Ren feeling isolated, out of control, and with no aid when she is having a panic attack.

What it does

We are solving a three part problem: People don’t always understand how they feel People don’t have the skills to change how they feel, especially in the moment Many don’t have the time or money to leverage a therapist to help them practice their skills

And in every scenario, no one is leveraging data to solve this problem.

So we created Zena.

Zena is a life assistant in the form of an app or a bot that can live in different ecosystems which allows a user to:

  • Collect data in the form of biometrics, geo-spatial, user inputs on how they are feeling and many other methods.
  • Analyze the data to detect patterns and identify critical moments for our users.
  • Recommend skills that the user can apply in this critical moment. These skills can be input by the user, based on core therapy techniques like CBT or DBT or loaded in by a therapist for a specific user.
  • Track outcomes for the user and across users to improve pattern detection and recommendations

How I built it

Functional prototype built on Adobe Experience Design

Challenges I ran into

  • Determining which data to leverage. We chose biometrics like heart rate because they are accurate and easy to gather leveraging existing devices. We also chose user input data to engage the user and get detailed data beyond biometrics.
  • UX must be sensitive to building trust with users and not triggering users further in critical moments.

Accomplishments that I'm proud of

  • Practical, people will actually use this
  • Complete prototype foundation from which we can actually build

What I learned

  • Keep it simple, easy to use and frequent use
  • Critical to be sensitive around language to the user

What's next for Zena

  • Expand data sources, including voice notes, diary entries, brain waves
  • Recommend more critical moments
  • Support areas beyond panic attacks such as substance abuse, suicide prevention
  • Add functionality around recommendations, such as loading Spotify playlists, changing the smart lighting or temperature in your house.
  • Improve recommendations based on learning from the data on outcomes
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