Glide: Improving Metabolic Health with a Companion App Concept for Wearable Continuous Glucose Monitors

Continuous Glucose Monitors (CGMs) and wearable health tech have become dynamic, fast-growing spaces, offering the potential to revolutionize how people manage their health. At UX studio, we saw an opportunity to make a meaningful impact by designing innovative solutions in this evolving landscape.

Our research showed that non-diabetic users—pre-diabetic, at-risk, and health-conscious individuals—are an underserved group with distinct challenges. By focusing on this population, we realized we could deliver a solution that simplifies glucose tracking and empowers users to experiment with their health, leading to better outcomes.

What is CGM and Why is it Important?

A Continuous Glucose Monitor (CGM) is a small wearable device that tracks glucose levels in real-time, offering insights into how various factors like diet, exercise, sleep, and stress affect glucose metabolism. Traditionally, CGMs have been used by individuals with diabetes to manage their condition, but non-diabetic users for health optimization purposes are increasingly adopting them.

By providing continuous data on glucose fluctuations, CGMs empower users to make informed decisions that promote metabolic health. For non-diabetic users, this technology is particularly valuable for preventing conditions like Type 2 diabetes, enhancing personal well-being, and discovering how lifestyle choices uniquely affect their bodies. The rise in non-diabetic users highlights the growing interest in self-monitoring and biohacking for health improvement​.

Part 1: Discovery

Desk Research

The goal of our desk research was to explore the landscape of CGM (Continuous Glucose Monitoring) devices and apps by reviewing user experiences, expert opinions, and relevant scientific studies.

Sources Reviewed:

• App reviews
• Expert videos and reviews
• Customer videos and reviews
• Scientific articles
• Sensors and apps - company pages
• Discussions in online CGM user communities

Outcome:

Our research provided a thorough understanding of the CGM ecosystem, focusing on user experiences, app functionalities, and the evolving role of CGMs in different patient segments: those with Type 1, Type 2 Diabetes, at-risk, or healthy groups. This research allowed us to identify key trends, challenges, and opportunities within the market, which will inform our approach moving forward. We also uncovered valuable insights into user behaviors and preferences, highlighting areas where existing solutions excel or fall short, paving the way for potential innovations.

Competitor Analysis

For our research, we conducted a comprehensive analysis of various CGM-related applications to understand their functionalities, strengths, and weaknesses. Our analysis focused on several key areas:

Official CGM apps: We reviewed the official applications of the most popular CGM devices, including Freestyle Libre, Dexcom, Caresens Air, and Simbionics (e.g., Freestyle LibreLink, Dexcom G6 App, CareSens Air App, Simbionics).

Third-party apps reading CGM data: We examined third-party apps that connect to CGM devices to read and interpret glucose data. This included apps that aggregate data from various CGM systems, providing additional analytics or features beyond those offered by the official apps (e.g., Glooko, MySugr).

Health optimization and prevention apps: Given the trend in the U.S. where CGM devices often require a prescription, we also explored third-party apps specifically targeted at health optimization and disease prevention (e.g., Levels, Veri, Nutrisense, January AI). These apps are designed for users who are proactive about their health and seek to use CGM data for lifestyle modifications and preventative measures, as opposed to treating existing conditions.

This analysis helped us to identify gaps in current offerings and uncover opportunities for improving user experience and functionality in CGM applications, especially for those using CGMs for health optimization and preventive care.

Expert Review

To gain firsthand experience and evaluate the usability of CGM systems, we conducted an expert review using two popular devices and their associated apps: the Freestyle Libre 2 with the LibreLink app, and the CareSens Air with the CareSens Air app.

Our expert review team consisted of one researcher and one designer who thoroughly tested both the sensors and connected apps. The evaluation was based on established usability heuristics, allowing us to systematically identify the severity of any issues encountered.

We made recommendations for improvement based on these findings and documented our personal experiences with both the CGM devices and their apps. This provided a well-rounded analysis of their strengths and areas needing enhancement, while we also identified opportunity spaces for our concept.

Semi-structured Interviews

Main goal

In our research, we conducted in-depth user interviews with individuals who use CGM systems to better understand their experiences, challenges, and motivations. The insights we gathered helped us design an app that enhances users' ability to manage their health effectively and integrates seamlessly with CGM devices. Our goal was to explore how users interacted with CGM data, its impact on their behavior and decisions, and their overall satisfaction with the technology.

While we initially aimed for equal representation from Hungary and the US, the final turnout included 3 participants from Hungary and 9 from the US. This shift was due to varying availability, insurance coverage, and adoption rates in each country. Approximately two-thirds of participants had Type 1 or Type 2 diabetes, while the remaining third were health-conscious individuals or those at risk for metabolic disorders. 

This uneven split can be attributed to the significantly lower awareness and adoption of CGM systems outside the Type 1 diabetes population, as well as accessibility issues such as the fact that CGMs in the US are available only by prescription.

In our screener, we asked participants about:

• their methods for checking blood glucose
• other health apps they used
• health-related data they track
• their motivations for using CGMs
• and the specific CGM brands they preferred. 

This helped us gain critical insights into their health habits and guided our research and design decisions.

The interviews lasted approximately 45 minutes and were conducted remotely through videoconferencing tools, allowing flexibility for participants and ensuring a broad reach.

Main Themes from Our Interview Script

• Overall Experience with Diabetes Management
  • Journey from diagnosis to present
  • Tools and processes used for diabetes management
  • Evolution in management practices over time
• Journey with CGMs
  • Initial motivation and experience with CGMs
  • Selection criteria and types of CGMs used
  • Changes in usage and perception of CGMs
• Data Management and Usage
  • Interaction and interpretation of CGM data
  • Use of data in daily decision-making (diet, medication, activity)
  • Tools and apps used for data management
• Behavioral and Psychological Impact
  • Behavioral changes and impact on confidence and control
  • Trust in the accuracy of CGM data
• Interaction with Health Care Professionals and Societal Context
  • Support or discouragement from those around the user
  • Insurance coverage and healthcare provider's role
  • Use of CGM data in consultations with healthcare providers
• User Experience with CGM Apps
  • Intuitiveness and features of the CGM app
  • Most helpful and frustrating features
  • Comparison with other CGM apps
• Impact of Alerts and Notifications
  • Perception and response to alerts and notifications
  • Likes and frustrations with alert systems
• Future Expectations and Innovations
  • Desired changes to CGM technology and apps
  • Areas for improvement in CGM systems

Applying Motivation Interviewing (MI):

We used Motivational Interviewing (MI) techniques, particularly the OARS framework—Open-ended questions, Affirmations, Reflective listening, and Summarizing—to gain deeper insights into users' experiences and motivations.

We also used scale questions like readiness and confidence rulers. For readiness, we asked, "On a scale of 1 to 10, how important is it for you to keep your blood glucose levels within the target?" This revealed users' commitment to glucose management. To assess confidence, we asked, "On a scale of 1 to 10, how confident are you that you can maintain target levels?" and followed up with, "What would it take to get you to a 10?" These questions helped us identify areas for support, and guiding app features that foster motivation, reinforcement, and goal-setting.

Interview analysis and synthesis

After finishing the interviews, we started the analysis by going through our notes and video recordings. We collected the findings in FigJam, and organized the information on an affinity map, where we grouped the findings into common patterns with supporting evidence for each (mostly in the form of direct quotes). 

We could already see at this stage that some categories were out of our control –  for example, pain points about how a CGM sensor works. We identified two main areas we could address with our new app concept,  namely education and learning based on direct feedback. Our findings showed that participants often felt the need for more education around reading glucose data, and they saw the immediate feedback that CGMs provide as one of the most important advantages of these devices. 

We also tried out AI-powered tools to speed up the analysis. Although these were useful, there were lots of nuances in the data that the AI couldn't properly interpret. As our interview topic was deeply personal, participants expressed lots of emotions of varying intensity. We felt that the AI couldn't properly interpret emotionally filled statements: it summarized them in a very general way, often understating the mental impact. Therefore, we still needed to do manual analysis and highlight direct quotes that reflect the actual thoughts and emotions of participants.  

Value Proposition Workshop

After completing our foundational research, synthesis, and developing shareable artifacts, we were ready to translate the gathered insights and market trends into a concrete strategy. Acknowledging our limitations as a third-party solution, our team of two researchers and two designers came together for an in-person workshop to determine the customer segment and value proposition for our digital solution.

By the end of the workshop, we defined:
For: Pre-diabetic, at-risk, and health-conscious individuals

Who: Want to better understand their metabolic health, prevent chronic diseases, adopt healthier behaviors, and optimize their nutrition

Our product: A Continuous Glucose Monitoring (CGM) companion app

That: Simplifies health-related behavior logging and empowers users with enhanced confidence, knowledge, and control over their blood glucose levels.

We used the Value Proposition Canvas by Strategyzer to guide this process. The outcome of this workshop gave us a clear direction for product development, ensuring our app would effectively address key user needs and market opportunities.

Our app focuses on two main themes to address the biggest unmet needs of our target audience: logging and experimentation.

Experimentation 

Many new CGM users, and even those with Type 1 diabetes, often find themselves needing to experiment with different food groups to understand how their blood glucose reacts. Users shared how they learned unique things about their metabolism through real-time CGM feedback, which allowed them to step beyond generalized nutritional guidelines or doctor’s orders. The constant feedback provided by CGMs makes this experimentation process safer and more personalized, helping users discover what works best for their bodies. Our app supports and encourages this experimentation, allowing users to safely explore their unique metabolic responses.

Logging

One of the biggest challenges users face is logging their food intake and other health behaviors. Many participants expressed that logging felt cumbersome and overwhelming, leading them to skip it altogether. To solve this, our app offers a simplified, conversational logging system, featuring voice-based logging in addition to traditional manual and picture recognition methods. This approach reduces friction and makes logging as easy and intuitive as possible. Users aren’t bombarded with requests for all details upfront (like macros); instead, the app calculates based on its knowledge, making data input optional and customizable to user preferences.

By building our value proposition around these two key themes, we’ve created an app that meets the core needs of our users: easy, intuitive logging and safe experimentation, all of which empower them to gain more control over their health and lifestyle choices.

Part 2: Definition and Concept Development

Following the extensive discovery research, we established our objective: 

• To empower users to better understand their metabolic health, prevent chronic metabolic diseases, adopt healthier behaviors, and optimize their nutrition. 
• We focus on guiding individuals in their use of CGMs and streamlining the process of logging health-related behaviors such as food intake, exercise, stress, and sleep.
• Our product concept is an app, accompanied by a smartphone widget and smartwatch face/widget, specifically designed for a pre-diabetic, at-risk, and health-conscious population.
• Given the complexity of CGM applications, we are focusing on creating a smooth and easy onboarding experience for non-diabetic users. 
• The goal is to provide a “fragmented” educational approach, rather than overwhelming users with all information at once. 

Our approach will rely on user feedback and adapt dynamically, helping them experiment with CGMs and integrate the technology into their daily routines. 

Behavioral Mapping

As our first activity in the second phase of the project, we created a behavioral map, a visual tool that outlines the end-to-end steps a user takes to achieve a target behavior, including actions, touchpoints, and potential barriers. 

We started by adapting a reference guide from the Center for Advanced Hindsight to our specific context. Then we mapped out user actions and steps, touchpoints, system activity/behind the scenes, and observations. 

We distinguished between those steps and actions that take place while interacting with the app (e.g.: logging meals) from those that are offline steps (e.g.: trying out an experiment recommended by the app). For each step, we then identified structural and psychological barriers, as well as problems, unknowns, questions, and opportunities. This process helped us identify a starting point, target behavior, and desired outcome for our app concept.

• Starting Scenario: Users begin with a CGM for a 14-day period, discover our app, and understand it will help them achieve better health outcomes and gain clarity on factors affecting glucose levels.
• Target Behavior: The desired behavior is for users to actively engage with the CGM data, experiment with different lifestyle factors (such as diet and exercise), and interpret the data to make informed decisions.
• Desired Outcome: Enhanced confidence, knowledge, and control regarding blood glucose levels.

The behavioral map informed by our data from interview study, competitor research, desk research, and literature review was a crucial research and design activity that applies a behavioral lens to ensure our app meets users’ needs and supports their goals effectively. Moreover, the behavioral map guides further research questions when testing the prototype and exploring the user experience post-launch.

By integrating this approach, we aim to ensure our design not only supports users in managing their glucose levels but also aligns with their personal health goals and values, ultimately leading to a more impactful and user-centered solution. 

Developing the Prototype

After completing our discovery phase and creating a behavioral map, we advanced to a mid-fidelity prototype in Figma. This prototype translated our research findings into interactive design elements and flows, focusing on essential features like meal logging, personalized insights, and self-experimentation.

The prototype prioritized a realistic user experience, incorporating real-like data based on nutritional science. For example, after logging a typical meal of oatmeal and orange juice, users would receive feedback on potential glucose spikes, helping them recognize patterns in their blood sugar responses. This level of detail gave users the impression of a working app, with many praising the accuracy and relevance of the insights.

To streamline meal logging, especially for the non-diabetic population, we prototyped an AI-powered logging feature that offered three logging options: typing, voice, and camera. This feature ensured fast and straightforward logging, addressing a known barrier for non-diabetic users, who often avoid logging because of its cumbersome process of providing and detailing all the macros and portions. With AI feasibility checks, such as using image-to-text and nutrition analysis models, we ensured that these logging methods could be both quick and sufficiently accurate.

We also incorporated real experiment suggestions in the Playbook, allowing users to see and try proven methods to stabilize glucose levels based on their unique responses. From logging to analyzing glucose data with experiment suggestions, this flow enabled users to understand their bodies better and make more informed decisions.

Usability tests and main insights

For the testing round, we recruited participants who had used CGMs within the past six months but had no known metabolic disorders. These individuals were monitoring their glucose levels with the goal of improving their health, optimizing their metabolism, or because they were at risk for pre-diabetes. 

During the session, we conducted short interviews to understand their past experiences with CGMs and the apps they used to interact with glucose data. Following this, participants walked through a scenario using our prototype, where they logged meals, viewed insights, and explored the Playbook’s experiments.

Key insights emerged from the testing. Participants initially misunderstood the Playbook feature, assuming it provided articles, tips, or recipes rather than "experiments" they could run. However, after opening individual experiment pages, they quickly understood the purpose and universally appreciated the concept of guided experiments. Additionally, participants expressed skepticism regarding the AI’s ability to analyze meals through photos, which they felt might lack accuracy. There was also some confusion about interpreting the "time in range" feature, indicating the need for clearer guidance on how this metric fits into the overall glucose tracking experience.

One highlight was the overwhelmingly positive reception of the app’s realistic content and data accuracy. Participants praised how accurately the system linked glucose spikes to meal timing, describing the insights as “good advice” and “realistic.” 

Users uniformly found the app’s structure intuitive, glucose data easy to interpret, and navigation seamless. Despite limited prior experience with AI-powered meal logging, they appreciated the smooth flow and found it easy to use. 

The concept of the Playbook and its guided experiments were also particularly well-received, with participants valuing the actionable insights and positive reinforcement. They noted how the app not only suggested areas for improvement but also affirmed what they did well, which boosted confidence and enhanced the overall experience. This level of detail and encouragement set the app apart from competitors, reinforcing its credibility.

Naming the App: Glide

The name "Glide" was carefully chosen to evoke a sense of smooth, effortless navigation, aligning with the app’s user experience. Beyond its literal meaning, "Glide" also incorporates elements of "Glucose" and "Guide," reinforcing the app’s role in helping users understand and manage their metabolic health. The app’s interface is designed to feel fluid and intuitive, enabling users to log meals and activities effortlessly. Features like AI-powered logging, insights into glucose stability, and the Playbook’s experiments all contribute to this seamless experience, guiding users in their journey toward better health outcomes.

Main features & functionality

In developing the Glide app, we applied behavioral design principles to make the broader goal of optimizing glucose metabolism feel manageable and motivating. Rather than overwhelming users with comprehensive metrics, we broke down health improvements into smaller, achievable goals. For example, users are guided to experiment with different types of breakfasts or to adjust their lunch composition—such as adding fat or fiber to flatten their glucose curve. The app also reinforces positive choices by commending users when their actions contribute to stabilizing blood glucose levels.

To enhance engagement and relevancy, Glide provides timely reminders, alerting users when new insights are ready. Additionally, if the system detects a rapid change in glucose levels, it prompts users to reflect on possible causes. For instance, they may be asked if they recently experienced stress, exercised, or had a snack. This approach not only encourages consistent logging but also fosters a reflective mindset, helping users connect their behaviors with their metabolic responses.

UI Design

Packed with the insights from our usability tests and the learnings of the discovery research, our designer started working on the UI design of Glide. Our goal was to create a visual language that combines the aspect of experimentation, the effortlessness of logging, and the power of education.

What are the first things that come to your mind about Glide? How do you want people to feel when they open the app? What’s the message you want to convey through shapes, colors, and typography? We asked these questions (and many more) from ourselves to give a head start to our ideation process.

We crafted vastly different mood boards that all represent individual visual directions in their raw nature while looking distinctive next to our competitors. We ended up moving forward with our glassmorph version that embodies both the playfulness and experimentation mindset of Glide, and the trust-invoking transparency of a see-through surface.

With the visual language in mind, the UI of Glide was built up step by step with continuous experimenting until it reached its final state. We focused on the key features and flows of the app to showcase our efforts. The soft colors, duotone icons, and the large rounded corners are gentle to the eye—while the large and bold numbers and images let the content and data drive the conversation.

Our work on Glide reflects our expertise in wearable tech, AI-powered features, and health behavior change. With deep experience in health tech and research-driven design, UX studio is well-equipped to create impactful solutions that promote healthier user experiences. If you’re interested in our work across other industries, explore our case studies for a closer look at what we can achieve.

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