Design the entire user interface of WurQ application
WurQ- Fitness Tracking App (Harvard spin-off startup)
My role: Lead User Researcher & Product Designer
Team: CEO, Product Manager, 2 Software Developers
Date: Jul 2022- May 2023
Background
The WurQ product features two wearable sensors that track every movement and exercise, measuring both performance quality and heart rate during each activity. This data helps users improve over time and compare their progress with other athletes.
App Flow
Challenge 1:
The process of starting a workout involved too many steps. Athletes had to:
Locate their sensors.
Manually connect them to their phone.
Calibrate the sensors before beginning their session.
This was especially frustrating when they were in a rush, such as before a class.
Technical Limitation:
Since users shared sensors rather than having personal ones, we couldn’t implement an automated connection feature for beta users.
Solution:
Automatic Sensor Detection:
As soon as users entered the connection screen, the app began searching for sensors—eliminating the need for manual scanning.
Seamless Connection: Once users selected both sensors, they were automatically paired without requiring an extra click.
Simplified Sensor Orientation:
Users often wore sensors incorrectly (e.g., placing a chest sensor on the wrist or wearing them in the wrong orientation), leading to inaccurate movement detection.
Instead of requiring multiple clicks for verification, users only needed to stand still for three seconds—no buttons to press.
The app automatically checked sensor placement and provided real-time guidance if adjustments were needed.
Results:
✅ 7 fewer clicks to connect sensors.
✅ Improved efficiency, fewer errors, and higher accuracy in movement detection.
✅ 100% of users understood and found the new process easy to navigate.
✅ SPARK Effect: By minimizing effort, users were more likely to take action. Simply standing still for three seconds allowed the app to handle the rest, making the experience effortless and intuitive.
Challenge 2:
The two wearable sensors captured a vast amount of workout data, offering valuable insights for users.
These sensors allowed us to track key performance metrics such as range of motion (ROM), workout pace, movement patterns, duration per exercise, and rest periods. However, accuracy was crucial—we couldn’t risk sharing unreliable performance data.
To ensure both accuracy and relevance, we focused on the most valuable metrics identified through interviews and focus groups. We prioritized metrics where we were at least 90% confident in data accuracy.
Key Metrics We Prioritized:
Timing:
Users cared most about how long it took to complete their workout and how much time they spent resting. Many were surprised by the actual rest time.
Pace:
They wanted insights into when they slowed down during their workout and how their pacing compared to others.
Heart Rate (HR):
While users already tracked HR with Whoop or Apple Watch, those devices only provided general HR trends. What was missing was HR data specific to each movement, helping users understand their exertion levels in real time.
Solution:
This page provides users with comprehensive timing data, including:
Total workout duration
Number of rounds completed
Total rest time
Detailed timing breakdown for each round
Key Feature: Pace Graph
The pace graph is the primary focus of this screen, visualizing an athlete’s speed fluctuations throughout the workout and comparing their pace to the group average.
Fitts’s Law: The graph occupies the largest space on the screen, ensuring users can easily focus on and interpret their performance.
Progressive Disclosure: Additional details are revealed as users hover over the graph, preventing information overload and supporting a step-by-step learning experience.
This page provides users with detailed heart rate data, including:
Average heart rate and max heart rate
Heart rate throughout the workout
Time spent in each heart rate zone per movement
Key Feature: Heart rate Graph
The heart rate graph highlights heart rate fluctuations during the workout, allowing users to see how their heart rate changes with each movement and rest period.
Fitts’s Law: The graph occupies the largest portion of the screen, allowing users to easily focus on their heart rate and observe its fluctuations during both movement and rest periods.
Progressive Disclosure: Additional details appear when users hover over the graph, showing heart rate at specific moments. Users can also select individual movements to highlight them on the graph, providing a clear, step-by-step experience while avoiding information overload.
Results:
✅ 98% User Comprehension: Users effectively understood and navigated the graphs.
✅ 87% Customer Satisfaction: Survey feedback indicated high satisfaction with the app design.
✅ Heart Rate Tracking: Approximately 70% of users prioritized heart rate tracking.
✅ Active vs. Rest Time: About 90% of users found the distinction between active and rest time highly engaging.
User Feedback
Pace graph:
📢 “Pretty easy to interpret what that meant. This is streamlined and easy to read. It’s perfect.”
📢 “This shows me where I lost time and pace—exactly what I needed to know.
Heart rate page:
📢“Cool. Oh yeah. Oh wow… This is really cool.”
📢 “I really like how you can see your heart rate over time and see which specific movement it corresponds to. I think that's really cool. I like this page a lot.”
📢 “Amazing. This is great. Oh my God. Whoop gives you this, but this is better.”