How can we better inform people who have sleep apnea about their sleeping patterns and empower them to have meaningful discussions with their physicians?


Doctors often prescribe CPAP (continuous positive airway pressure) machine therapy to sleep apnea sufferers. A CPAP machine generally consists of a mask that covers the mouth and/or nose, and it increases air pressure in the throat to prevent the airway from collapsing. While regulating breathing, CPAP machines can also record vast amounts of data, which can then be viewed and analyzed during medical visits by physicians.

It is our contention that people would be helped by gaining a better understanding of what happens while they sleep, enabling them to become better educated about their sleep history, and finally, to empower patients to have informed discussions about their sleep experiences with their physicians.

Our Process

Previous Work

First we did research on previous work in which we evaluated the effectiveness of two different CPAP visualization tools: SleepMapper and SleepyHead. We found that one offered too much information and the other offered too little. We wanted to create a visualization tool that stands in the middle for average CPAP users to view their own data on in a meaningful way.

User Research

Then, we did user resarch through in-person, semi-structured interviews. We discovered that most users have a heavy reliance upon their doctors to interpret their results and to inform them of any irregularities. When we conducted web research to investigate CPAP machine user statistics, we discovered that most people who are diagnosed with sleep apnea are male, overweight, and over the age of 40, another similarity shared between our participants. Although sleep apnea can affect anyone at any age, even children, our interviewees happened to be typical users of CPAP machines. From these as well as our previous work research, we created a provisional persona.


Then, I created initial sketches based off of the data points we found most imporant to users, keeping in mind our target users by referring to our persona.

CPAP Sketches


Of all the data measures available, respondents expressed highest interest in AHI (67%), apnea (61%), leak (60%), average AHI (54%), and pressure (51%). Based on this feedback, we then focused on those data points deemed most significant and extractable from our users’ SD cards. Then we utilized them in our next step: The Card Sort.

Card Sort

We conducted a card sorting exercise via OptimalSort, an online card sorting tool, and used terms that we identified from our interviews and surveys. In this round of research we had three CPAP user participants who helped us by sorting the terms into three categories: Important, Somewhat Important, and Not Important. Amongst their results we found that they all agreed that important terms included: AHI, Apnea, and Average Hours of Use. The popularity of the terms also coincided with our findings from the earlier survey.

Tableau Visualizations

Based on the results of the surveys and card sorting exercises, we incorporated participant and respondent preferences within our visualizations. We began with ideation as each team member experimented in Tableau to develop visualizations that incorporated user feedback. Afterwards, we collectively critiqued all visualization concepts and identified the designs with the most promise. We then began the task of relating visualizations to one another in a way that would introduce context to the dashboard stories. These dashboards were then tested by potential users by having them attempt a series of tasks. Finally, we refined our dashboards once more and arrived at our final versions.

User Evaluations

We created a usability test for our two initial dashboards below. We administered the tests to two CPAP users, and one Non-CPAP user. The tests consisted of four tasks for each dashboard and a questionnaire administered at the end. The first task used a “think aloud” protocol to acquire users first impressions of the data visualization. The remaining tasks were designed to evaluate how easy it was for users to answer questions about the data. The difficulty of the tasks increased as users moved through each. The results of all user tests were analyzed in a team review session. We discussed initial “aha” moments, reviewed the responses for each task, and noted any consistencies and discrepancies that were discovered.


From the list of findings we recieved from our evaluations, we made the following revisions to our visualizations:

  1. Changed the color scheme to adhere with a color-blind effective palette
  2. Added titles for the dashboards
  3. Clarified names of the data points
  4. Improved definitions
  5. Edited the names in the x and y axes to be more descriptive
  6. Increased the font size for the axes
  7. Eliminated AHI from dashboard 2
  8. Matched y-axis on dashboard 2
  9. Fixed the legends
  10. Disabled clickable legends since they do not filter and sort
  11. Included a help button with definitions in one consistent location

We also hosted them on a Wix website. By doing this, we were able to create a tutorial for first-time Tableau users. We were also able to congregate all the data point definitions into a "help" page.


Click on the images to redirect to our visualization website.

Chip Conner
Gail Thynes
Tanner Page
Jennifer Wong

Final Report
Tableau Visualizations

Research Findings
Design Sketches
Card Sort Results
Survey Results
Usability Results
Tableau Dashboards

Google Form