1. FUTURE HEALTH RISKS:
MISUNDERSTOOD, DEVALUED, AND DISCOUNTED
By
Alison K. Irvine
B.S. The University of South Dakota
M.S. The University of South Dakota
Dissertation Submitted in Partial Fulfillment
of the Requirements for the Degree of
Doctor of Philosophy in Psychology
______________________________________
Department of Psychology
Human Factors Program
In the Graduate School
The University of South Dakota

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Table of Contents
Abstract............................................................................................................................... 1
Introduction......................................................................................................................... 2
Heart Disease ...................................................................................................................... 2
Heart disease defined.............................................................................................. 5
Treatment and early detection................................................................................. 6
Prevalence............................................................................................................... 8
Public and Corporate Health Campaigns.............................................................. 11
Health Risk Information for the Public................................................................. 13
Computing probabilities........................................................................................ 16
Relative risk. ......................................................................................................... 16
Absolute risk reduction. ........................................................................................ 18
Decision ............................................................................................................................ 21
Uncertainty............................................................................................................ 23
Control, valence, and value................................................................................... 25
Statistical Illiteracy and Availability .................................................................... 28
Time...................................................................................................................... 31
Influences on discounting. .................................................................................... 33
Theories of discounting......................................................................................... 38
Do I feel lucky?................................................................................................................. 44
Method.............................................................................................................................. 47
Sample............................................................................................................................... 47
Apparatus and Materials ................................................................................................... 48
Procedure .......................................................................................................................... 54
Design ............................................................................................................................... 56
Results............................................................................................................................... 57
Tests for Assumptions ...................................................................................................... 60
Primary Analyses.............................................................................................................. 65
Exploratory Analyses........................................................................................................ 69
Discussion......................................................................................................................... 73
Conclusion ........................................................................................................................ 77
References......................................................................................................................... 79
Appendix B: Heart Disease Risk Information—Probability Format................................ 97
Appendix C Heart Disease Risk Information—Frequency Format.................................. 99
Appendix D: Heart Disease Knowledge Questionnaire.................................................. 101
Appendix E: Current Risk Status.................................................................................... 102
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Appendix F: Future Longevity........................................................................................ 106
Appendix G: UPPS Impulsive Behavior Scale............................................................... 107
Appendix H: Café Sano (Italian Translation, Healthy) Menus....................................... 109
Appendix I: Cafe Brutto (Italian Translation Bad) Menus............................................. 110
Appendix J: Nutrition Facts............................................................................................ 112
List of Tables and Figures
Table 1. Percentage of Adults who have had their blood cholesterol checked and were
told it was high.
...............................................................................................................................
17
Table 2. Percentage of Adults who have consumed fruits and vegetables five or more
times per day.
...................................................................................................................................
19
Table 3. Future Lifespan Assessment Scale
....................................................................................
52
Table 4. The combination of 2 experimental conditions with 3 levels
...................................
56
Table 5. Correlations between the three dependent
......................................................................
59
Table 6. Correlations between the three dependent and seven observational variables.
..
59
Table 7. Means, standard deviations, and subsample sizes for the dependent variable HD
Knowledge by the independent variable Risk Format.
......................................................
66
Table 8. Means, standard deviations, and subsample sizes for the dependent variable
View Nutrition Facts by the independent variables Cognitive Cue and Risk Format.
...............................................................................................................................................................
68
Table 9. Means, standard deviations, and subsample sizes for the dependent variable
Healthy Choices by the independent variables Cognitive Cue and Risk Format.
.....
69
Table 10. Percent of participants reporting HD Rates from the Heart Disease Risk
information by the two experimental conditions.
................................................................
70
Table 11. Cross tabulation of the variables View Nutrition Facts by Cognitive Cues.
.....
71
Table 12. Cross tabulation of the variables Healthy Choics by Cognitive Cues.
................
71
Table 13. Percent of participants that mentioned “Heart” and/or “Cholesterol” in the two
Cognitive Cue experimental conditions
..................................................................................
73
Figure 1. The distribution for the dependent variable—HD Knowledge.
62
Figure 2. The distribution of the dependent variable—View Nutrition Facts.
63
Figure 3. The distribution for the dependent variable—Healthy Choices.
64
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Abstract
Heart disease is the leading cause of death in all industrialized countries, and the
treatments for it are not as effective as prevention (WHO, 2014; Roger, et al., 2011).
Prevention means people need to change their behaviors, not when they are cued by their
doctor that their cholesterol is high, but before they get high cholesterol. Even though
people now have access to a wealth of health risk information, they still seem to believe
“it won't happen to me” (Weinstein, 1980).
Risks are hard to understand, and many people are left subjectively perceiving
them (Gigerenzer, Gaissmaier, Kurz-Milcke, Schwartz, & Woloshin, 2009; Loewenstein,
Weber, Hsee, & Welch, 2001). Research has shown that different risk formats are easier
to understand, and that different cues can reduce future discounting, but these two ideas
have not yet been tied together (Gigerenzer, et al., 2009; Weber, et al., 2007). This
dissertation explored these combined effects. It looked at the extent to which both the
format of health risk information and cues before decisions could influence knowledge
and behavior in an effort to get people to change their behaviors sooner rather than later.
Specifically, the goal was to show that college students, between the ages of 18
and 20, could better understand their risks of heart disease, be persuaded to read nutrition
information before they made a meal choice, and make better meal choices in the end. To
accomplish this, a 3 x 3 between subjects factorial design was used and analyses tested
the separate and combined effects of increasing the readability of health risk information
with cuing people to think about what they eat before making that decision.
ANOVA/ANCOVA results revealed that heart disease information was associated
with a better understanding of heart disease and that cuing was associated with reading
nutrition facts more. While heart disease risk information was recalled when cued to do
so, neither this information nor the cuing had an impact on actual meal choices. In all, the
findings from this research were not overwhelmingly supportive of the hypotheses but
were instead supportive of follow-up research.
Dissertation Advisor: __________________________________
Dr. Holly Straub

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Introduction
Decision-making is a cognitive process that results in a preference for a course of
action among alternatives. Of interest here are three components of this definition—
cognitive process, course of action, and among alternatives. This dissertation will explore
in detail each of these three components of decision making within the broader context of
heart disease. First, the “course of action” here is preventative health behaviors. Second,
“among alternatives” stands to imply risk, in this case the risk in a chosen course of
action. Third, the “cognitive processes” of interest are the ways in which people devalue
the preventative health behaviors, misunderstand the risks of doing so, and discount the
future consequences of their current preferences.
Risk, probability, and uncertainty are all common terms for the same idea—
unknown outcomes among alternatives. If we know that the lifetime risk of getting heart
disease is 0.33, we know something about this outcome. Yet, people demonstrate a
preference for certainty; decision-making research has revealed that people have an
aversion to uncertainty and undervalue central probabilities (Kahneman, 2003). In other
words, we prefer known outcomes, avoid unknown outcomes, and care little about
common outcomes. Computation and comprehension of probabilities are cognitively
laborious tasks for people; we are poor judges of them (Hoffrage, Lindsey, Hertwig, &
Gigerenzer, 2000). We simultaneously avoid, devalue, and misunderstand risks. We are
left with our own subjective perceptions of risks; we feel risks (Loewenstein, Weber,
Hsee, & Welch, 2001; Tversky & Kahneman, 1984; Weinstein, 1984). But, when risks
are presented as frequencies, it can help people to better understand them (Gigerenzer, et
al., 2009).

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Cognitive decision-making research rarely addresses the subjective perceptions of
heart disease risks. In the instances where heart disease related decisions have been
addressed, the focus has been on courses of action such as adherence to blood pressure
and cholesterol medications, rather than on preventative actions such as regular exercise
and a healthy diet (Chapman et al., 2001). Preventative health behaviors specific to heart
disease are not often studied directly, but risk factors for heart disease, such as smoking,
exercise, and being overweight, have been studied and are found to be associated with
time discounting—caring more about the present in spite of future consequences (Fuchs,
1982). Meanwhile, one process level account of decision-making, query theory, has
addressed time discounting, but not in the context of preventative health choices.
Other preventative health decisions such as getting a flu shot or a mammogram
have been well researched (Chapman & Coups, 1999; Chapman et al., 2001; Gigerenzer,
et al., 2008). But, these preventative behaviors are very different from the preventive
health behaviors associated with heart disease. A flu shot is a once per year choice that
decreases the likelihood of certain flu strains that year. Mammograms are a once every
few years choice that do not decrease the risk of getting breast cancer; instead they
increase the likelihood of early detection and false positive test results. Heart disease
preventative behaviors are multiple choices made daily that could eventually decrease the
likelihood of illness in the distant future.
It is widely accepted by medical professionals that the roots of heart disease lie in
a person’s preference for a course of action, or in this case a lack of action. If heart
disease risks were clearly and objectively understood would we see an increase in people
with no known risk factors? Alternatively, do people with no known risk factors better

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understand their risks of heart disease? It will be suggested here that heart disease has
become a large problem because at the time that it is developing people are devaluing the
risks, and people are devaluing the risks because they do not understand them. It will also
be suggested that in order to prevent heart disease people need to be cognizant of their
choices in activity and diet beginning in their early 20's, before the risks have bear a
tangible value. But first, it is important to know what heart disease is, how it occurs,
when it occurs, and who should be concerned. For that reason a brief medical and
epidemiological introduction to heart disease will be presented before going into the
problems with public campaigns against heart disease and theories in psychology that
could offer solutions.
Heart Disease
Heart disease has been the leading cause of death in the U.S. since 1921, where
roughly one in four people will die of heart disease (Ford & Capewell, 2007; CDC, n.d.).
The rates of heart disease fatalities are high, and while these rates have been on the
decline since the late 1960’s, they are projected to increase again soon (Schiller, et al.,
2012). Currently, 11.8% of the U.S. population actively lives with heart disease
(Schiller, et al., 2012). Coronary heart disease (the most common type of heart disease)
costs the U.S. $108.9 billion each year in health care services, medications, and most
importantly—lost productivity (CDC, 2014). In sum, peoples’ health in the US is poor, it
is costing billions of dollars each year, and this problem is projected to get worse.
This introduction to heart disease will cover its definition, methods for treatment,
prevalence, and public campaigns, primarily for the purpose of explicating the fact that

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prevention is ideal. In doing so, the following problems with heart disease will also be
made clear: (a) the projected future for heart disease is worse than the present state; (b)
neither treatment nor early detection is as effective as prevention; (c) public campaigns
aimed at decreasing heart disease rates are misdirected.
Heart disease defined.
Heart disease is a colloquial term used in the media, but is rarely used in medical
research. Oftentimes medical journals will refer to it more specifically as coronary heart
disease or cardiovascular disease. In fact, there are many lay terms that replace medical
terminology. For instance, coronary artery bypass grafting (CABG: pronounced cabbage)
is open-heart surgery and non ST-elevated myocardial infarction (NSTEMI) is a heart
attack.
The more common conditions that fall under the umbrella term Heart Disease
include atherosclerosis, arrhythmia, high blood pressure, heart failure, and heart attack.
Realistically, there are numerous other conditions that can be classified as heart disease,
but an exhaustive list is not necessary to adequately describe the problems with it. Three
sources contained a high level of consistency in their definitions of heart disease and its
associated risk factors: The American Heart Association (AHA), The Mayo Clinic, and
the U.S. National Library of Medicine (PubMed). Regarding this heart disease
introduction, information from these three sources was used (unless otherwise specified).
Atherosclerosis is a common condition associated with heart disease; it is
characterized by plaque build-up in the arteries that leads to arterial fibrosis and
calcification (i.e. a hardening of the arteries). This calcification develops over a long

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period of time, sometimes beginning in childhood, yet problems do not become evident
until mid to late adulthood (Kavey et al., 2003; Sillesen & Falk, 2011). One study found
the precursors for and/or early stages of atherosclerosis in men as young as 15 – 19 years
of age and women as young as 30 – 34 years of age (McGill, et al., 2000). There are other
common heart disease conditions that are related to atherosclerosis and include the
following:
• Arrhythmia, which is any change from the normal sequence of heartbeat:
too fast, too slow, early, fluttering, or quivering.
• High blood pressure, which is when the force of blood against the arterial
walls is too high.
• Heart failure, is said to occur when the heart muscle can no-longer pump
blood out of or into the heart effectively.
• Heart attacks, these are said to come about when blood clots in an artery
that damage or destroy part of the heart muscle (the heart does not
necessarily stop from a heart attack, whereas the heart does stop beating in
the case of cardiac arrest).
Treatment and early detection.
Treatments for heart disease vary greatly in their invasiveness and effectiveness.
Medical or drug therapies are used when the risk of heart attack is high (Bolookie &
Askari, 2010). As a preventative technique drug therapies have been shown to work as
well as invasive techniques (Stergiopoulos & Brown, 2012). One study found that rates

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of death, non-fatal heart attacks, unplanned open heart surgery, and persistent angina
were not significantly different between those that received medical therapies and those
that received stent implantation (Stergiopoulos & Brown, 2012).
Simple early detection tools include electrocardiographs and blood pressure
meters, which can quickly determine if people have arrhythmia, heart failure, heart attack
and/or high blood pressure (Mayo Clinic, 2014; McManus et al., 2011; Tavakoli, Sahba,
& Hajebi, 2009). However, of the many tests for atherosclerosis, the Farmingham Risk
Score (FRS) and Coronary Artery Calcium (CAC) scan are used most often, but these
tests are lacking in accuracy. Approximately 40% - 60% of the first signs of
atherosclerosis come in the form of a heart attack (Gibbons et al., 2008). Practicing
cardiologists are the biggest proponents for CAC scans, yet the American Heart
Association has been reluctant to recommend wide spread use of CAC scans as there is
limited support for improved patient outcomes and decreased future medical costs
(Hecht, 2008; Roger et al., 2012; Schlendorf, Nasir, & Blumenthal, 2009; Shah, 2010;
Sillesen & Falk, 2011).
When the initial lifetime risk of heart disease is low, or there are no major risk
factors throughout life, it tends to stay that way across time (Greenland & Lloyd-Jones,
2007; McGill, McMahan, & Gidding, 2008). Physical activity has been found to improve
functioning; it is linked to decreased blood pressure, decreased risk of diabetes, increased
weight loss, and decreased cholesterol levels (Hamman et al., 2006; Shaw, Gennat,
O’Rourke, & Del Mar, 2006; Zimmermann-Sloutskis, Warner, Zimmermann, & Martin,
2010). It has been found that more active or fit individuals tend to develop heart disease
less often and/or less severely (Myers, 2003).

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Prevalence.
The rate of heart disease fatalities is high, but the bigger concern is those living
with it. Among 34 developed countries, the U.S. ranking for life expectancy at birth has
recently dropped from 18th
to 27th
, more importantly healthy life expectancy dropped
from 14th
to 26th
(Murray et al., 2013). This should come as no surprise considering the
following:
• 45.1% of adults have at least one of three diagnosed or undiagnosed conditions—
high blood pressure, high cholesterol, or diabetes (Fryar, Hirsch, Eberhardt, Yoon,
& Wright, 2010);
• 16.2% of adults (≥ 20 years of age) have been diagnosed with high cholesterol
(Roger, et al., 2012);
• 31% of adults (≥18 years of age) have been diagnosed with high blood pressure
(Gillespie, Kuklina, Briss, Blair, & Hong, 2011);
• 70% of the adults (≥18 years of age) diagnosed with high blood pressure are
receiving treatment for it (Gillespie, Kuklina, Briss, Blair, & Hong, 2011);
• 20.3% of young people (12 – 19 years of age) have abnormal lipid levels (CDC,
2010);
• 40% of obese young people (12 – 19 years of age) have abnormal lipid levels
(CDC, 2010).
To further this point, obesity significantly increases the likelihood of type 2
diabetes, taken together these two conditions more than doubles a person’s risk of heart
disease and 68% of the U.S. population is currently overweight or obese (CDC, 2011;

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Fletcher et al., 2011; Hu et al., 2001; Roger et al., 2012; Wilson, D’Agostino, Sullivan,
Parise, & Kannel, 2002). Although estimates do not always agree, it has been found that
of adults ages 20 or more, 14% have been diagnosed with type 2 diabetes, and another
14% to 37% are considered pre-diabetic (CDC, 2011; Roger et al., 2012). The
Farmingham Heart Study reported a doubling in incidence of type 2 diabetes from 1971
to 2001, where most of the increase in cases occurred in those individuals with a body
mass index indicative of obesity (Fox et al., 2006). More recent data showed that in the
1980’s the crude average diagnosed cases of type 2 diabetes was 2.6% of the US
population, this rose to 3.23% in the 1990’s, another increase to 5.47% in the 2000’s, and
between 2010 and 2014 the average percent of people diagnosed with type 2 diabetes
rose to 7% (CDC, 2015a).
While some risk factors for heart disease are unavoidable (age, gender, and family
history) the health care community seems to agree that heart disease is primarily due to
personal behaviors such as physical inactivity, poor diet, smoking, excessive alcohol
consumption, and high levels of stress (Mayo Clinic, 2014). For instance, exercise has
many know benefits, but one study found that 33% of adults reported no engagement in
this personal behavior (i.e. they participated in no leisure-time aerobic activity that lasted
at least 10 minutes per week; Schiller et al., 2012). While this rate may not seem high, it
should be noted that there is a general inclination for people to over report physical
activity; research has shown that men tend to report 44% greater physical activity, and
women tend to report 138% greater physical activity (Prince et al., 2008). It has also been
found that physical activity consistently declines with age, more so for women than men
(Schiller et al., 2012; Zimmermann-Sloutskis et al., 2010).

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Of greater concern, even fewer U.S. adults eat a healthy diet — while fruit and
vegetable consumption has increased since the 1970's so has the consumption of added
sugars by 19% (Wells & Buzby, 2008). Research on spending patterns has shown that
U.S. households are out-of-step with USDA food recommendation, where fruits and
vegetables are still being consumed at a fraction of the rate they should, but cheese,
refined grains, red meat, and frozen entrees are consumed more often than recommended
(Wells & Buzby, 2008).
The research presented above suggests that the prevalence of heart disease is high
and costly, also that treatment and early detection of heart disease is not as effective as
prevention. Public and government agencies such as the USDA and CDC have been
reporting on heart disease. Organizations such as the American Heart Association (AHA)
have been campaigning against heart disease, and seek to engage specific populations
such as with their Go Red for Woman campaign. Also, First Lady Michelle Obama
brought personal health into popular culture with her Let's Move campaign that was
primarily directed at children. Finally, there is widespread popularity for company health
programs, further evidence that more incentive is necessary to effectively decrease the
prevalence of unhealthy lifestyles.
There are problems with these efforts, they are not working; the rates of
overweight and obesity are increasing. Reporting on the increasing problem of heart
disease does not appear to be causing alarm. Efforts directed at decreasing childhood
obesity and indirectly decreasing future cases of heart disease suffer due to the fact that
children rarely have a voice regarding what is prepared for dinner. Lastly, efforts directed

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at adults could be too little, too late—heart disease starts showing up in early adulthood
(20’s for men, 30’s for women; McGill, et al., 2000).
Public and Corporate Health Campaigns
There is more to the problem of public and corporate health efforts. This can be
seen in the contrasting the two main approaches to public health campaigns: downstream
approaches or upstream approaches. Efforts can either be directed towards helping those
people that have drifted downstream, and are really drowning, or preventing people from
getting in the stream altogether. The downstream approach would hold the individual
accountable, whereas the upstream approach would focus on the environment. Evidence
has already been presented to suggest that treating those that already have heart disease
(save those downstream that are drowning) is less effective, and could limit resources for
more effective prevention efforts (the events causing people to fall into the river in the
first place).
Many of the common risk factors for heart disease are starting to show up in
children and young-adults (Roger et al., 2012). The rate of heart disease has increased
from 13% in the 1960’s, to 30% in 2000, and it is believed to rise to 40% by 2030
(Heidenreich et al., 2011; Schiller et al., 2012; Wang & Beydoun, 2007). If we can see a
wave of people headed downstream why should we wait for them to begin drowning
before help is offered?
A food choice environment could provide people with knowledge
of their risks of heart disease in an effort to decrease the volume of people drowning; it
could support healthy food choices by making health-damaging choices more difficult to
make and health-promoting choices easier to make (Dorfman & Wallack, 2007).

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The downstream approach with heart disease focuses on personal responsibility,
poor character, and willpower; it orients peoples’ thinking towards weight loss, often for
appearance purposes (Dorfman & Wallack, 2007). In the case of obesity this can lead
people to adopt “10 pounds in 10 days” weight loss diets rather than investing in whole-
health lifestyle changes that include a nutritious diet (Cohen, Perales, & Steadman, 2005).
Nonetheless, a recent poll of major corporations found that 80 percent currently offer or
plan to offer monetary rewards to employees that participate in health initiatives such as
adopting a wellness plan or having their cholesterol tested; conversely, companies also
report plans to penalize those non-participants with consequences such as higher
insurance premiums (Toland, 2012).
This downstream approach does appear lucrative; Johnson & Johnson has
championed their workplace wellness program since 1970, and report that this has saved
the company $250 million in health care costs during the past decade (Berry, Mirabito, &
Baun, 2010). Many of these programs focus on weight loss, gloss over other health risk
factors, and poor general health; they are typically one size-fits-all wellness programs
purchased from outside consultants (Tu & Mayrell, 2010). But, the problem with this
preventative approach is that workplace health promotion programs are not universally
accepted by employees (Tu & Mayrell, 2010; Zoller, 2004).
Upstream approaches, which focus on nutrition, create environments that support
healthy food choices. A newer trend in the field of cognitive decision making—
behavioral economics—made popular by the book NUDGE, provides recommendations
and supporting evidence for how to guide peoples’ choices and produce measurable

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differences in outcomes (Thaler, Sunstein, & Balz, 2013). In other words, the upstream
approach is possible.
Heart disease, and its associated conditions, is not typically present in people with
no known risk factors—low blood pressure, normal cholesterol level, normal weight, lack
of type 2 diabetes, regular physical activity and a diet low in saturated fats and sodium. It
is widely accepted that heart disease’s roots lie in personal behavior/choice but it is
dangerous to suggest that the individual is solely to blame. It is not necessarily their fault
that they are drowning. Prevention is the solution; it has the potential to save billions of
dollars, but the “right” approach requires tailored programs for a variety of demographics
of people.
Health Risk Information for the Public
The preventative efforts of organizations like the American Heart Association
include providing people with information about the risks of heart disease. In general,
patient education materials are notorious for having poor readability, despite the
existence of objective measures of readability (Anderson, 2012). If health information
were more clearly presented to people, perhaps they would be less apt to engage in
unhealthy behaviors—perhaps. If heart disease risks were clearly and objectively
understood would people avoid unhealthy foods and sedentary activities? Creating better
health risk information is an upstream approach; there is a chance that it could improve
peoples' wellbeing.
Health information can be written in ways that are easier to read; improving the
readability of health information is important, but improving the recall of this information

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is even more important for people’s health (Marrow, et al., 2005). Likewise, health risk
information can be computed in ways that allow people to arrive at the right conclusions.
Transparency in health risk information could mean the difference between a person
feeling like it will or will not happen to them and knowing or not knowing their chances
of a heart attack.
Readability is not simply a matter of writing things better. Ambiguousness,
intelligibility, semantics, grammar, the ratio of words per sentence to syllables per
word—these all factor into the readability of a given text (Proctor & Van Zandt, 2008).
Readability can start with a simple test, for instance, a quick select/copy/paste of this
section tells us that it has a Flesch Reading Ease score of 65.4 and is written at an average
grade level of 7.6; whereas “the cat ate the mouse” earns a reading ease score of 117.2
and an average grade level of 0.7.
The Flesch reading ease formula rates text on a continuous scale where the higher
the score the easier it is to understand; for this readability score, average length of
sentence and average number of syllables per word are included in the calculation
(Kincaid, Fishburne, Rogers, & Chissom, 1975). The Flesch-Kincaid grade level test
offers a different objective way to assess the readability of text; it too examines sentence
length and number of syllables but the score suggests at what grade level a body of text
will be readable to all. For instance, a score of 8.0 means that an 8th
grader should
understand the text, this is also the score at which most newspapers are written. Yet
patient education materials are often found to be written at an average level of 10.2, too
high for the average consumer to read (Falconer, Reicherter, Billek-Sawhney, & Chesbro,
2011; Gal & Prigat, 2005; Wallace & Lennon, 2004).

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It has been suggested that in order to improve readability writers need to decrease
ambiguity, and using words with unfamiliar meanings and structuring sentences in
complex ways can confuse readers (Proctor & Van Zandt, 2008). For example, the
sentence “Colorless green ideas sleep furiously” famously coined by Noam Chompsky in
1957 demonstrates how a collection of words grouped in proper syntax is not necessarily
logical. Further, the use of words such as “myocardial infarction” versus “heart attack”
could leave a reader feeling unsure or confident in their understanding of the exact topic.
Presenting written materials in a way that is consistent with a reader's mental
representation of information can facilitate the consolidation of this new information
(Proctor & Van Zandt, 2008).
It is assumed that pamphlets, brochures, and webpages containing health
information are designed to either inform or raise awareness; in either case later recall
and application seem essential. But, recall is known to be inhibited by existing schemes
(e.g. people like me do not have heart attacks; Roediger & Marsh, 2003), biases (e.g. I am
in control, I will not let that happen to me; Schacter, 1999), and environmental or
contextual conditions (e.g. it just tastes better; Loftus & Palmer, 1974). To override these
schemes, biases, and environmental cues health information needs to be easily digestible
by the average person, and research on the presentation of health statistics has shown that
there is a way to present health information that is easier for people to understand
(Gigerenzer, Gaissmaier, Kurz-Milcke, Schwartz, & Woloshin, 2008; Gigerenzer,
Hoffrage, & Kleinbolting, 1991; Slovic, Monahan, & MacGregor, 2000; Tan et al.,
2005). This point will be explored in greater detail later, before doing so it is important to
make clear how health statistics are computed.

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Computing probabilities.
Effective health information would direct cognitive resources toward the act of
learning (recall and application) about one’s health; we know this from research on
cognitive load (Chandler & Sweller, 1991). While not the focus of this paper, it is easy to
see how Cognitive Load Theory applies here; information overload makes the act of
learning more difficult (Sweller, 1994). But health statistics can be presented in one of
two ways, relative or absolute, and there are benefits and problems with both. Relative
risk, for example, serves well to raise alarm, but can mislead a layperson to believe that
there is an autism epidemic (Gernsbacher, Dawson, & Goldsmith, 2005; Gigerenzer et
al., 2008; Spitalnic, 2005). Whereas absolute risk offers an accuracy or honesty to the
health risks, but is not always alarming enough to sway public opinion. These two
methods for computing risk, relative and absolute, will be further explored using
publically accessible prevalence and trends data from the CDC’s Behavioral Risk Factor
Surveillance System that is specific to heart disease risk factors in South Dakota (CDC,
2012).
Relative risk.
People might not understand how relative risk is computed; this could be why
statements such as, “the risk of death is decreased by nearly 67%.” can be so impactful.
When presented with findings in a relative risk format, people tend to endorse these
findings (Sarfati, Howden-Chapman, Woodward, & Salmond, 1998). The data in Table 1
shows base rate information for a common risk factor for heart disease—high

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cholesterol—at two points in time. An example of the computation of relative risk will
follow.
Table 1. Percentage of Adults who have had their blood cholesterol
checked and were told it was high.
Year Percent Yes (N) Percent No (N) Total
1995 24.79 (299) 75.21 (907) 1206
2009 42.56 (2463) 57.44 (3324) 5787
To calculate the relative risk of high blood cholesterol from Time 1 (T1=1995) to
Time 2 (T2=2009), the percentage of people with high blood cholesterol in 1995 is
divided by the percentage of people with high blood cholesterol in 2009:
𝑅𝑒𝑙𝑎𝑡𝑖𝑣𝑒 𝑅𝑖𝑠𝑘 =
!(!!!)
! !!!
=
!"#$ !""#
!"#$ !""#
(1)
𝑅𝑒𝑙𝑎𝑡𝑖𝑣𝑒 𝑅𝑖𝑠𝑘 =
!"#$
!"#"
!""
!"#$
=
!.!"#$
!.!"#$
= 1.718 (2)
The outcome in Equation 2 equates to a ratio greater than 1, which suggests the
risk of high cholesterol was higher in 2009 than in 1995. Had the ratio been less than 1
the risk of high cholesterol would have been in favor of 1995. Of course this could also
be inferred from Table 1, as the ratio of people with versus without high cholesterol in
2009 is closer to 1 than in 1995. Additionally, this method for understanding risk can be
used to suggest the following: the relative risk for high cholesterol has increased from
Time 1 to Time 2 by 71.77%. This finding seems easy to understand, and it is suggested
that for risks with an exceptionally low probability, relative risk format should be used, as

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it will lead to an increase in risk avoidant behaviors (Stone, Yates, & Parker, 1994).
People will go to great lengths to eliminate a small risk, a good example of this can be
found with asbestos, more on this later (Rabin & Thaler, 2001).
But there is a problem. Consider this example: before Policy X was enacted 3 in
1000 people perished due to Behavior Y, after Policy X was in place 1 in 1000 people
perished due to Behavior Y.
𝑅𝑒𝑙𝑎𝑡𝑖𝑣𝑒 𝑅𝑖𝑠𝑘 =
! !!! !!(!!!)
!(!!!)
=
!.!!"!!.!!"
!.!!"
= 0.666 (3)
Using relative risk reduction methods the following can be suggested about Policy X: (1)
Policy X is successful, its presence has a positive effect on reducing the risk of perishing
due to Behavior Y, (2) Enacting Policy X reduced Behavior Y by 66.6% thus decreasing
death by Behavior Y at the same rate. From Equation 4 it can be said that Policy X is
effective, it reduced the chance of dying due to behavior Y by about two thirds, which is
a relatively large reduction. In the absence of base rate information the reduction in risk
appears large.
Absolute risk reduction.
Absolute risk reduction (or absolute risk difference) is a computational approach
capable of assessing the difference between a risk factor's impact at two points in time
(Spitalnic, 2005). It can also test the effectiveness of treatments (treatment

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absent/treatment present) but in this instance it will provide a more realistic view of a
risk's reduction across time.
Table 2. Percentage of Adults who have consumed fruits and
vegetables five or more times per day.
Year Percent Yes (N) Percent No (N) Total
1996 24.5 (512) 75.5 (1576) 2088
2009 19.0 (1257) 81.0 (5352) 6609
𝐴𝑏𝑠𝑜𝑙𝑢𝑡𝑒 𝑅𝑖𝑠𝑘 = 𝑃 𝑌!! − 𝑃 𝑌!! = 𝑅𝑖𝑠𝑘 1996 − 𝑅𝑖𝑠𝑘 2009 (4)
𝐴𝑏𝑠𝑜𝑙𝑢𝑡𝑒 𝑅𝑖𝑠𝑘 =
!"#
!"##
−
!"#$
!!"#
= 0.245 − 0.193 = 0.052 (5)
Equation 5 helps to conclude the following: (1) about 1 in 4 people living in 1996
were “at risk” of eating the USDA recommended daily amount of fruits/vegetables and 1
in 5 people living in 2009 ran the same risk and (2) there was an absolute reduction in the
risk of “eating your veggies” by 5.2% over 13 years. Now, referring back to the Policy X
example in an absolute risk reduction context:
𝐴𝑏𝑠𝑜𝑙𝑢𝑡𝑒 𝑅𝑖𝑠𝑘 =
!
!!!!
−
!
!"""
= 0.002 (6)
Equation 6 produces an outcome of 0.002 meaning the risk of perishing from
Behavior Y was reduced by 0.2% after Policy X was enacted. Just as previously stated,
before Policy X was enacted 3 in 1000 people perished due to Behavior Y, after Policy X
was in place 1 in 1000 people perished due to Behavior Y.

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The way in which the conclusion for Policy X was worded comes from an
evidence based recommendation which is to present risks in a frequency format, this is
because people do better at properly identifying the greater of two risks (Gigerenzer et
al., 2008; Schwartz, Woloshin, & Welch, 2005). Frequency formats are found to be easy
to understand and to teach (Brase, 2002; Gigerenzer et al., 2008). Yet, this does not mean
that frequency formatted information is always fully understood.
Research has found that people have a bias towards larger values; when frequency
information is presented in larger versus smaller values—1,286 in 10,000 versus 24 in
100—the latter has been perceived as more likely to occur (Denes-Raj, Epstein, & Cole,
1995). Additionally, people appear to discount the relevance of base rate information; a
study of risk communication found that over half of respondents believed they could
make a conclusion about the health benefits of engaging versus not in a given behavior
where the denominator was absent (Schwartz et al., 2005).
In sum, the use of frequency format for the disambiguation of risks has become
quite common in public health information for laypersons, but there are still problems.
First, while people appear to have a better understanding of frequency formatted risks,
they still make errors with them. Second, even though the media or public health
campaigns could manipulate people’s subjective perceptions of risks, in the case for heart
disease it does not seem to be working.
In what follows, attention will be paid to explanations for the behavioral
precursors to heart disease from a cognitive decision making perspective. To begin, I will
describe the problems linked to risk, then the problems linked to time. Essentially both
factors are associated with a psychological devaluation for the future risk of heart

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disease. Yet, the rationale behind the discounting effects differ greatly. After a review of
the research on discounting, attention will then be paid to proposing cognitive strategies
aimed at preventative efforts for university populations.
Decision
Decisions with uncertainty, or risky choices, are focused on decisions that entail
trade-offs among costs and benefits with variable probabilities. Risk aversion and the
certainty effect are two sides to the same bias; they are theoretical terms describing the
tendency to prefer a certain outcome even when the payoff for the uncertain outcome is
higher (Baron, 2000; Tversky & Kahneman, 1992). These biases are in opposition to
rationality. Rationally, probabilities should be valued equal to the likelihood of their
payoff. But, this is not so, outcomes nearing certainty (p = 0 or p = 1) bear a subjective
value that exceeds the actual value of the probability; as outcomes grow more distant
from certainty (p = 0.50) their subjective value also becomes misaligned with their actual
value (Baron, 2000). Discounting, in this sense, is caring less about outcomes when the
probability is more disparate from certainty.
Decisions with delay, or intertemporal choices, are focused on decisions that
involve trade-offs among costs and benefits occurring at different times (Frederick,
Lowenstein, & O’Donoghue, 2003). Time discounting is a theoretical term for caring less
about future outcomes in favor of current outcomes (Chapman, 2003; Critchfield &
Kollins, 2001). This bias is also in opposition to rationality. Rationally, gains should be
valued equivalently to their sum total, regardless of when they are obtained. But, this is
not so, outcomes nearer to the present day bear a greater subjective value, as outcomes

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grow more distant from the present; their subjective value also decreases (Frederick,
Lowenstein, & O’Donoghue, 2003). Time discounting, in this sense, is caring less about
future outcomes and more about present outcomes.
Most often risk preference and time preference are analyzed as independent
entities. In its basic form, risk preference is analyzed by asking a series of, “which would
you prefer?” questions:
A smaller sum with a higher probability: 90% chance of gaining $5
A larger sum with a lower probability: 45% chance of gaining $10
After answering a series of such questions, a single score is derived which is indicative of
a person’s risk preference. Alternatively, in its basic form time preference is analyzed by
asking a series of, “which would you prefer?” questions:
A smaller sum with a shorter delay: $5 in 5 days
A larger sum with a longer delay: $10 in 10 days
From this, a single value is obtained which indicates a person’s time preference.
Researchers have attempted to apply principles and theories of risk to time. The
rationale seems sound, delay implies uncertainty, but the manner in which this idea has
been tested has not produced meaningfully conclusive findings (Frederick et al., 2003;
Soman, 2001). One problem with this work is that a parallel is not directly drawn
between time and risk; instead it is either conveniently or unnecessarily placed in the
context of money versus time (e.g. lose $10 for sure or a 50/50 chance of no loss versus
wait 10 minutes for sure or a 50/50 chance of no wait). Avoiding frivolous spending, or
adding diligently to a savings account, can save money. It feels as though time can be
saved in a similar way, for instance, taking a short cut to work or keeping up on grading.

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This feeling is irrational—time can never be saved, only lost. This is because each person
travels along a personal time continuum (i.e. their life span) with no way of stock piling
amounts of time. Also, the end point is unknown; no one is certain of when they will die.
This would suggest that time cannot be compared to money, but it can be compared to
uncertainty.
There are elements of both risk and delay with health behaviors and heart disease.
Healthy behaviors adopted early in one's lifespan and maintained throughout adulthood,
such as regularly exercising or sticking with a 2,000-calorie diet, lead to a decreased risk
of heart disease. Unhealthy behaviors throughout one's lifespan, such as abstaining from
exercise and indulging in overeating, lead to an increased risk of heart disease. In
essence, these behaviors should either increase or decrease the certainty of what people
may have come to expect, that their personal time continuum contains a total of 78.8
units of time. In other words, people on average live 78.8 years, but when it is commonly
referred to as “life expectancy”, rather than being understood as an average, it could
become an expectation.
Uncertainty
Research on decisions with uncertainty often shows deviations from normative
theory in the way of decision biases and paradoxes. Normative theories in decision-
making are essentially prescriptive statistical models that compute what we should
choose. Many descriptive theories in decision-making use different statistical models to
show how peoples’ choices systematically deviate from what we should choose. This

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approach ignores cognition as a process—how we choose—it also ignores the connection
between cognition and the environment.
It has been suggested that behavior is shaped by both the structure of task
environments and the computational capabilities of the actor (Gigerenzer & Goldstein,
1996; Simon, 1990). In the case of decision-making, a choice architect will shape the
structure of a decision environment, and the computational capabilities of the actor, the
decision maker, include their existing knowledge, biases and processing of information in
the environment. The information choice architects choose to provide for the decision
maker can take into account deviations from normative theory, biases, and paradoxes;
they can influence decisions via noticeable and unnoticeable features (Thaler, et al.,
2013).
Human factors, by definition, seeks to improve performance. In this context,
improving human performance on a cognitive task means creating an environment that
supports learning, retention, and retrieval (Proctor & Van Zandt, 2008). This is best
approached by taking into account cognitive processes. In doing so, normative theory or
how we should choose is irrelevant. However, how we use the information in an
environment to make a choice is relevant.
There are several findings in descriptive theory that are particularly relevant to
perceptions of health and heart disease. It will be shown that for risky choices, the
perceived subjectivity of a risk leads to a psychological devaluation of it, and an
additional devaluation occurs in when there is a delay in the outcome. After describing
the factors linked to the devaluation of the outcome heart disease, the focus will then turn
to theories best suited to offer solutions.

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Control, valence, and value.
This section will show that because probabilistic events are difficult for people to
compute they create strategies that tend to be good enough, but these strategies can lead
to errors in decision making. It will also be made clear that instead of knowing risks,
people subjectively perceive them. The subjectivity of risk perception can involve
control, valence, and value. Control, relates to a person’s varying perception of risk based
on their choice in the matter. Valence, in this context, refers to the positive or negative
label assigned to an outcome. Value, suggests that the actual probability of an event’s
occurrence is different from the subject value of the event’s occurrence. Each of these
will be further explained in turn.
A classic study suggested that people are willing to accept greater risks when they
are voluntarily engaging in behaviors such as smoking or sky diving, versus involuntarily
subjected to risks such as natural disaster or asbestos exposure (Starr, 1969). Others
support that control is associated with a denial of risk susceptibility; when people are in
control of the behavior they underestimate the likelihood of negative outcomes, whereas
when people have no control over an event they overestimate the likelihood of negative
outcomes (Slovic, Fischhoff, & Lichtenstein, 1986; Weinstein, 1980). More recently, this
has been found in vaccination decisions; parents that fully immunize their children
believe they do not have control over exposure and feel the risk of illness is high,
whereas parents that do not immunize believe they have control over exposure and feel
the risk of illness is low (Bond & Nolan, 2011; Palfreman, 2015).

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Risk and valence—the degree to which outcomes are viewed as positive or
negative—uncover another irrationality (Plous, 1993). Studies on why it won’t happen to
me, show that people have an unrealistic optimism for future events, they tend to believe
that they are more likely to have good things happen to them and less likely to have bad
things happen to them (Weinstein, 1984). For example, college students believe they are
more likely than their peers to receive a good starting wage and own their own home
soon after college; in contrast, they also believed themselves to be less likely to develop a
drinking problem or to have a heart attack (Weinstein, 1980). Valence finds it way into
food preferences as well; mutation bred foods are often perceived the same as genetically
modified, have a negative valence ascribed to them, and the risks associated with such
foods are overgeneralized and overestimated as simply bad for your health (Hagemann &
Scholderer, 2007).
To know that there are predictable biases in how people feel risks is a good
starting point. While control and valence provide surface descriptions that are applicable
to heart disease and food choices, these two factors do not offer a viable platform for
moving towards heart disease prevention. Knowing more about these biased perceptions
in the context of the subjective value of risks offers a deeper level of understanding.
Prospect Theory further explains decisions concerning probability and utility; it
suggests that people do not value stated probability’s values incrementally (Baron, 2000).
People are biased towards “certain” outcomes (probabilities closest to 0.0 and 1.0); there
is a tendency to view central probabilities as more equal, and the tails as more important
(Kahneman, 2003). An excellent example of this is the value people place on the
elimination of asbestos. The likelihood of undisturbed asbestos insulation becoming

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airborne is low; nonetheless people strongly value changing that slight possibility to an
impossibility.
When probability is plotted on the x-axis and psychological value on the y-axis an
S-shaped relation exemplifies the Pi Function. This function has three key characteristics:
(1) impossibilities are discarded, (2) low probabilities are psychologically over-weighted,
and (3) moderate and high probabilities are psychologically under-weighted (Tversky &
Kahneman, 1986). The latter effect is the focus here because the Pi Function
demonstrates an important finding: many moderately probabilistic events are
underweighted or psychologically valued as less of a risk than the actual risk. Applying
the Pi Function to heart disease, it can be understood that the psychological value for the
lifetime risk of heart disease is equal to the statistical value of it (π (x) = 0.33 = p (x)).
Yet, as a person accumulates more risk factors, or increases their risk of heart disease,
one could suggest that this would hold little more value to the person than their initial risk
of heart disease (π (x) < x = p (x)). This would be explained by the Pi Functions center
where moderate probabilities are psychologically under-weighted.
The other component to Prospect Theory, utility, separates out the subjective
value of gains versus losses, according to a person’s current reference point. Typically
this value function is applied to money or other material goods and suggests that it hurts
twice as much to lose a sum as it feels good to gain a similar sum (Thaler, 1985). The
shape of this function is similar to the Pi Function yet they represent very different
concepts. The y-axis, representing psychological value, divides the x-axis into losses on
the left and gains on the right. This value function supports the commonly found risk
averse behavior in decision-making research: losses are increasingly painful, thus we

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avoid them (Kahneman & Tversky, 1984). It also provides an explanation for risk taking
behaviors in different contexts; in general people avoid risks, but people are more willing
to take risks to avoid losses (Baron, 2000). This could help to explain why even the most
extreme treatments for heart disease are often considered viable solutions.
Prospect theory has been a dominant theory for decisions under uncertainty since
early 1980; it gained significant attention in 2002 when Daniel Kahneman won the Nobel
Memorial Prize in Economic Science. It provides evidence for decision strategies: first,
people tend to put probabilistic information into one of three simple categories,
impossible, possible, or certain; second, people view losses and gains differently
according to a reference point (Baron, 2000). Prospect theory also demonstrates that the
value of events is subjective and irrational (Kahneman & Tversky, 1984). This theory's
usefulness for description puts many behaviors into a context, especially the subjective
value of risks, but applying it to heart disease risk factors proves less useful. While
people may feel no more or less threatened by the accumulation of heart disease risk
factors, how would they feel about gains in weight or losses in cholesterol levels? Finally,
prospect theory describes decisions well, but offers little in the way of solutions.
Statistical Illiteracy and Availability
The work of Gigerenzer and colleagues, on the other hand, provides experimental
evidence for similar biases and heuristics while also providing usable solutions. Studies
have shown that people (doctors and patients alike) do not understand conditional
probabilities or draw the wrong conclusions from them, such as those encountered with
heart disease risks (Eddy, 1982; Gigerenzer et al., 2008). College students especially lack

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knowledge of heart disease risk factors; they demonstrate a greater knowledge of
psychological disorders and sexually transmitted diseases than heart disease (Bergman,
Reeve, Moser, Scholl, & Klein, 2011; Collins, Dantico, Shearer, & Mossman, 2004).
Research has found that college students will, on average, fail a true/false quiz for heart
disease knowledge (Bergman et al., 2011). Exacerbating this problem, findings that show
that as knowledge of heart disease decreases, cardiovascular risk increases (Lambert,
Vinson, Shofer, & Brice, 2013). That is to say, college students have demonstrated that
they possess poor knowledge of heart disease, and this lack of knowledge is associated
with an increased risk of future cardiovascular health problems.
Gigerenzer (et al., 2008) suggested that statistical literacy is a necessary
precondition in today’s technological atmosphere. With online health information readily
available, peoples’ notion of risk factors can easily be skewed and they can fall victim to
the availability heuristic. For example, contrasting the number of deaths due to heart
disease (611,105) versus cancer (584,881) in 2014 with the number of stories in the new
about heart disease (8,540,000) versus cancer (94,200,000) helps to explain why heart
disease may not seem as problematic. Additionally, college students are not looking for
information on heart disease; instead they tend to search for information regarding
illness/conditions, mental health, weight loss, exercise, and nutrition (Banas, 2008).
When knowledge of these health issues is greater and a majority of the health information
directed at college students is consistent with this knowledge, they could be more likely
to over estimate their risks due to the ease with which they can recall information
pertaining to it.

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Basic competence in statistics does not require a college degree; statistical literacy
implies that people are capable of recognizing that survival rates can vary based on many
personal factors (Gigerenzer et al., 2008). Another way to mislead, especially those who
lack statistical literacy, is to omit base rates for risks and study limitations, and frame
risks in sensationalized ways; as is often the case with the popular press (Gigerenzer et
al., 2008). For instance, a Newsweek article—The new obesity campaigns have it all
wrong—provided no rates to support the claim that exercise is an ineffective method for
weight loss, that the calorie/energy balance does not work, or that the USDA food guide
serves to fatten us up and increase our risk of heart disease (Taubes, 2012). However,
minimal and non-transparent statistical information was provided for the authors
proposed solution: no exercise and an Atkin’s style diet (a diet high in meat, eggs, and
cheese, and low in fruits, vegetables, and grains).
The problems with risk and uncertainty in personal health can be summed up in
three ways: people do not understand probability, the media easily misleads people, and
people feel their personal risks are low (Broadbent et al., 2006; Gigerenzer et al., 2008;
Kahneman & Tversky, 1984; Weinstein, 1980). For example, people do not understand
the risks with asbestos, they are easily misled about autism by the media, and they feel
that these risks are high. But it is those central probabilities, such as heart disease risks,
where people feel their risks are low. Unhealthy foods, when consumed in excess, can be
just as toxic as asbestos, obesity has been linked to cancer in that there are simply more
cells to become cancerous (AICR, 2014). Behavioral patterns of inactivity early in life
can be as difficult to manage as some of the behavioral patterns associate with autism;
children that rarely run can lack the skeletal integrity to effectively do so in adulthood

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(McKay & Smith, 2008). However, the time lag between these actions and their
consequences is great enough that they are further devalued. This leads us to the second
half of the problem.
Time
The salient feature of intertemporal choice is the psychological weighing out of
benefits between immediate and delayed outcomes, and the most commonly associated
subject matters are future discounting, time discounting, and time preference. Future
discounting is the rate at which future goods are devalued with delay indexes; Time
discounting is any reason for caring less about future consequences; Time preference is
the preference for immediate utility over delayed utility (Frederick et al., 2003; Wilson &
Daly, 2004). All of the above terms have been likened to impulsivity or impulsive
behaviors such as inadequate saving for retirement, drug and alcohol abuse, pathological
gambling, or health impairing habits (Bickel & Johnson, 2003; Kirby & Herrnstein, 1995;
Logue, 1995).
In fact, studies have found that 60% of Americans do not trust their own impulses
with their retirement savings (Laibson, Repetto, & Tobacman, 1998). Parallels can be
drawn between investing for retirement and investing in one’s future health. Not only
because many people do a very poor job of it, but also because people tend to put it off
(O’Donoghue & Rabin, 1998). But, the most common solution for improving retirement
saving behaviors, default options, could not possibly stand to work for long term health
decisions.

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While future discounting and time preference are focused on measurement that
results in a single value, time discounting is more general and appropriate to apply to
heart disease. The time lag between the actions and benefits of healthy behaviors helps to
explain why people often do not engage in them (Chapman, 2003). For example, the act
of jogging does not produce immediate health benefits; months of regular jogging will
produce benefits. But also, the act of eating a piece of chocolate cake does not produce
immediate health consequences; months of regular cake eating will produce these
consequences. Not surprisingly, health consequences such as heart disease are temporally
discounted more than four times that of monetary losses (Chapman et al., 2001).
A well-known finding in intertemporal choice is that time discounting is
hyperbolic. There is a declining rate of time preference relative to the delay; in other
words, people tend to value options that occur earlier rather than later (Bickel & Johnson,
2003; Frederick et al., 2003). For instance, the psychological value of $100 is valued at
its full worth when receipt is immediate. Yet, when there is a delay, the psychological
value of that $100 declines steadily. Interestingly, the shape of the curve is similar to that
of the loss curve in Prospect Theory’s s-shaped value function. Even more interesting, the
scope of this hyperbolic function is not limited to monetary gains; health decisions can be
modeled by the same function (Chapman, 2003).
The hyperbolic function of time discounting would suggest that there are two
ways to minimize discounting: decreasing the delay or increasing the magnitude of the
outcome. Taking this one step further, future discounting of heart disease could be
minimized getting heart disease earlier in life or increasing the severity of the symptoms.
Of course neither of these suggested solutions is plausible. Yet, living an extremely

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unhealthy lifestyle will increase the severity of health problems and the time with which
they are expressed. As it stands, these outcomes do not occur soon enough in one’s
lifetime, in turn young people do not value the risks. But, if young adults did have a clear
understanding of what it means to have heart disease, it may be possible for their future
discounting of heart disease to be lessened.
Influences on discounting.
Pigeons’, rats’, and humans’ future discount rates have all been found to follow
the same hyperbolic curve (Bickel & Johnson, 2003). Future discount rates can be
influenced by a variety of factors such as age, gender, SES, perceived life expectancy,
mating mindset, thoughts of death, blood sugar levels, personality traits, and drug
dependence (Bickel & Johnson, 2003; Daly & Wilson, 2005; Green, Fry, & Myerson,
1994; Kirby & Maraković, 1996; Liu & Aaker, 2007; Wang & Dvorak, 2010). The
factors most relevant to this work are individuals’ beliefs in their own life expectancy and
the personality traits that are known to be associated with impulsivity.
An individual's belief in their own life expectancy, perceived life expectancy, is
their personal perception of their total allotted time with which they can budget life
events. For example, a person may believe that they will live until they are well into their
80's, for this individual it is realistic spend more than 10 years pursuing advanced college
degrees and wait until their 30's to have children. However, a person who believes they
will only live until they are 50, may feel that 10 additional years in school is a “waste of
time” and child bearing in their 30's would mean that they would barely see their children
graduate from high school.

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The “expectation” of a lifespan may be psychologically salient, but not
necessarily a conscious expectation; people behave as if they adjust their discounting
rates in relation to local life expectancies (Wilson & Daly, 1997). The direct causes for
differences in perceived life expectancy are less understood, but relations have been
found between it and gender, personal/familial health history, age, and income; all of
which are factors that also directly impact heart disease risks (Fischhoff et al., 2000;
Hamermesh & Hamermesh, 1983; Klein, 2007; Wang & Beydoun, 2007; Wilson & Daly,
1997).
In some contexts people are fairly accurate in their perceptions of their own
mortality. For instance men tend to report a lower perceived life expectancy than do
women, as do smokers (Hamermesh & Hamermesh, 1983; Klein, 2007). Yet in other
contexts, namely exercise and familial longevity, people overestimate these positive
benefits to their own lifespan (Hamermesh & Hamermesh, 1983). The impact age has on
perceived life expectancy is of some concern due to findings that young teens (around the
age of 15) tend to overestimate their own likelihood of death before the age of 20; this
could be due to their perceived lack of control or belief in an uncertain future (Fischhoff
et al., 2000). However, other research finds that once people have reached young
adulthood, the average estimates of perceived life expectancy tend to better align with
national averages (Klein, 2007).
Research on income and perceived life expectancy offers a different description
of behaviors often judged as impulsive (e.g. reproduction earlier in the life-time).
Findings suggest that in some instances teen pregnancies, often deemed as impulsive,
could be the result of a shorter life expectancy (Daly & Wilson, 2005; Wilson & Daly,

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1997). Statistically, people with fewer resources have shorter life expectancies, as a
result, they have less time to accomplish life's milestones such as bear children and watch
them grow up. The link between perceived life expectancy and impulsivity is not often
drawn, neither is it with time discounting; but all of these connections can and should be
made with heart disease more directly.
People can be guided towards estimating or constructing higher life expectancies
just as they can have a greater value for the future (Payne, Sagara, Shu, Appelt, &
Johnson, 2012; Weber et al., 2007). When life expectancy is framed as “live to” (versus
“die by”) people consistently construct higher life expectancies (Payne et al., 2012).
Perceived life expectancy has also been tested in relation to the psychological value of
time added to the entire lifespan; where a positive relation was uncovered, people with
the belief in a higher life expectancy also increasingly value time added to their lifespan
(Klein, 2007). From all this, an interesting question arises: do people who believe their
lives will be longer make greater efforts to ensure it?
Perceived life expectancy, compared with personality, is a less popular area of
study in psychology; personality is largely regarded as relevant in most fields of
psychology. However, literature suggests that human factors psychologists tend to pay
less attention to topics regarding personality or emotion (Eccles et al., 2011). Yet, time
discounting rates are shown to be linked to personality traits, and personality traits are
shown to be linked to health behaviors, because of this it seems natural to introduce the
idea of understanding personality in relation to decision environments, especially for
health related choices (Edmonds, Bogg, & Roberts, 2009; Madden, Petry, Badger, &
Bickel, 1997; Mitchell, 1999; Ostaszewski, 1996).

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Thus far, some inconsistencies have been uncovered between some health related
behaviors and the personality measures linked to impulsivity; for example, substance
abusers in general show greater discounting while smokers and drinkers show less
consistency in personality measures (Bickel & Johnson, 2003; Kirby & Maraković, 1996;
Reynolds, Richards, Horn, & Karraker, 2004). Although, many of the studies examining
this specific relation employed the Barratt Impulsiveness Scale, the Impulsiveness and
Adventuresomenss sub-scales, or a measure of Sensation Seeking (Green & Myerson,
2004).
An alternative measure of impulsivity, the UPPS (Urgency, lack of Premeditation,
lack of Perseverance, and Sensation Seeking), drew from the above mentioned measures,
along with the EASI-III Impulsivity Scales, Dickman’s Functional and Dysfunctional
Impulsivity Scales, the I.7 Impulsiveness Questionnaire, Personality Research Form
Impulsivity Scale, Multidimensional Personality Questionnaire Control Scale,
Temperament and Character Inventory, and the Revised NEO Personality Inventory
(Whiteside & Lynam, 2001). In combining these measures, and arriving at a
parsimonious conclusion—that impulsivity is comprised of four factors—the UPPS has
proven useful in a wide variety of research contexts and in numerous cultures, it has also
been translated into many different languages.
The UPPS has been used to demonstrate that impulsivity has a mediating
relationship between time perception and health behaviors, a correlating relation with
impulsive decision-making, and predicting ability for eating disorders (Anestis, Selby,
Fink, & Joiner, 2007; Daugherty, 2011; Van der Linden et al., 2006). There is widespread
use of this measure within the contexts of personal health and obesity. UPPS related

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research that directly relates to heart disease has uncovered associations between
impulsivity and body mass index (BMI), the somatosensory cortex, binge eating, and
snack avoidance.
People with higher BMIs (overweight and obese) show elevations in Urgency,
Sensation Seeking, and lack of Perseverance (Delgado-Rico, Río-Valle, González-
Jiménez, Campoy, & Verdejo-García, 2012; Mobbs, Crépin, Thiéry, Golay, & Van der
Linden, 2010). In fact, when impulsivity was therapeutically treated, BMI levels were
significantly lowered compared to a control (Delgado-Rico, Río-Valle, Albein-Urios, et
al., 2012). Additionally, adolescents with higher BMIs, compared to their healthy weight
counterparts, showed differences in their somatosensory cortex that was linked to the
Urgency sub-scale (Moreno-López, Soriano-Mas, Delgado-Rico, Rio-Valle, & Verdejo-
García, 2012). Binge eaters also showed significant differences in Urgency and Sensation
Seeking (Kelly, Bulik, & Mazzeo, 2013). Whereas, those who plan to avoid snacking but
lack the ability to follow through, show higher scores on Urgency and lack of
Perseverance (Vainik, Dagher, Dubé, & Fellows, 2013). From this it is clear that Urgency
and Sensation Seeking play some role in overeating.
There is less research on the role of impulsivity in decision-making. Sensation
Seeking and Urgency have been linked to disadvantageous decisions, but lack of
Premeditation has been found to be linked with advantageous rapid or time-sensitive
decisions (Bayard, Raffard, & Gely-Nargeot, 2011). To know that impulsivity is related
to overeating is helpful in description alone. However, two findings prove actionable:
impulsivity can be treated and in certain contexts, it serves a functional purpose.

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Process level theories of discounting.
Many other factors contribute to discount rates; likewise, many of the research
models describe the differences in discounting. For instance, we know that there is a knee
in a hyperbolic curve for time discounting, and this will shift in relation to one's age; this
does well with description and prediction, yet it does not explain any underlying
processes (Brandstätter, Gigerenzer, & Hertwig, 2006). The argument has been made that
decision research will progress more rapidly by focusing on process instead of prediction
and models (Johnson, Schulte-Mecklenbeck, & Willemsen, 2008). Calls for a shift
towards process-level accounts of decision-making are nothing new, it is recognized that
they provide richer descriptions of preferences because the research is focused on
function over outcome (Einhorn, Kleinmuntz, & Kleinmuntz, 1979; Johnson et al., 2008).
It has been suggested that these process-level accounts are the key to opening the black
box that is decision-making (Brandstätter et al., 2006).
There are two process-level theories in cognitive decision making that provide
functional accounts and rich descriptions for how people arrive at a preference—
construal level theory and query theory. Construal level theory holds that temporal
distance influences intertemporal choices by systematically changing the way outcomes
are construed (Trope & Liberman, 2003). Query theory suggests that intertemporal
choices are constructed based on memory and the accessibility of information regarding
choice features, and these serve to determine preferences (Weber et al., 2007).
Construal level theory would explain the decision process for reading an
educational text based on the features being construed at abstract/high-levels and

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concrete/low-levels. The delayed or future benefits of reading this text are abstract:
expanding knowledge or gaining a new perspective. The immediate benefits are more
concrete and less rewarding: scanning words on a page to extrapolate meaning. When the
decision to read the text or not read the text is construed at a concrete/low-level, people
would be more likely to choose to read the text as it would simply be viewed as a task
(Trope & Liberman, 2003). Construing at high levels would be lofty and easily
discounted.
This theory also accounts for intertemporal preference reversals (Stephan,
Liberman, & Trope, 2010; Weber et al., 2007). The authors of construal level theory
suggest that so long as choice features are represented at a low-level or concretely,
discounting may not occur (Trope & Liberman, 2003). However, it is less capable of
accounting for the discounting asymmetries traditionally found via SS/LL choice task
methods (Weber et al., 2007). More importantly, research finds that construal level theory
does less well at accounting for healthy choices; people construe healthy and unhealthy
foods at low and high levels equally often and presenting health risk information at a
higher level (year rather than day) enhances the salience of future risks when it should
have a discounting effect (Bonner & Newell, 2008; Lo, Smith, Taylor, Good, & von
Wagner, 2012; Ronteltap, Sijtsema, Dagevos, & de Winter, 2012).
Construal level theory could be useful in constructing health risk information that
is more concrete, and according to this theory making it more concrete will make people
discount the risk less, but findings support the contrary (Bonner & Newell, 2008). The
theory of more interest—query theory—has provided process level accounts for attribute
framing, default effects, sunk cost biases, time discounting, and the endowment effect

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(Dinner, Johnson, Goldstein, & Liu, 2011; Hardisty, Johnson, & Weber, 2010; Ting &
Wallsten, 2011; Weber et al., 2007). While the endowment effect may seem slightly off
track for this research, the example nicely demonstrates this theory's ability to fully
account for a decision making process and corresponding bias, it offers insights into
possible solutions to reduce biases, and it is a seminal piece of query theory research.
In classic endowment studies half of the participants are randomly given an item
(a university coffee mug), then all the participants engage in a market experience where
there are selling prices from mug owners, and buyer bids from the other half of the
participants. Typically, findings suggest that those who were given a mug, have a selling
price 2 to 3 times more than the bidding prices. Historically, this finding has been
suggested to support loss aversion and the endowment effect—where people irrationally
avoid loss and place a higher value on items that they own—in this instance people place
a greater value on the mug (Kahneman & Tversky, 2000). But this is half an explanation,
it does not account for the perspective held by those that have money in their endowment.
Query theory provides explanations for both sides. The endowment effect, from a
query theory perspective, suggests that sellers and bidders have differing reference points
and that these different reference points predict differing valuations. Both sellers and
bidders ask themselves “why should I?” sellers ask, “why should I sell?” and bidders ask,
“why should I buy?” In shifting decision makers' attention to their alternate reference
point, the endowment effect can either be eliminated or exacerbated (Johnson, Häubl, &
Keinan, 2007).
The roots of query theory lie in the idea that preferences are constructed or
created in a way similar to the recreation of a memory. Recalling a scene from memory is

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not like reproducing a photograph, it is more like painting a picture; having a preference
is not a stamped-in scheme, it unfolds in the moment (Weber & Johnson, 2009).
Decisions depend equally on attention and memory processes where the principles of
proactive interference can be used to influence and even minimize irrational choices
(Anderson & Neely, 1996; Johnson et al., 2007; Weber & Johnson, 2009).
Returning now to the example of a decision to read (or not to read) a text, query
theory accounts for this decision based on four premises. The first two premises are that
decision makers will break down the choice into a series of mental inquiries starting with
a person's status quo, “why should I read this text?” and that these queries are executed
serially and automatically (Johnson et al., 2007; Sternberg, 1966). The features that a
decision maker will construct from these queries can be retrieved from memory and
acquired from the environment. The third and fourth premises of query theory hold that
order matters, due to retrieval interference, as does perspective (Johnson et al., 2007).
The first query will produce more features than subsequent queries, and these features
will effectively interfere or block one's ability to construct features to the contrary.
Interference is the reason perspective is also important; the first query is dependent on
one's perspective. In other words, query theory suggests that if a person begins by asking
“do I know enough about this topic?” they will be more likely to read the text than if they
start with “do I know about this topic?”
An appealing feature of query theory is its thoroughness in accounting for and
predicting intertemporal discounting. Construal level theory explains intertemporal
preference reversals but provides little explanation for asymmetries in discounting
between acceleration and delay decisions when they involve comparing the same two

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choice options, an immediate one and a later one (Weber et al., 2007). Whereas query
theory does, and in this case offers the following three insights into an intertemporal
decision process: (1) Decision makers decompose intertemporal choices into a series of
questions, in this instance two common queries would be “Why should I consume now?”
and “Why should I wait to get more later?” (2) Queries occur serially and begin with a
person's status quo or immediate state, “Why should I consume now?” (3) Retrieval
interference will occur for all but the first query; reasons for immediate consumption will
block peoples' ability to produce reasons for delayed consumption (Weber et al., 2007).
Due to the three insights outlined above, query theory suggests that query order is
critical to the decision process. It has been found that when query order begins with
“Why should I wait to get more later?” people demonstrate greater value for the future
than when query order begins with “Why should I consume now?” (Weber et al., 2007).
These insights provide design consideration for decision environments that are conducive
to the reduction of impulsive choices. While query theory has yet to be applied in a heart
healthy decision environment it has proven useful in accounting for methods to help
people pick the right paths and avoid the wrong ones (Dinner et al., 2011; Weber &
Johnson, 2009).
Research has also tested perceived life expectancy from a query theory
perspective and produced findings which support that this value is constructed just as
many other preferences are constructed; people in a “live to” condition produce more
positive thoughts about living to target age than those in a “die by” condition (Payne et
al., 2012). Live to conditions were also found to influence the decision to invest more in
one’s own future (Payne et al., 2012). The theory’s ability to account for similar decision

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biases, such as attribute framing, the endowment effect, and default effects, suggests that
it may be suited to address other similar consumption behaviors such as overeating or
impulsive food selections (Dinner et al., 2011).
Applying query theory to a hungry 18-year-old college student, research has
found that convenience will be their reference point or status quo, followed by price,
pleasure, then health and weight (Marquis, 2005). Query theory would suggest that their
first query would be “What food is easiest to get right now?” Given that this query would
be followed by “What’s affordable?” then “What will taste good?” It seems unlikely that
a college student would ever make it to “How will this meal benefit my long term
health?” or “Will this meal eventually cause me to have a heart attack?” These questions
are not related to their status quo. When this is the status quo (or starting point) it is easy
to see how a mental query would not often lead to the construction of a preference for
heart healthy nutritious foods.
It is being suggested here that college students should be aware of heart disease
risks when making food choices because this is the around the age where they are starting
to develop it, and when they can begin to choose foods autonomously. Devalued risks,
discounted futures, perceived life expectancy, and impulsivity are thought to contribute to
the unhealthy food choices and eating habits of young people. It is thus argued here that
if an information environment contained heart disease risks in a format that was more
salient and accessible, that information would be more easily recalled later, and if
peoples' attention was shifted to their future reference points, more people might choose
heart healthier foods.

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Preventative health behaviors had been gaining cultural, organizational, and
governmental support, but in the wake of the 2-foot bacon wrapped pizza, the future of
heart healthy campaigns is uncertain. The lifetime risk of heart disease is p = 0.33, and
indulging in one unhealthy meal will not ensure a heart attack later (p ≠ 1.00), just as
eating a single healthy meal will not ensure the absence of a heart attack (p ≠ 0.00).
Certainty is psychologically over-appreciated, but the future is full of uncertainties that
are not fully appreciated (Frederick et al., 2003; Kahneman, 2003).
Do I feel lucky?
~Dirty Harry, 1971
As previously mentioned, risks are generally understood subjectively; students
feel that they are less likely than their peers to have a heart attack. There are two
problems with this: they feel the risk, and the risk is too far in the future for students to
care. A study of the prevalence of heart disease risk factors and screening behaviors of
young adults (ages 20 – 35) found that less than 50% were screened for heart disease and
nearly 59% were at risk (Kuklina, Yoon, & Keenan, 2009). Realistically, heart disease
should be a concern for a sizable portion of the population of interest. Fortunately, there
is growing interest in this population.
Research on the topic of heart disease in college student populations is increasing.
While not a precise method, counting the results from Scholar.Google.com search terms
can provide a rough estimate for a trend. In searching for the exact terms “College
Student” and “Heart Disease” 30,800 results were produced. For comparison, “College

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Student” and “Sexually Transmitted Disease” produced 8,750 total results. Searching for
the College Student/Heart Disease term combination over time, it only ever accounts for
a fraction of a percent of total articles. Yet, the percentage has increased from less than
0.1% in the 1970’s to over 0.6% in the last 5 years.
While “College Students” and “Heart Disease” generates a sizeable list of results,
combined with “Cognitive Decision-Making” or “Human Factors Psychology” there are
only about 50 results retuned. But, with these two approaches a unified goal is
appropriate, optimizing human performance in a decision environment. More precisely,
optimizing college students’ objective understanding of the risks of heart disease and
recognizing the long-term consequences of their immediate actions could influence them
to make healthier choices and improve their wellbeing.
Health information materials are typically created to effect some change; to do
this the written material first needs to be comprehensible. This can be accomplished
through structuring sentences in less complex ways, using familiar words, decreasing the
ambiguity of the written information, and increasing the transparency of both the text and
statistics (Gigerenzer, 2009; Proctor & Van Zandt, 2008). The Flesch Reading Ease test
and the Flesch-Kincaid Grade Level test examine sentence length in the context of
syllables per word, and offer objective assessments of the readability of text.
While it is important for the wording of health information to be easy to
understand, it is just as important for the health risks to be easy to understand. Health
information, where the risks are presented in a frequency format, has been shown to
accomplish this (Gigerenzer et al., 2008, 1991; Slovic et al., 2000; Tan et al., 2005).

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Further, research supports that health information can be created in a way that is
associated with improved accuracy in comprehension, retention, and later retrieval
(Gigerenzer & Hoffrage, 1995; Proctor & Van Zandt, 2008; Sedlmeier & Gigerenzer,
2001).
But, in those moments when someone is making a decision about what to eat,
when it counts, it takes more than just having read good health information at one point
in time. People may need help tying the two together. Simple cues can direct peoples'
attention and prime their memory in ways that help them to avoid common decision
making biases (Weber et al., 2007). Research is needed which supports that the use of
cues can decrease other intertemporal choice biases, in this case the bias towards
choosing unhealthy foods. Choice architecture, oftentimes overlaps with findings in
human factors psychology, and suggests that steering peoples' choices in a healthy
direction is possible (Thaler, Sunstein, & Balz, 2010). The proposed study is designed
with a similar goal, and is guided by one question: Can health information and cues steer
peoples' choices in a heart healthy direction? From this question the following three
hypotheses were generated which take into consideration environmental and personal
factors:
H1: Young adults, ages 18 – 20, that read about heart disease, where the risks are
in a frequency format, will score higher on a test of heart disease knowledge than
those that read about risks in a probability format.
H2: Young adults, ages 18 – 20, that read about heart disease risks in frequency
format and/or get cognitively cued to think about the future, will look at nutrition

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facts more and choose healthy foods more than those that read about risks in a
probability format and/or get cued to think about the present.
H3: Young adults’, ages 18 – 20, individual differences in impulsivity, belief in
future vitality, future risk of heart disease, and/or body mass index will interfere
with the effectiveness of heart disease risk information and/or cognitive cues on
performing on a test of heart disease knowledge, looking at nutrition facts, and
choosing healthy foods.
Method
Sample
A total of 422 participants were recruited using The University of South Dakota’s
SONA Systems subject pool that is primarily, but not limited to, Introductory Psychology
students. Participation was voluntary, course credit was earned for participation, and
informed consent was obtained from all who participated. Because this was an online
survey clicking “continue” at the bottom of the informed consent statement (Appendix A)
equated to establishing informed consent.
While this population is typically considered a convenience sample they are the
ideal population for this work. In order to decrease variance due to demographic
characteristics participation was limited to students between the ages of 18 and 20 years
old.
There were 58 participants that dropped out of the study immediately after the
heart disease information was viewed (i.e. they did not answer any of the items). There
were two questions following the heart disease information page that were mandatory,

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two participants answered those questions and nothing further, and two others completed
the test of heart disease knowledge before dropping out. In all, 62 people dropped out
without completing the study. The remaining 360 participants all completed the research
protocol. Of the two experimental treatments, group sizes ranged from 97 to 132, and of
the nine possible experimental combinations, group sizes ranged from 31 to 51.
Apparatus and Materials
Stimuli were presented via PsychData, a survey application that enables users to
create and conduct web-based research. This tool is suitable for psychological surveys
and allows for anonymous response.
The first independent variable, Risk Format, was developed by writing two
versions of heart disease risk information (Appendix B and C). Information was focused
on heart disease and included a brief sub-section for women. A test of the readability of
these materials showed that it was written at the Flesch-Kincaid 9th
grade level of reading
ease.
The second independent variable, Cognitive Cue, was used in conjunction with
the meal choices. Participants were instructed, “Before making your choice: think of
some reasons why your preference would benefit your [present/future] state of wellbeing
and list those reasons here.”
There were seven measures used in this study, one with four distinct factors: HD
Knowledge, View Nutrition Facts, and Healthy Choices were measured as dependent
variables and Future Risk, Body Mass Index (BMI), Future Vitality, and Impulsivity

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(Urgency, (lack of) Premeditation, (lack of) Perseverance, and Sensation Seeking) were
covariate measures.
HD Knowledge, a dependent variable, was assessed using the Heart Disease
Knowledge Questionnaire (HDKQ; Appendix D). This measure was developed and
tested on undergraduate students enrolled in introductory psychology courses, whereas all
other similar measures have been tested on older adult populations, tailored to other sub-
populations, contained items leading to ceiling effects, and/or contain outdated terms
(Bergman, Reeve, Moser, Scholl, & Klein, 2011). This measure has a true/false format
with an “I don’t know” option to prevent guessing. The HDKQ was developed using
exploratory and confirmatory factor analytic techniques. A five-factor solution showed
good fit statistics (CFI = 0.96, TLI = 0.97, and RMSEA = 0.02) with factor loadings on
dietary knowledge, epidemiology, medical information, risk factors, and heart attack
symptoms (Bergman, Reeve, Moser, Scholl, & Klein, 2011).
The score on this measure is the cumulative number of statements correctly
identified as true or false. Statements incorrectly identified as true or false and all “I don’t
know” selections were counted as zero. For the purposes of this research the five factors
were not analyzed separately. Scores on this measure provided evidence for differences
in heart disease knowledge after receiving the treatment.
View Nutrition Facts, another dependent measure, asked participants the question,
“In making your menu choices did you view the nutrition information?” They were then
given a drop down option which included the following: never, for 1 meal, for 2 meals,
for all three meals. In other words, participants self-reported the frequency with which
they followed the provided links to nutrition information. This variable produced a