At Real (now Zeera), my role was to design how mental health content is delivered to
members of the app in a pedagogically sound and clinically effective way. I was
responsible for aligning content creation with an appropriate pedagogical framework, developing assessment
strategies to help users get a sense of their progress, and conducting relevant user research to identify areas of friction to improve and areas with potential for new approaches or features.
Example Project #1
How might reimagine the journaling experience to improve how users reflect on their mental health goals?
As Real was a self-directed experience, we noticed that users weren’t taking full advantage of the journaling feature which would greatly help them reflect on key mental health concepts. We knew that journaling was an effective way to consolidate learning delivered through video and written content, so we wanted to elevate it on the app.
Research:
To understand how users used the journaling feature, we performed a quantitative analysis of the rate at which the feature was used, the extent to which it was used, and asked ourselves whether there was any correlations between topic, delivery method, or user archetype and journal completion.
Redesign:
Based on our findings, we were able to start thinking of the journaling as a scaffolded experience rather than a standalone feature. The idea was that by including reflection questions throughout the content (before, within, and after a learning segment), we could achieve a few things: (1) alleviate the mental load and anxiety of having to reflect on larger chunks of content in one go, (2) use reflections points as a way to break up larger chunks of content into more digestable ones, (3) use reflections as knowledge checks throughout the content experience so users can get a clearer sense of what they learning as they are learning it.
Example Project #2
How might reimagine how content is recommended to users in order to ensure it builds on their mental health goals?
With months of data on how users navigated the app, we wanted to develop a more effective engine for content recommendation such that users could continue working on their mental health with content that felt relevant to them. This also meant implementing new quantitative review tools that would allow us to use an evidence-based approach to deciding how content was recommended and what new content to add to the platform.
Prototyping:
At the time this project was initiated, none of the content modules were related to each other in any way and were recommended at random. However, based on usage data, we were able to come up with 4 content recommendation approaches and model how they might change how content would be structured. The first approach was to identify trends in user engagement (who took what content) and create recommendations paths based on clusters of shared concerns. The second approach was to restructure all the content modules under learning trees, using a goals-based approach to identify which content modules were foundational and which fell under the same branches. The third approach was to redesign content modules along a depth axis (eg. easy, medium, hard) where the same topics could be reexplored at varied depths. The fourth approach was to focus on users’ strengths and use skill transferability to recommend content, essentially recommending content based on its overlap of skills with previously completed content.
Redesign:
Most of these prototyped approaches revealed to us that we had to work on building a larger pedagogical structure around the content, making sure we were intentional about how modules worked in relation to one another before nailing a content recommendation approach. I researched potential structures and found that a skills-based framework would work best for us. Through interviews with content designers, I was able to come up with a skills-based framework and aligning all existing modules to this framework. This exercise revealed many interesting findings that were later used to strengthen how content was developed internally.