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Quantified Self

Pondering on our ongoing conversations about the “quantified self” and relation to wellness, I decided to focus on tracking a damaging habit of mine. I’ve been biting my nails fervently since junior year of high school, likely spurred by an influx in academic stress and standardized exams. With my nails rarely reaching past my nail bed, I’ve tried everything from no-bite nail polish, to dipping my fingers in salt, and, in a particularly despite ploy, rubbing jalapenos on my nail beds. For this project, I tracked my nail biting habits from October 17th to November 3rd. by documenting each occurrence with the date and time via a note-taking application on my cell phone.

My research question is as follows: which dates, times, and days of the week during the two week recording period have the highest number of bite occurrences and why? Internally, I also wanted to consider the limitations of “quantifying the self” without a third party health tracking application. It is human nature to want to present the absolute best version of yourself. With kicking a bad habit, it’s a natural reflex to want to fudge the data to make yourself feel as though you are improving, even if you are not. There were several instances when I considered not recording nail biting occurrences for this exact reason, which may present some issues related to internal biases and data quality if I were to continue working on this project in the future.

Visualization 1: Occurrences/Day

This first visualization shows the number of bite occurrences from October 17th- November 3rd by day. I chose a line chart to be able to clearly visualize the pattern of my nail biting habit from the start to the end of recording. Overall, the number of occurrences seem to trend downwards from October 17th to November 3rd, with the highest bite count occurring on October 21st. I also included annotations for particularly high and low bite occurrences to provide some situational context for the data. For instance, the records of high bite counts seemed to occur on days of particularly high academic stress, and the lower bite counts seemed to occur on days when I was attending social gatherings. I also noticed that I seemed to bite my nails more frequently on days when I slept poorly, namely October 24th, October 26th, and November 2nd.

Visualization 2: Occurrences/ Day of Week

My second visualization shows the number of bite occurrences summarized by the day of the week. I decided to use a bar chart to be able to clearly visualize the total number of bite counts by each individual day of the week. This visualization clearly shows that Monday and Wednesday had the highest number of bite counts, while Sunday had the lowest number of bite counts. The high bite count on Wednesdays may be explained by the fact that I have two classes Thursday evening, and usually reserve Wednesday as the day to complete assignments for both classes. The stress of procrastination may have led me to bite my nails at a higher frequency. On Sundays, I usually attend a group potluck with friends. The combination of social enjoyment and participating in activities that involve both hands and all ten fingers (cooking, serving, and making drinks) may explain the non-existent bite count.

Visualization 3: Occurrences/Hour

This final visualization shows each bite count by the hour of occurrence. Initially, I wanted to visualize the data in a clock-like manner, with each bite occurrence visualized at the time of occurrence in a radial bar chart. However, I am still learning about Tableau’s time functions and was unable to create the visualization the way I initially planned to. Instead, I chose a circle chart indicating each occurrence of nail biting by the time of occurrence. The highest counts of nail biting occurred in the late morning to early afternoon, or 11:00 am-2:00 pm, with the remaining hours exhibiting a relatively steady stream of occurrences.

Dashboard

Final Thoughts and Next Steps

Overall, I think this was a solid start to kicking a bad habit, and provided some interesting insights on the merits of the quantified self. One major detriment to me during this project was the overall unwillingness to document instances of an embarrassing habit. As I myself was recording this data without the help of a health-tracking application, I had to make a significant effort to hold myself responsible. If Apple decides to one day include a feature that automatically documents when you bite your nails, similar to the step count in the Health application, then I will be immediately signing up.

One adaptation that I would consider moving forward is including separate data from a health-tracking application to see if external factors are influencing my nail biting. For example, while creating the first visualization, I noticed that I slept poorly on some of the days with higher bite counts. A more impactful future project may include data collected via a sleep documenting application, combined with the self quantified bite count data, to see if there is indeed a relationship between sleep quality and nail biting.

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