Google Fit is an app developed by Google in collaboration with the World Health Organization (WHO), designed to coach you into a healthier and more active lifestyle.
The app is heavily focused on physical wellness and activity. As a personal project, I wanted to see how users could also monitor their mental wellness, so that they can have more well-rounded understanding of their overall well-being.
In order to do this, I chose to design a new feature that allows users to track their mental and emotional states over time.
The emotional state data would then used in conjunction with their physical activity data to coach people into a healthier lifestyle that serves both body and mind.
Disclaimer: I developed this brief as an independent personal project. I am not affiliated with Google in any capacity, except as passionate user of their products.
Are people currently tracking their mental wellness and how are they doing it? To kick-start things, I embarked on a research project, conducting interviews and a completing a competitive analysis to understand how people were already solving this problem.
In the competitive analysis, I looked at the full spectrum of solutions, from pure Fitness and Activity Trackers, to micro-journaling solutions (such as bullet journals), to pure mood tracking solutions (focused solely on emotional well-being).
I also interviewed a sample of people, ages 18-40, who currently tracked their mood using various methods, in order to understand how and why they went about tracking their mood.
I synthesized the research data and identified five key themes that came up time and time again: Balance, Control, Motivation, Anxiety and Burnout. Based on these, I developed a persona and POV problem statement that would be my north star for the rest of the project.
The new features would need a home that fit seamlessly into the current app, in a way that was intuitive and familiar to new and veteran users alike.
I was important to carefully consider how these new features would fit into the existing architecture of the app, both from a front-end perspective and from a back-end perspective. After a careful inventory of the app’s existing architecture and visual assets, I created an updated sitemap and UI Requirements Documents outlining how these new features would integrate into the Fit system.
A quick round of user feedback based on the lo-fi sketches showed that when it comes to tracking moods (rather than emotions), ‘simple is best’. This is why I designed a 3-point system that could be easily translated into a quantifiable measure of mood.
Much like Google’s other products, Google Fit’s UI is based on the Material Design system. The High-Fidelity designs were crafted with careful attention to the Material Design framework and Fit’s current UI patterns, making for a seamless integration of the new feature into the app.
Adobe XD allowed me to create a high fidelity prototype, featuring Google Fit and Material Design’s distinctive micro-animations, giving the prototype an extremely polished look and feel.
This was crucial for user testing, in which current users of the app would test and evaluate the new feature.
I used a double pronged approach, conducting both moderated and unmoderated remote testing.
This will allowed me to collect a higher volume of data while still gaining deeper, qualitative insights from the moderated tests to validate what can only be inferred from unmoderated testing.
In both cases, participants were given a scenario and asked to complete some tasks.
The goal was to validate if users could successfully:
add/find/edit/delete a mood entry, and
find, interpret and understand mood data
The first round of usability testing provided a plethora of insights to move forward with. It was clear that certain aspects of the design need to be revisited to eliminate barriers and frictions from the user experience. But, many opportunities were discovered during the testing phase too.
Some new ideas for additional potential features were born from the qualitative feedback and observations that were made during testing.
After this initial delve into the world of wellness, there is still much to learn. But for now, the next steps are to evaluate the key findings from testing in order to develop priority revisions that can be tested again.
In addition to re-testing the priority revisions, this will also be a prime opportunity to explore what the role of social sharing and goal setting could play in the wellness tracking experience.
