At ElleHacks 2020, my team created an approachable iOS companion app that would teach young adults to recognize their emotions using natural language processing and provide resources to develop emotional intelligence.
Roles
Research
Prototyping
Business Model
Timeline
36 Hours
Many existing therapy apps on the iOS app store have limited input options for the user, are poorly rated, and don't provide resources to alleviate loneliness. We noticed, that while the audience of the therapy apps faced restrictions in expressing themselves, they were inquisitive and continued to track their emotions.
Our goal is to give students and young adults an experience that was simple, reduced input friction, and provided resources to navigate different emotions.
I conducted market research and competitive analysis. Using the pain points and constraints I focused on the flow of conversation between the app and users. I also prototyped the final product, and developed a scalabe business plan.
Design Goals
01
Track Emotions
Understanding emotions helps us manage them. By being aware, we can make informed decisions, prevent or avoid triggers of negative emotions, and communicate clearly to get support.
02
Provide Resources
Learning how to manage emotions can have positive outcomes despite negative emotions. Resources can offer: reflection, perspective, acceptance, and help in making rational choices.
03
Reduce Input Limitations
Understanding emotions helps us manage them. By being aware, we can make informed decisions, prevent or avoid triggers of negative emotions, and communicate clearly to get support.
04
Identify Patterns
Learning how to manage emotions can have positive outcomes despite negative emotions. Resources can offer: reflection, perspective, acceptance, and help in making rational choices.
Starting post-secondary means leaving behind valuable support systems. We wanted to understand how students dealt with this.
Target Users
We were most interested in people between the ages of 18-34. Students are the most vulnerable to loneliness.
We relied on two international students in the group for insights. We validated assumptions through secondary research.
Insights
01
Nearly 70% of students felt lonely over the past year.
02
Culprits include losing childhood friends & seeing picture-perfect social media posts.
03
40% of Canadians say they wish they had someone to talk to, but do not.
04
A change of environment can help combat feelings of isolation.
We need to understand which apps users are currently gravitating towards. What benefits do these applications provide our users? What are the disadvantages?
Key Points
01
Young adults are looking to vent and release emotions without relying on pre-existing support systems.
02
We want to focus on companionship rather than therapy to help alleviate loneliness.
03
Mental health apps provide the tools for mental success but do not address and relieve emotions being experienced in the moment.
04
Many apps restrict input options. Users feel the system is robotic, limited, and cold.
Woebot strives to coach users through chats and "offers insights and skills to help them grow into their best self". Woebot aims to help users with everyday stress and anxiety & loneliness.
Strengths
Weaknesses
1. Personalized Question
Prompt is focused on the user. UX writing similar to how friends and family would encourage and stimulate conversation.
2. Audio Input
Talking removes barriers, allowing the user to share raw feelings. Conversation starts with single button and feels natural.
3. History
Creates "journal-like" experience for users to track, revisit, and learn from their emotional history.
4. Recommendations
Insightful recommendations are accessible regardless of user's daily input. They will help the user learn about what to do in various emotional states.
Onboarding
Screening user's likes and dislikes will allow the algorithm to create recommendations based on the user's preferences.
Recommendations
Resources for the user to utilize on based on the emotions identified in the session, but are also accessible in the main navigation.
Identifying Emotions
Through ML (sentiment analysis), Ami identifies that Eden is excited and joyful and celebrates with him.
Answering the "Why?"
Tracking Emotions
Ami further helps Eden with identifying the emotions its responding to Eden with.
Why is this helpful?
Users are able to talk, chat, vent, track, acquire resources, and learn from Ami.
Companies such as Spotify and Headspace can be embedded to suggest recommendations for their products and services that are catered towards our users’ needs. Other revenue streams can include affiliate links, streaming services, destinations (museums/cafe's). By using this model we can easily add more sponsorships from a diverse range of services.
Ami was a thrilling experience and I'm really glad we were able to complete research, designs, and prototypes within 36 hours. To validate our solution, I would conduct surveys, user interviews, and spend more resources to gain insights on our target audience to validate our problem space. I would facilitate usability tests between each prototyping stage to test the ideas.
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