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A   Nutrient-Profiling Inspired A   Nutrient-Profiling Inspired

A Nutrient-Profiling Inspired - PowerPoint Presentation

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A Nutrient-Profiling Inspired - PPT Presentation

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

dietary nutrition food easy nutrition dietary easy food nutrient domain design nutritional expert diabetics evaluation nutrients diet ada research

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Slide1

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

Slide2

AgendaResearch Motivation Problem StatementBackground and Literature ReviewResearch Questions and GoalsResearch ApproachThe Evaluation Plan2

Slide3

Research 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

Slide4

Problem 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

Slide5

My Contribution5

Nutrition Education

Dietary Management

mHealth

Slide6

Nutrition Education and Dietary Management What has been done?6

Slide7

Dietary 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

Slide8

Nutrient Profiling “ the science of ranking food based on their nutritional composition in order to promote healthy behavior and prevent disease ”.

8

Slide9

ANDI: Aggregate Nutrient Density Index By Dr.Fuhrman, 20049

Health= Nutrients / Calories

Slide10

NuVal (Nutritional Value) System By Dr. David Katz, 2008 A single holistic measure (1-100)10

Slide11

Traffic 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

Slide12

The 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

Slide13

Triandis Theory of Interpersonal Behavior (Triandis, 1979)13

Slide14

Research 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

Slide15

Research 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

Slide16

Research ApproachResearch Method and Analysis16

Slide17

Design 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

Slide18

Adopted DSR Design Cycle (Meth, 2015)18

Slide19

19

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

Slide20

The 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

Slide21

21DR2: Recipes must be tailored to users’ food preferences (ADA) [15]

DR1:

menu plans

is used as a dietary management approach (literature Review) [19]

Slide22

22

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)

Slide23

23

DR5:

The recommended average of nutrients should be in line with ADA nutrition therapy recommendations (domain expert)

Slide24

The Intelligent Nutrition Engine

C

w

= Carbs

P

w

= Protein

F

w

= Fat

DF

w

= Dietary Fibers

S

w

= Sodium

RC

w

= Recommend daily calories

Slide25

The 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

Slide26

The Evaluation PlanTo Assess the utility and quality of Easy Nutrition26

Slide27

The Three-stage Evaluation Plan27

Effect in managing Diabetes

Usability and Users Interaction Satisfaction

Efficiency to improve Dietary Habits

Slide28

28

Slide29

First-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

29

Slide30

Pilot Study: Participants 30The percentage of users with diet-related health condition

Slide31

75.25

31

System Usability Score (SUS)

Results: Usability

a SUS score above 68 is considered above average

Slide32

Results: Participants’ Satisfaction32Figure. Participants' impression and overall reactions using Easy Nutrition on a scale (1-9).

Slide33

Results: Participants’ Satisfaction33Figure. Participants Reaction to Easy Nutrition Screens on a scale (1-9).

Slide34

FeedbackLabel the traffic-light scale34

Excellent nutritionPoor nutrition

Slide35

Expected 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

35

Slide36

36

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

Slide37

37THANKS!

Questions and Comments?!