jhea espares ︎︎︎






Learn Diabetes

UCSD Design Lab
Diabetes Design Initiative


winter
quarter project

January 2021 – 10 weeks

roles
UX Designer
UX Researcher
Lead Interviewer







Re-imagining Accessibility for Diabetes Learning



With the guidance of senior industry mentors at The UC San Diego Design Lab’s Diabetes Design Initiative, our team of student designers and researchers explored the question:

How might we simplify the complex learning curve of diabetes education through an accessible and interactive visual tool?



tools
Figma

Miro
Zoom


team
Yingxi (Ali) Wong
Meghan Yiu
Ed Angara


mentors
Lars Mueller
Heidi Rataj
Michelle Duong (PM)
Gayle Lorenzi RN, CDE
Sara Krugman
Rochelle Ardesher













I. Problem & Context

About 34.2 million people in America are diagnosed with diabetes, making up about 10.5% of the nation’s population.*

Despite millions affected, people with diabetes, with their shared curiosity and drive to learn more about their condition, are often left invalidated and unsupported by the lack of practical and accessible diabetes learning tools.




Existing tools primarily take the form of PDFs and infographics packed with many undefined variables, overly theoretical language, and stacked data sets.

Very few operate within the context of Continuous Glucose Monitor (CGM) usage
, a tool that has greatly shaped the progress of diabetes management.

Given this, people are not only left overwhelmed by their new diagnosis; they are also left overwhelmed by the cognitive overload of new learning material and the inaccessible frameworks they are often built upon.


︎Factors of Inaccessibility:
A. Overwhelming
B. Impractical


Learning about diabetes and its real-time effects on blood glucose levels causes unnecessary cognitive burden and lack of clarity for people who simply want to learn and gain a better understanding of their condition.

Based on this collective frustration and further exploration with our design brief, our design team prioritized exploring visual interactive graphs and their potential to create a more compartmentalized and easy-to-learn visual experience.






What if this learning experience, instead, was structured into a gradual learning framework, covering the smallest learning concepts first, gradually building up to larger, more complex concepts?















[ Main Hypothesis ]

Gradual and incremental visual learning will break apart the complexity and vastness of the “diabetes education” umbrella, allowing our users to sequentially focus on one small learning concept at a time.








A. Research Methods

Over the course of 10 weeks, our fully-remote team conducted three phases of interviews:


Phase 1: Introductory User Interviews

Understand: Who are our participants and what are their specific frustrations/needs when learning about diabetes?

Phase 2: Follow-up User Interviews

Understand: What are our participants’ cognitive processes and visual interpretations when making sense of diabetes concepts in the context of practical, real-life scenarios?

Phase 3: Prototype - Usability Testing

Understand: How might we reiterate our design ideas and prototypes to best meet the needs and perspective of our user participants?





Over the course of three phases of user research, our team conducted interviews with:



Out of all of our participants, 6 participants volunteered to participate in all phases. This provided our team a relatively longer duration of study with this group, observing the progression of their feedback on our overall design progress.

With our 10-week timeline, our team focused on researching and setting the foundation for our tool’s early stages, creating room for a buildable ecosystem where future possibilities in the tool’s evolution remain open for further exploration.




[ Research Constraints ]


1. Remote Setting

2. 10-week sprint timeframe

3. Convenience Sampling:
︎ Our team relied on a set of participants that have already been previously contacted by our DDI senior mentors. Most of our participants are highly involved in their care or their loved ones’ care, which gives us less insight to the perspectives of humans without access to exceptional technical and community resources, a perspective that is, nonetheless, equally as important.










II.

Understanding our participants’ cognitive learning processes



To test our main hypothesis and brainstorm solutions for our assigned project brief, our team found it valuable to understand our user participants’ respective learning processes of diabetes concepts in the context of their their daily usage of Continuous Glucose Monitors (CGMs).

For our early brainstorming phase, our team gathered visual representations, such as graphs, ratios, and semantic maps created by our participants real-time to understand their logical process in relation to the CGM graphs and formulas they interact with on a daily basis. We specifically based our questions on the context of Hypoglycemia due to its universality.
























︎HYPOGLYCEMIA: “occurs when the level of glucose in your blood drops below what is healthy for you... Symptoms of low blood glucose tend to come on quickly and can vary from person to person.”

Source: NIH - Low Blood Glucose (Hypoglycemia)








[ Main Interview Objectives ]

1. What is your process in calculating your food and insulin intake and their effects on your blood glucose?

2. How do your blood glucose levels fluctuate during your management of highs and lows? What does this look like on your CGM monitor?

3. What variables or concepts do you associate with prevention of hypoglycemia?

















Through this preliminary interview stage, our team discovered:


1. Each self-made diagram consisted of many intersecting variables and concepts that have yet to be synthesized.


2. Each of our participants tackled our questions from different angles and thought processes despite using the same graphs and graphic symbols.




Next Step: Connect the gaps and find the intersections between these various variables and concepts.

Next Step: Explore how we might create a tool that is both universal and applicable to a variety of perspectives.








III.

Structuring a logical learning framework

Learning about diabetes requires an understanding of multitudes of intersecting concepts and factors at play.



Based on our user interviews, we discovered a recurring pattern for many of our participants’ experiences with their initial diagnosis: being expected to learn various words, labels, and numbers without enough practical knowledge of how they take form in real life.

Many people have had to learn about diabetes directly through unpredictable and often dire real-life incidents.




















︎ There is a critical distinction between learning about diabetes through text and learning about diabetes through life. People with diabetes often have to learn through both processes simultaneously.

This causes significant cognitive overload for people who take the extra time and effort to understand complex diabetes concepts while also living through its real-time physiological effects on a daily basis.




Our team prioritized learning through the perspective of our participants’ logical flow by conducting two different frameworks:


A. Card Sorting Framework







[ Variable Sequence ]

GOAL: Observe how our participants sort a long, nonconsecutive list of diabetes concepts into two compartments: Level 1 & Level 2. In doing so, our team strived to better understand the web of variables that make up complex learning concepts.

– For instance, to comprehensively understand “Hypoglycemia” (Level 2 concept), one must first understand the function and context of variables such as: “fast acting carbs”, “basal insulin”, “carbs timing”, etc.


︎1. Level 1 - Simple/Primary Concepts
2. Level 2 - Complex Concepts: can and must be broken down into smaller, teachable concepts

 

B. Cause & Effect Framework







[ Contextual Sequence ]

GOAL:
How do our participants narrate their thought processes when provided a contextual scenario, such as the causes and effects of Hypoglycemia?

How do individual concepts and variables flow sequentially and lead to the fuller understanding of the greater concept of Hypoglycemia?

Why do specific variables belong under the umbrella of certain complex concepts?






Based on these two research frameworks, our team was able to synthesize our participants’ raw data into a much more focused and narrowed scope and sequential framework.

       



- final synthesized sequence

Based on our overall insights, our team was able to narrow down our largest Level 4 scope of “Insulin & Food” into its simplest, Level 1 learning block: Carb Counting & Simple Carbs.

Choosing “Carb Counting & Simple Carbs” as the initial point of entry for the greater “Insulin & Food” scope provided our team the primary foundation for our interactive visualization tool.























︎ [ Defined Variables ]

Level 1: Simple
Carb Counting, Simple Carbs

Level 2: Intermediate
Different types of food (complex carbs)

Level 3: Scenarios (Contextual) Hypoglycemia

Level 4: Insulin & Food
The concept of Insulin & Food is a large umbrella that houses many smaller concepts.







IV.

Fine-tuning our final prototype to uphold our design goals



Based on our main design processes, our team established 3 main design goals:

1. Realistic

Ensure that graphic visualizations are accurate representations of users’ real-life experiences with diabetes

2. Simple

Prioritize simple interactions, apply information hierarchy to avoid cognitive overload, and structure the tool into an incremental learning framework

3. Empathetic

be conscientious of our use of heavily theoretical, inaccurate language, and inaccessible visual symbols and colors





















V.
The Potential of Our Tool

A. How might our tool evolve overtime?


By focusing on the smallest learning block for our project cycle deliverables, we were able to establish a starting point, making way for future concepts. Overtime with more resources, we envision our tool to evolve into a more comprehensive one, covering many diabetes learning concepts that can consistently be built upon our tool’s design style and framework. This generative approach allows for a growing ecosystem with the intention of providing learning concepts that address diverse user backgrounds and expertise levels.



B. Who will best benefit from our tool?


This early stage of our tool aims to benefit all people with diabetes, especially those who want to educate themselves and others on the fundamentals of food-glucose-insulin interaction. Given our simple, incremental learning framework, people with diabetes, regardless of age, will be able to start learning from the foundation of Simple Carbs & Carb Counting and gradually make their way to more complex concepts that build upon this first learning block.

Alongside our hypothesis, we were able to gather feedback from our final two participants, who described themselves as tech-savvy and experienced with diabetes management and complex technological tools:









( Potential Concepts: Non-Exhaustive )

Complex Carbs, Effects of Exercise on Insulin Sensitivity, Full Meals & Their Nutritional Content, Effects of Co-existing Conditions, Effects of Steroids on Insulin Sensitivity, Effects of Intermittent Fasting


Apart from our tool’s potential learning content, our team also would have benefitted from further exploration for how our tool may be used in a guided learning setting, where a more experienced person educates a new learner with the guidance of our tool. Exploring this use case would have also allowed us to gain better insight into how communty support may provide a better quality learning experience.




At the end of our 10-week project cycle, our team facilitated a remote presentation to over 30 stakeholders ranging from health-tech professionals from DDI and Dexcom to people within the greater diabetes community. Afterwards, we held a community workshop where we facilitated an insightful discussion about future visions for our tool.




Special thanks to our mentors, Lars Mueller, Heidi Rataj, Michelle Duong (PM), Gayle Lorenzi RN, CDE, Sara Krugman, and Rochelle Ardesher, who all have allowed our team to grow and learn through such helpful guidance and critique.

Most special thanks to our amazing group of user participants. For their time, kindness, patience, and energy. Who have all given our team such fruitful learning experiences as growing designers and researchers.










Hello you’ve reached the end of this case study. Congrats on being a great and attentive reader! And thank you for being patient with my words. I hope you’ve learned some new things. If you have any questions about our work and or suggestions on how to improve this case study, I would love to discuss with you as I’m always finding new ways to explore previous projects in a new light. Thank you!
If you’d like access to our full presentation script, you can reach me @ jhea.espares@gmail.com