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Workshop Summary OVERVIEW With more than one-third of the U.S. adult population considered obese,1 a figure that has more than doubled since the mid-1970s (Flegal et al., 2010), obesity has emerged as a major public health challenge. Among children, obesity rates have more than tripled over the same period. Not only is obesity associated with numerous medical complications, but also it incurs significant economic cost. Although at its simplest, obesity is a result of an energy imbalance, with obese (and overweight2) people consuming more energy (calories3) than they are expending, in reality it is very diffi- cult for many people to balance calories consumed with calories expended. Human eating behavior is inordinately complex, with multiple layers of influence. Eating is impacted not only by the biological responses that occur when the presence of food or even the smell of food triggers physiological 1 For adults, obesity is defined as having a body mass index (BMI) of 30 or greater. For children, obesity is defined as a BMI at or above the 95th percentile for children of the same age and sex. For both adults and children, BMI is calculated from a person’s weight and height (weight [kg] / height [m]2). 2 For adults, overweight is defined as having a BMI between 25 and 29.9. 3 In this report, calorie (cal) is used synonymously with kilocalorie as a unit of measure for energy obtained from food and beverages. A kilocalorie (kcal) is defined as the amount of heat required to change the temperature of 1 g of water from 14.5°C (degrees Celsius) to 15.5°C. 1
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2 LEVERAGING FOOD TECHNOLOGY chain reactions but also by societal norms and values around portion size and other eating behaviors. Behavioral scientists have made significant progress over the last 10–20 years toward building an evidence base for understanding what drives energy imbalance in overweight and obese individuals. Meanwhile, food scientists have been tapping into this growing evidence base to improve existing technologies and create new technologies that can be applied to alter the food supply in ways that reduce the obesity burden on the Ameri- can population. As just one example, chemists at the Agricultural Research Service (ARS) of the U.S. Department of Agriculture (USDA) developed a novel, low-oil-uptake rice batter that absorbs 50 percent less oil than regular wheat batter and can be used for coating chicken, fish, vegetables, and other foods. Food scientists have developed a range of other fat-reducing tech- nologies as well, including new processing technologies for multiple grain doughs, new baking technologies, and technologies that incorporate fiber as a fat replacement. Reducing fat content might seem like the most obvious way to reduce the energy density of a food, given the high caloric value of fat,4 but there are other ways. For example, food scientists in the beverage industry have developed reduced-calorie sweetened beverages by replacing sucrose using various zero- and low-calorie sweetener technologies. Reducing the energy density of foods is by no means the only or best way to leverage food technologies in the effort to reduce and prevent obe- sity. Other technologies being leveraged for obesity prevention and reduc- tion efforts include ready-to-eat portion-controlled frozen meals, which have been shown to be associated with reduced energy intake and increased short-term weight loss; a variety of fruit- and vegetable-based technologies, based on the association between fruit and vegetable intake and mainte- nance of a healthy weight (when substituted for more energy dense foods) and reduced risk of many chronic diseases; and technologies that enhance micronutrient density, developed on evidence suggesting that micronutrient deficiencies may contribute to overeating. On November 2 and 3, 2010, the Institute of Medicine’s (IOM’s) Food Forum convened a public workshop in Washington, DC, to examine the complexity of human eating behavior and explore ways in which the food industry can continue to leverage modern food processing technologies to influence energy intake as one population-based change of the many 4 Fat contains 9 cal/g, compared to alcohol (7 cal/g), protein and most carbohydrates (4 cal/g), fiber (1.5–2.5 cal/g), and water (0 cal/g).
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3 WORKSHOP SUMMARY multifaceted societal changes that will help to reduce and prevent obesity. Through invited presentations and discussions, behavioral scientists, food scientists, and other experts from multiple sectors discussed evidence-based associations between various eating behaviors and weight gain and consid- ered the opportunities and challenges of altering the food supply—both at home and outside the home (e.g., in restaurants)—to alleviate overeating and help consumers with long-term weight maintenance. The workshop agenda and biographies for speakers and moderators are included in Appen- dixes A and B, respectively. This workshop summary was prepared by the rapporteurs for the Forum’s members and is organized into sections as a topic-by-topic description of the presentations and discussions that took place during the workshop. The main topics covered include, in order, the following: trends in overweight and obesity over the past 30 years; the complexity of eating behaviors; lessons learned and best practices; major challenges; and potential for innovation: next steps. These proceedings are not intended to be an exhaustive exploration of the subject matter. They summarize only statements made and information presented by participants at the work- shop. Although participants made several suggestions for moving forward with respect to leveraging technologies in obesity reduction and prevention efforts, the goal of this workshop was not to reach consensus on any issue(s). As such, the statements summarized here represent individual beliefs; they do not represent the findings, conclusions, or recommendations of a con- sensus committee process. TRENDS IN OVERWEIGHT AND OBESITY: FROM THE MID-1970s TO THE PRESENT5 In addition to 33.8 percent of the U.S. population aged 20 and over that is considered obese, another 34.2 percent is considered overweight, according to the most recently available National Health and Nutrition Examination Survey (NHANES) data (2007–2008) (Flegal et al., 2010). This makes for a staggering 68 percent of American adults who carry excess body weight, according to U.S. standards. Not only are all organ systems adversely affected by excess body weight, causing significant medical complications, but these medical complications in turn incur significant 5 This section summarizes the material presented during Gary Foster’s keynote presentation.
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4 LEVERAGING FOOD TECHNOLOGY 100 90 80 70 Percent of Adults 60 50 Overweight 40 Obese 30 20 10 0 1976-1980 1988-1994 2001-2002 2007-2008 Years FIGURE 1 Prevalence of overweight and obesity in adults aged 20 years or older, 1976–2008. Among U.S. adults, the prevalence of both overweight and obesity has been steadily increasing since the mid-1970s. Today, approximately 70 percent of American adults are either overweight or obese. 1 rev Fig SOURCE: Data adapted from Flegal et al., 1998, 2010; Ogden et al., 2006. economic cost. Between 1998 and 2006, the annual medical burden of obesity increased from 6.5 to 9.1 percent of annual medical spending,6 with per capita medical spending for obese persons being more than 40 percent greater than it is for persons of healthy weight (Finkelstein et al., 2009). Among adults, the prevalence of both overweight and obesity has been increasing steadily since the mid-1970s (Figure 1) (Flegal et al., 1998, 2010; Ogden et al., 2006). Likewise among children, the prevalence of obesity more than tripled between the early 1970s and mid-2000s (Figure 2) (Ogden and Carroll, 2010). As Gary Foster, professor and director of the Center for Obesity Research and Education at Temple University, remarked, childhood obesity is especially worrisome because obese children risk devel- oping adult conditions such as hypertension, increased cholesterol, and type 2 diabetes at a much younger age; also, obese children are more likely than normal-weight children to experience psychosocial complications such as 6 The main driver of the increase in obesity-attributable costs was the 37 percent increase in obesity prevalence from 1998 to 2006, not increases in per capita costs.
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5 WORKSHOP SUMMARY 100 6-11 years old 12-19 years old 90 Percent of Adolescents 80 70 60 50 40 30 20 10 0 1971- 1976- 1988- 1999- 2001- 2003- 2005- 2007- 1974 1980 1994 2000 2002 2004 2006 2008 Years FIGURE 2 Prevalence of obesity in children (6–11 years) and adolescents (12–19 years), 1971–2008. The prevalence of obesity among children and adolescents has tripled since Fig 2 rev the mid-1970s, with an estimated 18 percent of today’s 6–19 year olds considered obese. SOURCE: Foster presentation (November 2, 2010); data adapted from Ogden and Carroll, 2010. peer rejection, bullying, and impaired academic performance. Additionally, obese adults who were overweight as children have a greater prevalence of medical conditions than obese adults who were not overweight as children (Baker et al., 2005; Must and Anderson, 2003; Wearing et al., 2006). According to 2007–2008 NHANES data (Flegal et al., 2010), non- Hispanic blacks are disproportionately burdened by obesity. Non-Hispanic blacks not only have a higher prevalence of obesity than other ethnic groups (i.e., non-Hispanic whites, Hispanics, and Mexican Americans), they also have a higher prevalence of class II and class III obesity.7 Increasing trends in class II and III obesity are particularly alarming because they are associ- ated with greater impairment of quality of life, greater co-morbidity, and greater medical cost compared to the other classifications of overweight and obesity. Non-Hispanic blacks have also shown a slightly greater increase in the prevalence of obesity over time, since the mid-1970s, compared to non- Hispanic whites and Mexican Americans (Flegal et al., 1998; Ogden et al., 2006), with most of the divergence being among women. According to Foster, the fact that non-Hispanic blacks are dispropor- tionately impacted by obesity raises questions about the extent to which 7 There are three classes of obesity: class I (BMI of 30–34), class II (BMI of 35–39), and class III (BMI of 40 and greater).
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6 LEVERAGING FOOD TECHNOLOGY variation among different segments of the American population should be considered when exploring ways to leverage food technology for obesity pre- vention and reduction efforts. For example, are there certain types of food products that non-Hispanic blacks buy more frequently? If so, are there ways to aim interventions toward those products? Later in the workshop, other participants identified poverty as another important socioeconomic factor to consider when exploring the possibilities for intervention. For example, speaker Brendan Boyle, partner and chief invention officer at IDEO, suggested that product distribution is as important to consider as product innovation when devising technology-based strategies for obesity intervention, with a major challenge being the distribution of novel food products to lower-income neighborhoods where people would otherwise not have access to such products. Arguably one of the first and most obvious variables to consider when exploring possible causes of the obesity crisis is the amount of energy in food available for human consumption, as measured by calories per capita per day. Indeed, available daily dietary energy in the U.S. food supply increased from about 3,300–3,400 calories per capita to more than 4,000 calories between 1980 and 2004 (Hiza and Bente, 2007). As Foster explained, by assuming that energy expenditure remained constant during that time, an increase in daily energy per capita of that magnitude would be enough to account for the increased prevalence of obesity in the U.S. population. However, on closer examination, macronutrient contribution to the dietary energy supply changed very little over the same time. Although the share of the daily energy supply coming from carbohydrates increased slightly in the 1980s, it has since plateaued; none of the other macronutrient profiles have changed much. One might expect to see an increase in energy availability from fat, if anything, given the high caloric density of fat, but this is not the case. Nor has there been much change in the proportion of available energy coming from any particular major food group (i.e., grains; fats and oils; sugars; meat, poultry, fish; dairy; vegetable; fruit; eggs; nuts, soy; miscel- laneous). The only increases, and they have been slight (less than 5 percent change in share of total daily available calories derived from each), have been with grains and fats or oils, the latter slightly more than the former. In short, Foster concluded, while there have been slight increases in the proportion of available dietary energy coming from carbohydrates and fats or oils, the evidence does not implicate increased consumption of any particular mac- ronutrient or food group as a primary driver of the obesity crisis.
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7 WORKSHOP SUMMARY Changes in Eating Behavior Since the Mid-1970s: Three Illustrative Trends If it is not any particular macronutrient or major food group, then what is driving the increasing prevalence of obesity among U.S. adults and children? Rather than providing a comprehensive account of every change that has occurred in behavior over the past 30 years, Foster highlighted three trends by way of illustration: (1) increases in portion size; (2) increases in snacking frequency among adolescents; and (3) increases in meals eaten out- side the home (i.e., at restaurants). He identified portion size as a promising target for intervention, that is, through portion-controlled dieting, based on evidence from several studies comparing portion-controlled dieting to other diet methods. Portion Size The fact that available calories are increasing but without any major changes in the proportion of available energy coming from any particular macronutrient or major food group suggests that people are simply eat- ing more (of everything). Indeed, Nielsen and Popkin (2003) reported increases in portion sizes between 1977 and 1998 for many foods, includ- ing salty snacks, desserts, soft drinks, fruit drinks, French fries, hamburg- ers, cheeseburgers, pizza, and Mexican food. The most dramatic increases were with soft drinks and fruit drinks. In 1977–1978, the average por- tion size was 13.1 ounces (oz) for soft drinks and 11.3 ounces for fruit drinks; in 1989–1991, those figures jumped approximately 28 percent and 11 percent to 16.8 and 12.6 ounces, respectively; in 1994–1996, they jumped again by approximately 51 percent and 33 percent, to 19.9 and 15.1 ounces, respectively (Figure 3). Foster remarked that these data point to beverage consumption as a possible target for intervention, a strategy that Marge Leahy, director of health and wellness at the Coca-Cola Company, revisited during her presentation on zero-calorie and reduced- calorie sugar substitutes for beverages and other products. In another presentation, Jennifer Fisher, associate professor and research scientist at Temple University, explored in more detail the growing body of evidence showing that increased portion sizes are associated with increased energy intake. (Summaries of the information presented by Leahy and Fisher are provided later in this report.)
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8 LEVERAGING FOOD TECHNOLOGY + 60 Percent increase in portion size since 1977-1978 + 1989-1991 1994-1996 50 * 40 + * * + 30 + + 20 + * * * 10 + 0 -10 FIGURE 3 Changes in portion sizes, 1977–1998. Average portion sizes have increased since the mid-1970s, with the most dramatic increases for soft drinks and fruit drinks, Figure 3 rev pointing to beverage consumption as a possible target for obesity prevention and reduc- tion interventions. * Significant difference between 1977–1978 and 1989–1991 (p < 0.01). + Significant difference between 1977–1978 and 1994–1996 (p < 0.01). Note that no statistical inferences were drawn between 1989–1991 and 1994–1996 data. SOURCE: Data adapted from Nielsen and Popkin, 2003. In Foster’s opinion, one of the most promising obesity treatments is portion control.8 Several studies have shown that providing patients with portion-controlled meals is a more effective weight loss strategy than telling patients to maintain a restricted-energy diet by keeping track of calories. Ditschuneit and colleagues (1999) reported significantly greater weight loss among individuals who ate four portion-controlled meal or snack replace- ments daily, compared to individuals on an energy-restricted diet with conventional foods (with both diets totaling 1,200–1,500 calories daily). 8 Foster explained that there are several different approaches to obesity treatment, ranging from surgery (recommended for individuals with BMIs between 35 and 39.9 with co-morbidities and for individuals with BMIs greater than 39.9 regardless of co-morbidities), to pharmacotherapy (recommended for individuals with BMI between 27 and 29.9 with co- morbidities and for individuals with BMIs greater than 29.9 regardless of co-morbidities), to diet, exercise, and behavioral treatments (recommended for all individuals with BMIs of 25 and above) (NHLBI, 2000). Foster said that although surgery is the most effective obesity treatment, less than 1 percent of individuals eligible for surgery actually undergo surgery. He pointed to the Diabetes Prevention Program and Look AHEAD as examples of effective diet, exercise, and behavioral modification (or “lifestyle intervention”) programs.
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9 WORKSHOP SUMMARY The individuals were placed on their respective diets for three months and then placed on the same weight maintenance diet (energy restricted with two portion-controlled meals or snacks daily) for 24 months. Total weight loss over the entire 27 months, as a percentage of initial weight, was 5.9 kg for the energy-restricted group and 11.3 kg for the portion-controlled group. Similar results were observed in a four-year study comparing energy restriction and portion control (Flechtner-Mors et al., 2000). Finally, a meta-analysis of reduced-calorie diets versus partial meal replacement diets concluded that partial meal replacement diets resulted in signifi- cantly greater mean weight loss over both 3-month and 12-month periods (Heymsfield et al., 2003). Foster opined that part of the reason portion control works is its sim- plicity. The mountain of evidence and advice on how to eat is overwhelm- ing. By cultivating a “one-and-done” way of thinking, portion-controlled meals with fixed calorie amounts reduce much of the cognitive burden that is often placed on patients in nutrition-based obesity treatment programs. People do not need to weigh, measure, or calculate calories, fat, or any other component of what they are eating because that information is read- ily available on the package. Fixed-portion meals also reduce contact with “problem” food and are convenient to use because of their ready-to-eat nature. Portion control as a potentially effective target for intervention was revisited several times during the course of the workshop. Snacking Behavior Among Adolescents According to USDA data, snacking behavior among adolescents (12–19 years old) has changed dramatically over the past 30 years (ARS, 2010a; Hiza and Bente, 2007). In 1977, 40 percent of adolescents were not consuming any snacks at all. By 2005–2006, that figure had decreased by more than 50 percent, with only less than 20 percent of adolescents not consuming any snacks. Conversely, the percentage of adolescents consum- ing two or more snacks a day increased. About 15–17 percent of adolescents consumed two snacks a day in 1977, compared to nearly 30 percent in 2005–2006, and about 5 percent of adolescents consumed three snacks a day in 1977, compared to about 17 percent in 2005–2006. Not only has snacking frequency increased, but adolescents are also obtaining a greater percentage of their daily nutrients from snacks than they did in the past. In 1977–1978, adolescents obtained 14 percent of their daily nutrients (300 calories) from snacks, compared to 23 percent (a little more than 500 calories) in 2005–2006. In sum, Foster explained, adolescents are snacking
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10 LEVERAGING FOOD TECHNOLOGY 4000 * 3000 Calories * 2000 Boys 1000 Girls 0 0 1 2 3 4+ Number of snacks in a day FIGURE 4 Mean calorie intake by snacking frequency, adolescents aged 12–19, 2005–2006. * indicates a statistically significant trend. Fig 4 redrawn SOURCE: ARS, 2010a. more frequently and obtaining more absolute calories and a greater percent- age of their daily calories from snacks.9 Foster remarked that increases in snacking frequency and snacking- related energy intake do not reveal much about obesity unless they are associated with increases in total energy intake. If adolescents are simply distributing the same number of calories throughout the day in the form of snacks instead of meals, an increase in snacking frequency would not have an impact on obesity, but this is not the case. The same USDA data indicate that adolescents who consume more snacks also have higher total energy intakes (Figure 4).10 Although adolescents comprise only a small proportion of the population, these data point to snacking as another potential target for intervention. Food Consumption Outside the Home Foster observed that often when people think about the products that the food industry manufactures they have in mind foods that are 9 According to a recent study by Piernas and colleagues (2010b) on snacking trends from 1977–2006 among U.S. children, the largest increases in consumption have been in salty snacks and candy. The primary contributors of snacking calories are desserts and sweetened beverages. 10 Although the data indicate no significant variation in mean BMI among adolescents who snack more or less frequently, or not at all, Foster suggested that the self-reported nature of the data could be creating a bias in the results; the stigma of being obese may have prevented obese adolescents from being forthright about the number of times they snack.
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11 WORKSHOP SUMMARY being purchased in grocery stores and consumed in the home. In fact, a significant portion of the food supply is consumed outside the home. Kant and Graubard (2004) reported that the percentage of adults not eat- ing out decreased from 28 percent in 1987 to 24 percent in 1999–2000 (p < 0.0001). Not only are more people eating outside the home, but also they are eating outside the home more frequently. Kant and colleagues (2004) also reported that the percentage of adults eating three or more meals per week outside the home increased from 36 percent in 1987 to 41 percent in 1999–2000 (p < 0.0005). Even more compelling, Foster noted, are data showing that restaurant sales increased from $42.8 billion in 1970 to a forecasted $580.1 billion in 2010 (National Restaurant Association, 2010). Foster remarked that while these data do not in any way point to eat- ing outside the home as the primary driver of the obesity crisis in America, they do suggest that commercially prepared meals that are eaten outside the home serve as another potential target for intervention. IDENTIFICATION OF TARGETS FOR INTERVENTION: EVIDENCE FROM BEHAVIOR STUDIES Individuals make 200 to 300 food-related decisions a day (Wansink and Sobal, 2007). Multiple factors come into play when these decisions are made, creating several behavioral challenges for food scientists to tease apart when innovating technologies for the purpose of obesity prevention and reduction. This section summarizes the workshop presentations and discussions that revolved around those behavioral challenges, with a focus on portion size (and the challenge of moving the public toward eating more healthful portions); energy density (and the challenge of providing the public with less energy dense foods that taste as good or better than their counterparts); satiety (and the challenge of providing consumers with less energy dense foods that satisfy the appetite as much as their more energy dense counterparts do); and consumer perception of labels and pricing (and the challenge of providing the food industry with incen- tives to develop innovative technologies when faced with unpredictable consumer response). As much progress as behavioral scientists have made over the past 10–20 years toward building an evidence base for understanding what drives energy imbalance in overweight and obese individuals, there is still a great deal to learn. Richard Mattes, distinguished professor at Purdue University, argued that it is not even clear, at a fundamental level, whether eating is controlled by an internal biological system (i.e., homeostatically)
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70 LEVERAGING FOOD TECHNOLOGY food products for commercialization. For example, Brenner explained that ARS provides two mechanisms for partnering: licensing USDA-developed technologies for commercial production and establishing research part- nerships through CRADAs. Finally, an overarching theme expressed by many participants during the two-day dialogue was the lack of research, particularly long-term research. For example, Allison pointed to the need for “probative” research aimed at evaluating whether interventions actually impact obesity. Too often, studies stop at short-term purchasing or eating behaviors or energy intake. There was also a call by participants for a more systematic analysis of obesity in America—that is, research aimed at teasing apart not only behaviors that lead to excess energy consumption but also behaviors that lead to insufficient energy expenditure. An audience member remarked that a systematic analysis would help to manage expectations of the role and responsibilities of the private sector. These next steps suggested by workshop participants helped to establish a greater understanding of how food technology can be incorporated into the multifaceted response to the complex interplay of environmental, social, economic, and behavior factors that influence the prevention and reduction of obesity. REFERENCES Alfenas, R. C., and R. D. Mattes. 2005. Influence of glycemic index/load on glycemic response, appetite, and food intake in healthy humans. Diabetes Care 28(9):2123-2129. Ames, B. N. 2006. Low micronutrient intake may accelerate the degenerative diseases of aging through allocation of scarce micronutrients by triage. Proceedings of the National Academy of Sciences of the United States of America 103(47):17589-17594. Anderson, M., and D. Matsa. 2007. Are restaurants really supersizing America? http://are. berkeley.edu/Papers/anderson08.pdf (accessed April 5, 2011). ARS (Agricultural Research Service). 2010a. Snacking patterns of U.S. adolescents: What we eat in America, NHANES, 2005–2006. Food Surveys Research Group dietary data brief. http://ars.usda.gov/Services/docs.htm?docid=19476 (accessed May 13, 2011). ARS. 2010b. What we eat in America. NHANES, 2007–2008. http://www.ars.usda.gov/ SP2UserFiles/Place/12355000/pdf/0708 (accessed May 13, 2011). Bachman, J. L., J. Reedy, A. F. Subar, and S. M. Krebs-Smith. 2008. Sources of food group intakes among the U.S. population, 2001–2002. Journal of the American Dietetic Association 108(5):804-814. Baker, S., S. Barlow, W. Cochran, G. Fuchs, W. Klish, N. Krebs, R. Strauss, A. Tershakovec, and J. Udall. 2005. Overweight children and adolescents: A clinical report of the North American Society for Pediatric Gastroenterology, Hepatology, and Nutrition. Journal of Pediatric Gastroenterology and Nutrition 40(5):533-543.
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74 LEVERAGING FOOD TECHNOLOGY Hallfrisch, J., and K. M. Behall. 1997. Evaluation of foods and physiological responses to menus in which fat content was lowered by replacement with OATrim. Cereal Foods World 42(2):100-103. Hallfrisch, J., D. J. Scholfield, and K. M. Behall. 1995. Diets containing soluble oat extracts improve glucose and insulin responses of moderately hypercholesterolemic men and women. American Journal of Clinical Nutrition 61(2):379-384. Hannum, S. M., L. Carson, E. M. Evans, K. A. Canene, E. L. Petr, L. Bui, and J. W. Erdman, Jr. 2004. Use of portion-controlled entrees enhances weight loss in women. Obesity Research 12(3):538-546. Hannum, S. M., L. A. Carson, E. M. Evans, E. L. Petr, C. M. Wharton, L. Bui, and J. W. Erdman, Jr. 2006. Use of packaged entrees as part of a weight-loss diet in overweight men: An 8-week randomized clinical trial. Diabetes, Obesity, & Metabolism 8(2):146- 155. Harnack, L., S. A. H. Walters, and D. R. Jacobs. 2003. Dietary intake and food sources of whole grains among U.S. children and adolescents: Data from the 1994-1996 Continuing Survey of Food Intakes by Individuals. Journal of the American Dietetic Association 103(8):1015-1019. Heymsfield, S. B., C. A. van Mierlo, H. C. van der Knaap, M. Heo, and H. I. Frier. 2003. Weight management using a meal replacement strategy: Meta and pooling analysis from six studies. International Journal of Obesity and Related Metabolic Disorders 27(5):537- 549. Hiza, H. A. B., and L. Bente. 2007. Nutrient content of the U.S. food supply, 1909-2004. A summary report. Home Economics Research Report 57. http://www.cnpp.usda.gov/ publications/foodsupply/foodsupply1909-2004report.pdf (accessed April 5, 2011). Houchins, J. A., J. R. Burgess, W. W. Campbell, J. R. Daniel, M. G. Ferruzzi, G. P. McGabe, and R. D. Mattes. 2011. Beverage vs. soild fruits and vegetables: Effects on energy intake and body weight. Obesity (Silver Spring). Published electronically June 30, 2011. doi: 10.1038/oby.2011.192. Howarth, N. C., E. Saltzman, and S. B. Roberts. 2001. Dietary fiber and weight regulation. Nutrition Reviews 59(5):129-139. Jae, H., and D. Delvecchio. 2004. Decision making by low-literacy consumers in the presence of point-of-purchase information. Journal of Consumer Affairs 38(2):342-354. James, J., P. Thomas, D. Cavan, and D. Kerr. 2004. Preventing childhood obesity by reducing consumption of carbonated drinks: Cluster randomised controlled trial. British Medical Journal 328(7450):1237. Jeffery, R. W., R. R. Wing, C. Thorson, L. R. Burton, C. Raether, J. Harvey, and M. Mullen. 1993. Strengthening behavioral interventions for weight loss: A randomized trial of food provision and monetary incentives. Journal of Consulting and Clinical Psychology 61(6):1038-1045. Jenkins, D. J., C. W. Kendall, L. S. Augustin, S. Franceschi, M. Hamidi, A. Marchie, A. L. Jenkins, and M. Axelsen. 2002. Glycemic index: Overview of implications in health and disease. American Journal of Clinical Nutrition 76(1):266S-273S. John, L. K., G. Loewenstein, A. B. Troxel, L. Norton, J. E. Fassbender, and K. G. Volpp. 2011. Financial incentives for extended weight loss: A randomized, controlled trial. Journal of General Internal Medicine 26(6):621-626.
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75 WORKSHOP SUMMARY Johnson, L., A. P. Mander, L. R. Jones, P. M. Emmett, and S. A. Jebb. 2008a. Energy-dense, low-fiber, high-fat dietary pattern is associated with increased fatness in childhood. American Journal of Clinical Nutrition 87(4):846-854. Johnson, L., A. P. Mander, L. R. Jones, P. M. Emmett, and S. A. Jebb. 2008b. A prospective analysis of dietary energy density at age 5 and 7 years and fatness at 9 years among UK children. International Journal of Obesity 32(4):586-593. Johnson, S. L. 2000. Improving preschoolers’ self-regulation of energy intake. Pediatrics 106(6):1429-1435. Judd, J. T., B. A. Clevidence, R. A. Muesing, J. Wittes, M. E. Sunkin, and J. J. Podczasy. 1994. Dietary trans fatty acids: Effects on plasma lipids and lipoproteins of healthy men and women. American Journal of Clinical Nutrition 59(4):861-868. Kant, A. K., and B. I. Graubard. 2004. Eating out in America, 1987–2000: Trends and nutritional correlates. Preventive Medicine 38(2):243-249. Kant, A. K., M. B. Andon, T. J. Angelopoulos, and J. M. Rippe. 2008. Association of breakfast energy density with diet quality and body mass index in American adults: National Health and Nutrition Examination Surveys, 1999–2004. American Journal of Clinical Nutrition 88(5):1396-1404. Kessler, D. A., J. R. Mande, F. E. Scarbrough, R. Schapiro, and K. Feiden. 2003. Developing the “nutrition facts” food label. Harvard Health Policy Review 4(2):13-24. Koh-Banerjee, P., M. V. Franz, L. Sampson, S. M. Liu, D. R. Jacobs, D. Spiegelman, W. Willett, and E. Rimm. 2004. Changes in whole-grain, bran, and cereal fiber consumption in relation to 8-y weight gain among men. American Journal of Clinical Nutrition 80(5):1237-1245. Kral, T. V., A. C. Kabay, L. S. Roe, and B. J. Rolls. 2010. Effects of doubling the portion size of fruit and vegetable side dishes on children’s intake at a meal. Obesity (Silver Spring) 18(3):521-527. LaGrotte, C. A., and G. D. Foster. Forthcoming. Behavioral treatments for obesity. In Handbook of food and addiction, edited by K. D. Brownell and M. S. Gold. New York: Oxford University Press. Lang, V., F. Bellisle, J. M. Oppert, C. Craplet, F. R. Bornet, G. Slama, and B. Guy-Grand. 1998. Satiating effect of proteins in healthy subjects: A comparison of egg albumin, casein, gelatin, soy protein, pea protein, and wheat gluten. American Journal of Clinical Nutrition 67(6):1197-1204. Leahy, K. E., L. L. Birch, and B. J. Rolls. 2008. Reducing the energy density of multiple meals decreases the energy intake of preschool-age children. American Journal of Clinical Nutrition 88(6):1459-1468. Ledikwe, J. H., H. M. Blanck, L. K. Khan, M. K. Serdula, J. D. Seymour, B. C. Tohill, and B. J. Rolls. 2006. Low-energy-density diets are associated with high diet quality in adults in the United States. Journal of the American Dietetic Association 106(8):1172-1180. Ledoux, T. A., M. D. Hingle, and T. Baranowski. 2011. Relationship of fruit and vegetable intake with adiposity: A systematic review. Obesity Reviews 12:e143-e150. Li, X., M. B. Cope, M. S. Johnson, D. L. Smith, Jr., and T. R. Nagy. 2010. Mild calorie restriction induces fat accumulation in female C57BL/6J mice. Obesity (Silver Spring) 18(3):456-462.
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76 LEVERAGING FOOD TECHNOLOGY Liu, S. M., W. C. Willett, J. E. Manson, F. B. Hu, B. Rosner, and G. Colditz. 2003. Relation between changes in intakes of dietary fiber and grain products and changes in weight and development of obesity among middle-aged women. American Journal of Clinical Nutrition 78(5):920-927. Lowe, M. R. 2003. Self-regulation of energy intake in the prevention and treatment of obesity: Is it feasible? Obesity Research 11(Suppl):44S-59S. Lupton, J. R., D. A. Balentine, R. M. Black, R. Hildwine, B. J. Ivens, E. T. Kennedy, P. T. Packard, B. R. Sperber, D. Steffen, and M. Story. 2010. The Smart Choices front- of-package nutrition labeling program: Rationale and development of the nutrition criteria. American Journal of Clinical Nutrition 91(4):1078S-1089S. Mathias, K. C., B. J. Rolls, L. L. Birch, T. V. E. Kral, and J. O. Fisher. 2009. Does serving children larger portions of fruit affect vegetable intake? Obesity 17:S90-S90. Mattes, R. D., J. M. Shikany, K. A. Kaiser, and D. B. Allison. 2011. Nutritively sweetened beverage consumption and body weight: A systematic review and meta-analysis of randomized experiments. Obesity Reviews 12(5):346-365. McCaffrey, T. A., K. L. Rennie, M. A. Kerr, J. M. Wallace, M. P. Hannon-Fletcher, W. A. Coward, S. A. Jebb, and M. B. E. Livingstone. 2008. Energy density of the diet and change in body fatness from childhood to adolescence: Is there a relation? American Journal of Clinical Nutrition 87(5):1230-1237. McCarron, D. A., S. Oparil, A. Chait, R. B. Haynes, P. Kris-Etherton, J. S. Stern, L. M. Resnick, S. Clark, C. D. Morris, D. C. Hatton, J. A. Metz, M. McMahon, S. Holcomb, G. W. Snyder, and F. X. Pi-Sunyer. 1997. Nutritional management of cardiovascular risk factors. A randomized clinical trial. Archives of Internal Medicine 157(2):169-177. McConahy, K. L., H. Smiciklas-Wright, L. L. Birch, D. C. Mitchell, and M. F. Picciano. 2002. Food portions are positively related to energy intake and body weight in early childhood. Journal of Pediatrics 140(3):340-347. McKiernan, F., J. H. Hollis, and R. D. Mattes. 2008. Short-term dietary compensation in free-living adults. Physiology & Behavior 93(4-5):975-983. Mendoza, J. A., A. Drewnowski, and D. A. Christakis. 2007. Dietary energy density is associated with obesity and the metabolic syndrome in U.S. adults. Diabetes Care 30(4):974-979. Metz, J. A., J. S. Stern, P. Kris-Etherton, M. E. Reusser, C. D. Morris, D. C. Hatton, S. Oparil, R. B. Haynes, L. M. Resnick, F. X. Pi-Sunyer, S. Clark, L. Chester, M. McMahon, G. W. Snyder, and D. A. McCarron. 2000. A randomized trial of improved weight loss with a prepared meal plan in overweight and obese patients: Impact on cardiovascular risk reduction. Archives of Internal Medicine 160(14):2150-2158. Morales, L. 2010. In U.S., consumption of fruits and vegetables trails access. Gallup, September 22. Mourao, D. M., J. Bressan, W. W. Campbell, and R. D. Mattes. 2007. Effects of food form on appetite and energy intake in lean and obese young adults. International Journal of Obesity (London) 31(11):1688-1695. Must, A., and S. E. Anderson. 2003. Effects of obesity on morbidity in children and adolescents. Nutrition in Clinical Care 6(1):4-12.
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79 WORKSHOP SUMMARY Storey, M. 2010. The shifting beverage landscape. Physiology & Behavior 100(1):10-14. Stroebele, N., L. G. Ogden, and J. O. Hill. 2009. Do calorie-controlled portion sizes of snacks reduce energy intake? Appetite 52(3):793-796. Svendsen, M., R. Blomhoff, I. Holme, and S. Tonstad. 2007. The effect of an increased intake of vegetables and fruit on weight loss, blood pressure and antioxidant defense in subjects with sleep related breathing disorders. European Journal of Clinical Nutrition 61(11):1301-1311. Tiwary, C. M., J. A. Ward, and B. A. Jackson. 1997. Effect of pectin on satiety in healthy U.S. Army adults. Journal of the American College of Nutrition 16(5):423-428. Vartanian, L. R., M. B. Schwartz, and K. D. Brownell. 2007. Effects of soft drink consumption on nutrition and health: A systematic review and meta-analysis. American Journal of Public Health 97(4):667-675. Veerman, J. L., J. J. Barendregt, E. F. van Beeck, J. C. Seidell, and J. P. Mackenbach. 2007. Stemming the obesity epidemic: A tantalizing prospect. Obesity (Silver Spring) 15(9):2365-2370. Vermeer, W. M., I. H. Steenhuis, and J. C. Seidell. 2009. From the point-of-purchase perspective: A qualitative study of the feasibility of interventions aimed at portion-size. Health Policy 90(1):73-80. Vermeer, W. M., B. Bruins, and I. H. Steenhuis. 2010a. Two pack king size chocolate bars. Can we manage our consumption? Appetite 54(2):414-417. Vermeer, W. M., I. H. Steenhuis, and J. C. Seidell. 2010b. Portion size: A qualitative study of consumers’ attitudes toward point-of-purchase interventions aimed at portion size. Health Education Research 25(1):109-120. Volpp, K. G., L. K. John, A. B. Troxel, L. Norton, J. Fassbender, and G. Loewenstein. 2008. Financial incentive-based approaches for weight loss: A randomized trial. Journal of the American Medical Association 300(22):2631-2637. Wansink, B. 2004. Environmental factors that increase the food intake and consumption volume of unknowing consumers. Annual Reviews in Nutrition 24:455-479. Wansink, B., and J. Kim. 2005. Bad popcorn in big buckets: Portion size can influence intake as much as taste. Journal of Nutrition Education and Behavior 37(5):242-245. Wansink, B., and S. B. Park. 2002. Sensory suggestiveness and labeling: Do soy labels bias taste? Journal of Sensory Studies 17(5):483-491. Wansink, B., and J. Sobal. 2007. Mindless eating—The 200 daily food decisions we overlook. Environment and Behavior 39(1):106-123. Wansink, B., K. van Ittersum, and J. E. Painter. 2004. How diet and health labels influence taste and satiation. Journal of Food Science 69(9):S340-S346. Wansink, B., K. van Ittersum, and J. E. Painter. 2006. Ice cream illusions bowls, spoons, and self-served portion sizes. American Journal of Preventive Medicine 31(3):240-243. Wansink, B., C. Payne, and C. Werle. 2008. Consequences of belonging to the “clean plate club.” Archives of Pediatrics and Adolescent Medicine 162(10):994-995. Wearing, S. C., E. M. Hennig, N. M. Byrne, J. R. Steele, and A. P. Hills. 2006. The impact of childhood obesity on musculoskeletal form. Obesity Reviews 7(2):209-218. Whybrow, S., C. L. Harrison, C. Mayer, and R. James Stubbs. 2006. Effects of added fruits and vegetables on dietary intakes and body weight in Scottish adults. British Journal of Nutrition 95(3):496-503.
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