Overview
During my final semester (Spring 2021) at AU, I worked with Dr. Arthur Shapiro and fellow students Isabella Sims and Justice Suh in collaboration with ophthalmologists Mary Johnson, from the University of Maryland, and Amanda Henderson, from Johns Hopkins University, to develop an online tool to help diagnose photophobia in a clinical setting. I joined the project only after the first couple of weeks, but I got caught up on where the project was at that point.
Collaboration
AU held the Spring 2021 semester online, so Dr. Shapiro, Isabella, and myself would held virtual meetings (Zoom) every three weeks to discuss accomplishes and objectives. We would meet with our clients every three weeks through virtual means, as well. In terms of immediate questions and basic communication, we used email with our clients. Dr. Shapiro, Isabella, and I communicated consistently through Slack.
Roles/Tasks
- Coding the interface of the tests
- Transferred the code to Qualtrics
- Recorded an instructional video at the beginning of the tests
- Added disclaimer information and demographic questions to the beginning of the tests
- Distributed the tests to obtain initial results
Deliverables
The main deliverable was an easy-to-use, understandable platform that users can easily pick up on and that distributes the tests and collects data on them in graphical form. The two tests that we made were a Brightness Dots test and a Maximum Likelihood Difference Scaling (MLDS) test.
Tools and Technologies
Coding on OpenProcessing in p5.js to make tests, then integrating them into Qualtrics with JavaScript to host the tests and collect results from them.
Roadmap
Because I joined the project only after the first couple of weeks, by that point there were already some tasks completed. Dr. Shapiro and Isabella had coded the tests initially in OpenProcessing using p5.js.
Brightness Dots Test
Maximum Likelihood Difference Scaling (MLDS) Test
Then we took the code from OpenProcessing and implemented them into Qualtrics using pure JavaScript.
We added the disclaimers and demographic information to the tests, as well. Then we distributed the tests to several people to ensure that they were working properly and that they were easy to use and understand.
Final Product
Maximum Likelihood Difference Scaling (MLDS) test
Conclusion: Key Learnings
This was my first time working with a data-collecting tool. I also had never developed a technological tool for clinical purposes. I took into consideration the importance of the project because it will be a tool to aid health professionals in their practices. I also recognized the variations of JavaScript libraries and how they can be used for different purpose. For example, p5.js is more for graphic designs, and pure JavaScript has a multitude of functions.