Human-Computer Interaction, Computer Science Department, UIUC
Tools: Xcode, Swift, Sketch
Studies have found that as screen time increases, the risk of mental illnesses runs higher. However, spending time outdoors in open, public spaces has been proven to lift moods and combat conditions such as depression and anxiety. And just as people track their steps and their meals, people want to track their moods. Yet despite a positive link between open spaces and mental well-being, there are few applications that enable location-based mood tracking. Our team explored how we might offer data-driven insights on an individual's mood and motivate them to explore public spaces.
To begin tackling this issue, first we needed to learn more about what prevented people from going outside and exploring the free, public spaces nearby.
• "I try to take a 30 minute walk every day but I get bored of the same places."
• "Some days I can't motivate myself to get out of my apartment."
• "I just moved, so I'm still getting used to the area. I wouldn't know where to go."
Based on our preliminary interviews, we identified the following two themes. Barring bad weather, these reasons are the root of why people tended to stay inside:
A location-based iOS app, Emotigo, that breaks past the virtual barriers that prevent people from exploring open spaces and incentivizes individuals to track their emotions. Emotigo...
- prompts users to "check in" at nearby public spaces and log their current emotions
- suggests new places to check out based on user profile
- rewards check-ins and consistent tracking
- builds empathy for others in the community by displaying average data in a space
- offers insights and analytics on fluctuations in mood
The quantified self
In the age of Fitbit and food diaries, people are obsessed with data--especially data about themselves. With the help of technology, people are tracking their sleep, health, spending, fitness, and more. The quantified self movement, also called "lifelogging", characterizes our thirst for personal informatics. In a similar vein, our app leverages this movement, empowering users to record and track their mental health as it relates to their environment.
Applications to urban planning
We realized this could have powerful implications from a city planning perspective. With access to mass public data, urban planners can pinpoint areas where people generally feel unsafe or unhappy, and target those areas for improvement. The public comments are also provide an opportunity for the community to give feedback to their local government and communicate issues in their neighborhood.
Emotion indexing: the power of emojis
Emojis are a fun, simple way to represent emotions. People are already comfortable expressing themselves through emojis, so this reduces confusion and eases the learning curve. Rather than have users write long journals about their feelings or respond to restrictive prompts, selecting emojis is simple and quick. After all, our goal is to minimize screen time and make logging emotions as streamlined as possible. Emojis encourage users to respond almost instinctively, rather than overthinking their responses.
Next, we had to narrow our scope and quantify the emotions people might want to record. We needed to keep it simple, yet complex enough to capture a sufficient range of common mental states. After some deliberation and research, we came up with the following eight core emotions:
To quantify this further, we decided to add levels (ranging from mild to severe) to several of these emotions. This gives users more control over their logging and adds complexity without being cumbersome.
Based on user input, we also added productivity and energy levels.
Based on the statistics of other self-tracking apps, such as fitness loggers, we know that people tend to abandon personal tracking after three months (on average). On top of this, users with conditions like depression and anxiety would be more likely to lose motivation while logging.
I. Smart notifications: To combat logging fatigue, we incorporated gentle reminders for when a user hadn't checked in recently, along with quick suggestions on nearby locations to check out. Through tracking fluctuations in a person's mood, we could identify if they were feeling low, and suggest locations that they reacted positively to in the past.
II. Reward system: To incentivize check-ins, users are rewarded with points after each entry, and accumulating points leads to achievement badges and rewards. This acts as positive reinforcement for check-ins, congratulating users for their entries while also fostering some friendly competition among friends.
This was our first iteration of our application, which we programmed in Swift 5 and implemented in Xcode for iOS development. We tested using Xcode's iPhone 8 simulator.
Check out the demo on Github!
As both a designer and developer on this project, I loved being able to tackle this issue from not only a design perspective, but a technical point of view as well. In trying my hand at iOS development, I found Xcode's dev environment robust for storyboarding, interface building, and testing. While this project was my first foray into iOS development, I'm sure it won't be my last.
Exploring the quantified self and lifelogging movement was also fascinating, as self-tracking seems to be growing faster than ever. It was challenging to explore how to represent emotions and mood analytics, and also fulfilling to encourage public spaces in the community.