Duration: 10/01/2011 to 09/30/2016

Administrative Advisor(s):

NIFA Reps:

Non-Technical Summary

Statement of Issues and Justification

STATEMENT OF THE PROBLEM:
Excessive weight gain is associated with increased risk of developing many serious diseases, including cardiovascular disease, hypertension, and type 2 diabetes. Young adults are at a uniquely increased risk for weight gain because of rapidly changing social situations that influence eating and exercise behaviors. Despite extensive efforts to promote weight management, these efforts only reach a small proportion of the population at risk and even effective programs promoting individual behavior change may have limited effectiveness in environments that promote weight gain. Research is needed to elucidate the combination of individual and environmental factors associated with unhealthy weight gain among college students.

Participants in this multi-state research group have applied theory-based behavioral constructs to design intervention programs to promote healthful eating and exercise behaviors in young adults with a goal of preventing unhealthy weight gain. Although these programs have been effective in improving dietary behavior, they did not prevent weight gain. The previous five years of this multi-state research have been devoted to building community relationships with young adults using principles of community-based participatory research. The results of this work have led to the identification of environmental and behavioral barriers and facilitators, and to the development of instruments for assessing environmental and individual factors associated with health outcomes such as body weight. The purpose of this renewal is to refine and validate these instruments and to define the relationship between environmental and behavioral factors with a goal of quantifying these to create a Healthy Campus Index.

JUSTIFICATION:
Although the dramatic increases in rates of obesity may be leveling off, obesity is still at epidemic proportions in the U.S. (Flegal et al., 2010). As of 2004, 66.3% of adults (61.8% female and 70.8% male) in the U.S. were overweight or obese and 32.2% were obese (33% female and 31% male) (Ogden et al., 2006). Obesity contributes to the major causes of premature death in the U.S., with an estimated 300,000 deaths per year attributable (Allison et al., 1999) and is an important independent risk factor for atherosclerotic cardiovascular disease, type 2 diabetes, stroke, some forms of cancer, breathing problems, arthritis, reproductive complications and psychological disorders, such as depression. (Calle et al., 2003; Murphy et al., 2000).

Young Adults and Obesity. Young adults, aged 18-25, are at especially high risk for weight gain (Lewis et al. 2000; Klem, 2000). For many college students weight gain begins during their freshman year. Lloyd-Richardson et al. (2009) collected height and weight data from college freshmen and followed many of them through their sophomore year. Data were collected from two universities: one public and one private. During their freshman year, 77% of student participants from the public university gained weight (3.5 kg for men and women). Seventy percent of the students from the private university gained weight (2.5 kg for men and 1.6 kg for women). Most of the weight gain from both schools occurred during the first semester. Although many of the students still had a healthy body mass index (BMI) after the weight gain, rates of overweight/obesity increased from 22% to 36% in the public university and 15 to 18% in the private university. Students from the public university were followed through their sophomore year, and total weight gain for both years was 4.3 kg for men and 4.2 kg for women. Racette et al. (2005) reported similar findings from a longitudinal study to assess weight changes during freshman and sophomore years of college where 70% of the students had gained approximately 4.1 kg by the end of their sophomore year. Morrow et al. (2006) reported an increase in weight gain between fall and spring semesters of approximately 1.1 kg among freshman women during their first year of college.

The weight gain observed in the studies above may be due to the many different changes that occur in the lives of young adults. Many are transitioning into independent living and residing on college campuses where they are not making healthful choices as evidenced by The American College Health Association National College Health Assessment (ACHA-NCHA),. This assessment collects information on a broad range of student health behavior, health indicators, and perceptions. The Fall 2009 Reference Group includes ACHA-NCHA data from 34,208 students at 57 institutions of higher learning. Only 5.9% of college students (n=2,018) ate five or more servings of fruits and vegetables daily. Less than half met the recommendations for exercising vigorously for at least 20 minutes on three or more days per week or moderately for at least 30 minutes on five or more days per week (American College of Health Association, 2007). These negative health behaviors may be associated with the weight gain observed for young adults after entering college. This may be especially important for health because being overweight between the ages of 20-22 years is associated with an increased risk of obesity at 35-37 years of age (McTigue et al., 2002; Guo et al., 2000).

Many of these young adults will become, or are, parents. Parents dietary quality influences their childrens dietary quality: parents who are overweight or obese are more likely to have children who are overweight or obese. Given the concern with childhood obesity, it is important to target the young adult population to improve the young adults diet and weight management skills. Thus, weight management interventions with young adults may be important in preventing negative health conditions that may have deleterious lifelong health consequences for both the young adult and the next generation of young children.

Dietary Behavior Associated with Weight Management. Dietary behavior associated with weight management needs to be better understood. Weight gain results from chronic positive energy balance related to dietary and exercise behaviors (U.S. Food and Drug Administration, 2004); however, the underlying causes of overweight and obesity are multidimensional (Kumanyika & Obarzanek, 2003; U.S. Food and Drug Administration, 2004)

Specific food consumption behaviors have been associated with increased weight. For example, women who reported eating out a greater number of times per week also reported greater total energy intake and consumed a poorer-quality diet (Clemens et al., 1999). The frequency of consuming restaurant food also has been positively associated with increased body fat percentage in adults (McCrory et al., 1999). Using a multivariate model and data collected from the Health Risk Appraisal administered on a college campus, Adams and Rini (2007) reported that increases in BMI in women were associated with low intake in cruciferous vegetables and fiber, and high intakes of cholesterol-containing food and alcohol.

Individualized Factors and Obesity. There are many individualized factors that may play a role in eating behavior and the development of obesity. These factors include (but are not limited to): personality, parental status, income level, ethnicity, within-meal eating behavior, cognitive behavioral influences, and interpersonal influence susceptibility.

Personality. One way in which interventions may be customized is to tailor interventions for individuals based on personality characteristics. Elfhag and Rossner (2005) indicate that factors like possession of coping skills, presence of self-efficacy, and being a healthy narcissist are associated with success in losing weight, while characteristics such as disinhibited eating, perception of weight loss barriers, and psychosocial stressors can lead to weight gain.

Parental status. Parental obesity has been associated with higher risk for childhood obesity in cross-sectional (Danielzik et al.,2002; Sekine et al., 2002; Plachta-Danielzik et al., 2010; Whitaker et al., 2010) and longitudinal studies (Dubois and Girard, 2006). Recently, Whitaker et al. (2010) assessed the effect of maternal and paternal weight status individually and combined on risk for obesity in 7078 children in the UK. The incidence of obesity in children from families with two obese parents was 21.7% compared to 2.3% from families with two normal weight parents. In addition, children with two obese parents were 12 times more likely to be obese than children with normal weight parents. When obese parents were further stratified by severity of obesity, children with two severely obese parents were 22 times more likely to be obese than children of normal weight parents. Parental obesity has also been associated with more rapid weight gain between ages three and five (Griffiths et al., 2010), which may increase the risk of obesity in later childhood (Cole, 2004).

Income level. The association of low income levels with mortality related to obesity was found to be strong after controlling for major behavior risks in a 19-year prospective study of U.S. adults (Lantz et al., 2010). Obesity is more common among the less affluent, especially for the female population (Nikolaou and Nikolaou, 2008). Obesity rates were found to increase in females over time as neighborhood-level incomes decreased (Black and Macinko, 2010). Effective obesity preventive health policies need to consider individual and contextual determinants of obesity such as income levels.

Ethnicity. There are disparities in obesity prevalence among different ethnicities. According to NHANES 2007-2008 non-Hispanic black adults had the highest prevalence of obesity at 44% followed by Mexican Americans at 40 %, all Hispanics at 39 % and non-Hispanic whites at 32 % (Flegal et al., 2010). Within racial groups also had disparities by gender. Non-Hispanic black women consistently had a higher prevalence of obesity than non-Hispanic black men (52.9% vs. 37.2%, respectively, in NHANES 2005-2006 and 49.6% vs. 37.3%, respectively, in NHANES 2007-2008) (Flegal et al., 2010; Ogden, 2009).

In children racial and ethnic differences in the prevalence of obesity also exist. According to data from NHANES 2007-2008, the percentage of obesity and overweight and obesity combined in children 2 to 19 years of age was 20% and 35.9 % in non-Hispanic blacks, 20.8% and 38.9 % in Mexican Americans, 20.9% and 38.2 % in all Hispanics and 15.3% and 29.3 % in non-Hispanic whites. Hispanic boys had a significantly higher risk of obesity than non-Hispanic white boys. Non-Hispanic black girls had a significantly higher risk of obesity than non-Hispanic white girls (Ogden et al., 2010). The differences among racial groups may be explained by cultural influences on physical activity and family attitudes about food and eating, as well as access to healthful foods and physical activity facilities, which may promote weight gain.

Within-Meal Eating Behavior. Based on accumulating evidence, within-meal eating behavior, such as eating rate, bite size, chewing, oro-sensory processing, attentiveness to the development of satiation, and meal termination, have important implications in energy intake and body weight regulation (Westerterp-Plantenga, 2000; Laessle et al., 2007; Andrade et al., 2008; Llewellyn et al., 2008). Researchers of population-based studies have supported relationships between eating rates and body weight (Takayama et al., 2002; Otsuka et al., 2006; Sasaki et al., 2003; Greene et al., 2008). Independent effects have also been demonstrated between eating rate and insulin resistance (Otsuka et al., 2008), and components of metabolic syndrome (Kral et al., 2001).

Cognitive Behavioral Influences. Eating and exercise behavior, as well as attitudes towards weight status and related psychosocial determinants of weight differ by sex (Connor-Greene, 1988; Zmijewski and Howard, 2003; Mackey and La Greca, 2007). College-aged males tend to want to gain weight by building muscle, whereas females desire to lose weight (Connor-Greene, 1988; Neighbors and Sobal, 2007). Females are more likely to have higher dietary restraint and emotional eating scores relative to males (de Lauzon et al., 2004), indicative of problem eating behaviors that could lead to disordered eating (Lindeman and Stark, 2001) and/or weight gain (Hill and Peters, 1998). Specific patterns of psychosocial and behavioral variables were found in college students at elevated health risk (Greene et al., in press). Despite these differences, Economos and colleagues found weight gain in the freshman year did not differ by sex (Economos et al., 2008).

Interpersonal Influence Susceptibility. Research by Greaney, Less, White, Dayton, Riebe, Blissmer, Shoff, Walsh, & Greene (2009) implicated eating out with others as an interpersonal barrier for weight management faced by college students. The same research by Greavey et al. (2009) identified social support as a crucial factor for female college students more so than male college students for healthy eating. For some female college students, eating with a friend can result in eating more than if they ate with a stranger (Koh and Pliner. 2009). Not all college students may be equally influenced by social situations. To better understand the reasons why people make the food choices they do Rothman, Gillespie, & Johnson-Askew (2009) recommended that interpersonal behaviors be further studied.

The concept of personal food systems investigates how and why food decisions are made in different situations. According to research by Connors, Bisogni, Sobal and Devine (2001), food decisions are influenced by the maintenance of socially acceptable food choices and the values that groups and their members place on foods. Therefore, the food choice decisions college students make could depend on their social environment. The life course perspective (LCP) which has been used to examine factors related to health status over time can be used to explore the connection between peer social groups and the food decisions made by their members.

With its roots in sociology, LCP can be used to examine relationships between both family members and peers. One life change that could be studied is the time period when a young adult starts and attends college. Research by Vermeir and Verbeke (2006) has shown that a persons social network influences food choices. Understanding the influence that college students social groups have in different contexts could help to develop interventions to improve the nutritional status of groups of college students.

Environmental Factors and Obesity. The impact of the built environment on risk for obesity is pervasive in the obesity literature. The built environment encompasses a variety of components including, but not limited to, neighborhood walkability, access to parks, safety, cleanliness, and traffic flow (Renalds et al., 2010). These factors influence the ability and desire for people to be physically active. A review by Renalds et al. (2010) identified factors of the built environment that influenced physical activity. More lights, fewer intersections and traffic, and better scenery all encouraged physical activity, while poor security and poor neighborhood maintenance discouraged physical activity. The authors concluded that neighborhoods that encouraged walking had more physically activity residents with lower incidence of overweight. Many researchers have reported a positive association between some aspect of the built environment and obesity (Giles-Corti et al., 2003; Booth et. al, 2005; Papas et al., 2007; Timperio et al., 2010), possibly because of barriers to physical activity. In their review on obesity and the built environment, Booth et al. (2005) note that biological, psychological, behavioral, and social factors do not fully account for the current obesity epidemic, which provides support for the evaluation of the effect of the built environment. Increasing the ability for neighborhoods to promote physical activity may translate into a reduced risk for obesity but may be a challenge in socially disadvantaged neighborhoods where physical activity is lower for a variety of reasons (Giles-Corti and Donovan, 2002; Cerin and Leslie, 2008; Turrell et al., 2010). More affluent neighborhoods tend to have higher levels of physical activity, which may provide more protection against certain diseases in these communities (Turrell et al., 2010).

Currently there are few published studies documenting the effect a change in the built environment has had on the community. McCreedy and Leslie (2009) described a city-wide initiative in Orlando, Florida, called Get Active Orlando that brought together a multidisciplinary team of community partners passionate about changing the culture of their city to encourage physical activity. With a grant from the Robert Wood Johnsons Active Living by Design initiative, researchers designed and implemented a community-wide campaign that encouraged healthful lifestyle changes. After establishing baseline data by surveying their target low socioeconomic status neighborhood, researchers developed programs designed to increase physical activity such as bike giveaways, safe bike rides, free bike repair, a senior walking program, and a community garden. These programs were successful in getting the community involved and physically active. The program successes have led to policy changes for development projects in the community intended to make the city more active. Their website (www.getactiveorlando.com) provides information as well as a Design Standards Checklist to be used by developers to promote physical activity in developments. A similar program in Somerville, Massachusetts, (Burke et al., 2009) also achieved positive results. Although it is premature to determine if changes in the built environment will lead to a decrease in obesity, Orlando and Somerville can serve as models for community promotion of physical activity for the health benefit of residents.

Another environmental factor is the food environment. According to Hill and Peters (1998) one way in which the current environment promotes obesity is by providing more frequent opportunities for the consumption of large quantities of food. A variety of highly palatable, inexpensive foods is available nearly everywhere. A cross-sectional survey of rural adults indicated that frequency of eating at establishments that promote excessive food consumption such as buffets, cafeterias and fast food was positively associated with obesity (Casey et al., 2008). Young adults in the Coronary Artery Risk Development in Young Adults (CARDIA) study who ate at fast food restaurants more than twice a week had a significantly higher weight gain during the 15 year study period than those who ate fast food less than once a week (Pereira et al., 2005). Despite these positive associations, a recent review of the literature indicates insufficient evidence to support an association between fast food consumption and obesity, and more studies are needed (Giskes et al., 2007).

School Health Index. The Center for Disease Control has published manuals and guidelines for School Health Indexes for elementary (Center for Disease Control, 2005a), middle, and high schools (Center for Disease Control, 2005b). These School Health Indexes (SHI) are self-assessment and planning guides that involve teachers, parents, students and the community involvement in the process of identifying the strengths and weaknesses of the schools policies and programs for promoting health and safety and development of an action plan for improvement in these areas. The health topics assessed through this index include physical activity and physical education, nutrition, tobacco use prevention, asthma, and safety prevention. These indexes can be used by the schools for needs assessment, prioritization of needs, and implementation of prevention programs (Sherwood-Puzzello et al., 2007). There is no index currently available for higher education campuses.

By recognizing that a myriad of environmental and individualized factors can influence eating behavior and lifestyle choices, tailored intervention strategies that have both an environmental and individual focus can begin to be developed. Additionally, identification of the individual factors and the necessary environmental factors to support the individual change is the first step in the development of indexes for comparisons and benchmarking to support policies and programs for behavior change on college campuses and communities.

Benefits of this project:
This proposed research will continue to use the community-based participatory research (CBPR) design. Research investigators will continue to work side-by-side as partners with young adults to understand, develop, create, and tailor interventions desired by young adults. By using these CBPR approaches, results of this work are more likely to be effective. Grant funding will be pursued for this participatory research and for the resulting tailored intervention projects, as well as smaller state/local specific projects. The ultimate outcome of this work will be tailored intervention strategies and environmental support approaches that meet the young adult groups needs in their acquisition of healthful eating behavior to prevent excessive weight gain. These improvements in young adults eating behavior will likely also affect the eating behavior of young adults current and future young children. The collective power of the multiple states collaborating throughout the entire participatory process will significantly contribute to the understanding of how to best meet young adults needs as they strive to prevent weight gain and adopt healthful habits. The outcomes from this work address health promotion priorities of USDA and other agencies such as NIH and the Institute of Medicine.

Need for cooperative work:
Because of the multidimensional etiology of obesity and the equally multidimensional intervention designs needed to reduce incidence of obesity, significant large-scale progress toward weight management is unlikely to be achieved by a single investigator or University location; even the National Institutes of Health acknowledges this point. Thus, a multi-state research team approach will increase diversity of expertise, environmental locations, and population demographics. The procedures outlined in this proposal require an enormous amount of work and a diverse set of research skills, all factors that this research team can provide. Furthermore, the limitations of previous studies on how environmental factors affect diet quality and health have suffered from regional bias that restricts ability to generalize findings. By using multiple campuses to address specific objectives, this research team will be able to not only increase the power of analyses, but also enhance applicability of the findings and resolve regional disparities.

This effective collaborative research relationship is demonstrated through a strong publication record with multiple authors from different institutions. This multi-state research team has a strong record of collaborative research (NC200, NC219, NC219R, and NC1028 multi-state research) that will allow diversity needs to be met. Additionally, this research collaborative has successfully secured USDA grant funding, including an Initiative for Future Agriculture and Food Systems (IFAFS) grant and three National Research Initiative (NRI) grants.

Related, Current and Previous Work

Related: A CRIS review found 31 projects related to obesity and weight gain prevention. However, only four were targeted at young adults (18-24 years old): two focused on participatory research, two focused on tailoring interventions, and there was no duplication of the proposed project. Projects with components similar to our proposed project include work by: Devine, CM and Warren, BS which focused on weight prevention in breast cancer survivors; Devin, CM and Warren, BS which focused on worksite ecological interventions to prevent weight gain; Devine, CM and Warren, BS which focused on small steps as an exercise environmental intervention for worksite weight-gain prevention; Devine, CM, Warren, BS, Page RL, Maley M which is an integrated approach to prevention of obesity in high risk families; Hill, JO; Anderson, U and Doucette MM which utilized the America on the Move Family Program for weight gain prevention; Khouryieh, HA, Besong S, Giesecke, C and Mugia, C. which targeted healthy eating choices and prevention of weight gain in adolescents; Evans, E, Chapman-Novakofski, K, McAuley, E. and Motl. R which focused on peer education through exercising and eating right; Shoff, S. who focused on behavior change for obesity prevention in young adults; and our own NRI project (# 0217242; Kattelmann K, Greene G, Phillips B, White A, Hoerr S, Horacek T, Colby S, Kidd T, Nitzke S, Byrd-Bredbenner C, and Esters O).

Current: We are currently developing a web-based intervention for obesity prevention in college and non-college young adults, funded by a NRI grant (Kattelmann K, PI). Development of a randomized trial guided by the process of PRECEDE-PROCEED for the prevention of excessive weight gain in communities of young adults is a USDA/NRI integrated project, Kendra Kattelmann, P.I. (SD) with 11 funded subcontracts to RI, ME, NC, WI, MI, AL, KS, NY, IN, and NJ and 3 non-funded participating states WV, FL and NH and a total award of $1,499,491. This project uses the community-based research process of PRECEDE-PROCEED to develop and test a 12-week intervention tailored to the 18-24 year old for the prevention of obesity. Young adults (18-24 years old, n=2040) recruited from samples in all participating states will be randomized into intervention or control groups to test the effectiveness of this model intervention to reduce excessive weight gain, increase fruit and vegetable consumption and physical activity to a greater extent in the intervention group than the control group. This project will be the first to develop a model intervention for obesity prevention that uses the CBPR process to integrate research, Extension, and communities of young adults.

Previous: This renewal builds on the 20 years of research addressing eating behavior in young adults. It began with NC219, in which researchers developed significant depth in the understanding of young adult food choice behavior and differences by gender and stages of readiness to change. NC219 researchers in 10 states completed a behavioral intervention for low income young adults using extension/education/research partnerships. Over 2000 economically disadvantaged young adults were recruited and enrolled in a 6-month, stage based intervention. Follow-up interviews 6 months post intervention (12 months from enrollment) showed that positive results were achieved and maintained. Participants in the experimental group had higher intakes of fruit and vegetables than controls (perceived daily intakes of 4.90 vs. 4.60 servings per day, F=3.49, p<0.05 and 4.31 vs. 3.92 servings/day via 5-A-Day Screener, F=4.78, p<0.01) and greater progression to action or maintenance stages (66% progress in fruit for intervention vs. 55% progress in fruit for controls; 47% vs. 32% progress for vegetables, p=0.0080 and 0.0001, respectively) (Nitzke et al., 2007). In cooperation with Extension partners, a follow-up study converted the stage-based print materials and assessment instruments from the IFAFS-funded study to a web-based format (F and V Express Bites, http://www.nutrisci.wisc.edu/fav) with continued evidence of success (Park et al., 2008).

NC1028 researchers focused on college and non-college young adult populations with a goal of developing a CBPR model that integrates research, extension, and communities of the targeted population. Using the PRECEDE-PROCEED model of participatory research, each state worked with its target population to identify and prioritize the problems of significance to them, i.e. quality of life (Social Diagnosis); health/ environmental/ behavioral determinants (Health, Behavioral and Environmental Diagnosis); and predisposing, enabling and reinforcing factors (Educational and Ecological Diagnosis). During this processes researchers enhanced skills in participatory research techniques and built partnerships among researcher, extension and outreach educators, and populations of young adults to develop cooperative intervention programs. The work of this group has been augmented by research grants. Behavior Change for Obesity Prevention in Young Adults was a USDA/NRI Integrated Project, Geoffrey Greene, P.I. (RI), with subcontracts to NY, AL, MI, WI, SD, PA, and ME and a total award of $1,127,707. In addition, we continue to assess campus and community environments for promotion of physical activity and healthy eating, and discovered the influence environments have on the healthy behaviors of the young adult population. Physical environments conducive for walking and biking, and building and community environments that offered nutrient-dense low-cost foods, increase the likelihood of young adults adapting these healthier behaviors over time. Thus, the proposed 5-year project is designed to use CBPR to expand the scope of the web-based intervention to focus on environmental issues that support healthful lifestyles as well as behavioral and quality of life issues, as they relate to college students health and nutrition needs for obesity prevention. Multi-state collaborations, by researchers and young adults, as proposed for the current project will expand the experience in collaborative, community-based research that will complement the NRI-funded web-based intervention. Our long-range plan is to tailor intervention strategies developed during NC1028 as a model for integrated interventions for young adults.

Objectives

1. Develop instrument(s) and strategies to assess and evaluate individualized factors associated with eating behavior and health outcomes.
   
2. Refine and validate environmental assessment instruments for assessing and evaluating environmental factors that influence eating behavior and health outcomes.
   
3. Explore mechanisms of interaction between the identified individualized factors and environmental factors in influencing eating behavior.
   
4. Use the findings from Objectives 1-3 above to develop a Healthy Campus Index that can be used by higher education institutions around the nation to determine the how supportive their campuses are of promoting healthy weight among their students as well as identify areas of strength and areas needing improvement so that campuses can make meaningful changes that better support young adult health.
   

Methods

Objective 1 Procedures According to the PRECEDE-PROCEED planning model (Green & Krueter, 2004), etiological factors and determinants of health include genetics, behavioral patterns, and environmental factors. Assuming genetics are not subject to change, and that environmental factors will be assessed separately, the focus of this Objective is: "How do behavioral and perceived environmental factors affect predisposing, reinforcing and enabling factors for prevention of unhealthy weight gain in college students?" In NC 1028 we developed the Behavioral, Environmental, Changeability Survey (BECS) instrument to assess and prioritize behavioral determinants influencing unwanted weight gain in young adults. Objective 1 of the renewal project will focus on refining this instrument (i.e. modified BECS) and new instruments may be developed to assess emerging constructs such as personality. The factors addressed in this objective involve determinants that may be unique to each individual, may be represented by a unique combination of factors, or, as Greene and colleagues found, individuals may be clustered by a set of factors (Greene et al., in press). These factors include, but are not limited to, dietary and exercise behaviors, coping with stress, sleep, within-meal eating behavior, cognitions (cognitive restraint), susceptibility to emotional eating and external cues, personality, parental status, income level, and ethnicity. To date we have collected approximately 1300 responses from eight of the participating states using the BECS instrument. Young adults ranked the following behaviors as important to improve their health: getting seven to nine hours of sleep, exercising regularly and enjoying the food they eat. These young adults wanted to learn more about and were willing to change these factors in order to improve health. The top environmental factors they ranked as being supportive for prevention of weight gain were reducing the cost of healthful foods, availability of places to exercise, and availability of healthy foods. Preliminary psychometric work with BECS suggested six factors and 37 items but continued development and validation of the instrument will be necessary in Years 01 and 02 of this NC1028 renewal to validate this instrument and enhance its generalizability. Predisposing factors including current behavior will be added to the BECS along with motivational readiness to change to meet the behavioral targets. Because nutrition and exercise are broader than the targets above, more general nutrition and exercise behaviors and willingness to change factors will be assessed through these existing BECS scales. Attitudes will be assessed through the BECS scale "importance". Enabling factors will be assessed through BECS scales of environmental change and health programs. Recent work suggests the importance of social factors, especially social networks (Centola, 2010), therefore assessment of social factors will be added in Year 01. The goal of Year 01 will be to validate the modified BECS instrument and to add a social dimension assessing reinforcing factors. At least six to eight of the participating NC1028 states will conduct surveys of 200 young adults per state (n=1400) in Year 01. Surveys will include published instruments to use for concurrent validation. Additional developmental work with the BECS will require supplemental funding. For example, the influence of personality as an individualized factor associated with eating behavior patterns has recently emerged as a possible area of interest for this team. Other emerging factors such as within meal eating behaviors may also be assessed using the same model as described for personality. The specific research question is: What are the diet and physical activity behaviors and personality characteristics of individuals who effectively manage their weight and individuals who do not effectively manage their weight? To develop the eating behavior pattern survey, 20 participants at each of the 16 participating NC 1028 sites (n=320) will complete three-day food and physical activity records in Year 01. Researchers at NC and FL will content analyze the records and code behaviors. Researchers will develop a preliminary survey based on the identified coded behaviors. The preliminary survey will be content/face validated, and undergo cognitive interviewing procedures in Year 02. The revised survey will then be pilot tested and validated against three-day food and physical activity records in NC and FL in Year 02. The developed behavior assessment survey will be administered in an online format along with the International Personality Item Pool - Five Factor Model (Golberg, 1992 and 1999) and an assessment of effective weight management with a young adult population. The results of the online surveys will be statistically analyzed to identify weight management behaviors associated with specific personality types. Objective 2 Procedures Data from our 2008 higher education environmental audit, refined and simiplied in 2009, will be analyzed and published prior to the start of the proposed project. This environmental audit of campuses assessed the physical activity environment, food environments, obesity prevention building analysis, and campus policies. To accomplish Objective 2, principal investigators in at least six to eight institutions will continue to refine and validate the campus environmental audit in Year 01. Steering committees at each institution will work collaboratively with researchers to determine specific elements that will be assessed in environmental reassessments. Currently the total environmental audit, a categorical evaluation, consists of a total of 10 sections: 1) bike-ability/walk-ability, 2) food stores, 3) dining halls, 4) restaurants; 5) environmental appeal, 6) institutional policy audit, 7) building/stairwell health promotion, 8) vending machines, 9) recreation facilities and, 10) campus recreation services. Decisions will be made to determine other constitutients that should be included in data assessment (similar to the School Health Index); i.e. campus administrators, public safety personel, etc. Also in Year 01, the NEMS-dining hall food audit will be validated for diet quality by comparing its scores with findings from the Healthy Eating Index-2005 (Guenther et al., 2008) using the methods outlined by Reedy and colleagues for disappearance data (Reedy et al., 2010). That is, a HEI-2005 will be calculated for the foods prepared and served, minus the foods unserved, for an evening meal at a selection of campus dining halls in at least six to eight universities. In Year 02, at least six to eight universities, if funding is available, will each implement the refined environmental audit concurrently with ACSM American Fitness Index or the School Health Index (or other similar environmental assessment tool). Also in Year 02, with additional funding, the environmental audit will be validated by implementing it in at least three to five campuses known to have a healthful environment (beyond the current partners). With funding, we plan to develop a web version of environmental audit for easy administration and benchmarking. In addition, a data base system could be created for benchmarking to provide results for campus and advisory teams to start the change process. Objective 3 Procedures The interactions between the individualized factors identified in Objective 1 and the environmental factors identified in Objective 2 will be assessed. An example of one possible method of assessment would include conducting an online survey of individualized factors during the same time period that environmental factors are assessed. The online survey (based on the modified BECS) will be utilized that assesses the individualized factors identified in Objective 1 that are associated with prevention of unhealthy weight gain. To further validate the index, correlations between the healthful lifestyle facilitation scale and bikeability/walkability/student health indicators (i.e. BMI, percentage meeting recommended minutes of physical activity per week and recommended intakes of fruits and vegetables) will be determined. Two hundred participants will be recruited, over a three month period, from each site to take the online survey. The refined environmental assessment tool will also be used during the same three month time period. Results will be analyzed to identify relationships between the individual factors, environmental factors, and health outcomes including weight status (which will have a binomial or multinomial distribution) using a Chi-square test of independent variables and - for a group of selected environmental factors and their interactions - a generalized linear model (e.g. multivariate logistic or multinomial regression) will be fitted to test which of these factors explain a significant portion of the variability on individual behavior responses towards weight status. The results from the statistical analysis will be presented to steering committees for further evaluation of the data. Objective 4 Procedures Year 04 of this project will focus on development of a comprehensive Healthy Campus Index. Data collected in Year 03 (simultaneous assessment of environment and individualized factors) will be presented to participatory research committees and campus administrators on participating NC 1028 campuses. Campus specific scores as well as cross-campus comparisons will identify potential behavioral and environmental barriers. Significant factors related to healthful dietary and exercise behaviors will be utilized to construct a Healthy Campus Index. Participating institutions will be scored using this Index and results presented to Committees and administrators. Feedback from committees and administrators will be evaluated and potential changes in the Index will be proposed. If the Index is revised, reassessment of participating institutions will be conducted in Year 05. Reliability and validity of the Index must be determined prior to dissemination. Therefore, initial studies will be completed to assess the reliability of the assessment and the validity (related to health outcomes) of the Index. Funding will be sought for validation studies compared to behavior (e.g., comparisons with 24-hour food and activity recalls and objective measurements of body weight and activity using accelerometers). Funding will also be sought for benchmarking or identifying schools with high scores on the Healthy Campus Index and for investigating factors associated with high scores. Finally, funding will be sought for intervention studies to test whether individually tailored interventions including both individual factors and environmental awareness are effective in improving dietary and exercise behaviors to prevent unwanted weight gain in young adults.

Measurement of Progress and Results

Outputs

• Possible outputs include (1) a validated modified BECS instrument, 2) a dietary behavior pattern instrument for young adults; (3) an analysis of the relationship between personality, behavior and effective weight management.
• The output from this objective will be the finalized environmental audit which will be a component of the Healthy Campus Index. The audit can be used by campus residential life and foodservice administrators, health promotion specialists, and researchers to benchmark the degree the campus environment supports obesity prevention.
• The output from this objective will be the identification of individualized factors that may, in interaction with specific identified environmental factors, be most important to target when developing weight management intervention strategies. In addition, the simultaneous assessment of environmental and individual factors will provide a database to be used for Objective 4.
• The output from this objective will be a prototype for a Healthy Campus Index. Future research will further develop and validate this instrument.

Outcomes or Projected Impacts

• By recognizing that a myriad of environmental and individualized factors can influence eating behavior and lifestyle choices, tailored intervention strategies that have both an environmental and individual focus can begin to be developed.
• Identification of the individual factors and the necessary environmental factors to support the individual change is the first step in the development of indexes for comparisons and benchmarking to support policies and programs for behavior change on college campuses and communities.
• During this next 5 years, we will refine and validate assessment tools and develop a prototype Healthy Campus Index that can be used for planning and evaluation at both the personal and environmental levels of the socio-ecological model. Scores on the Healthy Campus Index will be provided to community partners, campus administrators, and other key stakeholders as the first step in making meaningful changes that address key factors affecting the health and nutrition of young adults.

Milestones

(1):ctober, 2011- September, 2012 New members of this multi-state project will gain knowledge on community based participatory process of PRECEDE-PROCEED. States will continue to work with their participatory research committees. At least six to eight states will complete Objective 1 to refine the BECS instrument for the assessment of individualized factors associated with eating and exercise behavior. At least six to eight different states will complete Objective 2 to refine and validate the environmental instruments. Data will be compiled, analyzed, and grant proposal(s) will be developed to fund assessment of additional individual factors as well as the assessment in Year 03. Manuscript preparation will continue.

(2):ctober, 2012- September, 2013 States will continue to work with their participatory research committees. BECS and environmental assessments reliability and validity studies will be finalized as needed. Additional individual factors may be explored if funding is obtained. Data will be compiled and analyzed, and funding proposals as well as manuscript preparation will continue.

(3):ctober, 2013- September, 2014 All states participating in the muli-state project will conduct an online assessment of individualized factors (n=200 per state) as well as an environmental assessment utilizing instruments for the assessment of individualized and environmental factors. States will continue to work with their participatory research committees. Data will be compiled and analyzed (Objective 3). Results will be reviewed and interpreted with community teams, and manuscript(s) preparation will continue as well as seeking additional funding.

(4):ctober, 2014- September, 2015 Results from Year 03 will be analyzed and a Healthy Campus Index developed (Objective 4). Participating institutions will be scored using the index. Results will be reviewed with participatory research committees and administrators, and Index will be revised as necessary. Funding support will be sought for reliability and validity testing, benchmarking, and intervention studies. Manuscript(s) preparation will continue.

(5):ctober, 2015- September, 2016 The prototype Healthy Campus Index will be finalized and results will be reviewed with participatory research committees and administrators. Funding support will be sought and manuscript(s) preparation will be finalized.

Projected Participation

View Appendix E: Participation

Outreach Plan

Results will be disseminated through abstracts, poster presentations, and planned sessions at national meetings (i.e Society for Nutrition Education, Experimental Biology, American Dietetics Associatoin and others) and manuscripts in refereed publications.

Organization/Governance

Each year a technical committee chair and secretary will be elected. Key communication tools, (website, listserve and roster) for the technical committee will be established and maintained by the secretary or designee. The chair and the secretary will assure that required reports are filed on a timely basis. Key communication tools, (website, listserve and roster) for the technical committee will be established and maintained by the secretary or designee. By the end of each annual technical committee meeting, the site and potential dates for the following year's annual meeting will be established. A site host will also be determined.

Each member will bring a written or submit an electronic annual report of activities associated with the multistate project to the group at or before the NC1028 annual meeting and provide a copy to the secretary. The report should summarize activities and include a list of citations (theses, abstracts, proceedings, journal articles, presentations, etc.) Each state and administrative agency will share the cost of the annual technical committee meeting by paying a registration fee that covers necessary costs such as meeting rooms, AV equipment rental, shared meals/refreshments, etc. Whenever possible, an estimate or notification of the registration fee will be announced in advance and receipts will be provided by the person hosting or designated as the site coordinator for the annual meeting.Establishing research topics

Research topics related to the objectives of the NC1028 research (i.e., spin-off projects that involve analysis of data collected by the multistate group) will be requested in writing using the Roots and Shoots template and posted to the project website via application to the chair of the publications committee. Roots forms designate temporary (up to 6 months in most circumstances) placeholders for ideas. Unless specific exceptions are requested and granted by the publications committee, root forms will expire 12 months from their original posting date, meaning any NC1028 partner is allowed to submit a root/shoot on that topic. Shoots forms will be used to describe planned analyses and partner inputs as details become available. The Decisions (topics/responsibilities for writing) related to multistate articles to be published from the data will be discussed during the teleconference meetings or at the NC1028 annual meeting. Each publication (journal article/abstract etc.) and/or presentation that addresses the NC1028s main objectives should name the state PIs as authors if they have made a substantial contribution to the collection and analysis of data. Others who have played leadership roles in gathering or analyzing NC1028 data (e.g., a primary statistical consultant or a graduate student who coordinated a critical component of the research) should also be included as authors, if relevant. Data related to a given state will be used for theses, articles, presentations etc. at the state's discretion. Authorship associated with such materials is at the state's discretion.The cost of submitting an abstract or poster to a scientific meeting will be borne by the senior author.Sharing page charges that exceed $100 will be negotiated among stations.Each scientist is responsible for filing an archival copy of publications that use NC1028 data or intellectual property (e.g., focus group manuals) on the multistate website. All NC1028 publications and related materials should give credit to the multistate project and other relevant grants.

The multistate technical committee will establish the procedures for data collection and coding. Once such procedures are established, all stations collecting and coding data will handle collection and coding in a uniform manner. If necessary, requests for modifications that do not jeopardize the integrity of the combined data set may be considered by the technical committee and must be requested/handled in a timely manner.

The group has established a maximum size of 15 members. New members must have applicable expertise and must strengthen the group research agenda.

Literature Cited

Adams, T. and A. Rini (2007) "Predicting 1-year change in body mass index among college students." J Am Coll Health 55(6): 361-365.

Allison, D. B., K. R. Fontaine, et al. (1999) "Annual deaths attributable to obesity in the United States." JAMA 282(16):1530-8.

American College Health Association (2007) "American College Health Association National College Health Assessment Spring 2006 Reference Group Data Report (Abridged)." J Am Coll Health 55(4): 195-206.

Andrade, A. M., G. W. Greene, et al. (2008) "Eating slowly led to decreases in energy intake within meals in healthy women." J Am Diet Assoc 108(7): 1186-91.

Black, J. L. and J. Macinko (2010) "The changing distribution and determinants of obesity in the neighborhoods of New York City, 2003-2007." Am J Epidemiol 171(7): 765-75.

Booth, K. M., M. M. Pinkston, et al. (2005) "Obesity and the built environment." J Am Diet Assoc 105(5 Suppl 1): S110-7.

Boyle-Dour C, Horacek T, Shoff S, Schembre S, Greene G, Kattelmann K, Lohse B, Phillips B. (2010) Process Evaluation of Project WebHealth: Behavior Change Intervention for Obesity Prevention in Young Adults. American Dietetic Association FNCE Meeting, Boston, MA November, 2010.

Burke, N. M., V. R. Chomitz, et al. (2009) "The path to active living: physical activity through community design in Somerville, Massachusetts." Am J Prev Med 37(6 Suppl 2): S386-94.

Calle, E., C. Rodriguez, et al. (2003) "Overweight, obesity, and mortality from cancer in a prospectively studied cohort of U.S. adults." N Engl J Med. 348(17):1625-38.

Casey, A. A., M. Elliott, et al. (2008) "Impact of the food environment and physical activity environment on behaviors and weight status in rural U.S. communities." Prev Med 47(6): 600-4.

Center for Disease Control (2005a) "School Health Index: A self-assessment and planning guide. Elementary school version." Altanta, Georgia. http:www.cdc.gov/HealthyYouth/SHI accessed September 7, 2010.

Center for Disease Control (2005b) "School Health Index: A self-assessment and planning guide. Middle school/high school version." Altanta, Georgia. http:www.cdc.gov/HealthyYouth/SHI accessed September 7, 2010.

Centola, D.(2010) "The spread of behavior in an online social network experiment." Science 329(5996):1194-7.

Cerin, E. and E. Leslie (2008) "How socio-economic status contributes to participation in leisure-time physical activity." Soc Sci Med 66(12): 2596-609.

Clemens, L. H., D. L. Slawson, et al. (1999) "The effect of eating out on quality of diet in premenopausal women." J Am Diet Assoc 99(4): 442-4.

Cole, T.J. (2004) "Children grow and horses race: is the adiposity rebound a critical period for later obesity?" BMC Pediatr. 12(4):6.

Connor-Greene, P. A. (1988) "Gender differences in body weight perception and weight-loss strategies of college students." Women Health 14(2): 27-42.

Connors, M., C. A. Bisogni, et al. (2001) "Managing values in personal food systems." Appetite 36(3): 189-200.

Danielzik, S., K. Langnase, et al. (2002) "Impact of parental BMI on the manifestation of overweight 5-7 year old children." Eur J Nutr 41(3): 132-8.

de Lauzon, B., M. Romon, et al. (2004) "The Three-Factor Eating Questionnaire-R18 is able to distinguish among different eating patterns in a general population." J Nutr 134(9): 2372-80.

Dubois, L. and M. Girard (2006) "Early determinants of overweight at 4.5 years in a population-based longitudinal study." Int J Obes (Lond) 30(4): 610-7.

Economos, C. D., M. L. Hildebrandt, et al. (2008) "College freshman stress and weight change: differences by gender." Am J Health Behav 32(1): 16-25.

Elfhag, K. and S. Rossner (2005) "Who succeeds in maintaining weight loss? A conceptual review of factors associated with weight loss maintenance and weight regain." Obesity Reviews 6(1): 67-85.

Erdman M, Horacek T, Phillips B, Guo W, Colby S, White A, Kidd T. Koenings M, Walsh J, Quick V, Greene G. Assessment of the Food and Eating Environment of College Campuses Using a Modified NEMS. American Dietetic Association FNCE Meeting, Boston, MA November, 2010

Flegal, K. M., M. D. Carroll, et al. (2010) "Prevalence and trends in obesity among US adults, 1999-2008." JAMA 303(3): 235-241.

Giles-Corti, B. and R. J. Donovan (2002) "Socioeconomic status differences in recreational physical activity levels and real and perceived access to a supportive physical environment." Prev Med 35(6): 601-11.

Giles-Corti, B., S. Macintyre, et al. (2003) "Environmental and lifestyle factors associated with overweight and obesity in Perth, Australia." Am J Health Promot 18(1): 93-102.

Giskes, K., C. B. Kamphuis, et al. (2007) "A systematic review of associations between environmental factors, energy and fat intakes among adults: is there evidence for environments that encourage obesogenic dietary intakes?" Public Health Nutr 10(10): 1005-17.

Goldberg, L.R (1992) "The development of markers for the Big-Five Factor Structure." Psychological Assessment 4: 26-42.

Goldberg, L.R (1999) "A broad-bandwidth, public-domain, personality inventory measuring the lower-level facets of several Five-Factor models." In: I. Mervielde, I.J. Deary, F. de Fruyt and F. Ostendorf (editors) Personality Psychology in Europe Vol. 7, Tilburg University Press, Tilburg (1999).

Greaney, M. L., F. D. Less, et al. (2009) "College students' barriers and enablers for healthful weight management: a qualitative study." J Nutr Educ Behav 41(4): 281-6.

Green, L.W. Krueter, M. W. (2004) Health Program Planning: An Educational Ecological Approach 4th Edition. McGraw Hill

Greene, G, Schembre, S, White, A, Hoerr, S, Lohse, B, Shoff, S, Horacek, T, Riebe, D, Patterson, J, Phillips, B, Kattelmann, K, Blissmer, B. (in press) "Identifying clusters of college students at elevated health risk based on eating and exercise behaviors and psychosocial determinants of body weight." J Am Diet Assoc (in press)

Greene, G.W., et al. (2008) "Eating Rate and d body mass index in college students." J Am Diet Assoc 108 (9): A26

Griffiths, L. J., S. S. Hawkins, et al. (2010) "Risk factors for rapid weight gain in preschool children: findings from a UK-wide prospective study." Int J Obes (Lond) 34(4): 624-32.

Guenther, P.M., J. Reedy, S. M. Krebs-Smith (2008) "Development of the Healthy Eating Index-2005." J Am Diet Assoc 108(11):1896-901.

Guo, S. S., C. Huang, et al. (2000) "Body mass index during childhood, adolescence and young adulthood in relation to adult overweight and adiposity: the Fels Longitudinal Study." Int J Obes Relat Metab Disord 24(12): 1628-35.

Guo W, Long K, Reznar M, Murashima M, Hoerr S. How well do campus buildings support healthy eating and physical activity? SNE. New Orleans, July 2009; MDA. Grand Rapids, May 2009.

Hill, J. O. and J. C. Peters (1998) "Environmental contributions to the obesity epidemic." Science 280(5368): 1371-4.

Hoerr S, Long K, Guo W, Reznar M, Murashima M, Horacek TM. How well does MSU campus support physical activity? ISBNPA. Lisbon, June 2009.

Horacek, T., Phillips, B., Herrick, M., White, A., Person, A., Greene, G., Quick, G., Kattelmann, K., Shelnutt, K., Morrell, J. (2010) Obesity Prevention: An Assessment and Comparison of 14 Campuses Walk-Ability and Bike-Ability. American Dietetic Association FNCE Meeting, Boston, MA November 2010.

Horacek T, Grimwade A, Bergen-Cico D, Decker E, Walsh J. (2010) Participatory Research with College Students Identifies Quality of Life and Stress as Key Issues for Obesity Prevention. American Dietetic Association FNCE Meeting, Boston, MA November, 2010

Horacek T, Byrd-Bredbenner C, Colby SE, Hoerr S, Johnson M, Kidd T, Phillips B, Walsh J. (2010) Healthy Vending Snacks on 10 College Campuses: A Comparison of Nutrient Density Scores Experimental Biology, Los Angeles, CA. April, 2010.

Kidd, T., Schrage, A., Snyder, C., and Horacek, T. (2010). Vending Machines Affect on Discretionary Calorie Consumption. American Dietetic Association FNCE Meeting, Boston, MA. November 2010.

Klem, M. L. (2000) "Successful losers. The habits of individuals who have maintained long-term weight loss." Minn Med 83(11): 43-5.

Koh, J. and P. Pliner (2009) "The effects of degree of acquaintance, plate size, and sharing on food intake." Appetite 52(3): 595-602.

Kral, J. G., M. C. Buckley, et al. (2001) "Metabolic correlates of eating behavior in severe obesity." Int J Obes Relat Metab Disord 25(2): 258-64.

Kumanyika, S. K. and E. Obarzanek (2003) "Pathways to obesity prevention: Report of a National Institutes of Health Workshop." Obesity Research 11(10): 1263-1274.

Laessle, R. G., S. Lehrke, et al. (2007) "Laboratory eating behavior in obesity." Appetite 49(2): 399-404.

Lantz, P. M., E. Golberstein, et al. (2010). "Socioeconomic and behavioral risk factors for mortality in a national 19-year prospective study of U.S. adults." Soc Sci Med 70(10): 1558-66.

Lewis, C. E., D. R. Jacobs, Jr., et al. (2000) "Weight gain continues in the 1990s: 10-year trends in weight and overweight from the CARDIA study. Coronary Artery Risk Development in Young Adults." Am J Epidemiol 151(12): 1172-81.

Lindeman, M. and K. Stark (2001) "Emotional eating and eating disorder psychopathology." Eat Disord 9(3): 251-9.

Llewellyn, C. H., C. H. van Jaarsveld, et al. (2008) "Eating rate is a heritable phenotype related to weight in children." Am J Clin Nutr 88(6): 1560-1566.

Lloyd-Richardson, E. E., S. Bailey, et al. (2009) "A prospective study of weight gain during the college freshman and sophomore years." Preventive Medicine 48(3): 256-261.

Ludwig, D. S., C. B. Ebbeling, et al. (2002) "A physiological basis for disparities in diabetes and heart disease risk among racial and ethnic groups." J Nutr 132(9): 2492-3.

Mackey, E. R. and A. M. La Greca (2007) "Adolescents' eating, exercise, and weight control behaviors: does peer crowd affiliation play a role?" J Pediatr Psychol 32(1): 13-23.

McCreedy, M. and J. G. Leslie (2009) "Get Active Orlando: changing the built environment to increase physical activity." Am J Prev Med 37(6 Suppl 2): S395-402.

McCrory, M. A., P. J. Fuss, et al. (1999) "Overeating in America: association between restaurant food consumption and body fatness in healthy adult men and women ages 19 to 80." Obes Res 7(6): 564-71.

McTigue, K. M., J. M. Garrett, et al. (2002) "The natural history of the development of obesity in a cohort of young U.S. adults between 1981 and 1998." Ann Intern Med 136(12): 857-64.

Morrow, M. L., K. C. Heesch, et al. (2006) "Freshman 15: Fact or fiction?" Obesity 14(8): 1438-1443.

Murphy, T.K., E. E. Calle, et al. (2000) "Body mass index and colon cancer mortality in a large prospective study". Am J Epidemiol 152 (9):847-54.

Neighbors, L. A. and J. Sobal (2007) "Prevalence and magnitude of body weight and shape dissatisfaction among university students." Eat Behav 8(4): 429-39.

Nikolaou, A. and D. Nikolaou (2008) "Income-related inequality in the distribution of obesity among Europeans." J Public Hlth 16(6): 403-411.

Nitzke S, Kritsch K, Boeckner L, Greene G, Hoerr S, Horacek T, Kattelmann K, Lohse B, Oakland MJ, Beatrice P, White A. (2007) "A stage-tailored multi-modal intervention increases fruit and vegetable intakes of low-income young adults." Am J Health Promot 22:6-14.

Ogden, C. L. (2009) "Disparities in obesity prevalence in the United States: black women at risk." Am J Clin Nutr 89(4): 1001-2.

Ogden, C. L., M. D. Carroll, et al. (2010) "Prevalence of high body mass index in US children and adolescents, 2007-2008." JAMA 303(3): 242-9.

Ogden, C. L., M. D. Carroll, et al. (2006) "Prevalence of overweight and obesity in the United States, 1999-2004." JAMA 295(13): 1549-1555.

Otsuka, R., K. Tamakoshi, et al. (2006) "Eating fast leads to obesity: findings based on self-administered questionnaires among middle-aged Japanese men and women." J Epidemiol 16(3): 117-24.

Otsuka, R., K. Tamakoshi, et al. (2008) "Eating fast leads to insulin resistance: findings in middle-aged Japanese men and women." Prev Med 46(2): 154-9.

Papas, M. A., A. J. Alberg, et al. (2007) "The built environment and obesity." Epidemiol Rev 29: 129

Park A., Nitzke S.., Kritsch K, Kattelmann K., White A., Boeckner L., Lohse B., Hoerr S., Greene G., Zhang Z. (2008) "Internet-based interventions have potential to affect short-term mediators and indicators of dietary behavior of young adults." J Nutr Educ Behav 40:288-97.

Pereira, M. A., A. I. Kartashov, et al. (2005) "Fast-food habits, weight gain, and insulin resistance (the CARDIA study): 15-year prospective analysis." Lancet 365(9453): 36-42.

Quick, V., Weiner, A., Shu. E., Horacek, T., Byrd-Bredbenner, C.. University Food Environment: An Assessment of the Nutrient-Density of Vending Machine Foods. (May 2010) New Jersey Dietetic Association Annual Conference, New Brunswick, NJ.

Quick, V., Byrd-Bredbenner, C. Behavioral Factors Affecting Young Adults Health and Body Weight: A PRECEDE-PROCEED Framework Approach. (May 2010) New Jersey Dietetic Association Annual Conference, New Brunswick, NJ.

Quick, V., Weiner, A., Shu. E., Johnson, E., Byrd-Bredbenner, C. Physical Activity and Obesity Prevention: An Assessment of Rutgers University, New Brunswick Campus Walk-ability and Bike-ability. (May 2009) New Jersey Dietetic Association Annual Conference, New Brunswick, NJ.

Quick, V., Weiner, A., Shu. E., Byrd-Bredbenner, C. An Evaluation of Buildings Frequently Used by Students at Rutgers University New Brunswick in Promoting Physical Activity. (May 2009) New Jersey Dietetic Association Annual Conference, New Brunswick, NJ.

Plachta-Danielzik, S., B. Landsberg, et al. (2010) "Determinants of the prevalence and incidence of overweight in children and adolescents." Public Health Nutr: 1-12.

Racette, S. B. Deusinger, S. S. et al. (2005) "Weight changes, exercise, and dietary patterns during freshman and sophomore years of college." J Am Coll Health 53(6):245-51.

Reedy, J, Krebs-Smith, S, Bosire, C. (2010) "Evaluating the food environment. Application of the Healthy Eating Index 2005". Am J Prev Med 38(5):465-471.

Renalds, A., T. H. Smith, et al. (2010) "A systematic review of built environment and health." Fam Community Health 33(1): 68-78.

Reznar MM, Guo W, Long KA, Lawson K, Murashima M, Horacek TM, Hoerr SL. Support for low-fat food choices at eating establishments on a large college campus. International Society of Behavioral Nutrition and Physical Activity Annual Meeting 2010. Minneapolis, MN, June 2010.

Rothman, A. J., A. H. Gillespie, et al. (2009) "Decision making in eating behavior: interacting perspectives from the individual, family, and environment: an introduction." Ann Behav Med 38 Suppl 1: S1-3.

Sasaki, S., A. Katagiri, et al. (2003) "Self-reported rate of eating correlates with body mass index in 18-y-old Japanese women." Int J Obes Relat Metab Disord 27(11): 1405-10.

Sekine, M., T. Yamagami, et al. (2002) "Parental obesity, lifestyle factors and obesity in preschool children: results of the Toyama Birth Cohort study." J Epidemiol 12(1): 33-9.

Sherwood-Puzzello, C.M., Miller, M., Lohrmann, D., and Gregory, P. (2007) "Implementation of CDC's School Health Index in 3 Midwest middle schools: motivation for change". J School Health 77:285-293.

Takayama, S., Y. Akamine, et al. (2002) "Rate of eating and body weight in patients with type 2 diabetes or hyperlipidaemia." J Int Med Res 30(4): 442-4.
Timperio, A., R. W. Jeffery, et al. (2010) "Neighbourhood physical activity environments and adiposity in children and mothers: a three-year longitudinal study." Int J Behav Nutr Phys Act 7(1): 18.

Turrell, G., M. Haynes, et al. (2010) "Neighborhood disadvantage and physical activity: baseline results from the HABITAT multilevel longitudinal study." Ann Epidemiol 20(3): 171-81.

U.S. Food and Drug Administration (2004) "Calories count. A report of the Working Group on Obesity". Rockville, MD.

Vermeir, I. and W. Verbeke (2006) "Sustainable Food Consumption: Exploring the Consumer Attitude  Behavioral Intention Gap." J Agric Environ Ethics 19(2): 169-194.

Walsh, J., White, A., Byrd-Bredbenner, C., Colby, S., Esters, O., Greene, G., Hoerr, S., Horacek, T., Kattelmann, K., Kidd, T., Nitzke, S., and Phillips, B. (2010). Use of a Multi-phase Community Assessment Model to Identify Behavioral and Environmental Influences of Obesity for Young Adults. American Dietetic Association FNCE Meeting, Boston, MA. November 2010.

Westerterp-Plantenga, M. S. (2000) "Eating behavior in humans, characterized by cumulative food intake curves--a review." Neurosci Biobehav Rev 24(2): 239-48.
Whitaker, K. L., M. J. Jarvis, et al. (2010) "Comparing maternal and paternal intergenerational transmission of obesity risk in a large population-based sample." Am J Clin Nutr 91(6): 1560-7.

Zmijewski, C. F. and M. O. Howard (2003) "Exercise dependence and attitudes toward eating among young adults." Eat Behav 4(2): 181-95.

Attachments

Land Grant Participating States/Institutions

AL, FL, KS, ME, MO, MS, NE, NJ, NM, RI, SD, WV

Non Land Grant Participating States/Institutions

New York - Syracuse University, University of Tenessee