Self: A Quantified Self Dashboard
There are a generally a few different types of people when it comes to exercising:
For those in every category, there are now more options than ever to track a myriad of personal data. The availability of so much data has led to a movement known as Quantified Self, the goal of which is self-improvement through quantified and analyzed self-knowledge.
One of the biggest limitations of the Quantified Self movement, however, is the difficulty to combine and draw big-picture insights from so many different data sources. This project, SELF, aims to solve that limitation through a dashboard that unifies and analyzes your data, tracks your goals, shows you data trends and, most importantly, gives you deep data insights into how to improve yourself.
My Design Process
I followed user centered design and lean UX processes to make sure that all of my design decisions were validated by user research, empathy and feedback.
I created three different personas for SELF users, bucketed into the three different exercise categories I discussed above. The personas were based on online research and interviews with my friends about everyday users of tracking apps and wearables. I made the personaswith a few key assumptions:
I used the Jobs To Be Done and How Might We frameworks to explore a few different use cases for SELF and to empathize with and understand users’ motivations and desired outcomes. These are the three main usage motivations I researched and validated:
Ideating the Solution
Early sketches and flows
After low-fi experimentation and research, I opened up Sketch to create hi-fi mockups and used Marvel to create a clickable prototype. I tested the prototype with a few friends and gym buddies, and did a few quick iterations based on the feedback. Initial feedback indicated that I had too much complexity, and needed to narrow down my initial feature set. Additionally, my first designs lacked a strong hierarchy and information architecture, so it was easy for users to get lost within the app.
My overarching architecture for SELF follows a goals structure. Users create goals, which track progress and create a motivational structure. Data and goals inform trends, which are historical comparisons of your data against each other. Trends are customizable, so a user can compare any and all data points against each other, completely agnostic of source. Trends are the deep dive into the data, the results of which are insights. Insights are smart recommendations from SELF on how to better achieve your goals based on trends.
After connecting data sources, users are encouraged to create goals. Goals are completely customizable and measurable. SELF detects all data sources from connected apps and wearables and presents them as potential info for every goal. Users tend to value a product that they’ve spent effort and time on more than a product in which they put no labor into, so goals are a good way to initially create user investment.
Upon completing their goals, users get rewarded with tips and encouragement from Trends and Insights.