(The sound quality is a bit rough so I recommend wearing headphones.)
I prototyped an app called 50UL in order to demonstrate that although abstract experiences such as human emotion seem difficult to quantify, with enough data and the proper technology, we can track our “soul” just like we track physical health. The name of the app is pronounced “soul” and is written in Leet (or “1337”) which is a writing system that uses character replacements that resemble the original symbols. In this case, the 5 is meant to look like an “S” and the 0 is the letter “O.” The app is named this because it symbolizes the quantification of something abstract, in this case, the “soul.” The number 50 is important because it represents neutrality, or balance, if we’re looking at a scale from 1 to 100 (though my app uses a scale of 1-10 to make things easier), and I’d like users to have the goal of achieving balance in their mental lives.
The idea arose from the frequent request to “rank your mood on a scale of one to ten.” This is a very difficult task because when it comes to intangible things like emotions, we are unaware of the difference in magnitude between a three and a four. Additionally, some people’s three might be another person’s six. And even if we did find out what emotion a three is (let’s say it’s anger) mental status is dynamic and changes over time.
My argument is that with enough data, we can create the parallel to a sampling distribution, in which tons of aggregated data will converge towards one point. For example, it is possible that initially, one ranks tiredness, anxiety, and anger all as a three. But over time, as they log more and more data, anxiety-associated words such as “worried,” “nervous,” or “antsy” are consistently ranked around three. Therefore, a subject will know exactly what a 3 feels like.
The significance of all of this is to be able to capitalize on our data just as we do in other ways we self-track. A good parallel is dieting apps on which people calculate their metabolic needs and log calories or exercise to achieve a goal of either losing or gaining weight. It’s important to note that everyone has different caloric needs and set points, making these apps quite inaccurate. By using them, we essentially compare ourselves to the “average” that has been determined by mass amounts of data. In 50UL, however, there is no population average emotion scale to be compared to. Each individual determines their own scale of feelings and then uses that to achieve their mental and emotional needs.
The app has several features to facilitate this. For example, there are five categories: work, relationship, family, friends, and self. The app enables users to see which part of their life is experiencing difficulties. Our emotions are more interconnected than we think, and it’s hard to pinpoint what exactly is making us feel what we feel or act the way we act. For example, low self-esteem can lead to isolating behavior patterns that affect connectedness with other people. This is inherently a “self” problem though it may be perceived as a relationship or friends issue. The idea is that a user would be able to “biohack” their soul, and be aware of which part of their life they really need to be focusing on.
Another feature of adding entries is adding detail. This part is for the user to evaluate repetitive patterns that are leading to the same situations, good or bad. For example, one may find that they feel a lot more content and focused in the mornings if they hadn’t picked up their phone first thing. Users are expected to see recurring lead-ups to certain situations and follow up by adjusting those behaviors to match their needs.
After enough entries have been inputted for each number to have a situation or emotion, the app will prompt a ranking by relevance. So if someone is experiencing “happiness” at 7.3, they will be shown several points around 7.3 and be asked whether what they are feeling is better or worse than “excitement” at 7.4 or “relief” at 7.2. This way, the app and the user can gauge a better idea of where their emotions fall in relation to other emotions. This may not always be consistent, for sometimes “happiness” can feel dull and not as high as 7.3. This is where we must keep reminding ourselves of aggregate data.
The app also incorporates other technologies such as a heart rate monitor, hormone monitor, menstrual calendar, and facial expression detector. An external device such as a watch or heart monitor will measure heart rate at the time of entry and accumulate this data to find any relations between different emotions and beats per minute. The idea is to use this data to predict one’s emotions before they occur. For example, the app may report that on gloomy days, one’s waking heart rate is 5 beats lower than usual, leading to lack of energy and depressed mood. Rather than finding a reason for their mood, users may recognize that their soul is reacting to a certain trend. It can also be used to understand long-run data, like Talithia Williams discussed in her TED Talk. A user may know that even at their most relaxed or depressed state, their heart rate never goes below 50 and move forward with that knowledge.
A chip in the individual’s body would detect hormone levels as frequently as possible, and the user would be expected to use that information in the same way. One may find that their endorphins are very high after a walk and that leads them to approach work with a positive attitude. These are more obvious examples that are already popular in health magazines today, but the idea is that new discoveries could be made. The menstrual calendar would be very useful for females with a regular cycles who experience emotional fluctuations before, during, or after their cycle. A logged emotion of “frustrated” may feel like a 3.4 normally but like a 1.8 during their cycle. Users can see this and recognize their emotions as a byproduct of physiological functions in their bodies. This could be used to discipline oneself. I know that I get irritated very easily if I am hungry, but knowing this helps me deal with things in an objective way and not let my emotions get the best of me. Lastly, the camera would detect facial expressions and associate them with emotions. This may not be very useful for normal people, but it could be useful for machine learning. It could also be useful for mentally ill individuals who can behave unpredictably. For example, my mother’s bottom lip quivers while slightly pouting when she is uneasy in a situation. The camera can pick this up and make associations going forward.
The app also doubles as a journal, not only because of the option to write journal entries but because of the fact that users have an entire database of emotions and situations that they have experienced. I found this fitting because many of the self-tracking pieces we read also discussed how the reason people self-track is to better understand themselves, and in my opinion, journaling is the best way to do that for emotions. However, it is also the most vulnerable information to be inputting in an app. In an ideal world, this app would not collect data on individuals for benefits, though doing so may provide a lot of insight into psychology.
The app has a clean and calm UI in order to make it more approachable and to make graphs and statistics easier to consume. I remember when I first got Facebook, I was very overwhelmed by all of the columns on the side and the variety of buttons and icons. This app opens up by asking the user for their preference of color, establishing user agency and thus appealing to the user. The font is also very thin and the background is white. Minimalism is the best way to make something very complicated and cluttered, like the mind and emotions, feel simpler and easier to process. Additionally, color is involved with the mood scale, for often times, emotions are associated with color.
In an app that involves inputting information, it’s important to not overwhelm the user with options to update their feelings yet make sure that the user is entering data frequently enough to benefit from it. Because of this, I decided the default notification setting would be four times a day, breakfast/morning, three hours later, around dinner, and before bed. Users could also choose to opt-out of notifications and make entries when they feel like it, or they can update it to hourly notifications to speed up their data collection process.
Perhaps the most useful part of the app would be the data and statistics page, which would show average moods daily, weekly, and based on category. There is also a calendar that has a different color for every day’s average mood because color is easier to see and quickly analyze than reading numbers. Additionally, the app would generate graphics that show other correlations it has found, such as which three categories are currently most pervasive in the user’s life or which emotions are associated with which app’s usage. This is the part of the app that really links to the thesis which implies that the data gathered will be useful to draw conclusions from.
That summarizes the basic features of the app. Of course, it has its faults as well. For one, it may take a very long time for data collection to actually be useful. With a scale that has 100 different points, it will take time to fill that up, especially of certain emotions are repeats. This is why I think it would have been interesting to consider an update that has pre-filled emotions on the scale, based on all of the aggregate data it has collected on users (but that would require violation of privacy). Therefore, when a user opens the app, “passionate” may already be at an 8 and users can place an emotion around that until enough entries are gathered to have a scale truly unique to the user.
Another area where I think I failed was embedding citations. Because I wanted the app to have a clean look, I tried to minimize words and explanations, though looking back, I realized I could have embedded sources in on the data analytics screen or as notifications from the app. Also, the Self-Tracking reading by Neff and Naufus describes how it’s important to consider different backgrounds before making sweeping statements about where a person’s health should be. I feel like that is especially important in mental health app in which a lot of factors can play into how one ranks emotions. This prototype only demonstrated the basic functions, but we can assume that the initial account-creation stages would involve inputting more information such as gender, race, socioeconomic status, mental health status, age, and more. This prototype shows the menstrual cycle feature, which could impact emotional perception, but there are definitely many more factors at play.
I want to make sure this app is distinct from other “mood trackers” because it is meant to go a step further and allow users to draw numerical conclusions from their data. The app has journal properties that could distract from the fact that it is meant to simplify complex emotions rather than dig too deep and complicate them. Things like fitness apps carve out a clear goal: to reach a certain number of calories eaten or burned or minutes worked out. However, this is harder to do with an app like 50UL. Do users want to get to an average mood of 7? Why not 10? Is that shooting too far? My personal recommendation would be that 5 is equilibrium, and that is where people should want to be at most times, but I am biased. It’s difficult to answer these questions because we don’t have tons of research on what the average population’s steady state for emotions as opposed to things like diet. The average person should eat at least 2000 calories a day, but we don’t know how many emotions below rank 5 are okay in one day. This inherently leads tracking the soul to be building off of a nonexistent basis.
Overall, however, I can see an app like this generating enough information to develop a basis. Maybe one day we will know the average mood of a human being on a good day. Maybe humans will change their behaviors and be less prone to making bad decisions in order to get there- now that it seems more concrete and structured to do so. At the beginning of this project, I actually didn’t believe that human emotions are quantifiable. However, I almost convinced myself otherwise. Though human emotions may not be generalizable to the entire population, they can definitely be monitored on a person by person basis.
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