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Article |

Randomized Controlled Trial to Improve Primary Care to Prevent and Manage Childhood Obesity:  The High Five for Kids Study FREE

Elsie M. Taveras, MD, MPH; Steven L. Gortmaker, PhD; Katherine H. Hohman, MPH; Christine M. Horan, MPH; Ken P. Kleinman, ScD; Kathleen Mitchell, MD; Sarah Price, MPH; Lisa A. Prosser, PhD; Sheryl L. Rifas-Shiman, MPH; Matthew W. Gillman, MD, SM
[+] Author Affiliations

Author Affiliations: Obesity Prevention Program, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute (Drs Taveras, Kleinman, and Gillman and Mss Hohman, Horan, Price, and Rifas-Shiman), Departments of Society, Human Development, and Health (Dr Gortmaker) and Nutrition (Dr Gillman), Harvard School of Public Health, and Harvard Vanguard Medical Associates (Dr Mitchell), Boston, Massachusetts; and Child Health Evaluation and Research Unit, Division of General Pediatrics, University of Michigan Health System, Ann Arbor (Dr Prosser).


Arch Pediatr Adolesc Med. 2011;165(8):714-722. doi:10.1001/archpediatrics.2011.44.
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Objective To examine the effectiveness of a primary care–based obesity intervention over the first year (6 intervention contacts) of a planned 2-year study.

Design Cluster randomized controlled trial.

Setting Ten pediatric practices, 5 intervention and 5 usual care.

Participants Four hundred seventy-five children aged 2 to 6 years with body mass index (BMI) in the 95th percentile or higher or 85th to less than 95th percentile if at least 1 parent was overweight; 445 (93%) had 1-year outcomes.

Intervention Intervention practices received primary care restructuring, and families received motivational interviewing by clinicians and educational modules targeting television viewing and fast food and sugar-sweetened beverage intake.

Outcome Measures Change in BMI and obesity-related behaviors from baseline to 1 year.

Results Compared with usual care, intervention participants had a smaller, nonsignificant change in BMI (−0.21; 95% confidence interval [CI], −0.50 to 0.07; P = .15), greater decreases in television viewing (−0.36 h/d; 95% CI, −0.64 to −0.09; P = .01), and slightly greater decreases in fast food (−0.16 serving/wk; 95% CI, −0.33 to 0.01; P = .07) and sugar-sweetened beverage (−0.22 serving/d; 95% CI, −0.52 to 0.08; P = .15) intake. In post hoc analyses, we observed significant effects on BMI among girls (−0.38; 95% CI, −0.73 to −0.03; P = .03) but not boys (0.04; 95% CI, −0.55 to 0.63; P = .89) and among participants in households with annual incomes of $50 000 or less (−0.93; 95% CI, −1.60 to −0.25; P = .01) but not in higher-income households (0.02; 95% CI, −0.30 to 0.33; P = .92).

Conclusion After 1 year, the High Five for Kids intervention was effective in reducing television viewing but did not significantly reduce BMI.

Trial Registration clinicaltrials.gov Identifier: NCT00377767

Figures in this Article

In the United States, approximately 21.2% of children aged 2 to 5 years are overweight (age- and sex-specific body mass index [BMI] in the 85th-94th percentile) and 10.4% are obese (BMI ≥ 95th percentile).1 Preschool-aged children who are overweight, especially those with overweight parents, tend themselves to become obese as adults2 and are at high risk of short-term3 and long-term adverse outcomes.48 The pediatric primary care team is well positioned to provide effective interventions to promote healthful behaviors among families of young children. Well-child visits occur at least annually from ages 2 through 6 years and additional problem-oriented visits provide other opportunities to develop a relationship with the child and family. The continuity of the relationship between pediatricians and families, embodied in the concept of the “medical home,”9 promotes receptivity to suggestions for changes in health-related behaviors.10

Few interventions to prevent childhood obesity have been conducted in the primary care setting.1123 Only 1 primary care–based randomized controlled trial23 and 2 nonrandomized trials have focused on children younger than 6 years.19,20 In the Live, Eat, and Play (LEAP) randomized controlled trial of 2112 children aged 5 to 9 years in Australia,23 consultations with general practitioners on obesity-related behaviors did not result in significant BMI reduction at 9 or 15 months postenrollment. In a nonrandomized study of 1128 children aged 3 to 6 years who attended primary care clinics in Singapore, Ray et al19 found that nurse-led counseling sessions were effective in reducing obesity prevalence. In another nonrandomized trial conducted within US-based primary care pediatric offices, motivational interviewing by pediatricians and dietitians was effective in reducing BMI percentile among 91 overweight children aged 3 to 7 years.20 Although each of these studies showed the feasibility and, in some, the effectiveness of primary care–based interventions for obesity management, none of these trials involved the entire primary health care team; 2 were further limited by their nonrandomized design; and the 1 US-based study had a small sample size.

The purpose of this study was to assess the extent to which a primary care–based intervention, compared with the usual care control condition, resulted in a smaller increase in BMI and improvement in obesity-related behaviors among children aged 2 through 6 years at elevated risk of obesity.

STUDY DESIGN, SETTING, AND RANDOMIZATION

High Five for Kids is a cluster randomized controlled trial in 10 primary care pediatric offices of Harvard Vanguard Medical Associates, a multisite group practice in Massachusetts. The intervention duration is 2 years and includes an intensive 1-year intervention period followed by a less intensive maintenance period. This article reports the results after the first year of intervention. To pair practices in preparation for blocked, or stratified, randomization, we first divided the practices into the biggest 4 and smallest 6, then matched within those groups as closely as possible on racial/ethnic composition. Within each of 5 pairs, a computerized routine randomly allocated one practice to the intervention group and one to the usual care control group.

PARTICIPANTS

Participants comprised children aged 2.0 to 6.9 years whose BMI (calculated as weight in kilograms divided by height in meters squared) was in the 95th percentile or higher or whose BMI was in the 85th to less than 95th percentile if at least 1 parent was overweight (BMI ≥ 25) and who received their pediatric care at Harvard Vanguard Medical Associates between August 2006 and October 2008. We excluded (1) children whose parent or guardian could not respond to interviews in English or Spanish, (2) children whose families were planning to leave Harvard Vanguard Medical Associates, (3) families for whom the primary care clinician thought the intervention was not appropriate, and (4) children with chronic medical conditions.

Using the electronic medical records, we identified 3253 children who had a BMI in the 85th percentile or higher sometime within the year prior to their index well-child care visit. After each pediatric provider offered medical clearance, and approximately 1 month prior to the child's scheduled well-child care visit, we mailed a letter to each parent introducing the study. The letter included an opt-out telephone number to call if the family did not want to participate. We telephoned those individuals who did not opt out within 7 days after mailing the letter. During the telephone call, research staff conducted a baseline interview and mailed a written informed consent to parents. Research assistants assessed parental BMI by interview. Participants were enrolled once we confirmed their BMI at the scheduled well-child care visit and we received written informed consent.

At 1 year, participants completed a telephone interview with research staff and had their heights and weights measured as part of their annual well-child care visit. We offered all participants $20 for completing each telephone interview. We also reimbursed intervention participants for the co-pay incurred at each visit with the nurse practitioners. All study procedures were approved by the human subjects committee of Harvard Pilgrim Health Care.

TREATMENT GROUPS
Usual Care

Participants randomized to usual care received the current standard of care offered by their pediatric practice. This included well-child care visits and follow-up appointments for weight checks with their pediatrician or a subspecialist (eg, nutritionist). Visits for families in the usual care group included the baseline and annual well-child care visits.

Intervention

The overarching model for this intervention was the Chronic Care Model,24 which posits that changes in primary care to produce functional patient outcomes require changes for all members of the practice team (Figure 1). Major components of the intervention involved changes to the health care system. We trained all members of the practice team to play an active role in the intervention. We enhanced the electronic medical record system to assist clinicians with decision support, patient tracking, follow-up, scheduling, and billing (Figure 1). After reorganization of the delivery of primary and acute care, the pediatric nurse practitioners conducted chronic disease management visits with intervention participants. Prior to the start of the intervention, we negotiated with the regional insurance companies to pay for up to 4 visits for both overweight and obese patients in the first year of the study.

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Figure 1. Conceptual framework, based on the Chronic Care Model, of the High Five for Kids study. EMR indicates electronic medical record.

We trained the pediatric nurse practitioners to be the key intervening clinicians and to use motivational interviewing during four 25-minute, in-person chronic disease management visits and three 15-minute telephone calls in the first year of the intervention. Motivational interviewing is a communication technique that enhances self-efficacy, increases recognition of inconsistencies between actual and desired behaviors, teaches skills for reduction of this dissonance, and enhances motivation for change.2528 Components include de-emphasizing labeling, giving the parent responsibility for identifying which behaviors are problematic, encouraging parents to clarify and resolve ambivalence about behavior change, and setting goals to initiate the change process.25,27,28 We trained the primary care pediatricians in the intervention practices to use brief, focused negotiation skills29 at all routine well-child care visits to endorse family behavior change. Brief, focused negotiation is based on the concepts of motivational interviewing but tailored for brief sessions such as the clinical encounter. To ensure accurate measurements of heights and weights, we trained all medical assistants in intervention and usual care practices on conducting research-standard anthropometric measurements. We also trained the medical receptionists to schedule initial and follow-up visits with the nurse practitioners based on the study protocol.

We developed several resources to assist the physicians and nurse practitioners in supporting participants and their family in behavior change. For the patient waiting rooms, we created posters highlighting our targeted behaviors to encourage dialogue during well-child care visits (Figure 2). For the chronic disease management visits with the nurse practitioners, we developed educational modules targeting television viewing and fast food and sugar-sweetened beverage intake that were matched to a family's stage of readiness to change27; printed and electronic tools for self-management support; lists of local resources for physical activity; and an interactive Web site with educational materials, recipes, and other features. To further support behavior change, the nurse practitioners provided small incentives such as water bottles, books, and snack containers. In addition, the nurse practitioners offered interested families an electronic television monitoring device to assist with the goal of reducing television viewing.

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Figure 2. High Five for Kids poster for pediatric primary care waiting rooms.

OUTCOME MEASURES

Our primary outcome was change in BMI from baseline to 1 year. Medical assistants measured children's weight, without shoes, using an electronic, calibrated scale (Seca, Birmingham, United Kingdom) and height using a stadiometer. We calculated BMI and age- and sex-specific BMI z scores and percentiles.30

The behavioral goals for children in the intervention were less than 1 h/d of television and video viewing, removing the television from or avoiding putting a television in the room where the child sleeps, 1 serving/wk or less of fast food, and 1 serving/d or less of sugar-sweetened beverages. To assess average daily television and video viewing, we used previously validated questions.31 We also asked if the child had a television in the room where he or she sleeps. We measured daily sugar-sweetened beverage intake using questions from a validated semiquantitative child food frequency questionnaire32 and we measured fast food intake using a single question shown to be associated with BMI in an adolescent cohort.33 We also measured the child's daily fruit and vegetable intake34 and outdoor physical activity time.35 During interviews with research staff, the parent who brought the child to his or her well-child care visit reported his or her height and weight range, from which we estimated his or her BMI. Research assistants asked the parent to report the height and weight of the child's other parent. Parents also reported their educational attainment, marital status, annual household income, and their child's race/ethnicity.

We culled data from the electronic medical record on completed visits and telephone calls. To assess parents' acceptance of and satisfaction with the intervention components, we asked parents in the intervention group during the 1-year interview to rate how satisfied they were with the program. We also asked parents if they would recommend the program to their family or friends and whether they had chosen to work on specific behaviors.

DATA ANALYSIS

We first examined baseline distributions of child and parent characteristics by intervention status. In intent-to-treat analyses, we used crude and adjusted multivariate regression models, corrected for clustering by practice, to examine differences from baseline to 1 year between the intervention and usual care groups. For continuous outcomes, we used linear regression models, and for dichotomous outcomes, we used logistic regression models. For all models, to account for intraclass correlation, we performed generalized linear mixed models that accounted for clustering by practices (PROC GLIMMIX in SAS version 9.2; SAS Institute Inc, Cary, North Carolina).

Figure 3 shows the participant flow in the High Five for Kids study. We enrolled 271 children in the intervention group and 204 in usual care. Two hundred fifty-three participants in the intervention group (93% of those enrolled) and 192 participants in usual care (94% of those enrolled) completed a 1-year telephone interview and well-child care visit for BMI measurement. Table 1 shows characteristics of our study sample overall and by intervention assignment. At baseline, mean (SD) BMI was 19.2 (2.6) among intervention children and 19.1 (2.0) among usual care children and BMI z scores were 1.88 (0.69) and 1.82 (0.56), respectively. Fifty-three percent of intervention children had a BMI in the 95th percentile or higher vs 60% of usual care children. Children randomized to the intervention group were more likely to be racial/ethnic minorities, have an obese parent, and live in lower-income households (Table 1). There were no group differences at baseline in health behaviors (Table 1).

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Figure 3. Participant flow for the High Five for Kids study. BMI indicates body mass index (calculated as weight in kilograms divided by height in meters squared); WCC, well-child care.

Table Graphic Jump LocationTable 1. Baseline Characteristics and Behaviors of Participants in the High Five for Kids Study Overall and by Intervention Assignment

Table 2 shows participants' BMI at baseline and at 1 year by intervention assignment. At 1 year, BMI had increased by a mean of 0.31 in the intervention group and 0.49 in the usual care group, yielding a crude difference of −0.19. After multivariable adjustment, compared with usual care, intervention participants had a smaller, nonsignificant change in mean BMI from baseline to 1 year than usual care participants (−0.21; 95% confidence interval [CI], −0.50 to 0.07; P = .15). We observed similar results using change in age- and sex-specific BMI z score as the outcome (−0.05 unit; 95% CI, −0.14 to 0.04; P = .28). In post hoc stratified analyses, we observed statistically significant intervention effects on BMI among girls (−0.38; 95% CI, −0.73 to −0.03; P = .03) but not boys (0.04; 95% CI, −0.55 to 0.63; P = .89) and among participants in households with annual incomes of $50 000 or less (−0.93; 95% CI, −1.60 to −0.25; P = .01) but not in higher-income households (0.02; 95% CI, −0.30 to 0.33; P = .92).

Table Graphic Jump LocationTable 2. Change in BMI From Baseline to 1 Year by Intervention Assignment and Within Subgroup

Table 3 shows baseline and 1-year levels of our behavioral outcomes. In adjusted models, intervention participants decreased their television and video viewing more than usual care participants (−0.36 h/d; 95% CI, −0.64 to −0.09; P = .01). We also observed greater decreases in fast food intake (−0.16 serving/wk; 95% CI, −0.33 to 0.01; P = .07) and sugar-sweetened beverage intake (−0.22 serving/d; 95% CI, −0.52 to 0.08; P = .15), though the confidence intervals for these effects did not exclude a null effect. For the dichotomous outcome of television in the room where the child sleeps, we did not observe an intervention effect (Table 3).

Table Graphic Jump LocationTable 3. Change in Health Behaviors From Baseline to 1 Year by Intervention Assignment

Over their multiple visits and telephone calls, participating families could choose to work on 1 or more behavioral targets. Of the 253 participants in the intervention group, 68% chose to work on decreasing their child's sugar-sweetened beverage intake, 62% chose to work on decreasing their child's fast food intake, 63% chose to work on decreasing their child's television and video viewing, but only 9% chose to work on removing the television from or avoiding putting a television in the room where their child sleeps. We stratified models by whether the family chose to work on the behavior and used usual care as the comparison for each model. In these stratified analyses, we observed greater intervention effects among participants who chose to work on specified behaviors (Figure 4).

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Figure 4. Change in health behaviors from baseline to 1 year according to whether the family chose to work on the behavior. CI indicates confidence interval; FF, fast food intake; OR, odds ratio; SSB, sugar-sweetened beverage intake; TV-BR, television in the child's bedroom; TV+Video, television and video viewing.

We aimed for intervention participants to complete 6 intervention activities with the nurse practitioner by 1 year. Among the 253 intervention participants, 141 (56%) had completed at least 2 of 6 activities. Compared with usual care, intervention participants who completed 2 or more activities by 1 year had greater decreases in television and video viewing (−0.58 h/d; 95% CI, −0.92 to −0.24; P = .001) and sugar-sweetened beverage intake (−0.31 serving/d; 95% CI, −0.74 to 0.12; P = .15). Intervention participants with fewer than 2 activities by 1 year had only minimal decreases in their television and video viewing (−0.04 h/d) and sugar-sweetened beverage intake (−0.02 serving/d). There was no difference in BMI or fast food intake change based on adherence to the intervention protocol.

Based on follow-up questions of the 253 intervention participants, 97% reported being “somewhat” or “very satisfied” with the High Five for Kids program and 91% reported they would recommend the program to their family and friends.

In this 1-year follow-up of a primary care–based, cluster randomized controlled trial we found that a multicomponent obesity intervention based on the Chronic Care Model improved television and video viewing particularly among families who chose to work on reducing television time and removing or avoiding putting a television in the room where the child sleeps. Children in the High Five for Kids intervention group had a smaller, but nonsignificant, increase in BMI overall. In addition, in post hoc analyses, the intervention significantly improved BMI among girls and those living in lower-income households.

To our knowledge, the High Five for Kids study is the first randomized controlled trial in a primary care setting aimed at reducing obesity among preschool-aged children. A recent review of primary care–based interventions for treating overweight and obese children and adolescents22 identified no moderate- to high-intensity interventions for children younger than 6 years and only the LEAP trial,23 a low-intensity intervention that involved consultations with general practitioners on nutrition, physical activity, and sedentary behavior, included children 5 years and older. Our intervention was also innovative in that we attempted to effect sustainable changes in the health care system to prevent and manage childhood obesity. We recognized that the complexity of childhood obesity as a chronic medical problem required a new paradigm to improve obesity-related outcomes. Thus, based on the Chronic Care Model, the High Five for Kids intervention involved changes in the roles and responsibilities for the entire practice team and retraining of clinicians to support family behavior change, as well as updating clinical information systems and providing families links to their community for physical activity. We designed intervention components to be sustainable in a “real-world” primary care setting by training existing clinical staff to deliver the intervention. The intervention was also designed to be of moderate to high intensity requiring 6 intervention activities over a 1-year period.

In our intervention, the overall adjusted mean difference (intervention vs usual care) in BMI was −0.21 at 1 year. This magnitude of effect is very similar to that of the LEAP study23 in which the adjusted mean difference in BMI was −0.20 (95% CI, −0.6 to 0.1) at 9 months. Several factors could have contributed to the lack of a statistically significant intervention effect on BMI. First, our intervention involved only the primary care setting and not children's communities or environment. It is possible that primary care–based interventions alone will not effect change in BMI but could complement and potentially enhance more comprehensive efforts in multiple settings. Second, adherence to intervention activities was relatively low; a little more than half of the participants completed at least 2 of the 6 visits/telephone calls. It is possible that the intervention “dose” delivered was not sufficient in effecting changes in BMI. Third, we taught the nurse practitioners to use motivational interviewing to structure their visits and telephone calls. Parents were provided a choice of behaviors to work on in a nonprescriptive style and this could have led to parents choosing behaviors that could have had a lower impact on BMI, eg, fruit and vegetable intake. Fourth, it is possible that BMI changes might lag behind the behavioral changes we observed in our intervention. Thus, we will evaluate the effect of the intervention after the planned 2-year intervention period.

Cross-sectional,3638 longitudinal,39,40 and experimental4143 evidence suggest that television viewing and televisions in bedrooms are associated with obesity risk in children. Although several interventions have attempted to reduce television viewing, only 3 published studies have included children younger than 6 years,41,42,44 only 2 of which successfully decreased television viewing.41,42 Using intervention strategies similar to Dennison et al41 and Epstein et al,42 we found that children in the intervention group decreased their television and video viewing by 0.36 h/d. The magnitude of effect was higher (−0.58 h/d) if parents chose to work on reducing their child's television and video viewing. This magnitude of effect was similar to the 2 published interventions that included preschool-aged children. Our results lend support to multimodal interventions to reduce television viewing among young children.

We observed greater intervention effects among female participants and among those living in lower-income households. It is possible that the sex differences we observed could be due to parents of girls being more attuned to issues of weight, diet, and activity and could have been more responsive to the intervention. A similar sex difference in intervention effect has been shown in other childhood obesity intervention studies.45 Participating children living in lower-income households had higher BMIs at baseline. It is possible the intervention was more effective among these children because they had more “room to move.” These findings deserve further investigation.

This intervention had several limitations. First, although we attempted to match the pediatric sites to obtain similar participant characteristics in intervention and usual care, unbalanced participant characteristics at baseline occurred. This imbalance may have also affected differences in parent obesity and household income. However, adjusted and unadjusted results were similar, suggesting that any imbalance in observed (or unobserved) characteristics did not affect inferences. Second, electronic medical records, which we used for decision support and recruiting and tracking of intervention participants, are not available in all pediatric practices. Thus, our intervention may not generalize to all pediatric settings. Third, although we used validated measures to assess our behavioral outcomes, we used parental report of behaviors rather than objective measures. Thus, it is possible that parents could exaggerate self-reported improvements in behaviors. For this reason, our primary outcome was BMI, a more objective measure. Fourth, because our intervention was not a factorial design, we are not able to specifically say which components were more effective. However, our results indicate that participants with more fidelity to protocol had greater improvement in their behaviors, possibly indicating that with greater fidelity to protocol, we could have had greater magnitudes of effects.

In summary, after 1 year, we found that the High Five for Kids study improved television-viewing behaviors among preschool-aged children but did not have significant effects on BMI or diet-related behaviors. We plan further follow-up to evaluate the intervention effects over a longer period and examine the components of such an intervention that are maximally effective, scalable, and cost-effective.

Correspondence: Elsie M. Taveras, MD, MPH, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, 133 Brookline Ave, Sixth Floor, Boston, MA 02215 (elsie_taveras@harvardpilgrim.org).

Accepted for Publication: January 12, 2011.

Published Online: April 4, 2011. doi:10.1001/archpediatrics.2011.44

Author Contributions: Dr Taveras had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Taveras, Hohman, Kleinman, Mitchell, Price, and Gillman. Acquisition of data: Hohman, Horan, Mitchell, and Price. Analysis and interpretation of data: Taveras, Gortmaker, Hohman, Horan, Kleinman, Prosser, Rifas-Shiman, and Gillman. Drafting of the manuscript: Taveras. Critical revision of the manuscript for important intellectual content: Gortmaker, Hohman, Horan, Kleinman, Mitchell, Price, Prosser, Rifas-Shiman, and Gillman. Statistical analysis: Gortmaker, Kleinman, Prosser, and Rifas-Shiman. Obtained funding: Gillman. Administrative, technical, and material support: Hohman, Horan, Price, and Gillman. Study supervision: Taveras and Gillman.

Financial Disclosure: None reported.

Funding/Support: This study was supported by grant R01 HD 050966 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development.

Previous Presentation: The abstract of this article was published as part of the proceedings of the 2010 Pediatric Academic Societies Meeting; May 1-4, 2010; Vancouver, British Columbia, Canada.

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PubMed   |  Link to Article
Miller W, Rollnick S. Motivational Interviewing2nd ed. New York, NY: Guilford Press; 2002
Rollnick S, Butler C. Health Behavior Change: a Guide for Practitioners. Edinburgh, Scotland: Churchill Livingstone; 1999
Rollnick S, Miller WR, Butler CC. Motivational Interviewing in Health Care: Helping Patients Change BehaviorNew York, NY: Guilford Press; 2008
Tyler DO, Horner SD. Family-centered collaborative negotiation: a model for facilitating behavior change in primary care.  J Am Acad Nurse Pract. 2008;20(4):194-203
PubMed   |  Link to Article
Tamura T, Goldenberg RL, Freeberg LE, Cliver SP, Cutter GR, Hoffman HJ. Maternal serum folate and zinc concentrations and their relationships to pregnancy outcome.  Am J Clin Nutr. 1992;56(2):365-370
PubMed
Baker PC, Keck CK, Mott FL, Quinlan SV. NLSY Child Handbook: A Guide to the 1986-90 National Longitudinal Survey of Youth Child DataRev ed. Columbus: Center for Human Resource Research, Ohio State University; 1993
Blum RE, Wei EK, Rockett HR,  et al.  Validation of a food frequency questionnaire in Native American and Caucasian children 1 to 5 years of age.  Matern Child Health J. 1999;3(3):167-172
PubMed   |  Link to Article
Taveras EM, Berkey CS, Rifas-Shiman SL,  et al.  Association of consumption of fried food away from home with body mass index and diet quality in older children and adolescents.  Pediatrics. 2005;116(4):e518-e524
PubMed   |  Link to Article
Rifas-Shiman SL, Willett WC, Lobb R, Kotch J, Dart C, Gillman MW. PrimeScreen, a brief dietary screening tool: reproducibility and comparability with both a longer food frequency questionnaire and biomarkers.  Public Health Nutr. 2001;4(2):249-254
PubMed   |  Link to Article
Burdette HL, Whitaker RC, Daniels SR. Parental report of outdoor playtime as a measure of physical activity in preschool-aged children.  Arch Pediatr Adolesc Med. 2004;158(4):353-357
PubMed   |  Link to Article
Andersen RE, Crespo CJ, Bartlett SJ, Cheskin LJ, Pratt M. Relationship of physical activity and television watching with body weight and level of fatness among children: results from the Third National Health and Nutrition Examination Survey.  JAMA. 1998;279(12):938-942
PubMed   |  Link to Article
Crespo CJ, Smit E, Troiano RP, Bartlett SJ, Macera CA, Andersen RE. Television watching, energy intake, and obesity in US children: results from the Third National Health and Nutrition Examination Survey, 1988-1994.  Arch Pediatr Adolesc Med. 2001;155(3):360-365
PubMed
Dennison BA, Erb TA, Jenkins PL. Television viewing and television in bedroom associated with overweight risk among low-income preschool children.  Pediatrics. 2002;109(6):1028-1035
PubMed   |  Link to Article
Dietz WH Jr, Gortmaker SL. Do we fatten our children at the television set? obesity and television viewing in children and adolescents.  Pediatrics. 1985;75(5):807-812
PubMed
Gortmaker SL, Must A, Sobol AM, Peterson K, Colditz GA, Dietz WH. Television viewing as a cause of increasing obesity among children in the United States, 1986-1990.  Arch Pediatr Adolesc Med. 1996;150(4):356-362
PubMed   |  Link to Article
Dennison BA, Russo TJ, Burdick PA, Jenkins PL. An intervention to reduce television viewing by preschool children.  Arch Pediatr Adolesc Med. 2004;158(2):170-176
PubMed   |  Link to Article
Epstein LH, Roemmich JN, Robinson JL,  et al.  A randomized trial of the effects of reducing television viewing and computer use on body mass index in young children.  Arch Pediatr Adolesc Med. 2008;162(3):239-245
PubMed   |  Link to Article
Robinson TN. Reducing children's television viewing to prevent obesity: a randomized controlled trial.  JAMA. 1999;282(16):1561-1567
PubMed   |  Link to Article
Fitzgibbon ML, Stolley MR, Schiffer L, Van Horn L, KauferChristoffel K, Dyer A. Two-year follow-up results for Hip-Hop to Health Jr.: a randomized controlled trial for overweight prevention in preschool minority children.  J Pediatr. 2005;146(5):618-625
PubMed   |  Link to Article
Gortmaker SL, Peterson K, Wiecha J,  et al.  Reducing obesity via a school-based interdisciplinary intervention among youth: Planet Health.  Arch Pediatr Adolesc Med. 1999;153(4):409-418
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Graphic Jump Location

Figure 1. Conceptual framework, based on the Chronic Care Model, of the High Five for Kids study. EMR indicates electronic medical record.

Place holder to copy figure label and caption
Graphic Jump Location

Figure 2. High Five for Kids poster for pediatric primary care waiting rooms.

Place holder to copy figure label and caption
Graphic Jump Location

Figure 3. Participant flow for the High Five for Kids study. BMI indicates body mass index (calculated as weight in kilograms divided by height in meters squared); WCC, well-child care.

Place holder to copy figure label and caption
Graphic Jump Location

Figure 4. Change in health behaviors from baseline to 1 year according to whether the family chose to work on the behavior. CI indicates confidence interval; FF, fast food intake; OR, odds ratio; SSB, sugar-sweetened beverage intake; TV-BR, television in the child's bedroom; TV+Video, television and video viewing.

Tables

Table Graphic Jump LocationTable 1. Baseline Characteristics and Behaviors of Participants in the High Five for Kids Study Overall and by Intervention Assignment
Table Graphic Jump LocationTable 2. Change in BMI From Baseline to 1 Year by Intervention Assignment and Within Subgroup
Table Graphic Jump LocationTable 3. Change in Health Behaviors From Baseline to 1 Year by Intervention Assignment

References

Ogden CL, Carroll MD, Curtin LR, Lamb MM, Flegal KM. Prevalence of high body mass index in US children and adolescents, 2007-2008.  JAMA. 2010;303(3):242-249
PubMed   |  Link to Article
Whitaker RC, Wright JA, Pepe MS, Seidel KD, Dietz WH Jr. Predicting obesity in young adulthood from childhood and parental obesity.  N Engl J Med. 1997;337(13):869-873
PubMed   |  Link to Article
Skinner AC, Steiner MJ, Henderson FW, Perrin EM. Multiple markers of inflammation and weight status: cross-sectional analyses throughout childhood.  Pediatrics. 2010;125(4):e801-e809
PubMed   |  Link to Article
Dietz WH. Health consequences of obesity in youth: childhood predictors of adult disease.  Pediatrics. 1998;101(3, pt 2):518-525
PubMed
Dietz WH Jr, Gross WL, Kirkpatrick JA Jr. Blount disease (tibia vara): another skeletal disorder associated with childhood obesity.  J Pediatr. 1982;101(5):735-737
PubMed   |  Link to Article
Freedman DS, Khan LK, Dietz WH, Srinivasan SR, Berenson GS. Relationship of childhood obesity to coronary heart disease risk factors in adulthood: the Bogalusa Heart Study.  Pediatrics. 2001;108(3):712-718
PubMed   |  Link to Article
Castro-Rodríguez JA, Holberg CJ, Morgan WJ, Wright AL, Martinez FD. Increased incidence of asthmalike symptoms in girls who become overweight or obese during the school years.  Am J Respir Crit Care Med. 2001;163(6):1344-1349
PubMed
Fagot-Campagna A, Pettitt DJ, Engelgau MM,  et al.  Type 2 diabetes among North American children and adolescents: an epidemiologic review and a public health perspective.  J Pediatr. 2000;136(5):664-672
PubMed   |  Link to Article
Medical Home Initiatives for Children With Special Needs Project Advisory Committee, American Academy of Pediatrics.  The medical home.  Pediatrics. 2002;110(1, pt 1):184-186
PubMed   |  Link to Article
Taveras EM, Capra AM, Braveman PA, Jensvold NG, Escobar GJ, Lieu TA. Clinician support and psychosocial risk factors associated with breastfeeding discontinuation.  Pediatrics. 2003;112(1, pt 1):108-115
PubMed   |  Link to Article
Díaz RG, Esparza-Romero J, Moya-Camarena SY, Robles-Sardín AE, Valencia ME. Lifestyle intervention in primary care settings improves obesity parameters among Mexican youth.  J Am Diet Assoc. 2010;110(2):285-290
PubMed   |  Link to Article
Siegel RM, Rich W, Joseph EC,  et al.  A 6-month, office-based, low-carbohydrate diet intervention in obese teens.  Clin Pediatr (Phila). 2009;48(7):745-749
PubMed   |  Link to Article
Ewing LJ, Cluss P, Goldstrohm S,  et al.  Translating an evidence-based intervention for pediatric overweight to a primary care setting.  Clin Pediatr (Phila). 2009;48(4):397-403
PubMed   |  Link to Article
Rattay KT, Ramakrishnan M, Atkinson A, Gilson M, Drayton V. Use of an electronic medical record system to support primary care recommendations to prevent, identify, and manage childhood obesity.  Pediatrics. 2009;123:(suppl 2)  S100-S107
PubMed   |  Link to Article
Kubik MY, Story M, Davey C, Dudovitz B, Zuehlke EU. Providing obesity prevention counseling to children during a primary care clinic visit: results from a pilot study.  J Am Diet Assoc. 2008;108(11):1902-1906
PubMed   |  Link to Article
Wake M, Gold L, McCallum Z, Gerner B, Waters E. Economic evaluation of a primary care trial to reduce weight gain in overweight/obese children: the LEAP trial.  Ambul Pediatr. 2008;8(5):336-341
PubMed   |  Link to Article
Patrick K, Calfas KJ, Norman GJ,  et al.  Randomized controlled trial of a primary care and home-based intervention for physical activity and nutrition behaviors: PACE+ for adolescents.  Arch Pediatr Adolesc Med. 2006;160(2):128-136
PubMed   |  Link to Article
Saelens BE, Sallis JF, Wilfley DE, Patrick K, Cella JA, Buchta R. Behavioral weight control for overweight adolescents initiated in primary care.  Obes Res. 2002;10(1):22-32
PubMed   |  Link to Article
Ray R, Lim LH, Ling SL. Obesity in preschool children: an intervention programme in primary health care in Singapore.  Ann Acad Med Singapore. 1994;23(3):335-341
PubMed
Schwartz RP, Hamre R, Dietz WH,  et al.  Office-based motivational interviewing to prevent childhood obesity: a feasibility study.  Arch Pediatr Adolesc Med. 2007;161(5):495-501
PubMed   |  Link to Article
Polacsek M, Orr J, Letourneau L,  et al.  Impact of a primary care intervention on physician practice and patient and family behavior: keep ME Healthy—the Maine Youth Overweight Collaborative.  Pediatrics. 2009;123:(suppl 5)  S258-S266
PubMed   |  Link to Article
Whitlock EP, O’Connor EA, Williams SB, Beil TL, Lutz KW. Effectiveness of weight management interventions in children: a targeted systematic review for the USPSTF.  Pediatrics. 2010;125(2):e396-e418
PubMed   |  Link to Article
McCallum Z, Wake M, Gerner B,  et al.  Outcome data from the LEAP (Live, Eat and Play) trial: a randomized controlled trial of a primary care intervention for childhood overweight/mild obesity.  Int J Obes (Lond). 2007;31(4):630-636
PubMed
Wagner EH. Chronic disease management: what will it take to improve care for chronic illness?  Eff Clin Pract. 1998;1(1):2-4
PubMed
Emmons KM, Rollnick S. Motivational interviewing in health care settings: opportunities and limitations.  Am J Prev Med. 2001;20(1):68-74
PubMed   |  Link to Article
Miller W, Rollnick S. Motivational Interviewing2nd ed. New York, NY: Guilford Press; 2002
Rollnick S, Butler C. Health Behavior Change: a Guide for Practitioners. Edinburgh, Scotland: Churchill Livingstone; 1999
Rollnick S, Miller WR, Butler CC. Motivational Interviewing in Health Care: Helping Patients Change BehaviorNew York, NY: Guilford Press; 2008
Tyler DO, Horner SD. Family-centered collaborative negotiation: a model for facilitating behavior change in primary care.  J Am Acad Nurse Pract. 2008;20(4):194-203
PubMed   |  Link to Article
Tamura T, Goldenberg RL, Freeberg LE, Cliver SP, Cutter GR, Hoffman HJ. Maternal serum folate and zinc concentrations and their relationships to pregnancy outcome.  Am J Clin Nutr. 1992;56(2):365-370
PubMed
Baker PC, Keck CK, Mott FL, Quinlan SV. NLSY Child Handbook: A Guide to the 1986-90 National Longitudinal Survey of Youth Child DataRev ed. Columbus: Center for Human Resource Research, Ohio State University; 1993
Blum RE, Wei EK, Rockett HR,  et al.  Validation of a food frequency questionnaire in Native American and Caucasian children 1 to 5 years of age.  Matern Child Health J. 1999;3(3):167-172
PubMed   |  Link to Article
Taveras EM, Berkey CS, Rifas-Shiman SL,  et al.  Association of consumption of fried food away from home with body mass index and diet quality in older children and adolescents.  Pediatrics. 2005;116(4):e518-e524
PubMed   |  Link to Article
Rifas-Shiman SL, Willett WC, Lobb R, Kotch J, Dart C, Gillman MW. PrimeScreen, a brief dietary screening tool: reproducibility and comparability with both a longer food frequency questionnaire and biomarkers.  Public Health Nutr. 2001;4(2):249-254
PubMed   |  Link to Article
Burdette HL, Whitaker RC, Daniels SR. Parental report of outdoor playtime as a measure of physical activity in preschool-aged children.  Arch Pediatr Adolesc Med. 2004;158(4):353-357
PubMed   |  Link to Article
Andersen RE, Crespo CJ, Bartlett SJ, Cheskin LJ, Pratt M. Relationship of physical activity and television watching with body weight and level of fatness among children: results from the Third National Health and Nutrition Examination Survey.  JAMA. 1998;279(12):938-942
PubMed   |  Link to Article
Crespo CJ, Smit E, Troiano RP, Bartlett SJ, Macera CA, Andersen RE. Television watching, energy intake, and obesity in US children: results from the Third National Health and Nutrition Examination Survey, 1988-1994.  Arch Pediatr Adolesc Med. 2001;155(3):360-365
PubMed
Dennison BA, Erb TA, Jenkins PL. Television viewing and television in bedroom associated with overweight risk among low-income preschool children.  Pediatrics. 2002;109(6):1028-1035
PubMed   |  Link to Article
Dietz WH Jr, Gortmaker SL. Do we fatten our children at the television set? obesity and television viewing in children and adolescents.  Pediatrics. 1985;75(5):807-812
PubMed
Gortmaker SL, Must A, Sobol AM, Peterson K, Colditz GA, Dietz WH. Television viewing as a cause of increasing obesity among children in the United States, 1986-1990.  Arch Pediatr Adolesc Med. 1996;150(4):356-362
PubMed   |  Link to Article
Dennison BA, Russo TJ, Burdick PA, Jenkins PL. An intervention to reduce television viewing by preschool children.  Arch Pediatr Adolesc Med. 2004;158(2):170-176
PubMed   |  Link to Article
Epstein LH, Roemmich JN, Robinson JL,  et al.  A randomized trial of the effects of reducing television viewing and computer use on body mass index in young children.  Arch Pediatr Adolesc Med. 2008;162(3):239-245
PubMed   |  Link to Article
Robinson TN. Reducing children's television viewing to prevent obesity: a randomized controlled trial.  JAMA. 1999;282(16):1561-1567
PubMed   |  Link to Article
Fitzgibbon ML, Stolley MR, Schiffer L, Van Horn L, KauferChristoffel K, Dyer A. Two-year follow-up results for Hip-Hop to Health Jr.: a randomized controlled trial for overweight prevention in preschool minority children.  J Pediatr. 2005;146(5):618-625
PubMed   |  Link to Article
Gortmaker SL, Peterson K, Wiecha J,  et al.  Reducing obesity via a school-based interdisciplinary intervention among youth: Planet Health.  Arch Pediatr Adolesc Med. 1999;153(4):409-418
PubMed   |  Link to Article

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Step-Cal Meter, an easy method to educate children, young people and adults
Posted on April 14, 2011
Santiago Garcia-Tornel, MD,PhD
Hospital Sant Joan de Deu,
Conflict of Interest: None Declared
Childhood obesity predisposes to insulin resistance and type 2 diabetes, hypertension, hyperlipidemia, liver and renal disease, and reproductive dysfunction. It also increases the risk of adult-onset obesity and cardiovascular diseases and has emerged as the number 1 health problem in the United States and another countries.1 Physicians and parents should encourage children to participate in vigorous physical activity throughout adolescence and young adulthood and to limit time spent watching television and videos and playing computer games. An energy -restricted and balanced diet, together with patient and parent education, behavioral changes and exercise, can limit weight gain in many pediatric patients who have mild or moderate obesity. The experts suggest that providers encourage healthy behaviors while using techniques to motivate patients and families, and interventions should be tailored to the individual child and family.2 A major educational barrier for both parents and children is the understanding of the simple equivalence between calorie intake and the exercise required to use this energy. Obesity prevention must clarify to caregivers and children the nutritional value associated with food and with the promotion of physical activity. Not all exercise demands the same energy expenditure. There are numerous tables showing the heat loss produced by the exercise performed. We believe this method is not very educational because it is difficult to learn and teach. The new proposal is based on the opposite reasoning. It pretends to teach the number of steps that should be walked up or down depending on the child's calorie intake. Several studies3-5 about the heart rate and oxygen uptake responses and the intensity and caloric cost of ascending and descending a public-access staircase, have showed that the caloric cost of stepping up and down a step were 0.11 and 0.05 kcal, respectively. The steps of the stairs have been standardized.6,7 With our method, it is possible to know the number of steps a child needs to walk up or down in order to burn the calories of the product he is about to eat. For educational purposes we have called it the Step-Cal Meter.
Nowadays, information on caloric values is available on the packaging and labeling of all foodstuffs. However, the public does not see on these labels the amount of effort it would take to burn those calories. Our simple tool transforms calories into steps, for educational purposes and the equivalence should be printed on all nutrition labels. For example, one McDonald's Biscuit (Breads), which is 84 gm in weight and has 290 calories, is equivalent to walking up and down 1812 steps, a bit more than going up and down the Empire State Building, which has 1576 stairs up to the 86th floor.
References
1. Moss BG, Yeaton WH. Young children's weight trajectories and associated risk factors: results from the Early Childhood Longitudinal Study-Birth Cohort. Am J Health Promot 2011; 25:190-8.
2. Spear BA, Barlow SE, Ludwig DS, Saelens BE, Schetzina KE, Taveras EM. Recommendations for treatment of child and adolescent overweight and obesity. Pediatrics 2007; 120 (Suppl): S254-S288.
3. Arvidsson D, Slinde F. Larsson S, Hulthe´n L. Energy cost in children assessed by multisensor activity monitors. Med. Sci Sports Exerc 2009: 41, 603-611.
4. Ridley K, Ods TS. Assigning energy cost to activities in children: A review and synthesis. Med Sci Sports Exerc 2009; 40:1439-1446. 5.Teh KC, Aziz AR. Heart rate, oxygen uptake, and energy cost of ascending and descending the stairs. Med Sci Sports Exerc. 2002 Apr;34(4):695-9.
6. Chiang C, Carter C. The Backstage Handbook: An Illustrated Almanac of Technical Information. Broadway Pr. 1994.
7. The American Institute of Architects Architectural Graphic Standards, 11th Edition. Wiley 2007.

Conflict of Interest: None declared
Number of children versus mean changes
Posted on April 5, 2011
Paul C. Young, M.D.
University of Utah
Conflict of Interest: None Declared
The article by Taveras et al showed some modest but important benefits children in the intervention group. I wish, however, that rather than reporting mean changes in the outcome variables, they would tell us how many children in the intervention group had a meaningful change compared with those in the comparison group. For example how many of the children who were initially in the overweight groups had a BMI that dropped below the 85th% in the two groups or how many in the obese groups dropped below the 95th or even how many in the overweight group did not progress to a BMI >95th, i.e., progressed from overweight to obese? Comparing the absolute risk reduction of the two groups allows the reader to estimate a NNT, which is perhaps the most useful outcome in an intervention study. The problem with comparing means is that, as the authors point out, obesity is complex and multifactorial. What works for one child may not work for another. We can't tell from this data whether the intervention worked for anybody or not. If there were differences in the number of children who achieved a meaningful improvement, it would be extremely interesting to know the characteristics of the children for whom the intervention worked compared with those for whom it did not. Conflict of Interest: None declared
Reporting Group changes in BMI may obscure useful information
Posted on August 9, 2011
Paul C. Young, M.D.
University of Utah
Conflict of Interest: None Declared
The article by Taveras and her Harvard colleagues reports an ambitious and well-conceived primary care intervention designed to prevent and manage obesity. The results suggest no differences in the mean BMI in the two groups at one year. I continue to wonder why obesity researchers report their data this way rather than the number of children who achieved the desired result, whatever that result might be. In the current study it would be much more useful to know the number of children who were in the > 95th% at baseline cohort and whose BMI decreased (or at least didn’t increase) at 1 year; for the 85-95 cohort, how many stayed below the 95th at one year? Since the pathway to obesity is complex involving multiple factors, it is unlikely that a single intervention, even a relatively comprehensive one as described here, will work for every one. If nobody improved or everybody continued to get worse, that would indeed be depressing. However, if anyone improved or at least didn’t get worse, knowing the characteristics of those who did and those who did not, might provide important insights into targeting interventions for those who share the characteristics of those who improve and going back to the drawing board for those for whom it looks like this type of intervention won’t work.
It seems a shame to go to so much work to do a study like this and then analyze the data in a way that might be misleading or obscure potentially useful information. No one would report an RCT of a drug for some disease without indicating the number of subjects who improved or were cured in the intervention group compared with the control group. Why do obesity researchers?

Conflict of Interest: None declared
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