difference-in-difference

n M (SD) p-value n M (SD) p-value Unadjusted Adjusted

BMI z-score

Pre 2.2 (0.4) 2.0 (0.3)

Post 2.1 (0.5) 2.0 (0.4)

Change 10 �0.05 (0.08) 0.13 13 �0.03 (0.14) 0.05 1.00 0.31

LIPIDS (mg/dL)

Cholesterol

Pre 150.3 (36.3) 167.9 (30.2)

Post 151.6 (34.1) 159.4 (23.4)

Change 12 0.9 (16.9) 0.85 14 �8.4 (19.0) 0.12 0.10 0.08

Triglycerides

Pre 91.0 (35.6) 108.6 (35.7)

Post 89.3 (45.7) 113.0 (45.4)

Change 8 4.5 (20.4) 0.55 7 4.4 (62.8) 0.86 1.00 0.97

HDL

Pre 48.1 (11.4) 47.0 (13.4)

Post 51.5 (12.7) 47.7 (14.7)

Change 8 3.8 (5.8) 0.11 7 0.7 (8.4) 0.83 0.42 0.43

VLDL

Pre 18.1 (7.1) 21.6 (7.2)

Post 17.9 (9.0) 22.6 (8.9)

Change 8 1.0 (3.9) 0.49 7 1.0 (12.4) 0.84 1.00 0.96

LDL

Pre 85.7 (37.0) 87.3 (28.4)

Post 89.6 (33.7) 83.3 (23.5)

Change 8 1.6 (16.1) 0.78 7 �4.0 (8.8) 0.78 0.43 0.44

LIVER (IU/L)

AST

Pre 25.6 (7.9) 24.1 (5.6)

Post 20.8 (5.9) 23.1 (7.2)

Change 11 �3.5 (2.7) 0.01 14 �1.1 (4.6) 0.40 0.06 0.17

ALT

Pre 24.6 (13.7) 24.4 (12.6)

Post 24.7 (18.7) 23.4 (15.5)

Change 11 �1.9 (3.6) 0.11 14 �1.1 (11.1) 0.72 0.60 0.49

GLUCOSE METABOLISM

HbA1c(%)

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team members were blinded to group allocation. Thismay have resulted in bias, although the null effects sug-gest otherwise. In addition, numerous measures in theevaluation protocol were not subject to self-report bias(e.g., biomarkers and other measured data). Analysis washindered by loss to follow-up among participants,thereby decreasing statistical power. Future initiativesworking with similar communities must focus attentionon explaining randomized trials, keeping participants en-gaged throughout the study, and tying data collection tovisits that have additional value to participants.One alternative strategy for engaging community

Table 2. Pre- and post-intervention BMI z-scoresa and medifference models (continued)

Control

n M (SD) p-value

Pre 5.6 (0.3)

Post 5.7 (0.3)

Change 11 0.1 (0.2) 0.31

Glucose (mg/dL)

Pre 83.3 (4.1)

Post 85.5 (5.2)

Change 8 2.3 (2.7) 0.05

Insulin (�IU/mL)

Pre 11.5 (4.9)

Post 13.2 (4.6)

Change 4 2.2 (2.3) 0.62

INFLAMMATION

C-reactive protein (mg/L)

Pre 3.0 (4.0)

Post 4.9 (9.1)

Change 12 1.8 (5.2) 0.25

IL-6 (pg/mL)

Pre 3.3 (1.9)

Post 3.4 (2.5)

Change 4 0.4 (2.2) 0.49

TNF-� (pg/mL)

Pre 3.1 (3.8)

Post 1.3 (0.8)

Change 5 �0.9 (0.5) 0.02

aAdjusted for caregiver education and maternal BMIbAdjusted for primary household languageALT, alanine aminotransferase; AST, aspartate aminotransferase; Hblow-density lipoprotein; TNF, tumor necrosis factor; VLDL, very-low-d

members, which may be particularly important in m

studies involving under-represented populations, iscommunity-based participatory research.51 In community-based participatory research, participants are engagedmembers along every stage of the project, from prob-lem and resource identifıcation, to study design, torecruitment and data collection, and ultimately to dis-semination of the fındings.52 Although the currenttudy worked to engage the community, and the healthoach incorporated and adjusted her specifıc recom-endations to accommodate community aspects, itid not utilize the rigor of the community-based par-icipatory research model, which may be successful in

lic markersb with unadjusted and adjusted difference-in-

Intervention Difference-in-difference

n M (SD) p-value Unadjusted Adjusted

5.7 (0.2)

5.7 (0.3)

2 0.0 (0.2) 0.80 0.42 0.38

83.7 (17.3)

84.2 (5.6)

6 1.8 (5.0) 0.41 0.48 0.80

21.8 (17.3)

24.2 (17.8)

4 2.4 (10.4) 0.32 0.98 0.98

2.1 (1.8)

2.5 (2.1)

4 0.4 (1.6) 0.42 0.91 0.90

2.6 (2.0)

2.2 (0.7)

6 �0.7 (2.1) 0.27 0.51 0.55

1.9 (0.8)

1.9 (0.8)

0 0.0 (1.1) 0.98 0.05 0.14

hemoglobin A1c; HDL, high-density lipoprotein; IL, interleukin; LDL,lipoprotein

tabo

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ConclusionHealthy Living Today!, an interactive educational pro-gram that models behavior coupled with family coachingto empower families to incorporate learned material intofamily and child lifestyles, has some promise for aug-menting the standard of care for obesity treatment ofchildren. It appeals to Latino’s sense of “familismo”53 andattempts to address individual and family barriers to suc-cess in the self-management of obesity. The current studyshowed that families are willing to participate, but furtherstudies are needed to show that such models will provideclinical benefıts over current models.This study is in line with other obesity interventions

showing null to minimal changes in outcomes.54 As par-icipants in the current study described the socio-nvironmental barriers that limit their ability to adoptealthy behaviors, we argue for the need to address child-ood obesity at numerous levels, including national,tate, and community policies. Public assistance pro-rams provide some relief in families’ fınancial stressors,ave the potential to support healthy lifestyle changes,nd can even directly intervene in children’s eating hab-ts, as in the case of school meals.Further work is needed to better understand the inter-lay between socio-environmental factors, family, andndividual barriers to treating obesity. One promisingtrategy emerging through the CDC’s Healthy Commu-ities Programs and theU.S. YMCA is a focus on improv-ng the built environment, especially in low-income com-unities, as a means to boost physical activity among

amilies.55 Caregivers in the present study articulated aeed for community settings where caregivers and chil-ren could exercise together. At the same time, they citedafety concerns as a barrier to physical activity. Community-evel interventions that engagemunicipal community de-elopment offıcials in an effort to renovate and buildublic park spaces that are well-utilized, well-lit, andppealing to family members across a wide age span maye especially important for Latino populations.55–59

Aslocalcommunitiesseektoaddress therisingobesityprev-alence by identifying the socio-environmental factors that in-fluence youth weight gain in the community, accompanyinglocal, state, andfederalpoliciesmustbe inplace to improveandmonitor the quality of foods served in schools, and provideadequate, public physical activity spaces, regardless of neigh-borhood demographics. Throughout, organized medicinemust likewise fındmodels that effectively andcompassionatelyaddress each individual’s experience of obesity.

Publication of this article was supported by the Robert WoodJohnson Foundation.This study was funded by the Robert Wood Johnson Foun-

dation through its national program, SaludAmerica!The RWJF

arch 2013

esearch Network to Prevent Obesity Among Latino Childrenwww.salud-america.org). Salud America!, led by the Instituteor Health Promotion Research at The University of Texasealth Science Center at San Antonio, Texas, unites Latinoesearchers and advocates seeking environmental and policyolutions to the epidemic.The study was also supported by the Massachusetts Generalospital Multicultural Affairs Career Development Award.he authors acknowledge the partnership of theMassachusettseneral Hospital Disparities Solution Center. In addition, theuthors acknowledge the generous support from the Harvardatalyst Clinical Research Center, Grant No. 1 UL1 RR025758-1, NIH, National Center for Research Resources, Generallinical Research Centers Program. Finally, we would like tocknowledgeDhruvKhullar for his excellent work as a researchssistant in helping to set up the initial phase of this project.No fınancial disclosures were reported by the authors of thisaper.

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