Motivation
When talking about eating, what is the first thing that pops up in your mind? When we look at the applications in the market, we have seen tons of apps focus on tracking food nutritional information, such as carbs, calories, sugar, etc. The goal of current tools is to promote awareness of healthy eating and potentially change eating behavior. However, eating experience can be influenced by a variety of dimensions, including elements that are external to an individual (e.g., time, location, companion, weather, distraction) and elements that are internal to an individual (e.g., mood, how hungry you are). In this project, we are interested in visualizing eating experiences to promote self-reflection on eating behavior.
Data
Since we didn’t find datasets suitable for the scope of this project, we collected the data by ourselves. We designed an eating experience journal using Google Forms and used the convenience sampling method to collect our first available primary data source. Oral consent forms were obtained from the participants who agreed to contribute their data. We received 420 responses from 10 users (4 male and 6 female) from February 16 to March 24, 2018. The data types include categorical, ordinal, and quantitative. To be specific, categorical data includes usernames, meal type, food content, eating companion, location of eating, eating distraction, weather, and mood. Ordinal data includes hunger scale before and after eating. Quantitative data includes date and time.
Task Analysis
INTERVIEW
Our team interviewed Professor Carmen Sceppa of Bouve College of Health Sciences, Northeastern University, on the merits and methodology of our project on visualization of the eating experience of people. She believed that we had all the subjective domains (e.g. food, environment, emotion level etc.) needed to visualize the eating experience. Furthermore, she added that, as implemented in our data collection system, it was a good idea to take company, distraction, taste of actual food, and after-effects of food into consideration as components of the overall eating experience. We also came to know about current dietary intervention in general and ways to help people promote self-reflection on eating behavior from an expert’s point of view. In her opinion, dietary intervention is following dietary guidelines that involve eating all food groups (fruits, vegetables, dairy etc.) and maintaining a regular food habit. She said that incentives for eating can help people promote self-reflection on eating behavior. By incentives, she meant “relating the benefits of eating healthy to what it means to improving health or maintaining health”. The takeaways from this interview are- (i) the key to eating well is to enjoy a variety of nutritious foods from all food groups, and (ii) external and internal drivers contribute to deciding what and how much to eat. So, we focused on external and internal factors when visualizing eating experience to help people self-reflect on eating behavior.
TASK TABLE
Based on our interview notes and background research, we created a list of domain asks on self-reflection on eating behavior.
| ID | TASK | HIGH LEVEL | LOW LEVEL | MID LEVEL |
|---|---|---|---|---|
| 1 | View summary of an individual’s eating pattern | Consume: Present | Search | Summarize |
| 2 | Identify eating regularity | Consume: Discover,Present,Enjoy | Search | Identify |
| 3 | Explore how internal elements (mood and hunger scale) influence eating pattern | Consume: Discover,Present,Enjoy | Search | Identify |
| 4 | Explore how food satisfaction, mood, and eating effort are related | Consume: Discover,Present,Enjoy | Search | Identify |
Design Process
Initial Sketches
We began the design process by preparing sketches of data collected during the survey period. Our initial sketches included various visualizations including timeseries of an individual's eating pattern, simulated map showing location-based eating habit, cloverleaf like analogy to summarize a user's diet, radial graph visualizing the gist of the diet, and bar chart to depict the effects of emotion on participant's level of food craving. Finally, we decided to pick and choose the best of our ideas taking into account the overall scope of the data we collected and settled on the following: a scatter plot to show the overview of eating pattern, a variation of box-plot to show the hunger level, a bar plot to explore how internal elements (e.g. mood) influence eating pattern, and a map-based visualization to identify how eating location is related to foods people eat.
View Initial SketchesInteractive Sketches
After settling on the data and the types of chart to be used in our visualization, we began to brainstorm ideas to include interactivity and color. We decided to use categorical colormap from colorbrewer to depict meal types in our scatterplot. We used filtering, brushing and linking, linked highlighting, and details on demand technique in the scatter plot for interactivity. When any of the circles is clicked, hunger level graph, bar plot with depiction of satisfaction with mood, and location-based visualizations will be shown - all of these again support details on demand.
View Interactive SketchesFinal Visualization Design
After data analysis, we had to make a few last tweaks to our interactive sketches. Instead of a variation of box plot to show hunger level, we resorted to a line graph to clearly visualize the change in hunger level over time. Moreover, we decided not to use map-based visualization as we couldn't find any interesting variation of eating location of people. Instead we agreed to present an individual's eating pattern using parallel visualization. We kept the scatter and bar plots from the previous design phase.