MobileBased Application for Type2 Diabetics to Increase their Knowledge about Nutrition and Improve their Dietary Behavior Mayda Alrige Samir Chatterjee Ernie Media Jeje Nuval Mayda ID: 805661
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A Nutrient-Profiling Inspired Mobile-Based Application for Type-2 Diabetics to Increase their Knowledge about Nutrition and Improve their Dietary BehaviorMayda Alrige, Samir Chatterjee, Ernie Media, Jeje Nuval
Mayda AlrigeAMIA 2017
Slide2AgendaResearch Motivation Problem StatementBackground and Literature ReviewResearch Questions and GoalsResearch ApproachThe Evaluation Plan2
Slide3Research Motivation The Burden of diet-related chronic diseases One-third of U.S. population are obese (Cynthia et al, 2015)Two thirds are overweight (CDC, 2015)9.3% are diabetics (ADA, 2016) The global prevalence
of diabetes has doubled since 1980 4.7% 8.5%
A
healthy diet role to prevent the onset of chronic
diseases
3
Slide4Problem Statement The focus on Food Quantity Calorie Counters/ Portion Size The issue of recall Tedious and unpractical taskCultural Differences By the American Diabetes Association
One size does not fit all! Diets eating plans4
Slide5My Contribution5
Nutrition Education
Dietary Management
mHealth
Slide6Nutrition Education and Dietary Management What has been done?6
Slide7Dietary ManagementThe importance of nutrition education. (Y. J. Woo, H. S. Lee, and W. Y. Kim , 2006)Awareness of the macro nutrients and micro nutrients Knowledge-based nutrition educationBehavioral Nutrition Education (Bader et al, 2013)
7
Slide8Nutrient Profiling “ the science of ranking food based on their nutritional composition in order to promote healthy behavior and prevent disease ”.
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Slide9ANDI: Aggregate Nutrient Density Index By Dr.Fuhrman, 20049
Health= Nutrients / Calories
Slide10NuVal (Nutritional Value) System By Dr. David Katz, 2008 A single holistic measure (1-100)10
Slide11Traffic Light DietBy Dr.Epstein in 1970’sBroadly recognized due to its ground-breaking natureUsing a tri-color palette to create an easy-to-follow diet
11
Slide12The Role of Technology mHealthFood trackers (eg. MyFitnessPaI)Diabetes tele-monitoring (eg. my Sugr)Healthy Lifestyle and wellbeing (
eg. Fooducate) GamificationSLIDES (Johnson et al
, 2014)
Diabetes-Island (Ruggiero
et al
, 2014)
Gustavo in
Gnam’s Planet (Marchetti et al, 2015)
12
Slide13Triandis Theory of Interpersonal Behavior (Triandis, 1979)13
Slide14Research QuestionCan Nutrient Profiling inspired food rating scale help improve the dietary behavior of type-2 diabetics by increasing their understanding of the nutritional value of the food they eat?How to develop an algorithm to underpin the nutrient-profiling food rating scale?How to develop and evaluate a mobile-based application as a customized dietary tool?
14
Slide15Research ObjectivesUnderstand the nutrition therapy recommendations for type-2 diabetics including the appropriate criteria for both macronutrients and micronutrientsDevelop an algorithm for the traffic-light food rating scale
Design and develop Easy NutritionFind recipes that are tailored to their food preferences
Understand the
overall nutritional value
of
these
choices
15
Slide16Research ApproachResearch Method and Analysis16
Slide17Design Science Research 17
People: Healthy subjects – T2D/pre-diabetics – Dietician
LLUMC and DMC
Nutrient profiling systems
Calorie counters and diet trackers apps
Build
Evaluate
Environment
Design
Knowledge Base
Theories: TIB- BCSS
American Dietary Guidelines
ADA Nutrition Therapy Recommendations
Domain Expert tacit knowledge
Relevance Cycle
Prototype
Design Cycle
Final
Design Cycle
Rigor Cycle
Art1: Intelligent Nutrition Engine
Art2: Easy Nutrition
Slide18Adopted DSR Design Cycle (Meth, 2015)18
Slide1919
Design Principle Design RequirementDesign FeatureCustomization
Recipes must be tailored to users’ food preferences (ADA)
Determination of favorite cuisines by users
The use of menu plans as a dietary management approach (literature Review)
Menu planning feature
Simplicity
The recommended average of nutrients should be in line with ADA nutrition therapy recommendations (domain expert)
Traffic-light bar that represent the nutritional value of the intelligent nutrition engineIngredients have to be presented in an easy to understand manner (domain expert)
Picture of the ingredient will be shown next to each ingredient To visualize portion size, an image of a hand will be presented to estimate portion size
The quantity of each nutrient have to be presented separately: fat, carbs, protein, dietary fibers, and sodium. (domain expert)Under the nutrition tab, users are allowed to see details of the five main nutrients comprising the nutritional value of the recipe
Slide20The proposed Solution: Main Design ArtifactA mobile-based application that allows consumers toFind recipes that are tailored to their food preferences Understand the overall nutritional value of their choices
Plan their meals in light of these preferred choices
20
Slide2121DR2: Recipes must be tailored to users’ food preferences (ADA) [15]
DR1:
menu plans
is used as a dietary management approach (literature Review) [19]
Slide2222
DR4
: Ingredients have to be presented in an
easy to understand manner
(domain expert)
DR3:
The quantity of each nutrient has to be presented separately: fat, carbs, protein, dietary fibers, and sodium. (Domain expert)
Slide2323
DR5:
The recommended average of nutrients should be in line with ADA nutrition therapy recommendations (domain expert)
Slide24The Intelligent Nutrition Engine
C
w
= Carbs
P
w
= Protein
F
w
= Fat
DF
w
= Dietary Fibers
S
w
= Sodium
RC
w
= Recommend daily calories
Slide25The Intelligent Nutrition EngineMacronutrients Micronutrients
25
Macronutrients
Carbs (C)
45 and 65 %
Fats (F)
25 and 35 %
For those with hypertension:
No more 7% of this percentage should come from saturated fat.
Protein(P)
15 and 20 %
Micronutrients
Dietary fibers (DF)
20-30 grams
Sodium (S)
no more than 2300 mg
no more than 1500 mg daily for diabetics who have hypertension.
Daily Needed Calories
Age
Gender
Height
Weigh
Harris Benedict Equation
Slide26The Evaluation PlanTo Assess the utility and quality of Easy Nutrition26
Slide27The Three-stage Evaluation Plan27
Effect in managing Diabetes
Usability and Users Interaction Satisfaction
Efficiency to improve Dietary Habits
Slide2828
Slide29First-Stage: Pilot StudyTarget Users: Adults with basic familiarity with smart phones (n=10)Goal: test the usability of Easy Nutrition and understandability of our approachCross-sectional study:Simulation of Easy Nutrition using its prototypeQuestions about the usability Questions about overall satisfaction interacting with Easy Nutrition: app screens, terminologies and interfaces Feedback Questions
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Slide30Pilot Study: Participants 30The percentage of users with diet-related health condition
Slide3175.25
31
System Usability Score (SUS)
Results: Usability
a SUS score above 68 is considered above average
Slide32Results: Participants’ Satisfaction32Figure. Participants' impression and overall reactions using Easy Nutrition on a scale (1-9).
Slide33Results: Participants’ Satisfaction33Figure. Participants Reaction to Easy Nutrition Screens on a scale (1-9).
Slide34FeedbackLabel the traffic-light scale34
Excellent nutritionPoor nutrition
Slide35Expected Contribution To the Society:Easy Nutrition Increase individuals understanding about nutrition and raise their awareness and consciousness of the nutritional value of the food they eatImprove their dietary behavior
To the Science Intelligent Nutrition Engine The steps of the algorithm can be tailored to tackle other diet-related chronic diseases. Results of the quasi-experimental study
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TaskSub-tasksDateLiterature Review and Problem Domain Investigation
meeting with domain experts
March 2016
Understanding both Nutrition Therapy Recommendations for diabetics
and
the science behind different Nutrient-ranking systems
September 2016
Requirements Identificationdeveloping the initial prototypeDec 2016finalizing the prototypeFebruary 2017Pilot StudyApplying for IRBFebruary 2017
Prototype evaluation (first- stage), the results have been submitted for publication (AMIA, 2017)March 2017Proposal defenseMay2017Experimental StudyTechnology developmentMay-July 2017
Second-stage of the evaluation planAug-Sep 2017Third-stage of the evaluation planOctober –December 2017Results and discussion write up January 2018
Dissertation final defense March 2018
Slide3737THANKS!
Questions and Comments?!