A data visualization-based self-management tool for youth with chronic pain
We designed myWeekInSight, a visualization app to help kids manage their chronic pain
Team Member (January - April 2021)
Project Lead (May 2021 - December 2022)
January 2021 - December 2022 (24 months)
Adobe Illustrator, Adobe XD, React.js, Balsamiq, Procreate, Adobe Premiere Pro, Miro, PowerBI, Tableau
Data Visualization, User Interviews, Affinity Diagramming, Literature Review, User-Centered Design, Prototyping, Requirement Gathering, Persona Building
Devarsh Bhonde, Rubia Guerra, Katra Farrah, Haomiao Zhang
Dr. Katelynn Boerner, Dr. Tim Oberlander, Dr. Karon MacLean, Dr. Tamara Munzner, Haley Foladare
myWeekInSight is a self-management tool for youth with chronic pain, that uses data visualization to help youth reflect on and manage their symptoms holistically. It uses a combination of daily surveys and data visualizations to represent youths' daily lived experiences.
myWeekInSight began as a course project at UBC. However, at the end of the course, I decided to continue the project as my Masters thesis, co-supervised by Drs. Tim Oberlander and Karon MacLean.
As a project team member, my role was conducting and analyzing user interviews, and co-creating the low and medium fidelity prototypes. As the project lead for my thesis, my work included designing and conducting user studies for feedback, collaborating with health tech firm CareTeam for deployment of the visualizations on their platform, and evaluating the application through interviews with youth with chronic pain.
So, what was the problem?
Chronic pain is a common and costly pediatric condition, with personalized and varied symptoms for each patient. This makes designing treatment plans for them complicated and hard to evaluate.
Clinicians often rely on patient records or accounts of their experiences, which can be biased, or altered due to time passed since the event. Lack of accurate records and tracking methods further complicates treatment for these patients.
Who was our target demographic?
This project focused on young chronic pain patients aged 12-18 years old, preferably from the patient network of the Complex Pain Service clinic at BC Children’s Hospital. However, this method can be applied to youth suffering from chronic pain anywhere, with some personalization based on their circumstances.
What was our plan?
The project consisted of three main phases:
- Context gathering and initial exploration
- Visualization design and prototype evaluation
- Clinical deployment and application evaluation
Context gathering and initial exploration:
We began by interviewing the clinicians to gain insights into chronic pain and our target demographic. Our interviews were semi-structured, with more open-ended questions, allowing the clinicians to share broader thoughts they felt would be relevant for us.
Clinicians highlighted the need for:
- Accurately recording the patient's lived experiences
- Storing the data securely and confidentially
- Reflecting the data back to the patient in an easily understandable format
- Enabling the patients to draw insights from their own data
Based on our interviews with the clinicians, we created personas of the target patient, their caregiver and clinician (click to zoom in):
Persona of a young chronic pain patient, Abigail
Abigail's mom and primary caregiver, Emily
Persona of Abigail's clinician, Alice
We scoped down to focus on the user's persona as they would be the main users of our application.
The main goals for this persona would be understanding how their pain impacted other aspects of their life. They would also want something that would be minimally invasive in their daily routine, and would allow them to share their information whenever and however they wanted.
This was followed by an initial round of prototyping - some pen-and-paper low-fidelity sketching of visualizations that could represent different data points, for instance:
using bars to show length and quality of sleep
showing pain location and intensity on a body silhouette
showing worry about things like school and family through emojis or icons
Low-fidelity prototypes: (from left to right) Showing sleep over one week, pain symptoms on a human silhouette, what the user is worried about
The full low-fidelity prototypes can be seen here: https://drive.google.com/drive/folders/1VF168378zV2z3-T93iVDHG-FDnU0GvtU?usp=sharing
We got initial feedback on our low-fidelity prototypes from two proxy users through semi-structured interviews, who mentioned they found simple charts like bars and lines easier to understand compared to a human silhouette form. They also felt the graphs overall were understandable, and an accurate representation of the survey data.
Medium Fidelity Prototype:
Based on the feedback from this round of interviews, we created refined medium-fidelity prototypes in PowerBI and Tableau by showing:
pain location and intensity on a body silhouette
symptoms and emotions through a bar chart
worries through penguin emojis
positive and negative emotions through ribbon charts
sleep length and quality through horizontal bar chart
Medium-fidelity prototypes: (above) in Tableau, showing the human silhouette chart, emotions through horizontal bars and the worries icon chart
(below) in PowerBI, showing the emotions through a ribbon chart, sleep through horizontal bars, worry though an area chart and sleep quality through a pie chart
We also created an application prototype integrating the visualizations showing daily, detailed and weekly summaries, along with detailed visualizations:
Prototype of the application, showing (from left to right) the daily screen, detailed summary per day, weekly summary, sleep chart and emotion data
To review the medium-fidelity prototypes of the visualizations and app, we interviewed 5 proxy users, who were either young adults, or people with previous chronic pain experience. Participants found the visualizations useful, and would want to continue using such an application in their daily lives.
Visualization design and prototype evaluation:
We then delved deeper into the visualization design process by beginning with a Data and Task Abstraction process to ascertain which user tasks we wanted our visualizations to enable.
Data and Task Abstraction results showing our patient-centered tasks, prioritized, and tagged with clinical relevance
We then iteratively designed the visualizations, moving from low-fidelity hand-drawn sketches to medium-fidelity in Balsamiq, to high-fidelity mockups in React.js. Below, we show the final visualizations in React.js:
Visualizations showing sleep (top-left), physical symptoms (bottom left), emotions (top center), worries (bottom center), expectations and reality (top right), and social interactions (bottom right)
These visualizations use familiar encodings like bar and line charts, are colour-coded by category, and are vertically aligned in a weekly format, to allow for easy cross-comparisons. They were designed in collaboration with HCI and visualization experts, and took input from clinicians and patient partners, for a patient-centered engaging application. The vertically aligned version can be seen here: https://drive.google.com/file/d/1TiB_9Cr2F0_7yO9bAp2R9EXnz2NLn4jE/view?usp=share_link
We evaluated the design of these visualizations with 10 youth with chronic pain, through semi-structured interviews.
Youth found the visualizations useful, and thought it would help them in tracking their pain.
Some liked tracking their physical symptoms, whereas for others mental health and emotions were more important.
Participants did note how there was a slight learning curve for the visualizations.
Overall, this was an application they could see themselves using in the long run.
Clinical deployment and application evaluation:
We then collaborated with healthtech firm CareTeam, to implement our visualizations on their platform. In order to assess the long-term impact of the application, we ran a three-week long clinical study, where 50 youth with chronic pain used the myWeekInSight application.
Post the three weeks, we conducted semi-structured interviews with a subset of 10 youth, to gather detailed feedback on their experience, aspects they liked, and what we could improve. These interviews were transcribed and qualitatively analyzed through affinity diagramming between 2 researchers. We saw 4 broad themes emerge, namely Potential Utility, Visualization Design, Continued Use and Personalization of the application. We show the affinity diagram below:
Broadly summarizing the findings from our interviews:
Potential Utility: Youth mentioned how looking and reflecting on their data encouraged them to change aspects of their daily lives. For some, the visualizations showed insights they otherwise would have missed.
Visualization Design: Different visualizations were relevant to different people. Participants were also able to cross-compare and relate aspects of their lives, for instance noticing the impact of sleep on pain or vice-versa.
Continued Use: The majority of our participants agreed they would want to continue using the application on a daily basis, but with some changes to the survey questions and frequency to make it more relevant for them. Some mentioned wanting to share it with their clinicians whereas others said they preferred private self-reflection.
Personalization: Youth mentioned survey preferences they would have liked, as well as additional survey questions and features we could add. For instance, one participant mentioned wanting to track diet and physical activity, another mentioned tracking their menstrual cycle.
How did that turn out?
We were able to broadly assess the feasibility of the myWeekInSight application through the clinical study. In the future, we would like to do a larger and longer study with an improved version of the application, taking into account the feedback we received through the studies.
Our demo video below explains this project up to the clinical deployment:
Who else knows?
We presented our project at the Designing for People Showcase 2021, and the video can found here
A later version of the project was presented at the International Symposium for Pediatric Pain 2022, as well as the Designing for People Showcase 2022 (where it won Best Demo)!. The video presentation for that can be found below:
A clinical publication for the feasibility study has been published by our clinical collaborators, and can be found here.
The entire project has also been documented in my thesis.
Challenges we faced
Challenges in industry-research collaborations: Our partnership with CareTeam for a real-world deployment showcased the challenges that arise in industry-research collaborations like timeline and scope mismatch, which are often the reason why many research projects do not get deployed in the real world. However, this partnership was crucial in deploying the application to assess long-term impact, thus showing the importance of such collaborations, even if they may be challenging.
Evaluating with restricted populations: This project involved accessing a population that was vulnerable in two ways - first, they were minors, and second, they were pain patients - thus requiring additional ethical considerations. Such populations are often not easy to access, and most design projects involving vulnerable populations are often not evaluated due to these very reasons. However, getting feedback from the target demographic is crucial for an accurate assessment of designs, so this highlighted the need for inter-disciplinary collaborations in such design projects.
What did we learn?
Designing from a youth-centered perspective: Through our design process and interactions with youth, we were able to highlight unique constraints in designing from a youth-centered perspective. Factors like graph literacy and the adolescent context, which are not applicable when designing for more general audiences, play pivotal roles here. Our designs thus had to be carefully thought out and evaluated.
The uniqueness of personal data: Our interactions with youth highlighted the diversity in their experiences - from varying lifestyles, to different physical symptoms and clinical experiences, different graph literacies to varying opinions on which charts they liked the most - each participant in our interviews had strong, unique opinions. This highlights the difficulty in creating a general application that can work for everyone, and the potential for smaller customizations that can help each user refine the application to make it more relevant to their life.
What’s gonna happen next?
We're keen to share this research with others in the field, particularly those working at similar intersections of healthcare and design.
An industry-research partnership focused publication is under review, and a visualization design paper is currently in progress! Stay tuned!