OSE

OVERVIEW

Project Duration: 6 months

OSE is a health analytical service that utilises the Internet of Things (IoT), Artificial Intelligence (AI) and Machine Learning to help first degree family members of newly diagnosed diabetics reduce their risk level of developing the disease. The service takes data that is currently being collected on the user, through fitness trackers and other devices, to analyse their current state. It can predict when the user is more likely to revert to old habits, eating a chocolate bar for example, and when they need to know they are not alone on their lifestyle change journey. The service learns by being used, giving the user a stress and hassle free experience.

BACKGROUND

The Type 2 Diabetes Epidemic

Diabetes is a lifelong disease that can affect the individual’s quality of life if it is left undiagnosed and untreated. It is the build-up of glucose in the bloodstream, causing blood sugar levels to become dangerously high. It is manageable through medication or lifestyle control (Diabetes UK, 2019).

There is an estimated 1 in 15 people in the UK who have developed the disease. This is 2.7 times more than in 1996 (Diabetes UK, 2019). It’s not just in the UK where diabetes has become a growing issue. One in nine adults in the U.S. have diabetes with Centres for Disease Control and Prevention (CDC) estimating that this will shorten to one in five by 2025 without intervention. 90-95% of diabetes cases are type 2. Historically, the disease has mainly affected people aged 65+ but now younger adults are developing the disease and even some cases affecting children and adolescents.

Target Users

In order to combat the increasing number of people who are developing diabetes, I have decided to focus on first degree relatives of newly diagnosed diabetics. This is one of the factors that increases a persons risk, that they can’t change. This target user group will have had the shock of someone close to them being diagnosed, and it is an opportunity to help educate or change their lifestyle in order to prevent themselves developing type 2 diabetes, as well as helping their diabetic family member to manage the disease.

GOALS AND CHALLENGES

The subject of the diabetes can be personal to the person with the disease or to their family members. It can trigger emotional issues, especially when someone is first diagnosed. For this project, I conducted research with users that had been diagnosed for a minimum of two years and those who volunteered to take part in the research.

One of the biggest challenges that came from the research was to be able to come up with a solution that would keep users motivated after an initial shock of a diagnosis wore out. It was discovered that many of the first degree family members wanted to make sure that they didn’t develop the disease, but after changing their habits, they were revert back.

Another challenge was to get a solution that was different in an already overcrowded market. There are many fitness and health apps currently available, and even some apps that help to monitor sugar intake. Currently in development, there are glucose monitors that would allow the user to check what their blood sugar levels are without having to break the skin. Taking this challenge into considerations, a decision was made to use what is currently being used to make something innovative.

APPROACH

The project follows the double diamond framework and split in two phases.

Discover / Define

Using different research methods in order to uncover the users feelings about a recent diagnosis, what they are currently doing and using to help support the diabetic and reduce their own risk.

Develop / Deliver

Taking the insights to ideate an innovative solution that can help the users on their journey to reduce their diabetic risk.

DISCOVER

Research

In order to understand the current experience and key issues surrounding type 2 diabetes from the users perspective, a range of research methods were utilised in order to gain perspective.

Auto-ethnography

Through self-reflection, I mapped out my personal experiences with diabetes of having two family members being diagnosed with type 2 diabetes, and how each case affected me. With this method, I had to be objective with each experience and not let my feelings about one experience affect my memory of the previous one. To map these out, I created empathy maps of each case, before putting them together to compare the two.

“I don’t want that to happen to me, but I don’t know how to prevent it!”

Semi-structured Interviews

Interviews with 3 families who had recently had a type 2 diabetic diagnosis in the family. The main areas that I wanted to find out about were:

What was the lead up to the diagnosis?
What was the initial reaction that the diabetic and family members had when they first learnt about the diagnosis?
What support is currently out there for the diabetic and their family?
What, if anything, did the family do to help the diabetic manage their diabetes?

Online Ethnography

In order to collect more user experiences around a diagnosis, I looked at diabetes forums to gather more stories and to learn more about the different circumstances that a diagnosis happened and what they user did in order to manage it. I focused on posted from newly diagnosed, and followed their story from initial diagnosis to each time they posted which ranged from every couple of months to a jump in time of a year. Each of the posts was thematically analysed, focusing on what the users felt, thought and did at each stage of their journey.

“So, I guess all the warning signs were there. Except I could and did easily attribute them elsewhere.”

‘What if’ scenario

A ‘what if’ scenario was created and conducted with potential users who had no previous experience with type 2 diabetes. The activity used storytelling, wildcard and card sorting elements. The main areas that I wanted to gain insight into were:

What was the initial reaction to a diabetic diagnosis of a close family?
What research or information would they most want to know about?

DEFINE

Synthesis

In order to gain valuable insights from the research, each finding was coded and written onto a post it note. The notes were then made into an affinity diagram, with the each finding being categorised into themes, and then into over arching themes.

Key Insights

A number of key insights came from the data analysis, and a few are shown below.

In order to create an experience that could successfully help users to change their lifestyles and reduce their risk relies on a number of key components.

Support
Motivation
Knowledge
Family
Lifestyle (food, exercise and sleep)

Refined Target User Group

Due to this target user group being broad, I split to group up into the different attributes that I uncovered during the discovery phase.

Changers - those who actively want to prevent type 2 diabetes.
Disbelievers - those who don’t believe that they are at risk of type 2 diabetes.
Accepters - those who have accepted they will develop type 2 diabetes.
Unaware - those who don’t know they are at risk of type 2 diabetes.

A decision was made to focus on changers because while they are already trying to change their lifestyles and had an experience that could be improved. This would be the first stage of a large project in order to try and persuade the other types of users to try and reduce their risk of type 2 diabetes.

Persona

In order to make sure that the user was always at the centre of decisions and evaluations during the ideation and development stage, a persona was created of the target user group.

Experience Mapping

From all of the data collected, an experience map of the target user group was developed to give an over view of their experience, focusing on what their pain points are, and where there is an opportunity to implement a solution.

UX Vision

From the first stage of the project, I discovered what the issues surrounding the prevention and management of type diabetes and uncovered opportunities that could be used to create a new experience. In order to solidify the project direction, I created a vision statement.

There is an opportunity for a product or service for changers who want to reduce their risk of developing type 2 diabetes, but worry about their own risk, frustrated that they have lost their motivation over time and reverted to old habits and want it to fit into everyday life.

DEVELOP

How Might We Statements

To begin the ideation part of the project, some how might we statements were rapidly created based on the potential opportunity areas that were uncovered during the define stage. The how might we statements help to focus the ideation on one opportunity that could be solved in several different way. A large list of potential statements was created, and put to a fellow designer in order to narrow down. The list was then further refined with an evaluation matrix in order to focus on the more interesting opportunities and would lead to more innovative solutions.

Design Principles

In order to make sure that the solution suited the user’s needs, a list of principles was created using the MoSCoW technique. The principles were list in order of priority, and they were used later in the development stages to help structure the design and also to evaluate back once the solution was created in order to make sure that the solution fits the user’s needs.

Ideation

Using the how might we statements that were developed and refined, a group Crazy 8s session with fellow designers was organised. Each designer was given 5 minutes to explain their project, distribute different HMWs and 8 minutes for each designer to quickly ideate solutions and then feedback to the group on the ideas that they have come up with. This was done several time for each designers projects.

This technique allowed each designer to get a new perspective on topics, and also gave each designer a chance to partake in discussions that could be related back to their own projects, such as using different technologies and methods to develop a solution.

One resource that I used to help with ideation was Brainstorm cards created by Board of Innovation. These helped to think about how technologies are currently being used, and how they can be related to reducing diabetic risk. I listed potential ideas and how different technologies could be used together in order to create an innovative solution.

As a final technique I created a Digital Trends Matrix. This was done by using HMW statements on one side of the table, and different types of technology along the other. Each HMW was answered with ideas by using the different technologies. This method combined the Crazy 8s and the Brainstorm Cards.

Evaluation of Ideas

In order to narrow down all of the ideas that were created and decide upon a small number of idea to develop further, a HOW-NOW-WOW matrix was used. All the ideas were placed on the matrix dependant on how innovative the idea is and how simple it would be to innovate.

Once the list was refined, each of the ideas was evaluated against the experience goals, task goals and pain points that were developed for the persona. This lead to a few of the initial ideas being selected to take forward into the experience prototyping phase and to develop further.

Storyboarding and Experience Prototyping

A couple of the ideas were chosen to develop further. Basic storyboards for potential user interactions were drawn up and iterated in order to test out the experience that the proposed solution. Two of the chosen concept ideas were chosen to take forward. The storyboards were used to conduct experience prototyping and then iterated to improve the concept. The earlier iterations were conducted with fellow designers before the last version was conducted with a target user to get any changes and feedback on the concept.

DELIVER

Co-design

Once the final concept was decided upon, a co-design session with a target user was organised to discuss the details of the experience and how to make the experience better. The target user is also a Solutions Architect that specialises in Amazon AWS. Together, we created proof of concept through a framework that shows how the solution could be created using current technologies.

Concept Statement

A concept statement was created and iterated throughout the development stage of the process. The final concept statement is as follows.

OSE is a smart health analytical service that utilizes the Internet of Things, Artificial Intelligence and Machine Learning for family members of newly diagnosed diabetics, who actively want change by taking advantage of their sudden motivation from a recent diagnosis to reduce their diabetic risk.

OSE provides the user with a supportive platform, continuous motivation and feedback to keep them on track and give them a stress and hassle-free experience.

Concept Diagrams

To be able to fully understand the full concept, I created different diagrams throughout the different iterations to be able to communicate the idea.

The current market is already over populated with different versions of fitness trackers and food/health/exercise apps. While these apps and devices are useful and help keep users on target with their goals, there is a lot of users who need something else to keep on tracker. The novelty of wearing or using these apps wears out and they are back to where they started. Creating a similar product with other features may work for some users, however many who already own other devices won’t want to spend more money on something that they does most of what their own device does. The OSE concept proposed to use the data that is already being collected by these devices and apps to analyse the data in a different way.

Eco-System

Research showed that there were 6 key areas which help or hinder the users when they trying to change their lifestyles to prevent diabetes.

Food
Exercise
Glucose
Behaviour
Social
Sleep

During the iterations, a decision was made to remove the social aspect for the solution, and this could be made into another solution that could be linked at a later stage.

This diagram shows that each area is linked, creating an eco-system that is vital to take into consideration to help the users achieve their goals.

Inputs and Outputs

The main technologies that the OSE solution uses is Artificial Intelligence and Machine Learning. The algorithms that are proposed to be used in the solution are able to give specific outcomes, and through the use of an Internet of Things button, the parameters of the algorithm will be altered. This will determine when the system will communicate with the user and what information it will show.

Compared to the eco-system, there are only 4 inputs of information that will be used that will to enable the main features. The sleep data will still be input to view through the application, however during this stage does not have an output.

User Flows and Potential Interactions

User flows of how the system would work were drawn out at a high level, and then in further detail in each of the areas. These were iterated throughout development.

Potential interactions that the user could have with the system were mapped out in order to decide upon the user journeys that would be prototyped and user tested.

**Initial high level user flow diagram**

Five user journeys were decided upon. These were chosen as the most important areas to show, and how the solution would be used by the user and in what context.

Lo-Fi Prototype

A decision was made to create a prototype of the system using a mobile app as the interface and interaction with the users. In the future, users would be able to potentially access the system through their smart watches and through a web browser, but a mobile phone is the device that the majority of the target users kept on them for the largest portion of the day.

A lo-fi prototype was developed and was given to target users to test at different stages of development. This allowed constant feedback from the users who would be using the solution and certain features altered to make sure that their experience goals were being met.

User Testing

The lo-fi prototype was iterated 3 times, each being tested with a target user. Each testing session, the user was asked to complete the user journeys and then asked questions about their experience, what worked and what needed altering.

Hi-Fi Prototype

Once the features and the experience was refined into the final version, the lo-fi prototype was modified into a hi-fi prototype. User interface design and branding was researched and developed, aligning with the design principles that were set out in the earlier stage of the project. Accessibility was considered when choosing the UI and colour scheme, with select screen being checked through a colour-blindness simulator.

The hi-fi prototype was tested and usability tested with a target user who gave feedback on the UI of the app and suggested minor changes in order to improve it’s usability.

Hi-Fi Prototype Visuals

Final Concept

OSE is a smart health analytical service that utilizes the Internet of Things, Artificial Intelligence and Machine Learning for family members of newly diagnosed diabetics. OSE takes advantage of users motivation to change by taking advantage of their sudden motivation from a recent diagnosis to reduce their diabetic risk. OSE provides the user with a supportive platform, continuous motivation and feedback to keep them on track and give them a stress and hassle-free experience through the use of a mobile app.

Prototype

Features

An adaptable system - through connected accounts. Not all the data inputs are needed for it to work, however some features may be limited.
Connected accounts - All the data collected in one place to review.
A support network - with family members to give each other encouragement and show their support when the user needs it most.
Notifications - based on current health statistics to intervene before entering a ‘weak zone’.
A clicker - that notifies the system you are having a weak moment. This alters the algorithm in order to improve intervention in future.
Risk score - continually updated so you can see your progress.
Knowledge centre - to learn tips and tricks on how to keep yourself on target, about diabetes and your risk.

Video Prototype

In addition to a click-through prototype, I created a video in order to show the full capabilities of the solution. It showcases the technologies used, and how the data relates to interactions with the platform.

OSE