PainCare
Mar. 2019
A conversational mobile app that helps tracking patient's chronic pain
My Role
Designer/Researcher
Duration
1 year
The Context
This project is my graduate thesis project. I created a self-care application for patients with chronic pain to track their symptoms. The reason I chose this topic is that I have close relatives who suffer from chronic pain. I've always been interested in learning more about people's motivations. I took this opportunity to experiment with motivational psychological theory in interaction design for self-care.
The Process
Because this is also an academic research project, I conducted theoretical research to identify the theories to experiment. The idea is to stand on the shoulder of the giants and build upon the previous academic success.
User Research
I conducted six user interviews to learn about the patient's life and daily struggles. I also created a survey to gather information from a larger patient group.
Testing Methodology
25 patients
All participants tested have at least one type of chronic pain.
A/B testing
We're testing both the proposed design and the original design with every participant.
Survey
Participants fill out a survey after they used both prototypes.
Result analysis
We compare scores for all metrics.
Correlation analysis
Calculate the correlation between overall interest and the satisfaction of the three key needs.
Proposed Design
Give user choices
Users can select areas to focus on and improve.
Awareness
Help user see what they get after logging
Simple & easy
Simplified questions and flow
Conversational and
colloquial language
Use a conversational interface and daily language
Positive language
Use a positive tone only
Easily shareable
Share information with their care providers
Prototypes
It took me a while to find the best way to prototype for the conversational user interface. I used Axure to build the final prototype, which was able to simulate a real and intuitive experience.
The Evaluation
To evaluate the results, I developed a list of questions and metrics for the final user evaluation. A total of 25 patients have participated and completed the testing and evaluation.
Correlation Analysis
I conducted a correlation analysis to understand the results. Results from both design prototypes were gathered and analyzed to see the difference. Correlation analysis does not show cause and effect, and it is still an excellent way to see what works and what doesn't, and how it relates to other metrics.
Results
The results of this testing were generally positive. It especially highlighted the importance of positive messaging and content design, affecting whether a user would like to continue using the product. Based on the results, we can conclude that designing for autonomy and relatedness has a positive effect on encouraging continual usage.
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