Content uploaded by Ernie Medina
Author content
All content in this area was uploaded by Ernie Medina on Sep 25, 2019
Content may be subject to copyright.
Applying the Concept of Nutrient-Profiling to Promote Healthy Eating and
Raise Individuals’ Awareness of the Nutritional Quality of their Food
Mayda A. Alrige, MSc1, Samir Chatterjee, PhD1, Ernie Medina, DrPH2, Jeje Nuval, R.D.2,
1Claremont Graduate University, Claremont, California, USA;
2Loma Linda University, Loma Linda, California, USA
Abstract
Diet-related chronic diseases are on the rise. Current dietary management approaches are mostly calorie-counter
tools that draw our attention away from the nutritional quality of our food choices. To improve consumers’ dietary
behavior, we need a simple technique to educate them about nutrition and increase their understanding of the
nutritional quality of their food. This study aims to design a dietary tool to promote a nutrient-dense diet. To this
end, we applied the concept of Nutrient Profiling to classify food recipes based on their nutritional quality, by
developing the Intelligent Nutrition Engine. This engine undergirds our mobile-based application, Easy Nutrition,
which was designed to enable users to find food recipes and understand their nutritional quality. To evaluate the
usability and understandability of our approach, we piloted the prototype of Easy Nutrition on 24 consumers. The
results indicate that our approach provides a sustainable avenue to help consumers manage their diets.
Introduction
Diet-related chronic health conditions, such as diabetes, obesity, hypertension, and cardiovascular disease, present a
major public health concern. As of 2014, more than one-third (36.5%) of U.S. adults are obese, according to the
National Center for Health Statistics (2011-2014)1. Two-thirds are overwheight2. These trends are spreading world
wide. According to a global WHO report from 2016, an estimated 422 million adults world wide were living with
diabetes, compared to 108 million in 1980. The global prevalence of diabetes has nearly doubled since 1980, as it
had raised from 4.7% to 8.5%3. Furthermore, the American Diabetes Association reports that 29.1 million
Americans (9.3% of the population) had been diagnosed with diabetes in 20124. Another WHO report from 2004 on
food and health in Europe states that diseases with major nutritional determinates account for 41% of disability-
adjusted life years among all diagnosed diseases in Europe5. The root of the problem of all these conditions is a poor
diet. Tackling this problem is by no mean easy and requires complex lifestyle changes. However, a healthy diet is a
key component of a healthy lifestyle that can prevent the onset of these chronic diseases or mitigate their severity6 .
Technology-tailored interventions to facilitate dietary management have been introduced in different formats,
mainly through mobile-based applications (mHealth). Although evidence for the efficacy of mHealth is generally
sparse7, research has shown that the use of the hand-held devices can improve the dietary intake of healthy food
groups, such as whole grains and vegetables8. Also, the use of mHealth has the potential to reduce heath costs, and
to improve well-being in various ways, for instance, through the promotion of a healthy lifestyle by constant
monitoring and self-management9,10. In their systematic review, Kroeze et al. conclude that there is sufficient
evidence in favor of computer-tailored interventions for improving dietary behavior11. These findings have also been
supported by Long and colleagues in their review on the technology employed for dietary assessment12. This
suggests that mHealth may have a significant effect on individuals sustaining a healthy lifestyle.
Most of the current technology-tailored dietary tools deploy diet recalls and food records as the main dietary
management approaches13,15. These approaches focus on the quantity of the food. That is, users are prompted to
quantify the portion of the food that they consumed. One of the critical issues in this context is time. It is very
tedious and time-consuming to track food intake. Another problem in addition to the time it takes to track food
intake is the issue of recall6. Users have to sit down at least once a day, remember what they had eaten during the
day in correct portions, and type in their food intake. Despite their effectiveness, these tools require performing
some tasks that are unpractical to apply on a daily basis. Research suggests that too much detailed information on
mobile-phone may result in user being discouraged from using these tools16. The focus on food quantity with
relation to the issues of time and recall has undermined the effectiveness of technology-tailored dietary tools.
The notion of nutrition profiling can alleviate these issues because it presents how healthy a food item is in a single
holistic measure that is easy to follow and intuitive to understand. Nutrient profiling is defined as the science of
393
ranking foods according to their nutritional composition for reasons related to preventing disease and promoting
health (WHO). By focusing on the food quality instead of the quantity, nutrient -profiling based systems aim to
educate users about the overall nutritional quality that constitute a good or bad food choice. In light of this notion,
systems such as Nuval17 (Nutritional Value) and ANDI18 (Aggregate Nutrition Density Index) have been developed
to rank foods according to their nutritional quality. These systems have been tested, and widely accepted in the
market landscape to rank food products based on their nutritional content.
This study aims to apply the concept of nutrient profiling in a mobile-based dietary application called Easy
Nutrition. Easy Nutrition aims to increase consumers’ understanding about nutrition and raise their awareness about
the nutritional value of the food recipes they choose. To rank different food recipes, we developed the Intelligent
Nutrition Engine. This algorithm takes into account the three major macronutrients, two micronutrients and the
number of calories the consumer needs daily. To present the nutritional quality of a particular food recipe in a
simple, easy to understand manner, we adopted the notion of the traffic-light diet. The nutritional information is not
given as a strict tri-color output. Rather, it is by analogy a color-coding food rating scale of eight values that rates
the food recipe based on its nutrition from red (for less nutritious choices) to green (for nutritious choices).
While the goal of this study is to increase individuals’ understanding about nutrition and ultimately improve their
dietary behavior by avoiding poor nutrition diets, the goal of this paper is to presents consumers’ feedback regarding
our approach in presenting nutritional information. The rest of the paper is organized as follows. The literature will
be reviewed from two perspectives. First we will investigate the state of dietary management and nutrition education
to prevent the onset of chronic diseases. Second we will investigate the role of technology to facilitate this process.
The method section will elaborate on the design science research, the approach we followed to develop the
Intelligent Nutrition Engine and Easy Nutrition. This section will additionally outline the three-stage evaluation plan
for my research, of which this paper represents the first stage. In the fourth section, the results of this stage (pilot
study) will be illustrated and discussed in terms of the usability of Easy Nutrition and the understandability of its
interfaces. Finally, we will conclude by arguing that our study contributes equally to both science and society. Our
approach provides a new methodology (the Intelligent Nutrition Engine) to rank food and a dietary tool (Easy
Nutrition) to help consumers better manage their diet by focusing the nutritional quality of their food choices.
Literature Review
Dietary Management
Managing diets is essential when it comes to treating diet-related chronic diseases. Furthermore, for healthy
individuals, it is a preventative measure to maintain a healthy weight and overall wellbeing. Essentially, there are
four different approaches to manage diets6. These approaches encompass: dietary recall, food records, self-
management and menu planning. In the first approach, the patient is asked over the phone about the amount of food,
drinks consumed typically in a 24-hour period along with the method of preparation and the brand of the food items.
Using food records, patients will have the chance do the same by writing this information down. In the self-
management approach, personal digital assistant software is used. The patient will set some pre-defined goals on
what quantities of food to consume. In this case, patients are in charge of how to keep their diets under control no
matter what kind of food is consumed. The last approach is menu planning where patients get to have their meals
planned based on previously identified food preferences. As the first three approaches are prone to the tediousness
of counting and recording food intake multiple times a day, the focus in this study is to utilize the last approach,
which is menu planning where users get to plan their meals after learning about their nutritional value.
Many studies have been conducted to develop and evaluate computerized dietary tools that are based on diet recall
and food record15,16. These two methods are designed as diet trackers and calories counter to assist individuals better
manage their diet. However, the notion of calorie counting is very tedious and entails many issues from the patient’s
perspective. First, the underlying method can suffer from recall issues, where users have to log their daily food
intake. Examples of these food and calorie tracker apps include “MyFitnessPal”, “Lose It!”, or “Calorie Count”
among many others. These apps allow users to log their food on a daily basis, define personal weight loss goals and
review and analyze the gathered data against these goals. In this context, the second issue stems from a realistic
limitation, which is the food database where users get to pick from. The quality of the food tracking/dairy app is
tightly dependent on this underlying database. However, even the largest current food databases are still far from
being complete and often contain only country-specific products21. The notion of nutrition education or nutrition
394
profiling help mitigate these issues as it aims to educate patients about the underlying nutrients that constitute a good
or a bad food choice.
Nutrition Education
To better manage diets and sustain a healthy lifestyle, one has to be aware and knowledgeable about the nutritional
content in the food consumed. Being educated and aware of the macro nutrition and micro nutrition contributes to
one’s overall health by minimizing poor nutrition diet. This leads to a healthy behavior that holds the promise of
preventing the onset of chronic diseases and sustaining a healthy lifestyle. Diet education is effective to both
understand diet requirements and control body weight and blood sugar levels22. This is especially true when such
educational material is delivered in an easy to follow, and convenient to understand manner. Indeed, knowledge-
based nutrition education alone does not change dietary behavior. Behavioral nutrition education does a better job to
change dietary behavior. The effectiveness of the behavioral nutrition approaches are investigated by Bader and her
colleagues in23. These approaches range from gaining familiarity with general nutrition principles, acquiring general
planning frameworks, and finally planning tools to use meal replacements or prepared meals.
Bader and colleagues in their attempt to evaluate the effectiveness of nutritional education conducted a pilot study
where they investigated one of the most preferred dietary management approaches: menu planning. The study
targets type-2 diabetics. Authors conducted a single-arm clinical trial to evaluate one of the commercial internet-
based menu planning tools. They examined pre- to post intervention changes in body weight, blood pressure, and
glycaemia among overweight patients with T2DM (n=33). Nutritional recommendations were operationalized into
weekly Internet-delivered menu plans. The findings indicate that there was 5% weight reeducation in 18% of the
participants. The study highlights the effectiveness of the behavioral nutrition approaches that are less structured.
These approaches range from gaining familiarity with general nutrition principles, acquiring general planning
frameworks (eg, carbohydrate exchanges, “points”) and finally planning tools to use meal replacements or prepared
meals23.
Nutrient Profiling
Nutrition profiling is one of these behavioral nutrition approaches that do not dictate people what to eat or what not
to eat. Rather, it aims at educating users about the overall nutritional quality of the food and leaving the choice of the
meal to them. Nutritional profiling aims to rank food based on their nutritional content, as it is driven by the focus
on food quality instead of food quantity. Individuals who follow high-scored food would most likely improve their
dietary behavior. Simplicity is a key when it comes to presenting nutritional information. It has been suggested that
nutritional information on mobile phones should be easy to read and understand. Approximate information is better
than accurate facts that are harder to access but more precise. It was further suggested that the food information on
phones should not be too fine-grained, as too much detailed information may result in user discouragement and little
user friendliness16.This stresses the idea that carb and calories intake counting used for dietary management has
become less preferred in favor of a more generalized nutritional information about the quality of the food
compositions. Nutrient profiling is the science of ranking foods according to their nutritional composition for
reasons related to preventing disease and promoting health (WHO).
This idea has led to the creation of many nutritional rating systems. Examples of these systems include Nuval17, and
ANDI18. In addition, Leonard H. Epstein and his colleagues have developed the Traffic-Light Diet in the 1970’s. In
this dietary approach, Epstein used a tri-color palette to create an easy-to-follow diet for overweight children. The
notion of traffic-light diet had for two decades inspired new research due to its groundbreaking nature. The traffic-
light diet is a structured eating plan that divides food by the color of the traffic signals. Green is for low-calorie food
(go) that can be eaten at any time, orange (caution) is for moderate–calorie food that can be eaten occasionally, and
red (stop)is for high-calorie food that should be eaten rarely. Since it was launched, the Traffic Light Diet has been
used widely by pediatricians to encourage healthy eating habits among their patients24. Many studies have been
conducted utilizing the “Traffic-light” dietary approach and showed promising results. The Traffic Light Diet is
used as a part of a comprehensive treatment, and the results show a significant decrease in obesity in preadolescents
children25–28. Significant changes in eating patterns have been reported when comprehensive obesity treatment has
been combined with the Traffic-Light Diet29,30. Reductions in “red foods” have been observed after treatment with
significant associations between changes in intake of “red food” and weight loss29 or decrease in percent of
395
overweight30. In our study, we adopted the dietary approach of Traffic Light Diet to present the nutritional
information.
The Role of Technology
mHealth (short for mobile health) is a term for using mobile devices for health services and information. These
devices can be mobile phones, patient monitoring devices, tablets, personal digital assistants, or other wireless
devices31. Although evidence for the efficacy of mHealth is generally sparse7, research has shown that the use of the
hand-held devices can improve the dietary intake of healthy food groups, such as whole grains and vegetables8.
Also, the use of mHealth has the potential to reduce heath costs, and to improve well-being in various ways, for
instance, through the promotion of a healthy lifestyle by constant monitoring and self-management9,10. In their
systematic review, Kroeze et al. conclude that there is sufficient evidence in favor of computer-tailored interventions
for improving dietary behavior11. These findings have also been supported by Long and colleagues in their review
on technology employed for dietary assessment12. This suggests that mHealth may have a significant effect on
individuals sustaining a healthy lifestyle.
Method
Research Approach
This study follows the design science research approach, DSR, suggested by Hevner and Chatterjee32. Alan and
colleagues also present design science as a legitimate research paradigm to be employed in Information System
research projects, where the goal is to solve practical problems33. DSR aims to design IT-based artifacts to gain a
better understanding of the problem in an iterative process. It involves the two main activities of building and
evaluating. In this paper, we have developed two main artifacts. First is the Intelligent Nutrition Engine, the
algorithm that is used to rank different food recipes based on their nutritional compositions. Second is Easy
Nutrition, a mobile-based application that was designed to present how the algorithm performs to classify different
food recipes. To ensure the utility and quality of these artifacts, we evaluated the prototype of Easy Nutrition for its
usability and the understandability of its interfaces.
The Intelligent Nutrition Engine
We developed the Intelligent Nutrition Engine. This algorithm encompasses five different nutrients: the three major
macronutrients that derive calories, which are fat, protein and carb and two micronutrients, which are dietary fibers
and sodium. The algorithm checks the calories, the percentage of macronutrients, and also the amount of
micronutrients in a selected recipe. These nutrients determine a healthful, or unhealthful choice. For people with
diabetes, we consult the nutrition therapy recommendations by the American Diabetes Association34 to find the
important nutrients that have to be considered, along with the appropriate limit for each nutrient. Some of these
criteria have slight different specification for comorbid type-2 diabetes and hypertension. For example, the sodium
recommendation for the general population is less than 2,300 mg/day. However for individuals with both diabetes
and hypertension, further reduction in sodium intake should be individualized (no more than 1500 mg/day). In
addition, for comorbid T2D and hypertension, no more than 7% of fat should come from saturated fat. These criteria
have been reviewed and validated by a registered dietician in Loma Linda university medical center. The list of
these nutrients is outlined in Table 1.
Table 1. The recommended amounts/percentages for the considered nutrients in the Intelligent Nutrition Engine.!
Macronutrients
Carbs
45 and 65 %
Fats
25 and 35 %
For those with hypertension:
No more 7% of this percentage should come from saturated fat
Protein
15 and 20 %
Micronutrients
Dietary fibers
20-30 grams
Sodium
no more than 2300 mg
no more than 1500 mg daily for those with hypertension
396
Harris Benedict Equation
All these nutrition amounts/percentages are based off one’s daily calories intake. Because the recommended caloric
intake differs based on age, gender, height and weight, we apply the Harris Benedict Equation to determine that
basal metabolic rate (BMR) based on these factors (See Equation 1)
The final formula encompassing all these nutrients as well as the calories will produce one single holistic number
that represent the overall nutritional quality of a particular recipe (Figure 1). For the sake of simplicity, we will
present this number in a traffic-light scale that ranges from red to green through some intermediate colors. This is
illustrated in Figure 1.
Figure 1. The nutritional score presented behind a traffic-light scale.
The cursor would start right in the middle of the traffic-light scale as an initial score for any given food recipe, as
can be seen in Figure 1. The cursor would move to the right as a certain nutrient is within the recommended
percentage/amount. On the other hand, the cursor would move to the left if a certain nutrient exceeds the maximum
limit or fail to meet the minimum limit of the recommended range. This algorithm will be applied to Spoonacular,
the largest online food API in order to rank different food recipes accordingly*
Design of Easy Nutrition
We developed an initial prototype of Easy Nutrition. Using Easy Nutrition, end-users will have the option to find
online recipes that are tailored to their favorite cuisine and learn about their nutritional quality though our Intelligent
Nutrition Engine. This algorithm will present the nutritional quality of the recipe in a traffic light scale as can be
seen in Figure 2. This particular example shows that “Mahi Mahi Tacos” recipe is moderately unhealthy choice as
the cursor is more toward the red indicator.
* Details of the algorithm and how it works can be requested from the first author (mayda.alrige@cgu.edu).
BMR for Men = 66.47 + ( 13.75 x weight in kg ) + ( 5.003 x height in cm ) - ( 6.755 x age in years )
BMR for Women = 655.1 + ( 9.563 x weight in kg ) + ( 1.850 x height in cm ) - ( 4.676 x age in years ).
Equation 1 Harris Benedict Equation
397
Figure 2. The nutritional quality in a traffic-light scale. Figure 3.!The detailed nutrition in the recipe.
If the user is interested to know more about the nutrients that lower the overall nutritional quality, he/she can click
on the “nutrition” tap to find out which nutrient is beyond the recommended range, as can be seen in Figure3. Other
pertinent information for each recipe is presented as well. These include the ingredients, instructions, and some
healthy tips on how to maximize the nutritional value of the selected recipe.
Evaluation
Evaluation is a significant part of any design science research, to ensure the artifact(s) utility, efficacy and quality.
To this end, we evaluated Easy Nutrition from a socio-technical perspective in a three-stage plan. The present study
conducted the first stage of the evaluation plan, which is a pilot study to test the usability and understandability of
our approach represented in Easy Nutrition. Put differently, in the first stage, the goal is to test whether or not
presenting the nutritional quality in a traffic-light scale appeals to consumers and motivates them to select healthier,
more nutrient-dense recipes. Since this stage is a pilot study, we intended to obtain participants’ feedback for
improvement. Once we have established that the technology is sufficient to be employed, we will finalize the full
version of Easy Nutrition and evaluate it in the second stage. The second stage aims to test Easy Nutrition as an
enabling tool to help users increase their knowledge and understanding of the nutritional quality of their food and
hence improve their dietary behavior. Based on participants’ answers to the questions related to their dietary
behavior questions, we will generate a healthy behavior score. This score will be used as a baseline for post
comparison. An increase in this score suggests improving in the dietary behavior in a pre-post intervention study. In
the third stage, we will test Easy Nutrition effect in managing diabetes. In this paper, we will present the results of
the first stage.
Participants
The first stage targets adults both with and without diet-related chronic conditions to evaluate the usability and
understandability of our approach. The inclusion criteria are basic familiarity with smartphones so they can navigate
through the app. This study is conducted in compliance with the ethical principles of Institutional Board or Review
(IRB), at Claremont Graduate University.
Intervention: Procedures and Measures
Users were asked to perform basic tasks on the prototype of Easy Nutrition so they grasp an idea of what it is like to
present the nutritional information in a traffic-light scale. After navigating through the app, participants were asked
to answer a set of questions about Easy Nutrition usability. The System Usability Scale is utilized for this purpose.
398
SUS is a reliable and valid instrument used for usability assessment. It is a 10-item questionnaire that has five
response options (strongly agree to strongly disagree)†. Prior research suggests that a SUS score above 68 is
considered above average and that the website or mobile-app under investigation is usable. On the other hand,
anything below 68 has to go under a lot of improvement to increase its usability35. To obtain the SUS score, we
followed the instructions of interpreting a participant’s score for each question that ranges from 1 (for strongly
disagree) to 5 (for strongly agree). We added all the converted scores for the 10 questions for all the participants.
Then, we divided this number by 24 (number of participants) and finally multiplied it by 2.5 to convert the original
scores to 100-point scale.
In addition, in order to assess user satisfaction with the way we are presenting the nutritional quality of a recipe, we
adopted the QUIS instruments. QUIS is the Questionnaire for User Interaction Satisfaction. QUIS aims to o gauge
users' subjective satisfaction with specific aspects of the human-computer interface, such as screen visibility,
terminology, system feedback and learning factors. Each area measures the users' overall satisfaction with that facet
of the interface on a 9-point scale. The questionnaire is designed to be configured according to the needs of each
interface analysis by including only the sections that are of interest to the researcher.
Our goal is to capture users’ reaction mainly to the way the nutritional information is presented. We utilized the
latest version of QUIS, in particular version 7.0 (the short form). Specifically, we used three measures out of nine:
screen, terminology and users’ overall reaction to the application. As this is a pilot study, we concluded our survey
with four open-ended questions to obtain users feedback regarding Easy Nutrition and the way we present
nutritional information in a traffic-light scale.
Results
We utilized the Email channel to invite individuals to participate. Out of 50 recipients, about 6 participants have
partially completed the survey, and 24 have fully completed the survey. 5 out of 24 participants have been diagnosed
with diet-related condition, 2 are prediabetics, 2 are diabetics, and one had previously been diagnosed with Cancer
(Figure 4).
Usability
After navigating through the prototype of Easy Nutrition, participants were directed to an online survey, which
asked them about the usability of Easy Nutrition. As mentioned above, we utilized the System Usability Scale
(SUS). The usability score of Easy Nutrition came to 71.13. This is a good indicator that Easy Nutrition fits the
context of dietary management and is appropriate to use in this context
† For a full list of the questions, please contact the first author at (mayda.alrige@cgu.edu).
Figure 4 The number of participants with diet-related health condition
399
Users’ Satisfaction
To capture participants’ subjective satisfaction, we asked them questions about their experience interacting with
Easy Nutrition. For this purpose, we utilized the QUIS instrument, which asks questions about the app screens,
terminologies and users’ overall reaction to the interfaces. As opposed to SUS, QUIS is a diagnostic tool, which
means it provides a basis for improvement. This is because it asks very detailed questions about all the elements that
make up the app interface, such as messages, characters, highlights, screen layouts, and sequence of screens.
Participants in this study were totally satisfied with all interfaces relevant aspects in Easy Nutrition including the
way we present the nutritional quality of the recipe in a traffic-light scale. Figure 5 and Figure 6 illustrate users’
satisfaction on 9-point scale.
One of the useful feedback comments we have obtained is to use some labels (e.g. “nutrient-dense” vs. “poor
nutrition”) in the traffic-light scale that is used to represent the nutritional quality of the food recipe. The
participant’s comment states that, “in order for Easy Nutrition to reach its full potential, I recommend adding some
brief labels in conjunction with the scale”. We will consider such feedback when developing the full-version of Easy
Nutrition for the second stage of this research. We will address this by labeling the traffic-light scale to range from
poor nutrition (by the red side) to excellent nutrition (by the green side).
Figure 5. Participants' impression and overall reactions using Easy Nutrition on a scale (1-9).
Figure 6. Participants Reaction to Easy Nutrition Screens.
Discussion and Conclusion
The current study applies the concept of nutrient profiling on the domain of dietary management to help consumers
gain a better understanding of the nutrition in their food and maximize the nutritional value of their choices. To this
end, we have developed an algorithm, the Intelligent Nutrition Engine, to produce a single nutritional indicator of
the food recipe. This algorithm takes into account the amount of calories, carbs, fat, protein, sodium and fibers. The
Intelligent Nutrition Engine works by comparing the amount/percentages of these nutrients in a recipe against the
recommended range. We applied this algorithm to rate some food recipes and present their nutritional quality in a
traffic-light scale. To test the usability and understandability of this approach, we developed and piloted a mobile-
based application called Easy Nutrition. We evaluated Easy Nutrition for its usability and understandability of its
interfaces in a small sample size of 24 participants. The results of this pilot study show that Easy Nutrition is usable,
400
and that all of its interfaces are easy to understand. In addition, the feedback we obtained from these consumers gave
us some basis for improvement.
This research contributes to the body of knowledge in two different levels: to the society and to the science. The
first level of contribution is presented to the society as a nutrition-based dietary tool, called Easy Nutrition. Easy
Nutrition presents the nutritional content of different food recipes in a traffic-light scale. The nutritional information
is better absorbed if they are presented in a practical, easy to follow and easy to understand manner. The second and
more important level of contribution is presented to the science as a new method for nutrition education. This is the
Intelligent Nutrition Engine that is used to rank food recipes based on their nutritional quality. Nutrition education is
a key component when it comes to dietary management. Thus, it is very important to find a simple, intuitive
mechanism to present nutritional information. Our algorithm is based on the notion of behavioral nutrition
approaches stressed by Bader and her colleagues23. The notion behind behavioral nutrition approaches is to increase
the likelihood that individuals will implement the nutritional strategy learned. With Easy Nutrition, we don’t offer
general nutritional information. Rather, the nutritional information is presented through individual recipes in a
practical]’/ manner. This study can add to the evidence base that nutritional behavior strategies may be a modern
adjunct to diabetes (or any other diet-related chronic condition) dietary management. Our study suggests that Easy
Nutrition may have some beneficial effects to improve the dietary behavior of consumers.
In addition, the steps of this algorithm can be viewed as a set of design principles. The algorithm can be tailored to
tackle different health conditions. Both the nutrients and the criteria for each nutrient can be tailored according to the
health condition being treated. For example, cardiovascular diseases have certain nutrition therapy recommendations
that are slightly different than those for diabetics. While diabetes management give priority to carbs consumption,
CVD gives a special attention to fat consumption. The selection of the nutrients and the criteria for each nutrient can
be adjusted to target different diet-related health conditions.
References
1. Cynthia L. Ogden, Ph.D.; Margaret D. Carroll, M.S.P.H.; Cheryl D. Fryar, M.S.P.H.;, and Katherine M.
Flegal, Ph.D. Prevalence of Obesity Among Adults and Youth:United States, 2011–2014 [Internet]. 2015
[cited 2016 Dec 13]. Available from: https://www.cdc.gov/nchs/data/databriefs/db219.pdf
2. CDC, National Center for Health Statistics. FastStats [Internet]. [cited 2017 Feb 23]. Available from:
http://www.cdc.gov/nchs/fastats/obesity-overweight.htm
3. WHO. GLOBAL REPORT ON DIABETES [Internet]. [cited 2017 Feb 18]. Available from:
http://apps.who.int/iris/bitstream/10665/204871/1/9789241565257_eng.pdf
4. Drive ADA 2451 C. Statistics About Diabetes [Internet]. American Diabetes Association. [cited 2016 Dec
12]. Available from: http://www.diabetes.org/diabetes-basics/statistics/
5. WHO. Food and health in Europe: a new basis for action [Internet]. [cited 2016 Dec 12]. Available from:
http://www.euro.who.int/__data/assets/pdf_file/0005/74417/E82161.pdf
6. Arens-Volland AG, Spassova L, Bohn T. Promising approaches of computer-supported dietary assessment
and management-Current research status and available applications. Int J Med Inf. 2015 Dec;84(12):997–
1008.
7. Kumar S, Nilsen WJ, Abernethy A, Atienza A, Patrick K, Pavel M, et al. Mobile health technology
evaluation: the mHealth evidence workshop. Am J Prev Med. 2013;45(2):228–236.
8. Atienza AA, King AC, Oliveira BM, Ahn DK, Gardner CD. Using Hand-Held Computer Technologies to
Improve Dietary Intake. Am J Prev Med. 2008 Jun;34(6):514–8.
9. Or CKL, Tao D. Does the use of consumer health information technology improve outcomes in the patient
self-management of diabetes? A meta-analysis and narrative review of randomized controlled trials. Int J Med
Inf. 2014 May;83(5):320–9.
10. El-Gayar O, Timsina P, Nawar N, Eid W. A systematic review of IT for diabetes self-management: are we
there yet? Int J Med Inf. 2013;82(8):637–652.
11. Kroeze W, Werkman A, Brug J. A systematic review of randomized trials on the effectiveness of computer-
tailored education on physical activity and dietary behaviors. Ann Behav Med. 2006;31(3):205–223.
12. Long JD, Littlefield LA, Estep G, Martin H, Rogers TJ, Boswell C, et al. Evidence review of technology and
dietary assessment. Worldviews Evid Based Nurs. 2010;7(4):191–204.
13. Ma Y, Olendzki BC, Chiriboga D, Rosal M, Sinagra E, Crawford S, et al. PDA-assisted low glycemic index
dietary intervention for type II diabetes: a pilot study. Eur J Clin Nutr. 2006 May 17;60(10):1235–43.
401
14. Theng Y-L, Lee JWY, Patinadan PV, Foo SSB. The Use of Videogames, Gamification, and Virtual
Environments in the Self-Management of Diabetes: A Systematic Review of Evidence. Games Health J. 2015
Oct;4(5):352–61.
15. El-Gayar O, Timsina P, Nawar N, Eid W. Mobile Applications for Diabetes Self-Management: Status and
Potential. J Diabetes Sci Technol. 2013 Jan 1;7(1):247–62.
16. Arsand E, Frøisland DH, Skrøvseth SO, Chomutare T, Tatara N, Hartvigsen G, et al. Mobile health
applications to assist patients with diabetes: lessons learned and design implications. J Diabetes Sci Technol.
2012;6(5):1197–1206.
17. Nuval System [Internet]. Available from: http://www.nuval.com/How
18. ANDI Food Scores: Rating the Nutrient Density of Foods [Internet]. [cited 2016 Oct 31]. Available from:
https://www.drfuhrman.com/learn/library/articles/95/andi-food-scores-rating-the-nutrient-density-of-foods
19. Rossi MCE, Nicolucci A, Pellegrini F, Bruttomesso D, Bartolo PD, Marelli G, et al. Interactive Diary for
Diabetes: A Useful and Easy-to-Use New Telemedicine System to Support the Decision-Making Process in
Type 1 Diabetes. Diabetes Technol Ther. 2009 Jan;11(1):19–24.
20. Kollmann A, Riedl M, Kastner P, Schreier G, Ludvik B. Feasibility of a Mobile Phone–Based Data Service
for Functional Insulin Treatment of Type 1 Diabetes Mellitus Patients. J Med Internet Res. 2007;9(5):e36.
21. United States Department of Agriculture, Agricultural Research Service. USDA Food Composition Databases
[Internet]. [cited 2016 Nov 3]. Available from: https://ndb.nal.usda.gov/ndb/search/list
22. Woo YJ, Lee HS, Kim WY. Individual Diabetes Nutrition Education Can Help Management for Type II
Diabetes. Korean J Nutr. 2006 Oct 1;39(7):641–8.
23. Bader A, Gougeon R, Joseph L, Da Costa D, Dasgupta K. Nutritional Education Through Internet-Delivered
Menu Plans Among Adults With Type 2 Diabetes Mellitus: Pilot Study. JMIR Res Protoc. 2013 Oct
11;2(2):e41.
24. Leonard H. Epstein, PhD; Michelle D. Myers, MA; Hollie A. Raynor, MS, RD; and Brian E. Saelens, MA.
Treatment of Pediatric Obesity [Internet]. [cited 2016 Dec 9]. Available from:
http://www.ohsu.edu/xd/outreach/oregon-rural-health/hospitals/chip/upload/Treatment-of-Pediatric-
Obesity.pdf
25. A Valocki. Nutrient intake of obese children in a family-based behavioral weight control program. 1990
Aug;14(8):667–77.
26. Leonard H. Epstein, Rena R. Wing, Randi Koeske, Deborah Ossip, Steven Beck. A comparison of lifestyle
change and programmed aerobic exercise on weight and fitness changes in obese children. 1982 [cited 2016
Dec 15]; Available from:
http://www.sciencedirect.com.ccl.idm.oclc.org/science/article/pii/S0005789482800221
27. Epstein LH, Wing RR, Steranchak L, Dickson B, Michelson J. Comparison of family-based behavior
modification and nutrition education for childhood obesity. J Pediatr Psychol. 1980;5(1):25–36.
28. Epstein LH, Valoski AM, Vara LS, McCurley J, Wisniewski L, Kalarchian MA, et al. Effects of decreasing
sedentary behavior and increasing activity on weight change in obese children. Health Psychol.
1995;14(2):109.
29. LH Epstein. Child andparent weight loss in family-based behavior modification programs. [cited 2016 Dec
15]; Available from: https://www.google.com/search?q=Child++andparent+weight+loss+in+family-
based+behavior+modification+programs&ie=utf-8&oe=utf-8
30. Gabrielle Duffy, Susan H. Spence. The effectiveness of cognitive self-management as an adjunct to a
behavioural intervention for childhood obesity: a research note - Google Search. J Child Psychol Psychiatry.
1993 Sep;34(6):1043–50.
31. Klonoff DC. The current status of mHealth for diabetes: will it be the next big thing? J Diabetes Sci Technol.
2013;7(3):749–758.
32. Hevner A, Chatterjee S. Design Science Research in Information Systems. In: Design Research in Information
Systems [Internet]. Boston, MA: Springer US; 2010 [cited 2015 Feb 11]. p. 9–22. Available from:
http://link.springer.com/10.1007/978-1-4419-5653-8_2
33. von Alan RH, March ST, Park J, Ram S. Design science in information systems research. MIS Q.
2004;28(1):75–105.
34. Evert AB, Boucher JL, Cypress M, Dunbar SA, Franz MJ, Mayer-Davis EJ, et al. Nutrition Therapy
Recommendations for the Management of Adults With Diabetes. Diabetes Care. 2014 Jan
1;37(Supplement_1):S120–43.
35. Aaron Bangor, PhD, CHFP, James Miller, PhD, Philip Kortum, PhD. Determining What Individual SUS
Scores Mean: Adding an Adjective Rating ScaleJUS. JUS - J Usability Stud. :114–23.
402
