Translational Research in Childhood Obesity ... - SAGE Journals

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This article explores the state of translational research in childhood obesity prevention. Five phases of translational research and emerging findings from basic science that could be useful in the design of obesity prevention programs are described. Few intervention studies have been published, and these are dominated by Phase 3 trials (efficacy), conducted in school settings, with a limited number of studies finding significant effects on Body Mass Index or adiposity. Cost-benefit analyses are lacking. Research is needed to translate basic science findings into novel interventions (Phases 1 and 2) and to translate programs with known behavior change capability into dissemination trials (Phase 5). Translation can be facilitated by enhanced communication between basic science and prevention science researchers, the development of interventions for underused settings, using novel strategies, support by institutions and funding agencies for translation, and the design of interventions with translation in mind.

TRANSLATIONAL RESEARCH IN CHILDHOOD OBESITY PREVENTION KIM D. REYNOLDS DONNA SPRUIJT-METZ University of Southern California

Keywords: translation; childhood obesity; prevention; intervention

AUTHORS’ NOTE: Please address correspondence to Kim D. Reynolds, PhD, University of Southern California, Institute for Health Promotion and Disease Prevention Research, 1000 S. Fremont Avenue, Unit #8, Bldg. A-5, #5235, Alhambra, CA 91803; e-mail: [email protected]. EVALUATION & THE HEALTH PROFESSIONS, Vol. 29 No. 2, June 2006 219-245 DOI: 10.1177/0163278706287346 © 2006 Sage Publications

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Evaluation & the Health Professions / June 2006

he prevalence of overweight and obesity continues to rise in the United States. For pediatric populations, obesity and overweight are determined using distributions of Body Mass Index (BMI) specific to age and gender and with children falling into one of four categories, including underweight (BMI for age less than 5th percentile), normal (BMI for age 5th to less than 85th percentile), at risk for overweight (85th percentile to less than 95th percentile), and overweight (BMI for age equal to or greater than 95th percentile). Prevalence of overweight has been estimated at 11% to 16% among children 6 to 19 years of age (Centers for Disease Control and Prevention, 2001; Hedley et al., 2004; Troiano & Flegal, 1998). Prevalence rates for overweight or at risk for overweight among children 4 to 12 years of age appear higher for African Americans (21.5%) and Hispanics (21.8%) compared to non-Hispanic Whites (12.3%; Strauss & Pollack, 2001). Although major efforts should continue to target the treatment of obesity in children, the long-term maintenance of weight loss is difficult to achieve, and efforts are warranted for the prevention of obesity (Baranowski et al., 2000; Gill, 1997; Lobstein, Baur, & Uauy, 2004; Story et al., 1999). With the rapidly increasing incidence of obesity in children, the need for effective prevention programs is particularly acute. This has led to a call for the rapid implementation of effective strategies for preventing obesity (Robinson & Sirard, 2005) and putting translational research squarely in the center of the debate on our national strategy for the prevention of obesity. In this article, we use the definition for translation described by Sussman, Valente, Rohrbach, Stacy, and Pentz (in press) including the adoption of novel ideas from basic science for use in the design of interventions to modify behavior and reduce risk for disease. This definition of translation also includes the progression of interventions from initial feasibility testing to the verification of their behavior change properties and then to the eventual dissemination of programs with known behavior change capability. Because effective programs to prevent obesity for children and adolescents will differ substantially from programs designed to reach adults, we will restrict our discussion of translation to studies of the prevention of obesity in pediatric populations. What does research in health behavior and public health have to contribute in terms of programs known to be effective for obesity prevention, and how can we best translate these programs to eventual dissemination and widespread use? How can we draw on basic

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science research to develop more effective intervention strategies for the prevention of obesity? These are essential questions for the field, as it works to create effective strategies for the prevention of childhood obesity. In this article, we will address these critical questions and suggest key issues that must be considered to facilitate translational research in childhood obesity prevention.

MODELS OF TRANSLATIONAL RESEARCH FOR CHILDHOOD OBESITY PREVENTION

Various models have been developed for the conceptualization of translational research and have been described well in Sussman et al. (in press). We will provide a brief summary and relate these models to research in childhood obesity prevention. One common framework is the five-phase model initially put forward by Greenwald and Cullen (1985). In this model, the five phases of translational research include (a) basic research, (b) methods development, (c) efficacy trials, (d) effectiveness trials, and (e) dissemination trials. The lines between phases of translation can be fuzzy, and the paragraphs below attempt to illustrate the definition of each phase using examples from research in obesity prevention. In Phase 1, basic research is the generation of etiologic models to explain and predict phenomena of interest including obesity-related behavior. Basic research can include behavioral and physiological findings that inform the design of intervention components, the selection of behaviors to change, or the selection of outcomes to measure. For instance, advances in the understanding of the neurobiological basis of eating have shown that feeding modulatory gut peptides such as ghrelin and cholecystokinin are released before and during food intake and cue feeding behaviors. However, obesity may be related to insensitivity to these peptides, resulting in a lack of ability to either produce or process food signals that help to determine how much food is consumed during a meal (Chen et al., 2005; DelParigi et al., 2002; Schwartz, 2004). These findings indicate that in some cases, biological determinants of behavior may trump psychological or environmental determinants of behavior and that biological imbalances will need to be considered to intervene effectively. In Phase 2, methods development includes the specification of technology, research methods, and intervention methods needed to apply

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basic research concepts to an applied setting. An example is provided by Story et al. (2003) in which an innovative behavioral intervention using small group meetings and a family component was developed and pilot tested through an after-school program. A small number of African American girls (N = 54) between the ages of 8 and 10 were randomized to an intervention or control condition and received a behavioral intervention delivered by research staff. Feasibility of the intervention approach and of the research operations were emphasized using this approach and will provide essential information for the design of a Phase 3 or Phase 4 trial. This falls into Phase 2 because of the primary focus on the feasibility testing of a novel intervention based on basic behavioral science findings and the research operations required to evaluate the program in a community setting previously underused for childhood obesity prevention efforts (after-school programs). In Phase 3, efficacy trials involve the evaluation of an intervention program under ideal conditions and typically involve a high degree of control over research operations to maximize internal validity. An example is provided by Goran and Reynolds (2005) in which a multicomponent intervention was developed using an interactive computerized game, classroom activities, and homework done collaboratively with parents. The study targeted a sample of fourth graders in four elementary schools in Southern California. The schools were carefully selected to have adequate computer facilities and were randomized to experimental conditions, the intervention was delivered by trained program staff, and the program produced positive effects in girls but not in boys. This evaluation yielded well-measured data on the efficacy of the program when delivered in a limited number of schools under ideal conditions of control over computer laboratory facilities and delivery of the intervention components. This study delivered a full intervention and was more than a pilot of selected intervention components and research operations, thereby distinguishing it from a Phase 2 study. However, it maintained a high degree of control over research operations to maximize internal validity, thereby limiting external validity and preventing its classification as a Phase 4 study. Phase 4 effectiveness trials involve the implementation and testing of intervention programs under real-world conditions and with substantially reduced control by the investigators. Thus, data showing effectiveness bolster confidence that the program can be widely disseminated and retain its ability to produce behavior change. Gortmaker and colleagues (1999) provide an example of a Phase 4 effectiveness trial in the Planet Health program. In Planet Health, regular classroom

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teachers delivered a largely classroom-based intervention to a diverse sample of 6th and 7th grade students. Careful efforts were made to integrate the obesity prevention subject matter in with existing coursework in other subject areas. A carefully conducted evaluation was then completed on the intervention documenting effectiveness among girls but not boys. The use of a diverse and relatively large sample, regular classroom teachers, and other features designed to integrate the intervention into routine educational practices at the school limited investigator control, increased external validity, and allowed this study to be seen as a Phase 4 translation study. Finally, Phase 5 translation involves the evaluation of conditions that facilitate or impede the widespread distribution, adoption, and maintenance of an effective intervention by government, health care, or other community organizations responsible for delivering and managing the intervention. In this case, the school-based program might be systematically tested to determine whether different styles of training, different types of schools, or different methods of approach to school districts facilitate adoption, use, and maintenance of the program. To our knowledge, there is no Phase 5 intervention published in the childhood obesity prevention literature.

THE TRANSLATION OF BASIC SCIENCE RESEARCH IN CHILDHOOD OBESITY PREVENTION

Although the translation of basic science research into novel intervention operations holds promise to increase the effect sizes produced by childhood obesity prevention interventions, the systematic translation of basic science research into novel intervention approaches is a relatively rare occurrence. There are several reasons why this might be the case. First, basic science research and obesity prevention research are conducted in different domains with few shared scientists and relatively little communication across the domains. As a result, ideas developed in basic science are seldom communicated to obesity prevention researchers in a systematic fashion. Second, when attempts at communication across these domains occur there are substantial differences in core knowledge, terminology, and methodological and statistical approaches (Chou, Spruijt-Metz, & Azen, 2004; SpruijtMetz & Chou, 2004) that limit the ability to communicate. Third, few colloquia exist dedicated to breaching the communication divide between basic science and intervention research. Fourth, funding for

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highly innovative translation research is hard to come by. Enhanced funding for these pursuits would greatly facilitate the formation of teams, the communication occurring between basic and behavioral scientists, and ultimately the development of efficacious interventions. To stimulate discussion on promising areas for the translation of basic science research into obesity prevention efforts and to facilitate communication of these findings across investigators from multiple perspectives, we have outlined several promising areas of basic research in the sections below. There are few well-designed pediatric obesity prevention efforts that rest on solid basic research, methods development, and efficacy trials, let alone any that have progressed to Phase 4 or 5 effectiveness and dissemination trials. Results of these trials have, more often than not, been disappointing. However, the increasing research focus on pediatric obesity is providing a steadily increasing stream of innovative and transdisciplinary basic pediatric obesity research. Preventionists are therefore uniquely positioned to tap these findings for the purpose of translation to effective, evidence-based interventions. There exist a vast array of basic sciences that could potentially contribute to prevention of pediatric obesity. The trick is to find the “low-hanging fruit,” or the most promising findings that lend themselves to the development of innovative interventions to prevent childhood obesity, in particular, findings that incorporate (a) basic behavioral sciences including psychology, neurocognition, and sociology, (b) environmental sciences, and (c) basic bench and epidemiological sciences that are converging on new approaches to prevention of pediatric obesity. It is beyond the scope of this article to catalogue the many innovative studies that have uncovered promising strategies for intervention. However, examples of low-hanging fruit from behavioral, environmental, and bench science will be enumerated, and possible approaches to translation will be discussed. BASIC BEHAVIORAL SCIENCE

Major contributions to obesity prevention in basic behavioral sciences include moving toward the identification of discrete behaviors that can prevent obesity and the understanding of determinants of those behaviors. Approaches most frequently focus on creating and maintaining energy balance through modification of diet and physical

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activity. These interventions boast several intermediate goals, such as improved fitness levels (Eliakim et al., 2002; Gutin, Barbeau, Litaker, Ferguson, & Owens, 2000) or higher consumption of fruit and vegetables (Baranowski et al., 2003), with the ultimate goal of achieving and maintaining lower BMI (Cruz et al., 2005). Reduction of both soft drink consumption (Berkey, Rockett, Field, Gillman, & Colditz, 2004; Schulze et al., 2004) and television viewing (Caroli, Argentieri, Cardone, & Masi, 2004) have emerged as key behavioral targets for obesity prevention. However, changing lifestyle behaviors such as these in pediatric populations has proven challenging, in particular because the underlying psychosocial determinants of target behaviors are often not well understood (Baranowski, Klesges, Cullen, & Himes, 2004; Spruijt-Metz & Saelens, in press). The importance of understanding behavioral determinants, developing theory-based interventions, and testing the mediators of effective interventions, cannot be overstated (Baranowski, Anderson, & Carmack, 1998; MacKinnon, 1994; MacKinnon & Dwyer, 1993; Reynolds, Spruijt-Metz, & Unger, in press; Reynolds, Yaroch, Franklin, & Maloy, 2002). No matter how solidly a behavior is related to obesity risk, effective interventions depend on the inclusion of strategies to successfully convince children to adopt that behavior. Interventions to change behaviors focus on a combination of individual, social, and environmental determinants. Recent reviews of the most promising of the more than 100 behavioral theories currently in use suggest several promising theories or mini theories that might best be suited to interventions to change pediatric obesity-related behavior (Baranowski, Cullen, Nicklas, Thompson, & Baranowski, 2003; Biddle, Sallis, & Cavill, 1998). Two examples will be given here: one muchused theory and one theory that is relatively new in pediatric obesity research. One prominent example of a much-used theory in pediatric obesity research is the Theory of Reasoned Action (TRA). The TRA was originally formulated to explain the relation between attitudes and behavior (Ajzen & Fishbein, 1980).The TRA suggests that the intention to be active or eat a healthy diet is the strongest influence on children’s behavior. Intention is influenced by attitudes and subjective norms regarding physical activity and diet, which are in turn affected by perceived consequences and the influence of others, respectively. In the course of research, the TRA came to be considered limited because it did not take into account the fact that some behaviors are not under a

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person’s control (e.g., healthier food choices may not be available at home or at school). Therefore, the construct of perceived behavioral control was added as another factor influencing the intention to engage in a given behavior to produce the Theory of Planned Behavior (TPB; Ajzen, 1991). Despite years of research using this theory, the explanatory powers remain limited in youth, particularly when objective measures of behavior (as opposed to self-report measures) are used. The TPB accounted for only 7% of the variance in fruit and vegetable intake in adolescents (Lien, Lytle, & Komro, 2002). In another study, however, the TPB could explain a full 15% to 28% of the variance in soft drink consumption in adolescent girls and boys (Kassem & Lee, 2004; Kassem, Lee, Modeste, & Johnston, 2003). A metaanalysis of the TPB in regard to physical activity supported the model constructs but questioned the amount of variance in physical activity that this model could predict (Hagger, Chatzisarantis, & Biddle, 2002). In one study, the TPB accounted for only 6% of the variance in adolescents’ moderate to vigorous physical activity when physical activity was objectively measured (Trost, Saunders, & Ward, 2002). However, several modifications to this theory have been proposed, including procedures to enhance assessment of belief salience, past behavior (or habit), moral norms, self-identity, affect, and motivation. Much of the current TPB research involves these possible enhancements, and results are pending (Baranowski et al., 2003). A relative newcomer to pediatric obesity prevention research is SelfDetermination Theory. This theory postulates that behavior is more or less self-determined (from nonself-determined to self-determined). Levels of self-determination coincide with types of motivation (from amotivation to intrinsic motivation) and regulatory styles. According to this theory, people enjoy physical activity most if it is intrinsically motivated; conversely, one could say that the behaviors that people enjoy are most likely to be intrinsically motivated (Frederick-Recascino, 2002). Children and adolescents are increasingly concerned with autonomy and self-determination (Spruijt-Metz, 1999). Furthermore, enjoyment seems to predict physical activity better than many other, more commonly used constructs (Dishman et al., 2005). Therefore, SelfDetermination Theory offers a promising theoretical capstone for the development of effective interventions involving changes in dietary and exercise behaviors in pediatric populations. These findings from behavioral science suggest that (a) reduction of television viewing and sugarladen beverage consumption reduces obesity risk and (b) interventions

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to change these behaviors might best be approached through enhancing intrinsic motivation to reduce the targeted behaviors and/or replace them with other behaviors. GEOGRAPHY, BUILT ENVIRONMENT, AND BEHAVIORAL SCIENCE

In the emerging marriage of environmental, geographical, and behavioral sciences, exciting new evidence documents the influence of the built environment on behavior and obesity. Although there is a lack of research on built environmental determinants of obesity in youth (Gordon-Larsen & Reynolds, 2005), low levels of both actual (Gordon-Larsen, McMurray, & Popkin, 2000) and perceived (Timperio, Salmon, Telford, & Crawford, 2005) neighborhood safety have been related to physical inactivity and obesity. Urban sprawl, characterized by (a) a population widely dispersed in low density residential development; (b) separation of homes, shops, and workplaces; (c) lack of town centers; and (d) large block size and poor access from one place to another, particularly on foot, have been related to lower levels of physical activity (Ewing, Schmid, Killingsworth, Zlot, & Raudenbush, 2003). However, neighborhoods that are walkable, consisting of safe and desirable walking routes, have been related to lower levels of obesity (Booth, Pinkston, & Poston, 2005; Frank, Schmid, Sallis, Chapman, & Saelens, 2005). Land-use mix or the location of different types of land uses (residential, commercial, institutional, recreational, etc.) close together, has been shown to positively affect BMI. One striking study showed a strong association between land-use mix and obesity in adults (BMI ≥ 30), with each quartile increase in an indicator of the mix of four land uses (residential, commercial, office, institutional) being associated with a 12.2% reduction in the likelihood of obesity across gender and ethnicity (Frank, Andresen, & Schmid, 2004). Access and proximity to facilities such as recreational spaces and playgrounds have been shown to positively affect levels of pediatric physical activity (Sallis, Prochaska, & Taylor, 2000). Environmental influences on diet, such as lack of access to supermarkets that offer affordable and healthy foods (Fitzgibbon & Stolley, 2004) and school food environments that emphasize high calorie foods and sugary beverages (French, Story, & Jeffery, 2001; Neumark-Sztainer, French, Hannan, Story, & Fulkerson, 2005), are also related to pediatric obesity.

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The translation of these findings into intervention poses stiff, albeit not insurmountable challenges because implementation can be costly and often involves entire communities, policy makers, and/or politicians. Improving walkability can be as simple as providing green cover by planting trees or as complex as revamping entire neighborhood topographies. Changing land-use mix can be as simple as implementing a neighborhood vegetable garden or as complex as rezoning. These findings point toward innovative approaches to developing healthy environments involving community coalition building and action research. BASIC PHYSIOLOGICAL SCIENCE

Research on metabolism, physiology, and other bench sciences has produced several intriguing lines of study that could provide the basis for new ways to think about intervening to prevent childhood obesity. In the area of metabolism, the Glycemic Index (GI) of foods has emerged as an influence on overeating and weight gain. Foods that are quickly digested and transformed into glucose have a high GI. Starchy, refined grain products and potatoes have a high GI, up to 50% higher than table sugar, whereas vegetables, legumes, and fruits generally have a low GI (Foster-Powell, Holt, & Brand-Miller, 2002). The rapid absorption of glucose after consumption of meals with a high-glycemic load sets off a sequence of hormonal and metabolic changes that exacerbate hunger, and through the long term, promote weight gain (Ebbeling & Ludwig, 2001; Ludwig et al., 1999). Low GI diets may improve the body’s sensitivity to insulin, whereas high GI foods may refuel carbohydrate stores after exercise. Taking GI into account would change, at least to some extent, the concept of healthy and unhealthy foods. This would in turn alter the healthy “foodscape” to include some foods that are both attractive to most children and surprisingly low GI (for instance, some brands of chocolate ice cream). Although more research needs to be conducted, current literature suggests that interventions to promote healthy eating based on GI might be able to provide more palatable dietary choices, reduce hunger, and promote activity. With the frequent failure of the energy balance approach to achieve lasting health improvements, researchers have begun to include strategies to improve obesity-related metabolic abnormalities such as insulin dynamics, cholesterol levels, blood pressure, and

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other metabolic abnormalities included in the metabolic syndrome (Cruz et al., 2005). In the area of physiology, evidence is emerging that strength training (resistance training) may improve insulin sensitivity, increase muscle mass, decrease fat mass, and in particular reduce visceral fat (Campbell, Crim, Young, & Evans, 1994; Campbell, Crim, Young, Joseph, & Evans, 1995; Ryan, Pratley, Elahi, & Goldberg, 1995; Treuth et al., 1995). Strength training has also shown some favorable effects on psychosocial variables such as self-confidence in adolescent girls (Holloway, Beuter, & Duda, 1988). The American Academy of Pediatrics has endorsed resistance training (with appropriate supervision and instruction) for children and adolescents as a means to improve strength and decrease risk of sports-related injuries (Faigenbaum, 2003). However, few interventions have incorporated strength training for children. One study examined a 6-week program that combined a dietary intervention (low energy, 20% to 25% calories from fat, high in complex carbohydrates) with strength training in obese children and showed a 6% improvement in cholesterol (Sung et al., 2002). Another study showed that resistance training in 12 overweight White girls, (Treuth et al., 1995; Treuth et al., 1994; 20 min sessions; 3 days per week for 5 months) led to increased strength and improvement in visceral fat. Incorporation of strength training in pediatric physical activity interventions might be able to improve physical activity levels and important psychosocial and metabolic variables simultaneously. Finally, exciting developments in the area of hormones and peptides are suggesting new ways of understanding the whole child and may point to new ways of intervening accordingly. Levels of the gut peptides such as ghrelin, amylin, PYY 3-36, GLP-1, and several others, are tightly linked to feeding and energy balance (Stanley, Wynne, & Bloom, 2004). For instance, ghrelin levels rise immediately preceding each meal and decline sharply following the meal. This raises the possibility that ghrelin signals to the brain a desire to eat and, following ingestion of the meal, sends a signal to terminate the desire to keep eating. Some peptides and hormones, such as amylin (Baldo & Kelley, 2001; Rushing, Hagan, Seeley, Lutz, & Woods, 2000) and leptin (Romon et al., 2004), are linked to insulin secretion and insulin resistance, food intake, and physical activity. Understanding these metabolic influences on behavior will help us refine our understanding of the relative contributions of metabolism, cognition, emotion, and environment on eating behavior.

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TRANSLATION OF CHILDHOOD OBESITY PREVENTION INTERVENTIONS: MOVING FROM EARLIER TO LATER PHASES

We will now address the issue of translating intervention programs from the earlier stages (Phases 2 and 3) to later stages (Phases 4 and 5) and address factors that may facilitate this translation. Failure to translate efficacy studies into effectiveness studies is a common problem facing translational research across a number of behaviors and diseases (Glasgow, Lichtenstein, & Marcus, 2003; Green, 2001; Orlandi, 1987). It is too early to say whether this problem will also affect childhood obesity prevention; however, it is plausible to think that many of the same issues limiting translation in other areas of prevention research will also influence the prevention of obesity. A number of factors limiting translation have been identified (Glasgow et al., 2003; Green, 2001; Orlandi, 1987). First, the time and resources available for practitioners to deliver childhood obesity prevention is limited. For example, in health care settings, physicians and nurses are concerned primarily with clinical care. If time is available for addressing obesity issues, it will typically be directed to those children at the highest percentile levels on BMI or weight. Clinicians do not have the time, training, or support staff to deliver obesity prevention intervention to healthy children at lower levels of BMI. When resources are available, evaluated programs may not be available or known to the clinical staff. Second, there is a lack of incentives for the use of evidence-based obesity prevention programs. For example, schools might be used to reach large numbers of children with obesity prevention interventions. However, school personnel are under tremendous pressure to achieve adequate scores on standardized tests of basic academic subjects (e.g., spelling, arithmetic). Incentives are provided for performing well on these tests, and punishments can result if schools do not achieve strong test scores. By contrast, few incentives are present to deliver obesity prevention programs, and time spent addressing prevention allows less time for education on basic academic subjects. As a result, few incentives can be put forth for the use of substantial class time or other school resources for the implementation of childhood obesity prevention programs. Third, the types of products flowing from efficacy trials may be a poor fit for practitioners who must deliver the interventions in the field. That is, obesityprevention interventions must be developed with an understanding of

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the settings in which they will be delivered and the personnel who will deliver them. Fourth, bias toward internal validity in the testing of interventions may lead to the development of programs that cannot be generalized across settings and populations (Glasgow et al., 2003; Klesges, Estabrooks, Dzewaltowski, Bull, & Glasgow, 2005). The incentive for many researchers is to obtain strong intervention effects and findings that can be causally attributed to the program. Thus, there is a tendency to maintain control over the setting, populations targeted, and delivery of the intervention material to increase the chances of obtaining significant intervention effects. In childhood obesity prevention, this might lead to interventions delivered in a narrow sample of children as defined by ethnicity or gender, a sample of schools with infrastructure and personnel likely to be supportive of the intervention, and the use of highly trained project staff rather than regular school staff to deliver intervention components (Chin, 2004; Green, 2004). TRANSLATION STATUS OF THE EXISTING CHILDHOOD OBESITY PREVENTION INTERVENTIONS

Despite the critical importance of the ongoing obesity epidemic, relatively few childhood obesity prevention intervention studies have been published. Thorough reviews of this literature have been conducted, and we will not duplicate those efforts (Ells et al., 2005; Summerbell et al., 2005). In the sections below, we comment on the state of the published childhood obesity prevention literature with an emphasis on translation. Reviews by Ells and colleagues (2005) and Summerbell and colleagues (2005) provided an initial list of studies for review. Additional studies appearing in the literature since their publications were also examined. Following the Cochrane review criteria, only controlled trials of at least 12 weeks duration, with children less than 18 years of age at the beginning of the study, interventions involving diet, physical activity, lifestyle and social support, and a measure of BMI or body composition were included in the database. Studies focusing exclusively on obesity treatment were excluded. EXTENT OF STUDIES IN CHILDHOOD OBESITY PREVENTION

Using the criteria listed above, 25 published interventions were identified. This is a small number given the importance of the

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obesity epidemic and may be due to the relatively recent interest in childhood obesity. The scope of the obesity epidemic, particularly as it pertains to children and adolescents, has become evident only in perhaps the past 10 years. Prior interventions have focused on the treatment of obesity in overweight and at risk for overweight children and adolescents rather than the prevention of obesity (Epstein, 1996; Epstein, Myers, Raynor, & Saelens, 1998). In addition, many prevention studies with children and adolescents have focused on the modification of diet and physical activity, without a primary focus on the prevention of obesity, in an effort to reduce risk for chronic diseases such as cardiovascular disease and cancer (Resnicow & Robinson, 1997). Therefore, the perceived need to develop interventions with components specifically targeting obesity has been a recent occurrence. Interventions are now beginning to emerge in part as a consequence of national and international calls for action (Lobstein et al., 2004; U.S. Department of Health and Human Services, 2001) and funding initiatives such as the Girls Health Enrichment Multisite Studies (GEMS) funded by National Institutes of Health as a “multicenter research program to develop and test interventions designed to prevent excess weight gain by African American girls” (Rochon et al., 2003, p. S1-6). PHASE OF INTERVENTION

The 25 studies were classified according to the five-phase translation model described earlier in this article (Greenwald & Cullen, 1985; Sussman et al., in press). Five studies described the development and evaluation of Phase 2 interventions (Baranowski et al., 2003; Beech et al., 2003; Robinson et al., 2003; Story et al., 2003; Warren, Henry, Lightowler, Bradshaw, & Perwaiz, 2003). These studies were typically defined by the authors as pilot studies with an emphasis on the measurement of feasibility and insufficient statistical power to detect treatment group differences. These studies also maintained a high degree of control over intervention delivery. Four of the Phase 2 studies came from the GEMS funding mechanism and were well designed and conducted. These Phase 2 studies were attentive to issues of setting, on-site staff delivery and training, and incentives that will maximize external validity and eventual dissemination of the interventions when shown to be effective. As such, the latest intervention efforts are demonstrating attentiveness to issues of translation with widespread dissemination in mind.

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The majority of the published studies to date, 14 of 25, were classified as Phase 3 efficacy studies (Davis, Gomez, Lambert, & Skipper, 1993; Dennison, Russo, Burdick, & Jenkins, 2004; Epstein et al., 2001; Fitzgibbon et al., 2005; Flores, 1995; Goran & Reynolds, 2005; Gortmaker et al., 1999; Harvey-Berino & Rourke, 2003; James, Thomas, Cavan, & Kerr, 2004; Mo-suwan, Junjana, & Puetpaiboon, 1993; Mo-suwan, Pongprapai, Junjana, & Puetpaiboon, 1998; Muller, Asbeck, Mast, Langnase, & Grund, 2001; Neumark-Sztainer, Story, Hannan, & Rex, 2003; Robinson, 1999; Stolley & Fitzgibbon, 1997). The interventions were delivered under conditions that provided substantial control by the investigators and might be considered a best case scenario for the behavior change capability of the programs. This is most evident in the staff used to deliver the interventions. For the majority of the studies, research project staff delivered the intervention directly. For several additional Phase 3 studies, substantial oversight and corrective feedback was provided by research staff to practitioners, or a hybrid model was used in which a portion of the intervention was delivered by research staff and a portion by practitioners. Thus, we consider a study to be in Phase 3 if it is doubtful that sufficient intervention integrity could be obtained without a substantial level of research staff oversight. In addition, limitation of the sample and selection of settings that were highly supportive of intervention delivery also led to classification as a Phase 3 study. Six of the studies reviewed were considered Phase 4 (effectiveness) trials (Caballero et al., 2003; Donnelly et al., 1996; Gortmaker et al., 1999; Kain et al., 2004; Pangrazi, Beighle, Vehige, & Vack, 2003; Sahota et al., 2001a, 2001b). All of these trials used school as a setting. This is perhaps not surprising given the relatively long history of schools as a setting for intervention with children and adolescents, including the modification of diet and physical activity to prevent chronic disease. An interesting finding was that many of the most recent studies are in Phase 2 and are being conducted in either nonschool settings or in a combination of settings that includes schools and also family, after school, and other opportunities for intervention. This probably reflects an understanding among intervention researchers that nonschool settings have been understudied and hold potential for affecting the obesity epidemic. No studies were found that were in Phase 5. The implications for the distribution of published studies across phases of translation are clear. First, those studies that have been

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developed and shown to be effective in Phases 2 and 3 are well positioned to move forward to Phases 4 and 5. Funding agencies and scientific review groups must be willing to provide financial support for this final level of dissemination testing. An emphasis on the translation of promising Phase 2 and Phase 3 studies to later stages of translation is warranted to produce long-term reductions in risk for overweight and obesity. EFFICACY AND EFFECTIVENESS OF THE STUDIES

Most studies produced effects on at least one outcome of interest including psychosocial constructs, dietary behavior, or physical activity. However, only 8 of 25 interventions had significant effects on the ultimate outcome of BMI or body composition in their primary statistical tests of program effectiveness (Flores, 1995; Goran & Reynolds, 2005; Gortmaker et al., 1999; Harvey-Berino & Rourke, 2003; Kain et al., 2004; Mo-suwan et al., 1993; Muller et al., 2001; Robinson, 1999). Effect sizes were not reported in these studies. Significant effects favoring the intervention condition were reported, as noted above, and included BMI change scores ranging from 0.2 to 1.1, a difference of 0.07 using BMI z scores, a 1.7% triceps skinfold difference at posttest, and a 5.5% change score difference in the classification of participants as obese or not obese. For several studies, effects were found in only one gender (Goran & Reynolds, 2005; Gortmaker et al., 1999; Robinson, 1999). Two additional studies found effects when secondary statistical analyses were conducted in the face of nonsignificant primary analyses (Baranowski et al., 2003; Mo-suwan et al., 1998). Thus, relatively few of the interventions had significant effects on the primary outcomes of BMI or adiposity. COST-EFFECTIVENESS

Systematic cost-effectiveness analyses were not observed in any of the studies reviewed. In a few cases, minimal calculations of the costs of an intervention were compiled (Donnelly et al., 1996; Epstein et al., 2001; Flores, 1995). The reasons for failure to conduct costeffectiveness analyses are unknown but may include the additional data collection burden required and the failure to involve qualified economics researchers. In addition, cost-effectiveness analysis may not have received sufficient emphasis in the field to date as an important

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component of childhood obesity prevention research. This again highlights the need to design intervention trials with translation and dissemination in mind. Cost benefit analysis will be a major consideration for practitioners who will be charged with the delivery and maintenance of prevention programs (Glasgow et al., 2003; Klesges et al., 2005). SETTINGS

The majority of interventions used schools as a primary intervention setting (18 of 25 studies). Eleven of the 18 school-based studies also included a home-based or a family-based component. The remaining 7 studies included 2 conducted solely among families, 1 using a summer camp component combined with an internet-based family intervention, 2 that used community centers, 1 that used a tutoring program, and 1 using a preschool environment combined with a family component. The evidence is not yet sufficient to conclude that obesity prevention studies can be delivered feasibly in a wide range of conditions and in a diverse range of settings. Notably absent are interventions designed for delivery through health care organizations. There were no published interventions that used primary care as a means to reach children or families with obesity prevention interventions. In addition, no prevention studies were identified that used places of worship as a setting; however, at least one recent study has used churches for weight reduction with overweight girls (Resnicow, Taylor, Baskin, & McCarty, 2005). Finally, modifications of the built environment and of policy are notably absent. These are potentially fruitful areas for future intervention research given the likely capability of these techniques to produce and sustain behavior change without ongoing external supports. INTERVENTION STRATEGIES

The studies reviewed have used a mix of intervention strategies shaped by the setting in which the intervention was delivered and the target population to be reached. Many of the interventions, and particularly the school-based interventions, used curricula or classes as a major intervention strategy (Caballero et al., 2003; Davis et al., 1993; Fitzgibbon et al., 2005; Flores, 1995; Gortmaker et al., 1999; HarveyBerino & Rourke, 2003; Robinson, 1999; Stolley & Fitzgibbon, 1997).

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Other small group activities that did not involve curricula, such as dance classes, have been used and perhaps more often in recent interventions (Flores, 1995; Robinson et al., 2003). Efforts to modify the environment are evident in some studies (e.g., changes in food service practices in schools [Caballero et al., 2003; Donnelly et al., 1996], reduction of snack food availability at schools [Kain et al., 2004], changes across the entire school environment [Sahota et al., 2001a, 2001b], and modifications in physical education [Donnelly et al., 1996; Kain et al., 2004; Neumark-Sztainer, Martin, & Story, 2000; Neumark-Sztainer et al., 2003]. Periodically, efforts have been made to integrate community members and resources into interventions (Davis et al., 1993; Neumark-Sztainer et al., 2003; Robinson et al., 2003; Sahota et al., 2001a, 2001b). Novel tools such as television time monitoring devices have also been used (Robinson, 1999), and several studies have targeted reductions in television use (Dennison et al., 2004; Gortmaker et al., 1999; Robinson, 1999; Robinson et al., 2003; Warren et al., 2003). As noted earlier, many studies have attempted to reach parents using print materials, home visits, smallgroup meetings, and the Internet (Baranowski et al., 2003; Beech et al., 2003; Caballero et al., 2003; Dennison et al., 2004; Epstein et al., 2001; Fitzgibbon et al., 2005; Kain et al., 2004; Muller et al., 2001; Story et al., 2003). Some interactive electronic media has been used, but this approach is relatively unexplored as a strategy for obesity prevention (Baranowski et al., 2003; Goran & Reynolds, 2005). The theories used to design interventions are not always described; however, Social Cognitive Theory (Bandura, 1986, 2001) was the most commonly cited (Beech et al., 2003; Caballero et al., 2003; Davis et al., 1993; Goran & Reynolds, 2005; Gortmaker et al., 1999; NeumarkSztainer et al., 2003; Robinson et al., 2003; Rochon et al., 2003). Therefore, the constructs described by Social Cognitive Theory are highly evident in the intervention designs (e.g., goal setting and selfmonitoring). In addition, at least two studies have integrated culturally relevant learning approaches into the intervention approach used (Caballero et al., 2003; Davis et al., 1999; Davis et al., 1993). COLLABORATIVE RELATIONSHIPS

All of the cited studies could not be completed without the cooperation of individuals in the settings in which the interventions were delivered. Undoubtedly, strong efforts were made toward establishing

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fruitful relationships with the communities served. However, the use of community-based participatory research (CBPR) to actively engage community partners as a part of the research team was not evident in these studies despite strong and recent advocacy for CBPR (Chin, 2004). The use of CBPR may constitute an innovation enhancing the effect sizes obtained by intervention studies, regardless of phase, and may also facilitate the eventual translation to Phase 5 intervention.

DISCUSSION

Given the scant number of obesity prevention interventions developed for children and adolescents, funding is clearly needed for new intervention development and testing. This is true for all phases of translational research in obesity prevention. More work needs to be conducted at Phases 1 (basic science) and 2 (methods development) to translate promising areas of basic science into novel intervention approaches with an eye on increasing the impact of our prevention strategies on obesity risk (e.g., BMI, percentage body fat). It is clear that BMI and/or adiposity will be difficult outcomes to effect in children and adolescents and that stronger interventions are needed (Resnicow & Robinson, 1997; Summerbell et al., 2005). To support Phase 1 and Phase 2 translation, efforts to bridge the gap between basic science and prevention research are essential. Mechanisms to foster collaboration might include transdisciplinary RFAs, conferences and seminars focusing on Phase 1 and Phase 2 translation, and the creation of translational research teams. Academic institutions must nurture and protect transdisciplinary collaboration and allow time for fruitful collaborations to emerge without the need to quickly obtain large amounts of extramural funding. Additional Phase 3 and Phase 4 studies are needed to test promising ideas and intervention approaches identified in Phases 1 and 2. Attention to principles that will facilitate wider use in the community is needed in the design of these trials (Glasgow et al., 2003; Green & Glasgow, in press). Work is also needed to translate Phase 4 interventions to Phase 5 and conduct the systematic testing of approaches that can have a broad public health impact. Clearly needed is the cooperation of scientific review groups and funding agencies that see the value of higher risk translational research (Phases 1 and 2). Also needed is an understanding of the importance of dissemination testing on programs that may not be

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seen as novel because they have been extensively researched in the past, that is, interventions in Phase 5 translation. Selected settings have received very little attention in the development of childhood obesity prevention programs. Primary care settings, places of worship, policy interventions, and interventions involving the modification of the built environment have all received scant attention as settings for the controlled testing of childhood obesity prevention interventions. Primary care settings and places of worship can be readily targeted by new intervention strategies and can draw on substantial bodies of work in other content areas. Policy as an intervention strategy has been less frequently used by researchers in public health but is receiving more attention in recent years (Buzbee, 2003; King et al., 1995; Sallis, Bauman, & Pratt, 1998). Interventions using a policy approach may require a longer period of time in Phases 1 and 2 to test promising methods for intervention before Phase 3 efficacy trials are attempted. The built environment holds great promise for increasing our understanding of factors that determine obesity. The question remains whether we have sufficient knowledge of built environmental influences to develop effective interventions that maximize scarce intervention resources. The latter is critical given that modification of the built environment may be slow and costly. An increased understanding of built environmental influences will also help translational researchers develop intervention strategies that maximize use of the existing environmental structure to increase physical activity and improve nutrition (e.g., form community coalitions to increase use of urban trails). A better understanding is needed of why some programs affect obesity and others do not. In this way, intervention strategies known to affect adiposity or BMI can be repeated in revisions of old programs and used in new programs, noneffective strategies can be discarded, and much more effective interventions can be developed in the long term. Mediational analysis has been developed to facilitate this type of testing and has been used successfully in a number of research areas (Baranowski et al., 1998; Judd & Kenny, 1981; MacKinnon & Dwyer, 1993; MacKinnon, Johnson, Pentz, & Dwyer, 1991; Reynolds et al., 2002). We strongly advocate the consistent use of mediational analysis techniques across obesity intervention studies. To enhance this effort, the creation of an archive, perhaps Web based, for the results of mediational tests has been advocated (Reynolds et al., 2002). As clearly articulated by several authors, maximum translation is likely to be accomplished by designing trials with translation in mind

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(Glasgow et al., 2003; Klesges et al., 2005). One guiding framework for the analysis of research for translational potential is the RE-AIM (reach, efficacy and effectiveness, adoption, implementation, and maintenance) framework (Glasgow, Vogt, & Boles, 1999; Green & Glasgow, in press). The five components of RE-AIM are used to determine the dissemination potential of health behavior research. The framework has been used in the Behavior Change Consortium projects, funded by the National Institutes of Health, illustrating areas of adequate coverage within the RE-AIM framework and methods by which each component can be maximized in behavior change research (Klesges et al., 2005). This same approach could be used in the design of future childhood obesity prevention intervention trials, and we encourage translational researchers to do so. Several final points can be raised. First, transition to Phase 5 implies that a study has been sufficiently tested in Phase 4 and is known to produce effects in real-world settings across a diverse set of participants. However, it is unclear when sufficient data have been amassed to conclude this in the affirmative. Second, to address perceived needs of community partners and to maximize Phase 5 translation, the increased use of Community-Based Participatory Research is warranted (Chin, 2004; Green, 2004). This approach involves the community in a truly collaborative relationship with researchers, creating an effective partnership that will address barriers for the end users of an intervention and facilitate its use and long-term maintenance. Third, we need to create an archive of best practices interventions, particularly at the Phase 4 and 5 levels, and make this archive available to researchers and practitioners nationally and internationally. Efforts toward this end are in progress through the Guide to Community Preventive Services (www.thecommunityguide.org). Archives of effective interventions will facilitate the development and delivery of high-quality obesity intervention programs and help reduce obesity rates nationally by making programs of known effectiveness available.

REFERENCES Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Making, 50, 179-211. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice Hall.

EHP287346.qxd

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240

Page 240


Baldo, B. A., & Kelley, A. E. (2001). Amylin infusion into rat nucleus accumbens potently depresses motor activity and ingestive behavior. American Journal of Physiology Regulatory Integrative & Comparative Physiology, 281(4), 1232-1242. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall. Bandura, A. (2001). Social cognitive theory: An agentic perspective. In S. T. Fiske, D. L. Schacter, & C. Zahn-Waxler, Annual review of psychology (Vol. 52, pp. 1-26). Palo Alto, CA: Annual Reviews. Baranowski, T., Anderson, C., & Carmack, C. (1998). Mediating variable framework in physical activity interventions. How are we doing? How might we do better? American Journal of Preventive Medicine, 15(4), 266-297. Baranowski, T., Baranowski, J. C., Cullen, K. W., Thompson, D. L., Nicklas, T., Zakeri, I. E., et al. (2003). The fun, food, and fitness project (FFFP): The Baylor GEMS pilot study. Ethnicity & Disease, 13, S130-S139. Baranowski, T., Cullen, K. W., Nicklas, T., Thompson, D., & Baranowski, J. (2003). Are current health behavioral change models helpful in guiding prevention of weight gain efforts? Obesity Research, 11(Suppl. 1), 23S-43. Baranowski, T., Klesges, L. M., Cullen, K. W., & Himes, J. H. (2004). Measurement of outcomes, mediators, and moderators in behavioral obesity prevention research. 38(Suppl. 1), 1. Baranowski, T., Mendlein, J., Resnicow, K., Frank, E., Cullen, K., & Baranowski, J. (2000). Physical activity and nutrition in children and youth: An overview of obesity prevention. Preventive Medicine, 31, S1-S10. Beech, B., Klesges, R., Kumanyika, S., Murray, D., Klesges, L. M., McClanahan, B., et al. (2003). Child and parent-targeted interventions: The Memphis GEMS pilot study. Ethnicity & Disease, 13(Suppl. 1), S40-S53. Berkey, C. S., Rockett, H. R. H., Field, A. E., Gillman, M. W., & Colditz, G. A. (2004). Sugaradded beverages and adolescent weight change. Obesity Research, 12(5), 778-788. Biddle, S., Sallis, J. F., & Cavill, N. (1998). Policy framework for young people and healthenhancing physical activity—Evidence and implications. London: Health Education Authority. Booth, K. M., Pinkston, M. M., & Poston, W. S. (2005). Obesity and the built environment. Journal of the American Dietetic Association, 105(Suppl. 1), S110-117. Buzbee, W. (2003). Urban form, health, and the law’s limits. American Journal of Public Health, 93(9), 1395-1398. Caballero, B., Clay, T., Davis, S., Ethelbah, B., Rock, B., Lohman, T., et al. (2003). Pathways: A school-based, randomized controlled trial for the prevention of obesity in American Indian schoolchildren. American Journal of Clinical Nutrition, 78, 1030-1038. Campbell, W. W., Crim, M. C., Young, V. R., & Evans, W. J. (1994). Increased energy requirements and changes in body composition with resistance training in older adults. American Journal of Clinical Nutrition, 60(2), 167-175. Campbell, W. W., Crim, M. C., Young, V. R., Joseph, L. J., & Evans, W. J. (1995). Effects of resistance training and dietary protein intake on protein metabolism in older adults. American Journal of Physiology: Endocrinology and Metabolism, 268, E1143-E1153. Caroli, M., Argentieri, L., Cardone, M., & Masi, A. (2004). Role of television in childhood obesity prevention. International Journal of Obesity & Related Metabolic Disorders: Journal of the International Association for the Study of Obesity, 28(Suppl. 3), S104-108. Centers for Disease Control and Prevention. (2001). Prevalence of overweight among children and adolescents: United States, 1999-2002. Retrieved May 17, 2005, from http://www .cdc.gov/nchs/products/pubs/pubd/hestats/overwght99.htm Chen, C. Y., Chao, Y., Chang, F. Y., Chien, E. J., Lee, S. D., & Doong, M. L. (2005). Intracisternal des-acyl ghrelin inhibits food intake and non-nutrient gastric emptying in conscious rats. International Journal of Molecular Medicine, 16(4), 695-699.

EHP287346.qxd

4/4/2006

10:37 PM

Page 241


241

Chin, M. (2004, January). Community-based participatory research for diabetes translation and multi-level, multi-factorial interventions. Paper presented at the meeting of the National Institutes of Health and the Centers for Disease Control and Prevention, From clinical trials to community: The science of translating diabetes and obesity research, Bethesda, MD. Chou, C., Spruijt-Metz, D., & Azen, S. (2004). How can statistical approaches enhance transdisciplinary study of drug misuse prevention? Substance Use & Misuse, 39, 1867-1906. Cruz, M. L., Shaibi, G. Q., Weigensberg, M. J., Spruijt-Metz, D., Ball, G. D., & Goran, M. I. (2005). Pediatric obesity and insulin resistance: Chronic disease risk and implications for treatment and prevention beyond body weight modification. Annual Review of Nutrition, 25, 435-468. Davis, S., Going, S., Helitzer, D., Teufel, N., Snyder, P., Gittelsohn, J., et al. (1999). Pathways: A culturally appropriate obesity-prevention program for American Indian school children. American Journal of Clinical Nutrition, 69(Suppl. 4), 796S-802S. Davis, S., Gomez, Y., Lambert, L., & Skipper, B. (1993). Primary prevention of obesity in American Indian children. Annals of the New York Academy of Sciences, 699, 167-180. DelParigi, A., Tschop, M., Heiman, M. L., Salbe, A. D., Vozarova, B., Sell, S. M., et al. (2002). High circulating ghrelin: A potential cause for hyperphagia and obesity in Prader-Willi Syndrome. Journal of Clinical Endocrinology & Metabolism, 87(12), 5461-5464. Dennison, B., Russo, T., Burdick, P., & Jenkins, P. (2004). An intervention to reduce television viewing by preschool children. Archives of Pediatric Adolescent Medicine, 158, 170-176. Dishman, R. K., Motl, R. W., Saunders, R., Felton, G., Ward, D. S., Dowda, M., et al. (2005). Enjoyment mediates effects of a school-based physical-activity intervention. Medicine & Science in Sports & Exercise, 37(3), 478-487. Donnelly, J., Jacobsen, D., Whatley, J., Hill, J., Swift, L., Cherrington, A., et al. (1996). Nutrition and physical activity program to attenuate obesity and promote physical and metabolic fitness in elementary school children. Obesity Research, 4(3), 229-243. Ebbeling, C. B., & Ludwig, D. S. (2001). Treating obesity in youth: Should dietary glycemic load be a consideration? Advances in Pediatrics, 48, 179-212. Eliakim, A., Kaven, G., Berger, I., Friedland, O., Wolach, B., & Nemet, D. (2002). The effect of a combined intervention on body mass index and fitness in obese children and adolescents— A clinical experience. European Journal of Pediatrics, 161(8), 449-454. Ells, L., Campbell, K., Lidstone, J., Kelly, S., Lang, R., & Summerbell, C. (2005). Prevention of childhood obesity. Best Practice & Research Clinical Endocrinology & Metabolism, 19(3), 441-454. Epstein, L. (1996). Family-based behavioural intervention for obese children. International Journal of Obesity & Related Metabolic Disorders, 20(Suppl. 1), S14-S21. Epstein, L., Gordy, C., Raynor, H., Beddome, M., Kilanowski, C., & Paluch, R. (2001). Increasing fruit and vegetable intake and decreasing fat and sugar intake in families at risk for childhood obesity. Obesity Research, 9(3), 171-178. Epstein, L., Myers, M., Raynor, H., & Saelens, B. (1998). Treatment of pediatric obesity. Pediatrics, 101, 554-570. Ewing, R., Schmid, T., Killingsworth, R., Zlot, A., & Raudenbush, S. (2003). Relationship between urban sprawl and physical activity, obesity, and morbidity. American Journal of Health Promotion, 18(1), 47-57. Faigenbaum, A. D. (2003). Youth strength training. Clinics in Sports Medicine, 19(4), 593-619. Fitzgibbon, M. L., & Stolley, M. R. (2004). Environmental changes may be needed for prevention of overweight in minority children. Pediatric Annals, 33(1), 45-49. Fitzgibbon, M. L., Stolley, M. R., Schiffer, L., Van Horn, L., Kaufer-Christoffel, K., & Dyer, A. (2005). Two-year follow-up results for HIP-HOP to health jr.: A randomized controlled trial for overweight prevention in preschool minority children. Journal of Pediatrics, 146(5), 618-625.

EHP287346.qxd

4/4/2006

10:37 PM

242

Page 242


Flores, M. (1995). Dance for health: Improving fitness in African American and Hispanic adolescents. Public Health Reports, 110(2), 189-193. Foster-Powell, K., Holt, S. H. A., & Brand-Miller, J. C. (2002). International table of glycemic index and glycemic load values: 2002. American Journal of Clinical Nutrition, 76(1), 5-56. Frank, L. D., Andresen, M. A., & Schmid, T. L. (2004). Obesity relationships with community design, physical activity, and time spent in cars. American Journal of Preventive Medicine, 27(2), 87-96. Frank, L. D., Schmid, T. L., Sallis, J. F., Chapman, J., & Saelens, B. E. (2005). Linking objectively measured physical activity with objectively measured urban form: Findings from SMARTRAQ. American Journal of Preventive Medicine, 28(Suppl. 2), 117-125. Frederick-Recascino, C. (2002). Self-Determination Theory and participation: Motivation research in the sport and exercise domain. In E. L. Deci & R. M. Ryan (Eds.), Handbook of self-determination research (pp. 277-294). Rochester, NY: University of Rochester Press. French, S. A., Story, M., & Jeffery, R. W. (2001). Environmental influences on eating and physical activity. Annual Review of Public Health, 22, 309-335. Gill, T. (1997). Key issues in the prevention of obesity. British Medical Bulletin, 53(2), 359-388. Glasgow, R., Lichtenstein, E., & Marcus, A. (2003). Why don’t we see more translation of health promotion research to practice? Rethinking the efficacy to effectiveness transition. American Journal of Public Health, 93(8), 1261-1267. Glasgow, R., Vogt, T., & Boles, S. (1999). Evaluating the public health impact of health promotion interventions: The RE-AIM framework. American Journal of Public Health, 89, 1323-1327. Goran, M., & Reynolds, K. (2005). Interactive multimedia for promoting physical activity (IMPACT) in children. Obesity Research, 13(4), 762-771. Gordon-Larsen, P., McMurray, R. G., & Popkin, B. M. (2000). Determinants of adolescent physical activity and inactivity patterns. Pediatrics, 105(6), E83. Gordon-Larsen, P., & Reynolds, K. D. (2005). Influence of the built environment on physical activity and obesity in children and adolescents. In M. I. Goran & M. Southern (Eds.), Handbook of pediatric obesity: Etiology, pathophysiology and prevention (pp. 251-270). Boca Raton, FL: Taylor & Francis Books/CRC Press. Gortmaker, S., Peterson, K., Wiecha, J., Sobol, A., Dixit, S., Fox, M., et al. (1999). Reducing obesity via a school-based interdisciplinary intervention among youth. Archives of Pediatric and Adolescent Medicine, 153, 409-418. Green, L. (2001). From research to “best practices” in other settings and populations. American Journal of Health Behavior, 25, 165-178. Green, L. (2004, December). From efficacy to effectiveness to community and back: Evidencebased practice vs practice-based evidence. Paper presented at the meeting of the National Institutes of Health and the Centers for Disease Control and Prevention, From clinical trials to community: The science of translating diabetes and obesity research, Bethesda, MD. Green, L., & Glasgow, R. (in press). Evaluating the relevance, generalization, and applicability of research: Issues in translation methodology. Evaluation & the Health Professions. Greenwald, P., & Cullen, J. (1985). The new emphasis in cancer control. Journal of the National Cancer Institute, 74(3), 543-551. Gutin, B., Barbeau, P., Litaker, M. S., Ferguson, M., & Owens, S. (2000). Heart rate variability in obese children: Relations to total body and visceral adiposity, and changes with physical training and detraining. Obesity Research, 8(1), 12-19. Hagger, M. S., Chatzisarantis, N. L. D., & Biddle, S. J. H. (2002). A meta-analytic review of the theories of reasoned action and planned behavior in physical activity: Predictive validity and the contribution of additional variables. Journal of Sport & Exercise Psychology, 24(1), 3-32.

EHP287346.qxd

4/4/2006

10:37 PM

Page 243


243

Harvey-Berino, J., & Rourke, J. (2003). Obesity prevention in preschool Native-American children: A pilot study using home visiting. Obesity Research, 11(5), 606-611. Hedley, A., Ogden, C., Johnson, C., Carroll, M., Curtin, L., & Flegal, K. (2004). Prevalence of overweight and obesity among U.S. children, adolescents, and adults, 1999-2002. Journal of the American Medical Association, 291(23), 2847-2850. Holloway, J. B., Beuter, A., & Duda, J. L. (1988). Self-efficacy and training for strength in adolescent girls. Journal of Applied Social Psychology, 18(8, Pt. 2), 699-719. James, J., Thomas, P., Cavan, D., & Kerr, D. (2004). Preventing childhood obesity by reducing consumption of carbonated drinks: Cluster randomised controlled trial. British Medical Journal, 328, 1237-1239. Judd, C., & Kenny, D. (1981). Process analysis: Estimating mediation in treatment evaluations. Evaluation Review, 5(5), 602-619. Kain, J., Uauy, R., Albala, C., Vio, F., Cerda, R., & Leyton, B. (2004). School-based obesity prevention in Chilean primary school children: Methodology and evaluation of a controlled study. International Journal of Obesity, 28, 483-493. Kassem, N. O., & Lee, J. W. (2004). Understanding soft drink consumption among male adolescents using the theory of planned behavior. Journal of Behavioral Medicine, 27(3), 273-296. Kassem, N. O., Lee, J. W., Modeste, N. N., & Johnston, P. K. (2003). Understanding soft drink consumption among female adolescents using the Theory of Planned Behavior. Health Education Research, 18(3), 278-291. King, A., Jeffery, R., Fridinger, F., Dusenbury, L., Provence, S., & Hedlund, S. (1995). Environmental and policy approaches to cardiovascular disease prevention through physical activity: Issues and opportunities. Health Education Quarterly, 22, 499-511. Klesges, L., Estabrooks, P., Dzewaltowski, D., Bull, S., & Glasgow, R. (2005). Beginning with the application in mind: Designing and planning health behavior change interventions to enhance dissemination. Annals of Behavioral Medicine, 29(Special supplement), 66-75. Lien, N., Lytle, L. A., & Komro, K. A. (2002). Applying theory of planned behavior to fruit and vegetable consumption of young adolescents. American Journal of Health Promotion, 16(4), 189-197. Lobstein, T., Baur, L., & Uauy, R. (2004). Obesity in children and young people: A crisis in public health. Obesity Reviews, 5(Suppl. 1), 4-85. Ludwig, D. S., Majzoub, J. A., Al-Zahrani, A., Dallal, G. E., Blanco, I., & Roberts, S. B. (1999). High glycemic index foods, overeating, and obesity. Pediatrics, 103(3), E26. MacKinnon, D. (1994). Analysis of mediating variables in prevention and intervention research. National Institute on Drug Abuse Research Monograph, 139, 127-153. MacKinnon, D., & Dwyer, J. (1993). Estimating mediated effects in prevention studies. Evaluation Review, 17(2), 144-158. MacKinnon, D., Johnson, C., Pentz, M., & Dwyer, J. (1991). Mediating mechanisms in a school-based drug prevention program: First-year effects of the midwestern prevention project. Health Psychology, 10(3), 164-172. Mo-suwan, L., Junjana, C., & Puetpaiboon, A. (1993). Increasing obesity in school children in a transitional society and the effect of the weight control program. Southeast Asian Journal of Tropical Medicine and Public Health, 24(3), 590-594. Mo-suwan, L., Pongprapai, S., Junjana, C., & Puetpaiboon, A. (1998). Effects of a controlled trial of a school-based exercise program on the obesity indexes of preschool children. American Journal of Clinical Nutrition, 68, 1006-1011. Muller, M., Asbeck, I., Mast, M., Langnase, K., & Grund, A. (2001). Prevention of obesity— More than an intention: Concept and first results of the Kiel Obesity Prevention Study (KOPS). International Journal of Obesity, 25(Suppl. 1), S66-S74.

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244

Page 244


Neumark-Sztainer, D., French, S., Hannan, P., Story, M., & Fulkerson, J. (2005). School lunch and snacking patterns among high school students: Associations with school food environment and policies. International Journal of Behavioral Nutrition and Physical Activity, 2(1), 14. Neumark-Sztainer, D., Martin, S., & Story, M. (2000). School-based programs for obesity prevention: What do adolescents recommend? American Journal of Health Promotion, 14(4), 232-235. Neumark-Sztainer, D., Story, M., Hannan, P. J., & Rex, J. (2003). New moves: A school-based obesity prevention program for adolescent girls. Preventive Medicine, 37, 41-51. Orlandi, M. (1987). Promoting health and preventing disease in health care settings: An analysis of barriers. Preventive Medicine, 16, 119-130. Pangrazi, R., Beighle, A., Vehige, T., & Vack, C. (2003). Impact of Promoting Lifestyle Activity for Youth (PLAY) on children’s physical activity. Journal of School Health, 73(8), 317-321. Resnicow, K., & Robinson, T. (1997). School-based cardiovascular disease prevention studies: Review and synthesis. Annals of Epidemiology, S7, S14-31. Resnicow, K., Taylor, R., Baskin, M., & McCarty, F. (2005). Results of go girls: A weight control program for overweight African-American adolescent females. Obesity Research, 13(10), 1739-1748. Reynolds, K., Spruijt-Metz, D., & Unger, J. (in press). Health behavior research and intervention. In R. B. Wallace (Ed.), Maxcy-Rosenau-Last Public Health & Preventive Medicine (15th ed.). Stamford, CT: Appleton & Lange. Reynolds, K., Yaroch, A., Franklin, F., & Maloy, J. (2002). Testing mediating variables in a school-based nutrition intervention program. Health Psychology, 21(1), 51-60. Robinson, T. (1999). Reducing children’s television viewing to prevent obesity: A randomized controlled trial. Journal of American Medical Association, 282(16), 1561-1567. Robinson, T., Kraemer, H., Matheson, D., Pruitt, L., Owens, A., Flint-Moore, N., et al. (2003). Dance and reducing television viewing to prevent weight gain in African-American girls: The Stanford GEMS Pilot Study. Ethnicity & Disease, 13, S1-65-S61-77. Robinson, T., & Sirard, J. (2005). Preventing childhood obesity: A solution-oriented research paradigm. American Journal of Preventive Medicine, 28(2S2), 194-201. Rochon, J., Klesges, R., Story, M., Robinson, T., Baranowski, T., Obarzanek, E., et al. (2003). Common design elements of the Girls Health Enrichment Multi-Site Studies (GEMS). Ethnicity & Disease, 13, S1-6–S1-14. Romon, M., Lafay, L., Bresson, J. L., Oppert, J. M., Borys, J. M., Kettaneh, A., et al. (2004). Relationships between physical activity and plasma leptin levels in healthy children: The Fleurbaix-Laventie Ville Sante II Study. International Journal of Obesity & Related Metabolic Disorders: Journal of the International Association for the Study of Obesity, 28(10), 1227-1232. Rushing, P. A., Hagan, M. M., Seeley, R. J., Lutz, T. A., & Woods, S. C. (2000). Amylin: A novel action in the brain to reduce body weight. Endocrinology, 141(2), 850-853. Ryan, A. S., Pratley, R. E., Elahi, D., & Goldberg, A. P. (1995). Resistive training increases fat-free mass and maintains RMR despite weight loss in postmenopausal women. Journal of Applied Physiology, 79, 818-823. Sahota, P., Rudolf, M. C., Dixey, R., Hill, A. J., Barth, J., & Cade, J. (2001a). Evaluation of implementation and effect of primary school–based intervention to reduce risk factors for obesity. British Medical Journal, 323, 1027-1029. Sahota, P., Rudolf, M. C. J., Dixey, R., Hill, A. J., Barth, J. H., & Cade, J. (2001b). Randomised controlled trial of primary school–based intervention to reduce risk factors for obesity. British Medical Journal, 323(7320), 1029-1032. Sallis, J., Bauman, A., & Pratt, M. (1998). Environmental and policy interventions to promote physical activity. American Journal of Preventive Medicine, 15(4), 379-397. Sallis, J. F., Prochaska, J. J., & Taylor, W. C. (2000). A review of correlates of physical activity of children and adolescents. Medicine & Science in Sports & Exercise, 32(5), 963-975.

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Schulze, M. B., Manson, J. E., Ludwig, D. S., Colditz, G. A., Stampfer, M. J., Willett, W. C., et al. (2004). Sugar-sweetened beverages, weight gain, and incidence of type 2 diabetes in young and middle-aged women. Journal of American Medical Association, 292(8), 927-934. Schwartz, G. J. (2004). Biology of eating behavior in obesity. Obesity Research, 12(Suppl. 2), 102S-106S. Spruijt-Metz, D. (1999). Adolescence, affect and health. London: Psychology Press. Spruijt-Metz, D., & Chou, C. (2004). The most critical unresolved issues associated with transdisciplinary substance use prevention research: Programs, models, paradigms, concepts, and processes. Substance Use & Misuse, 39, 2071-2072. Spruijt-Metz, D., & Saelens, B. (in press). Behavioral aspects of physical activity in childhood and adolescence. In M. I. Goran & M. Southern (Eds.), Handbook of pediatric obesity: Etiology, pathophysiology. Boca Raton, FL: Taylor & Francis Books/CRC Press. Stanley, S., Wynne, K., & Bloom, S. (2004). Gastrointestinal satiety signals III. Glucagon-like peptide 1, oxyntomodulin, peptide YY, and pancreatic polypeptide. American Journal of Physiology—Gastrointestinal & Liver Physiology, 286(5), G693-697. Stolley, M., & Fitzgibbon, M. (1997). Effects of an obesity prevention program on the eating behavior of African American mothers and daughters. Health Education & Behavior, 24(2), 152-164. Story, M., Evans, M., Fabsitz, R., Clay, T., Rock, B., & Broussard, B. (1999). The epidemic of obesity in American Indian communities and the need for childhood obesity–prevention programs. American Journal of Clinical Nutrition, 69(Suppl.), 747S-754S. Story, M., Sherwood, N., Himes, J., Davis, M., Jacobs, D., Cartwright, Y., et al. (2003). An after-school obesity prevention program for African-American girls: The Minnesota GEMS pilot study. Ethnicity & Disease, 13(Suppl. 1), S54-S64. Strauss, R., & Pollack, H. (2001). Epidemic increase in childhood overweight. Journal of American Medical Association, 286(22), 2845-2848. Summerbell, C., Waters, E., Edmunds, L., Kelly, S., Brown, T., & Campbell, K. (2005). Interventions for preventing obesity in children (Vol. 3). New York: John Wiley & Sons. Sung, R. Y., Yu, C. W., Chang, S. K., Mo, S. W., Woo, K. S., & Lam, C. W. (2002). Effects of dietary intervention and strength training on blood lipid level in obese children. Archives of Diseased Children, 86, 407-410. Sussman, S., Valente, T., Rohrbach, L., Stacy, A., & Pentz, M. (in press). Translation in the health professions: Converting science into action. Evaluation & the Health Professions, 29(1), 7-32. Timperio, A., Salmon, J., Telford, A., & Crawford, D. (2005). Perceptions of local neighbourhood environments and their relationship to childhood overweight and obesity. International Journal of Obesity & Related Metabolic Disorders: Journal of the International Association for the Study of Obesity, 29(2), 170-175. Treuth, M. S., Hunter, G. R., Kekes-szabo, T., Weinsier, R. L., Goran, M. I., & Berland, L. (1995). Strength training reduces intra-abdominal adipose tissue in older women. Journal of Applied Physiology, 78, 1425-1431. Treuth, M. S., Ryan, R. E., Pratley, R. E., Rubin, M. A., Miller, J. P., Nicklas, B. J., et al. (1994). Effects of strength training on total and regional body composition in older men. Journal of Applied Physiology, 77, 614-620. Troiano, R., & Flegal, K. (1998). Overweight children and adolescents: Description, epidemiology, and demographics. Pediatrics, 101(3), 497-504. Trost, S. G., Saunders, R., & Ward, D. S. (2002). Determinants of physical activity in middle school children. American Journal of Health Behavior, 26(2), 95-102. U.S. Department of Health and Human Services. (2001). The surgeon general’s call to action to prevent and decrease overweight and obesity—2001. Rockville, MD: Author. Warren, J., Henry, C., Lightowler, H., Bradshaw, S., & Perwaiz, S. (2003). Evaluation of a pilot school programme aimed at the prevention of obesity in children. Health Promotion International, 18(4), 287-296.