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OCR for page 1
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
OCR for page 2
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.
OCR for page 9
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
OCR for page 10
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.
OCR for page 11
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)
OCR for page 70
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.
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