How Usability Testing Supported and Shaped the Design and Development of the JOOL Platform



Can we wow users by providing a forecast of their energy and willpower for the next day just by knowing their zip code?

Can users quickly grasp how to use the app?

Do users understand the different terms used within the app?

Which sections of the app should the users be exposed to during their initial onboarding?

These were the types of questions my colleague Mandi and I helped answer over the course of last summer and fall as User Experience Interns at JOOL Health. It was the first time I was working on helping design a mobile app and it felt great to be embedded as a team member in the early stages of this exciting startup. As a company, JOOL Health is committed to an agile, user-centered design approach and this ultimately meant our conducting more than 100 hours of usability testing to gather feedback before investing time and resources in fully developing any new features on the app.

Mandi and I scouted coffee shops in Ann Arbor, popular hangouts at the University of Michigan, and met with employees at Steelcase and MHealthy to conduct a series of usability tests to help clarify various product and design questions. During each usability test, we’d meet with 5 to 6 users and observe them using a prototype of the feature. Followup questions would then help us understand which aspects of the app worked for them, which features they liked, and the areas needing more clarity or refinement.                                 

Based on the feedback we received from these usability tests, the JOOL team would adjust the designs or, in some cases, totally reinvent a specific feature. Sometimes, the findings were significant enough to warrant a radical rethinking of our approach.

Tomorrow’s Outlook

The JOOL platform’s Tomorrow’s Forecast feature is a perfect example of how this process worked to our advantage. We first tested a prototype version of our “forecast” interface — one in which users are presented with a projection of their energy and willpower for the next day merely by entering their zip code.


When we introduced this concept and interface to users, we received plenty of questions back.

Would everyone in the same zip code get the same forecast? How are you able to predict my energy and willpower? What does confidence mean? Is it my confidence or the app’s confidence in the energy and willpower?

Based on this feedback, we updated this section by requesting two additional pieces of information: age and gender. We also removed the numbers to reduce cognitive load for the user and make it easier for them to process the information visually.


Still, users seemed confused and skeptical. What we thought would be a truly intriguing feature to offer up immediately continued to be problematic for many test participants. In response, we radically modified our approach. First, we decided to make this feature available only after our JOOL Insights Engine (a powerful back-end correlation algorithm) had adequate time to create an individualized personal model, lending users more confidence in our assessment of their expected energy and willpower. We also decided to rename the section “Tomorrow’s Outlook” to make it more relatable and less potentially daunting.

In the most recent update of the Tomorrow’s Outlook section, we decided to simplify it further by indicating whether a user would have low or high energy and willpower, the determining factors behind the outlook, and an actionable tip to help the user make the most of the projection.


This is just one example of how our rigorous usability testing played a significant role in determining the best approach to building our MVP (Minimum Viable Product) version of the JOOL app. The feedback from usability tests consistently informed the direction we took in designing and developing the app, affording Mandi and I a great opportunity to apply our coursework from the University of Michigan’s School of Information to help build a powerful and entirely unique mobile app experience.

– Ram Kumarasubramanian (University of Michigan School of Information)