myWeekInSight

A data visualization-based self-management tool for youth with chronic pain

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Conceptual model of the myWeekInSight system

Role

Team Member (January - April 2021)

Project Lead (May 2021 - now)

Tools

Adobe Illustrator, Adobe XD, React.js, Balsamiq, Procreate, Adobe Premiere Pro, Miro, PowerBI, Tableau

Date

January 2021 - Present (16+ months)

Team

Devarsh Bhonde, Rubia Guerra, Katra Farrah, Haomiao Zhang

Dr. Katelynn Boerner, Dr. Tim Oberlander, Dr. Karon MacLean, Dr. Tamara Munzer, Haley Foladare 

Methods

Data Visualization, User Interviews, Affinity Diagramming, Literature Review, User-Centered Design, Prototyping, Requirement Gathering, Persona Building

Overview/Context

myWeekInSight began as a course project at UBC, for CPSC 554K (Designing for People Project) in January 2021. 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. 

 

myWeekInSight is a self-management tool targeted toward youth with chronic pain, that attempts to use 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.

As a project team member, my role was an equal share of conducting and analyzing user interviews, and co-creating the low and medium fidelity prototypes. As the project lead now, my work has included designing and conducting user studies for feedback, refining and creating new prototypes, and working with health tech firm CareTeam, to implement the project on their platform for a secondary user study.

So, what was the problem?

Chronic pain is a common pediatric condition that affects multiple aspects of patients lives, including their mental, physical and social well-being. Symptoms of chronic pain are often varied and personalized to each individual, which makes designing effective treatment plans difficult. 

 

Existing methods for self-management of pain include mobile applications and wearables, but most of them do not integrate clinician input or have not been tested with the target user group of patients. There are also very few apps specifically for a younger audience, and within that more specifically for chronic pain.

 

To try to combat this, Dr. Tim Oberlander and Dr. Katelynn Boerner, clinicians at BC Children’s Hospital, Vancouver, developed an EMA (Ecological Momentary Assessment) approach to gather timestamped data about patients’ daily lived experiences. This comprises an EMA survey administered thrice a day that gathers data about the user’s mental, physical, and social experiences through a series of quantitative and qualitative questions. However, the representation of this data in a teen-friendly, readable format, back to the users was a challenge. 

Who was our target demographic?

For the purposes of this project, we chose to focus on youth in the age range of 12-18 years old, preferably from the patient network of the Complex Pain Service clinic at BC Children’s Hospital. However, this method could be applied to youth suffering from chronic pain anywhere, with some personalization based on their circumstances.

What was our plan?

Ideation:

The EMA survey for this project comprises of ~15 questions - a mix of qualitative and quantitative - administered thrice a day. Over a period of one week, that generates ~315 data points. This data is stored and accessed in the form of a spreadsheet, for clinician reference. However, for patients, viewing this data in its raw form is close to meaningless. 

 

Given the highly individual aspect of chronic pain experience,  we decided to use data visualization and personalization to represent the data.

Design:
Interviews:

We began by interviewing the clinicians to gain subject matter 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. 

Persona:

Based on our interviews with the clinicians, we created personas of the target patient, their caregiver and clinician:

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Persona of a young chronic pain patient, Abigail

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Persona of Abigail's mom and primary caregiver, Emily

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Persona of Abigail's clinician, Alice

The personas helped us ensure we kept all three perspectives in mind while designing the visualizations - the patient, the caregiver, and the clinician view. They also helped us understand the diverse user requirements and how we could best integrate them into our prototypes.

Low-fidelity prototype:

This was followed by an initial round of prototyping - some pen-and-paper low-fidelity sketching:

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Low-fidelity prototypes: (clockwise from top left) Showing sleep over one week, pain symptoms on a human silhouette, emotions over one week, and category of worries through charts and icons

The full low-fidelity prototypes can be seen here: https://drive.google.com/drive/folders/1VF168378zV2z3-T93iVDHG-FDnU0GvtU?usp=sharing

These were based on different visualization concepts and ideas from existing commercial health tracking apps, using traditional graph forms as well as more creative icons and silhouettes. The visualization concepts focused on four different categories: physical pain, sleep, emotional health, and peer interactions. We got initial feedback on our low-fidelity prototypes from two proxy users, through semi-structured interviews and a UEQ-based survey for each of the visualizations. 

 

Overall, the participants 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 a good representation of the survey data.

Medium Fidelity Prototype:

Based on the feedback from this round of interviews, we created refined medium-fidelity prototypes. As at this stage we weren’t bound to a particular platform, we decided to split our efforts across Tableau and PowerBI, which would allow us to test both platforms and their capabilities for our intended purposes. We also tried the EMA surveys for a period of two weeks ourselves, to gather sample data as well as develop insight as to what the EMA experience would be like for patients. We used this data to create the visualizations in PowerBI and Tableau.

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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 designed the workflow of the visualization application:

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Wireframing for the visualization application, showing the workflow across different screens

This is what the app would look like:

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Prototype of the visualization 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 (since our access to the actual target demographic was restricted at this point). Similar to the interviews for our low-fi prototype, we had semi-structured interviews with UEQ-based surveys for the visualizations. 

How did that turn out?

We ended up performing thematic analysis on all the interviews and came up with the following design recommendations for visualizing for youth:

  • Use simple graphs like line or bar charts, as opposed to more complex ones like scatterplots, or graphs with multiple coordinate systems like combo graphs. Multiple simple graphs are preferred over single complicated ones.

  • Use guides to help users understand their data and provide analyses that enable them to reflect on the findings.

  • Clinicians and patients would find different aspects of the information useful, hence visualizing the data in ways most efficient for each user demographic is required.

  • Patients might not fill the survey each time accurately, which means designers need to account for missing data points when visualizing the data.

  • Images, icons and colors are acceptable when they are easily associated with the intended purpose, the difference between ratings is easily distinguishable and accessibility is considered.

  • Temporal visualization of data is useful for patients to track their progress

Our final medium-fidelity prototype of the visualizations can be found here:

PowerBI: https://drive.google.com/file/d/1kGgGeKQhf_V3h06vaNjrhpOkQQDXcB44/view?usp=sharing

Tableau: https://drive.google.com/file/d/1yGZY5A71ct26br16Y6gkc-FfqKevcGRO/view?usp=sharing

Our final prototype of the visualization application can be found here:

https://drive.google.com/file/d/181Xt8HSsd54H0QrAEfYRg7lNDGe798_S/view?usp=sharing

You can view an interactive version of the application prototype here: https://xd.adobe.com/view/1e096285-14b4-4dab-9393-bff9f1dc0620-4b5a/?fullscreen&hints=off !

Who else knows?

We presented our project at the Designing for People Showcase 2021, and the video can found here:

Outside of everyone who viewed that, now you know too! :)

Challenges we faced

 Our biggest challenge was that we were unable to gain access to our target demographic of teenagers with chronic pain, to involve them in our design process. We chose to substitute for the same using literature review, personas, and users nearest to the target demographic like teenagers who had just turned adults, or people with past chronic pain experiences, as well as clinicians. 

 

We were also unable to fully implement the solution in the form of an application as it was beyond the time and scope of the course project.

What did we learn?

The biggest takeaway for me here was the importance of personalization and understanding users in this project. The complex and highly individual nature of chronic pain made it vital for us to understand what patients felt on a daily basis, so we could best represent that data. While personalization remains a key aspect in any project, it stood out even more here due to the highly diverse nature of chronic pain. 

 

It was also very insightful working with the clinicians - taking their medical perspectives into account was crucial in our prototypes being well-received by our proxy users. That simply serves as a reminder of the importance of every stakeholder in a project.

What’s gonna happen next?

Glad you asked! 

 

Post the culmination of this course project, I decided to take it on as my Masters thesis, and see it to completion. While I cannot reveal more details since my thesis is still in progress, we did end up doing several more design iterations on the visualizations as well as a round of feedback interviews with the target demographic (finally!). We’re also working with CareTeam (a health tech firm) to implement the visualizations on their platform, and will then be running a second study!

 

We did present this work partially at ISPP 2022, you can see that video here: https://www.virtual2022.nz/demonstrations.html (Data Viz 4 Youth in Pain (DV4YP): A Data Visualization-based Holistic Pain Management Web Application for Youth with Chronic Pain)

 

Feel free to get in touch to discuss more about this project if you’d like!

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