1. Robert Wood Johnson Foundation
Games To Generate Data Challenge
WHealth - A Window into your Future Health
Edward Kim PhD, James Park MD MPH MSHP
1. INTRODUCTION
The Centers for Disease Control and Prevention currently recommends regular physical activity with a well
balanced diet for maintaining good health. However, engaging individuals to participate in healthy lifestyle
habits has been a difficult task. Obesity continues to be a growing epidemic negatively impacting the health of
millions of Americans. Compounded by other chronic, lifestyle-associated diseases such as hypertension, diabetes
mellitus, and coronary heart disease, obesity remains a significant public health threat. The imperative to address
such chronic diseases has never been greater.
Encouraging individuals to participate in healthy lifestyle modifications is complex and requires a multifaceted
approach - encouraging individuals to modify their diet to healthier foods, encouraging individuals to participate
in physical activity regularly, addressing the built environment to increase physical activity or gain access to
fresh fruit and vegetables. To promote this goal, we have developed an interactive storytelling application that
both generates large amounts of user specific data while positively impacting the health of the application user.
Our application will be designed to compose an interactive story that will show users their future selves and how
we predict their life will progress. The story will be tailored specific to each user through both collected data
and existing community data including the County Health Rankings and Roadmap data and Aligning Forces
performance data. We envision our application having the potential to collect significant amounts of user data
including basic data ranging from height, weight, age, and zip code, to more abstract data including, drive to
work status, exercise data, hobbies, children, pets, leisure activities, etc.
1.1 Application Scenario
We believe a good way to give an overview of our proposed application is through a sample scenario. The user
is first introduced to our application when he signs onto Facebook and sees a friend has posted a video of, “A
day in my future 15 years from now.” Upon viewing the video, the user is asked if he would like to see a day
in his own future and is directed to our application either through a web browser or mobile device. Our app
states, “To view your future, we need to know some things about you...”, such as, your gender, weight, height,
age, zip code, working status, hobbies, etc. The user optionally takes a headshot of themselves to create an
avatar, or manually creates one, and can now get a brief glimpse of his avatar in the near future. However, at
this point, the application only gives the option to create a story 1 year in advance, and the avatar’s actions are
very limited. For example, the avatar wakes up, goes to work, comes home from work, watches TV, and goes to
sleep. The user is presented a user interface to put in more information about themselves. The more information
they provide, the more detailed the avatar’s storyline can be, and the application will also unlock story lines
further in the future.
To see story lines further in their life, the user inputs their history of health and disease, and weekly data
related to their exercise habits and eating habits. The 10 year storyline is unlocked as a reward. Given the user
data, this storyline is much more detailed, and predictions for the avatar’s health are made. In this scenario,
our user has indicated that he does not exercise, smokes, and eats unhealthy 3 or 4 days out of the week.
Additionally, he has indicated that he has a history of heart disease and he lives in 02139 zip code. Thus, in

2. Figure 1. Example of using existing external sources of health data to drive the story as well as provide useful information
for the user. This has the additional effect of bringing recent, relevant quality healthcare data to the forefront. Lastly,
we are able to suggest behavioral modifications or institutional changes based upon the user’s current health conditions.
the 10 year storyline, the user has put on some weight, has a high risk of heart attack, and his overall health
score has significantly lowered. He also watches as his avatar goes to work, takes a short work break outside and
is offered a cigarette, but surprisingly declines. There is a window that pops up saying, “why did I decline the
cigarette?”. The user clicks on this and sees that according to the County Health Ranking Data for Middlesex
county, the tobacco use is extremely low, and because of your environment, in 10 years we predict that you will
have quit smoking. Later on in the story, the user visits a friend who is having a routine checkup at Mount
Auburn Hospital. A window pops up saying that you have switch hospitals and you can find out more information
by clicking, “why did I switch hospitals?” The user again clicks on this and is informed that according to the
Aligning Forces for Quality data, Mount Auburn Hospital has above state average quality care in Cardiovascular
Disease, and 71% of patients gave this hospital the highest overall rating. Your current hospital, the Cambridge
Health Alliance - Cambridge Hospital has below state average quality and scored only a 60% for highest overall
rating. Your avatar has performed research for you and decided this is the best hospital option due to the user’s
current heart disease history. See Figure 1 for a possible depiction of the scene.
After viewing his story, the user decides to eat healthier and exercise once or twice a week. The application
prompts him to update his general feelings of health and behavior on a weekly basis. Some information collected
includes, “how many days this week did you feel good/poor?”, “have you lost any weight?”, and “how many
days this week did you exercise?” Upon updating this information, the application notifies the user that their
10 year story has changed! The avatar is now in better shape and frequents the gym after work. The user is
asked if he would like to email this story to a friend or post to Facebook.
1.2 Related Background Research
User engagement, and our application gamification, is based upon the idea of interactive storytelling. Interactive
storytelling is a medium where the narrative and the evolution of the narrative can be influenced by the user.
The medium highlights many of the captivating and compelling elements of games that we would like to leverage
in our application. Several elements include a challenge to improve the health outcome of your future self, the
input of more and more data to unlock future story lines, the design of a character avatar that the user will
identify and empathize with, and ultimately an entertaining interactive story that engages the user and produces
strong emotional results.

3. Our application is designed, not with the present, but with the future of our application user in mind. The
key reason for this is due to the concept of future discounting in health. In regards to an individuals decision to
exercise, behavioral economics helps explain why some individuals may choose to forgo physical activity. First,
individuals overly discount their future health. In doing so, they do not value their future health high enough
to invest in it in the present day.1 In the case of obesity, individuals, in the present day, do not appreciate the
magnitude or time horizon of the potential, future health consequences, which results in under-valuing of their
future health. Second, individuals have present-biased preferences where they seek immediate rewards despite
delayed costs (e.g. - eating potato chips at the risk of weight gain, high cholesterol) and postpone immediate
costs despite delayed rewards (e.g. exercising to achieve weight control, improved cardiovascular health).2, 3
Hence, the present-biased preferences would explain an individuals tendency to forgo exercising with latitude for
dietary indiscretions.To help address future discounting and present-day preferences, we present our application,
WHealth, that helps individuals appreciate their future health through their health avatar. Individuals are able
to see and quantify their future health based on their current health status and lifestyle and monitor how their
future health changes with their everyday health-related activities.
For the rest of our proposal, we would like to focus on the potential for our application to generate new and
exciting health data, our software specifications and implementation, and our ideas for community deployment.
2. DATA GENERATION AND COLLECTION
Our application is in a unique position to collect a significant amount of user demographic, health, and behavior
data. Because of the design of our application, the input of more data directly correlates with increasing the
user’s engagement, experience, and story outcome. Also, the more data the user inputs, the more relevant the
story becomes to the user. Additionally, we believe the more relevant the story, the more empathetic the user
will become to the online avatar. As a bonus outcome, we are hopeful that the user will then be more open and
receptive to suggestions (through their online avatar) that may improve their overall health.
2.1 Demographic Data Collection
When the user first signs up to use our application, we will need to provide a premise, or background, to our
story. We will require the user to input their demographic profile as a one time registration. The signup screen
will minimally collect the following information from our users: name, email, and zip code.
2.2 Avatar Data Collection
Additionally, at the start of the application, we require the user to create their own avatar. For the avatar’s
face, the user can take a snapshot of their own face and we can paste that onto the the body of an avatar, or
alternatively, the avatar’s facial characteristics can be selected from a menu of different face possibilities. The
body of our avatar will be procedurally generated after the user inputs their height, weight, age, gender, and
ethnicity. Since we are making a story around the user’s future, we also have the leeway of asking more personal
questions about the avatar including, are you employed, do you drive to work, do you have children, do you have
pets, what are your hobbies, etc. We note that in many other applications (like biometric apps, weight loss apps,
diet maintenance apps), these types of questions could not be asked without raising the suspicion of the user.
2.3 Health Data Collection
At this point, the storyline is still very limited and we urge the user to give health related data to fill in
some details. Rewards for providing this information would be a more detailed storyline and possibly further
predictions in the user’s future. Some health related data that we might collect include, history of hypertension,
diabetes mellitus, coronary heart disease, and obesity. We would also collect health behavioral data including
how often do they exercise, how often do they eat out, how healthy is their diet, etc. We emphasize that the
nature of our framework would allow us to ask a very broad range of questions as long as we can support the
question with a storyline element.

4. 2.4 Continuous Behavioral Data Collection
We are very interested in collecting health and behavioral related data continuously over time. We plan to enable
the application to collect continuous data including, “how do you feel today”, “how many days this week did
you feel poorly”, “how many days this week did you exercise”, etc. The categories we believe we would collect
are: exercise data, general feeling of health, weight, eating habits. This will inform the future story lines of the
user as well as give us temporal data related to the user’s overall health and behavior.
We believe there are some very interesting possibilities with continuous data in a county or state. For example,
we envision that we might be able to track the spread of some disease by viewing the response to people’s general
feeling of health, see Figure 2. Or we might be able to monitor the effect of seasonal changes on weight, health,
or exercise.
Figure 2. A possibility with the collection of continuous temporal health data might be the trending of the general health
of a region. This could be used to track the spread of disease or illness, like influenza. Or this could monitor the exercise
increase of a population due to weather conditions or other variables.
2.5 Integration of Existing Data
For our initial prototype, we plan to use two external sources of information. The first is the County Health
Ranking Data and the second is the Aligning Forces performance data. We envision the use of this information
in several aspects of our application.
The first area that we plan on using external data is with the prediction of the avatar’s future health. We
would primarily use the County Health Ranking Data, “Z-scores”, from the county that corresponds to the zip
code provided by the user in our calculation. The Z-scores from this dataset is a standardized measure of a
category (like mortality, health behaviors, clinical care, social and economic factors, and physical environment).
A Z-score of 0 is the mean and the standard deviation is 1. Also a higher Z-score indicates poorer health. These
scores would be combined with user specific data that we collect to compute an overall health score for the avatar
in the future. More details on an example combination of scores can be seen later in software description Section
3.2.
Secondly, we see the use of external data in driving the user’s story. For this aspect of our application, we
primarily see the use of the Aligning Forces performance data. This data provides information regarding the
quality of care of physicians and hospitals, and also patient experience data. We can tailor decisions that the
avatar would take in the story based upon the medical history of the user and/or what the user has indicated is
important to him. For example, if the user has diabetes, and is highly interested in a good patient experience,
we can introduce a scenario in the avatar’s story where he is introduced to a physician or hospital close to him
that is ranked highly in both of these categories.

5. 2.6 Gamification of Data Collection
In our application, we want to be particularly sensitive to recent research in gamification of health related
applications in the use of badges, levels, and point systems. If used improperly, the utilization of these reward
incentives for health may undermine the intrinsic motivation of the user to be healthier. Known in the literature
as the “undermining effect”,4 “motivation crowding-out effect”, or “overjustification effect”, the extrinsic reward
of getting to the next level, or getting the most number of badges (e.g. by increasing their exercise) crowds out
the more important intrinsic motivation of the user to perform some action for the sole benefit of being healthier.
The rationale in defense of these extrinsic rewards is that the user is exercising, so it does not matter that
the motivation behind the behavior is based upon levels and badges. However research shows that once the
extrinsic reward is removed, the intrinsic motivation has been crowded out, and the user is even less likely to
freely exercise than if they had never received any extrinsic rewards in the first place.
Our gamification technique takes this research into account. To unlock future storylines, the user is not
required to “perform” in a typical reward system. Rather details are added to stories, and future story lines
are unlocked with the addition of more data. There are no point benefits or level ups related to giving healthy
related information, and no negative points related to informing the application that you did not exercise this
week. Rather, by immediately realizing the user’s future in our interactive story, we are rewarding positive,
healthy behavior in the avatar’s health, well being, and story. We believe we are keeping the focus and goal on
maintaining one’s health and personal growth rather than focusing on extrinsic rewards.
On a related note, we plan to leverage additional research in the area of social norms to influence behavior
changes. WHealth will use normative influence, or social norms, to help motivate individuals to engage in
physical activity. Because individuals tend to overestimate the pervasiveness of undesirable health behaviors,
these individuals tend to continue such behaviors under the perception that they are acting within social norms.
By making individuals aware of the true social norm, individuals are compelled to conform to the social norm
and thus change their behavior. Harnessing this power of normative influence has been successful at motivating
change for several undesirable behaviors, including alcohol consumption, drug use, as well as other non-health
related behaviors.5–7 WHealth creates an anonymous, online community of participants that define the social
norm of a particular health behavior, such as physical activity. Participants can see how their health and daily
activities compare to that of their online peers and hopefully modify their behavior accordingly.
For example, a user would be able to see the average number of minutes a day that people exercise in their
zip code. If the user exercises 15 minutes a day, but he sees that the average user in his zip code exercises 30
minutes a day, then the user would hopefully be more motivated to increase his daily physical activity to stay
within the social norm.
3. SOFTWARE DESCRIPTION
We will be developing our application in a variety of different programming languages under the umbrella
category of HTML 5. HTML 5 is the fifth revision of the HTML standard and it contains some new features
that make this an ideal environment for our application. The integration of Canvas and SVG open up new
application programming interfaces (APIs) that easily handle graphical and animation content. Additionally,
the development of our game in HTML 5 enables immediate compatibility across mobile devices (tablets, iOS,
android) and also provides an compatible interface for standalone browsers.
3.1 Programming Languages
Specifically, for our graphics and rendering, we will be using SVG. The SVG standard is an XML based file format
that describes two dimensional vector graphic shapes, images and text. Vector images and shapes are defined
by mathematical instructions rather than traditional image formats based on individual pixels. Thus, vector
objects are scalable and resolution independent (on a side note, SVG does allow the inclusion of bitmap images).
Different vector objects include lines, circles, shapes, and polygons that are alterable by spatial transformations,
alpha masks, and other effects defined by the W3C standard. These shapes can be customized in color, fill,

6. texture, and stroke style. The XML Document Object Model (DOM) enables the use of existing technology for
efficient searching and modification of the SVG objects. As an example, the background cityscape in Figure 1
was created using SVG elements.
Because of SVG’s compatibility with other web standards, interactivity and animation effects can be leveraged
by scripting languages such as Javascript. Interactivity can be added by manipulating the XML DOM. Animation
would be performed by timed Javascript events on the elements of the SVG document. Also, button or mouse
clicks can be captured by events attached to different elements.
There are several ways that user provided input can be obtained. SVG allows for the inclusion of HTML
forms, which would be handled by a web server running PHP. For the prototype, we would likely limit the possible
inputs to our questions by the use of select HTML tags with defined options. For example, a question of, “how
do you get to work?”, would have the options, “bus”, “car”, “train”, and “walk”. A second way that we might
capture user input is through XMLHttpRequests. Due to the asynchronous nature of most XMLHttpRequests,
this may be a better option for our application. Again, we would handle the data using PHP on the server side.
The data would be stored on a Linux web server running PHP and MySQL. Data tables would be built
to store user data, avatar data, health data, and continuous data. We would also parse relevant information
from the external sources of data (AF4Q, County Health Rankings) and store this information in our MySQL
database.
3.2 Procedural Modeling and Animation of Characters and Health Statistics
The creation or modeling of our characters would be controlled procedurally through the parameters of the
avatar model. Thus, by changing the height, weight, or age parameters, we would obtain a different visualization
of the body of the avatar. This is possible using SVG since the shapes are built on vector graphics and can
be easily manipulated by scaling and other spatial transformations. An example visualization of our procedural
modeling would look something like Figure 3. Procedural animation of our characters and other objects would be
similarly programmed by manipulating the transformation of polygons and objects in the SVG DOM. Character
animation of body parts will be programmed using joint inverse kinematics(IK). We have found open source
Javascript libraries for IK solvers that will simplify our initial animation process. We note that once an animation
is created for one character (for example a hand wave) we can reuse this animation on any other avatar by applying
the animation script to the SVG element(s).
We would also compute the risks and health scores of an avatar based upon a combination of features e.g.
mortality, health behaviors, diet, exercise, etc. These features would be obtained from user input as well as
external data. The final score or risk could be computed as a weighted normalized sum of all the features
present.
n
α ∗datapoint
The combination could be represented by the following equation, score = i=1 i Z i
, where αi is a
weight parameter given to a specific datapointi , and Z is a normalization constant. The datapoints could be
any combination of County Health Ranking data, AF4Q data, or user inputted data. We would precompute the
weights for each datapoint before prototyping the system live.
Additionally, we would look to include in our model, other well known health prediction models, such as
the 10 year cardiovascular risk calculation based upon the Framingham Risk Score. This model is a well known
tool that uses age, gender, cholesterol, smoking status, and blood pressure to estimate the likelihood of having
a heart attack in the next ten years.
3.3 Device Compatibility
The underlying technology of our application lends itself nicely to cross compatibility across operating systems,
browsers, platforms, and mobile devices. Most likely, there will be changes or tweaks necessary to ensure
compatibility with different devices, but luckily the technology that we are using (vector graphics) is highly
dynamic. This means we can scale or crop programmatically for the appropriate device. The technology is also
resolution independent, so images or multimedia created for a small smart phone screen will not look pixelated
on larger tablets or computer screens.

7. Figure 3. An example where we could use model parameters and a combination of features to both visualize the avatars
and also compute the avatar statistics.
Time permitting, we may include a feature that would render out the animation, or a particular part of
an animation, to a movie file. We envision that if we create an interesting and creative future for a user, he
may want to share this with a family member or friend. A movie file could be a more ubiquitous medium for
deployment and sharing, such as posting a movie on Facebook or YouTube. We will describe this aspect of our
application further in the community deployment section.
4. COMMUNITY DEPLOYMENT
In this section, we will describe our plans for immediate, short-term deployment. If given the opportunity, this
will be our tactic for the time period between Phase I and Phase II. Furthermore, we have outlined some future
ideas in features and deployment beyond the Phase II time period. Lastly, we present our qualifications as
justification towards software development and deployment.
4.1 Short-term Deployment
WHealth will initially be deployed through advertisements at local physician offices. While this application is
open to any individual, it will likely gain attraction among those individuals interested in understanding their
health over time. Hence, patients waiting at physician offices are ideal individuals to download the app and
primed to engage in it. Once deployed, WHealth will allow users to share their unique, interactive stories to
friends via email or social media, thus expanding its exposure to more and more individuals.
4.2 Future Deployment
Once a significant user base has been established, WHealth will be structured to create personalized communities
of friends. In doing so, users can invite more friends to use WHealth and virtually interact with them through
their avatar. Users will then have intertwining storylines of their future health with other avatars within the
personalized community. The personalized communities provide an excellent platform to foster accountability
and peer mentoring, which are proven methods to improve health, amongst friends who are working towards
healthier lifestyles.8
4.3 Software Production and Deployment - Qualifications of the Applicants
Edward Kim holds a Ph.D. from Lehigh University in Computer Science. He has a MSE in Computer Graphics
and Game Technology and a BSE in Computer Science from the University of Pennsylvania. He is currently an
Assistant Professor at The College of New Jersey (TCNJ), jointly appointed in the Department of Computer

8. Science and in the Interactive Multimedia Department. His research is in the area of computer vision, game
development, and computer graphics and he has several publications in peer-reviewed journals and conferences on
the topics of multimedia, vector graphics, and web technology. He is also currently teaching a game development
class at TCNJ.
James D. Park, MD, MPH, MSHP is a general internist and health services researcher interested in promoting
healthy lifestyles through principles of behavioral economics. After completing a combined MD and MPH degree
at UMDNJ - Robert Johnson Medical School, he then went to the University of Michigan for his internal medicine
residency training. With continued interest in public health and health promotion, James entered a post-doctoral
research fellowship at the Perelman School of Medicine at the University of Pennsylvania, where he focused his
research on patient and provider-directed financial incentives to improve health. Currently, he is an Instructor in
the Division of General Internal Medicine at UMDNJ - Robert Wood Johnson Medical School where he continues
his research promoting healthy choices through behavioral economics.
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