Zena — Identify Moments of Need & Make Recommendations to Feel Better
Designing a solution to assist in panic attacks
A few weeks ago, I attended a Mental Health Hackathon focused on creating solutions in the mental health field. There were hundreds of attendees, each with the goal of making something that will improve the lives of others.
Initially, I wasn’t sure what kind of solution I wanted to create and didn’t really have ideas in mind that I wanted to work on. After listening to a pitch session, I went up to Ren, who had the idea of making an application to help ease intense feelings in moments of need.
A team of us who were interested in this idea came together, and we built an app concept called Zena, which assists the user in the most critical moments and offer actionable solutions that may help them in that moment.
Our team was made up of Ren Lee, software engineer interested in hardware & original owner of the idea; Anamika and Suresh Khanna, product strategist & writer; Anil Vaitla, Belinda Chin, strategic contributors; and me, the product designer responsible for visuals & UX. We also had amazing mentors — Kwiri Yang, and Dr. Ning Zhou, who offered expert advice in the field of business and psychiatry, respectively. We had less than 24 hours to work on the project once we had the team together.
We had a lengthy discussion about the problem we wanted to solve and features we wanted to implement. Everyone had great ideas, and I led a brainstorm session with the team, combing through and combining ideas, refining our approach in order to make the most efficient use of our time.
Our Process
1. Defining the problem
What is the specific problem we wanted to solve?
We wrote on sticky notes, talked through our ideas, and gathered our thoughts. The problem then became clear:
How might we identify critical moments and make recommendations on how to feel better?
We defined a “critical moment” through a specific use case — a panic attack. When someone is going through a panic attack, they are frozen in the moment. Coping skills that they might be aware of or have used in the past are buried deep, not immediately retrievable or executable — because the person is physically under pressure. They are sweating, their heart is racing, they’re feeling faint and feel like they might be going crazy. It’s a frightening experience that is hard to come out of, no matter how much you’ve experienced it.
We talked extensively among our teammates who have experience with mental health issues; they describe panic attacks as something that’s difficult to come out of because they’re completely taken over by it. With the knowledge of their personal experience, we then framed the problem into three parts:
a. People don’t always understand how they feel
b. People don’t have the skills to change how they feel, especially in the moment
c. Many don’t have the time or money to see a therapist to help them practice their skills
2. Exploring solutions
What might the solution look like? We explored ideas on how best to solve the problem at hand.
Some of our ideas included an AI chatbot, using CBT (Cognitive Behavioral Therapy) and DBT (Dialectical Behavior Therapy) methods, reminders of coping skills, database of coping mechanisms, etc. Through it all, a unifying theme emerged, which was finding a way to measure & record biometric data in order to quickly identify someone’s critical moment.
3. Defining our audience
Who is our audience? Who should we target?
In our discussion, two distinct groups of potential users emerged: the person who needs occasional assistance in managing random outbursts of intense feelings, and the person who has been diagnosed with a condition who needs ongoing assistance in managing their illness.
We discussed what we wanted to do and decided that our audience, for now, will be the latter — those who have been diagnosed and need regular assistance. This way, we can use algorithms to constantly add coping skills and provide suggestions to the user that may work best for them.
Challenges
Throughout our process / discussion, we dealt with two major challenges:
1. Determining what data to use
The human body is complex. There are many types of biometric indicators that can be measured. We ultimately chose heart rate to use in our prototype, because it is an easy-to-gather metric. Due to the fact that we did not have access to wearable devices during the Hackathon, we allowed users to manually input data to better engage them where biometrics through wearables aren’t accessible. Heart rate is something that’s easy to measure through pulse and the usage of a timer, so we thought it was appropriate.
2. Making it approachable
We talked extensively about making the app approachable and easy to use. This is crucial as we do not want to further agitate someone who’s in the middle of a stressful body response. We decided to use language that is nuanced, that evokes trust, and that isn’t triggering. This way, we are better able to assist the user in critical moments. An example is, instead of telling the user “You’re having a panic attack, here’s what to do”, we decided to use phrases such as “I’ve noticed a change, can I assist you in some way?” and “try these below” for coping skills that the user can adopt.
The goal was to act as a guiding hand in making the user feel better while appearing caring and neutral. As we further explored this personable / neutral trait, we named our app Zena — a play on the word “Zen” — a name that was approachable in nature, to give it a feel of a personal assistant.
Our Solution
Our final product concept is a mobile app that connects FitBit for biometric data, and identifies critical moments through heart rate. Because we did not have a FitBit or other wearable devices on hand, we devised a solution where the user can input manual data, including heart rate, temperature, and mood, mostly for demonstration purposes.
With our use case panic attack as an example, the app provides suggestions on how to best cope in the situation. It would alert the user with a notification when such a physical state is detected, and offer actionable suggestions on what to do to feel better.
Suggestions can go anywhere from touching pressure points on your face, walking a block down the street, or taking a series of deep breaths.
The app also allows the user to input coping skills that has worked for them in the past, so that we can capture a database of actionable coping skills that we may be able to pull from to give to the user.
Because of our time constraint, our app concept was designed using Adobe XD by me. It’s a visual prototype that shows our core features. See it in action below.
Demo
In our prototype, we have made a very crude decision where inputting a heart rate of 120 bpm will output suggestions for dealing with a panic attack. We are aware that this is by no means a scientific measurement or conclusion that always rings true. In real-life development, we would have a much more sophisticated way of measuring and delivering an assessment that best fits the physical response of the user. Ideally, data would come from a wearable that can capture biometrics much more effectively and holistically, which would allow us to deliver a more nuanced assessment of someone’s wellbeing in that moment.
You can also try the prototype below.
For the Future
We limited the amount of features on Zena due to time constraint. Looking into potential developments, we think that there are many additional ways where Zena can expand. These include:
- Using additional data sources, including brain waves or someone’s browsing behavior,
- Supporting areas beyond panic attacks such as substance abuse or other types of distress,
- Adding customized recommendations, such as loading Spotify playlists, and
- Improving recommendations based on learning from outcomes.
If Zena was to be made into a real product, many additional considerations will need to be taken in order to create a solution that solves a specific problem for a specific population. We will need to decide what features we want to include, and what are just fluff that we don’t necessarily need, in order to create the best value for the users we’re targeting.
Thanks for reading! Feel free to tweet / msg / stalk me on the interwebs @sheneral. Or see my work at gaodesigns.com
