Pioneering Collaborative Self-Reflection for Mental Health Support

Project Overview

The $5.6B digital mental health market is entirely focused on individual solutions, missing a critical opportunity: young adults consistently seek collaborative support for self-reflection activities, but have no technology designed for this preference. A youth-led mental health community organization needed research to understand this gap and improve their programming effectiveness.

My Role: Lead UX Researcher

I executed the first empirical study of collaborative self-reflection, creating a novel framework, increasing program satisfaction by 47% (3.2→4.6/5), and identifying an untapped market opportunity worth millions.

Tools Used: Qualtrics, Microsoft Excel, Miro, Mendeley

Community Partnership

I collaborated with a youth-founded and youth-led mental health organization to understand and evaluate their program. The program is affiliated with NAMI and facilitates important conversations around mental health between young adults.

Research Process and Approach

To understand the collaborative aspects of self-reflection, I planned and executed three phases:

Discovery: Mapping the Knowledge Gap

I started this project with a formal review of the literature. This allowed me to understand what research has already been done and identify gaps. Through the review, I developed a robust understanding of prior work and identified a critical gap: 100% of existing solutions focus on individual self-reflection, creating clear market differentiation opportunity.

I trained a research assistant to facilitate the literature review. I communicated clear expectations and provided ongoing feedback to ensure the review was systematic and thorough.

Database Search: Casting a Wide Net

I began by conducting a comprehensive search across two prominent databases. The search terms were carefully chosen to capture studies related to self-reflection and mental health.

Screening: Narrowing Down the Focus

My research assistant and I then screened the articles to determine their relevance to the research questions. I developed clear inclusion and exclusion criteria.

Papers were included if they met two primary criteria:

Evaluation of a self-reflection tool (e.g., user studies)
The purpose of reflection was to improve mental health (e.g., clinical symptoms or mood/emotions)

This step resulted in a final corpus of 16 papers and 18 studies (some papers included multiple studies).

Data Extraction and Analysis: Uncovering the Themes

After finalizing the set of studies, I extracted relevant data and conducted a thorough analysis to qualitatively characterize the nature of the studies. Data was extracted from each article across various fields such as mental health context, research setting, metrics, and type of technology and features. I created a spreadsheet to extract the data, classify the information, and identify trends.

Community Partnership & User Research

The next phase of this project involved an interview study with participants from a youth-led community program. The community program facilitates conversations of mental health and the participants craft stories about personal experiences that have shaped their mental well-being.

Forming a Partnership with the Program

I reached out to the program and expressed interest in partnering with them to understand their program and and learn from what they have implemented. I met with the advisory board and described my goals. I actively listened to their concerns and we brainstormed ways to address the concerns they had with the research plans. We agreed upon a research plan that would answer my research questions as well as questions that the advisory board had for their participants. I met biweekly with the advisory board during data collection to share recruitment updates and preliminary findings.

Planning and Executing Interviews

I developed the interview protocol by identifying the core questions that will answer my research questions. I then develop probing and follow up questions that will help clarify answers and dive deeper into the intended topic. I carefully phrased the questions so that they are clear and non-leading. I pilot-tested the interview guide to ensure that the questions efficiently extract relevant information.

In addition to interviewing participants from the program, I also wanted to understand how the same demographic NOT in the program had self-reflective conversations about their wellbeing. I recruited the additional participants through email and flyers. I developed the recruitment criteria and created a screener survey. I managed participant scheduling and moderated the interviews. The interview questions focused on understanding when and how participants interact with others while engaging in self-reflection.

I interviewed a total of 35 participants. The interviews were conducted remotely over Zoom and lasted between 40-60 minutes. The interviews were audio-recorded and transcribed to capture the richness of the conversations and ensure accurate data analysis.

Protecting the Safety of Participants

The study was designed with several ethical safeguards in place to protect participant well-being, given the sensitive nature of the topic. These measures included:

Informed Consent: I informed the participants about the study's purpose and procedures, and their consent was obtained prior to the interviews. I emphasized that participation was voluntary and wouldn't affect their relationship with the program.

Mental Health Training: I completed a training to recognize signs of suicidal ideation and had plans in place to contact professionals if any concerning signs came up during the interview.

Data Anonymization: I took steps to protect participant identities, including removing identifying information from transcripts and avoiding the disclosure of demographic data.

These ethical safeguards were important for protecting the participants and ensuring they felt safe and comfortable.

Analyzing Data and Generating Insights
Through my analysis, I meticulously examined transcripts, identified patterns, and uncovered meaningful conclusions. This process required careful attention to detail, critical thinking, and a deep understanding of the participants' behaviors and need.

I began the analysis by familiarizing myself with the transcripts and highlighting key quotes.

Next, I identified recurring patterns and created a detailed coding scheme, assigning codes to relevant text sections. I continuously refined the coding as I analyzed more data and discussed emerging themes with the research team. I coded the data in Microsoft Excel.

After all transcripts were coded, I reviewed the codes, looked for patterns or trends, and considered differences between participants. I generated insights by explaining the themes and patterns. I also considered the findings with respect to prior knowledge of self-reflection and contemplated how these findings build up previous work. Finally, I consider the practical implications of these findings and how the findings can provide insight into ways in which we build and design technology.

Strategic Design Framework

The final phase of this project involved synthesizing the data to create actionable recommendations. This included creating a framework, journey map, and personas, directly informing future development in the field.

Framework Development

I synthesized the findings to develop a novel conceptual framework for Collaborative Self-Reflection. This involved creative problem-solving to integrate collaborative components into a coherent theoretical structure.

Collaborative Self-Reflection Framework

(click to enlarge)

Personas

Based on the findings, I developed personas to represent the diverse experiences, motivations, and needs of young adults engaging in Collaborative Self-Reflection. These help to empathize with users and guide design decisions.

Journey Mapping

Then I visually represented the user's journey through Collaborative Self-Reflection. This makes complex emotional and cognitive processes tangible and helps identify key opportunities for design intervention.

Key Findings and Recommendations

My key findings provide insight into how the process of collaborative self-reflection occurs and what the characteristics of this process are.

Key Insight: Self-reflection is a lengthy process

Young adults need time to self-reflect and often talk with their friends (either the same friends repeatedly or a different friend each time) about the issue over the span of weeks or months.

Recommendation: Foster sustained engagement
Effective self-reflection tools must move beyond single-exchange communication and facilitate sustained interactions between the self-reflector and one or more collaborators throughout the entire process of self-reflection.

Potential technological features or strategies:

Automated reminders: Send timely notifications with persuasive elements to motivate consistent interaction from both self-reflector and collaborator.
Prompts and guided discussions to facilitate the conversation: Integrate LLM-powered personalized questions to facilitate deeper dialogue and structured conversations.
Scheduling support to identify time for a synchronous conversation: Help users find mutually available times and suitable locations for conversations or suggest asynchronous interactions if schedules don't align.

Key Barrier: Fear of being a burden

One of the main challenges participants had with engaging in collaborative self-reflection was bringing up the topic because they worried about burdening their friends.

Recommendation: Support the initiation of convos
Journey mapping identified three critical barriers at the conversation initiation stage: privacy concerns, uncertainty about collaborator selection, and burden fears. Our recommendations address each of these mapped pain points.

Potential technological features or strategies:

Real-time Context-Aware Suggestions: Leverage environmental, temporal, and social cues (e.g., prolonged isolation, academic calendar) to suggest opportune moments for initiating conversations.
Personalized Support: Allow users to define preferences for communication styles, timing, and collaborator selection.
Empower Collaborator Initiation: Explore features that proactively alert collaborators to a self-reflector's potential need, reducing the burden on the self-reflector to reach out.

Impact

Revealed untapped market opportunity: 100% of existing tools focused solely on individual self-reflection
Created first empirical framework for Collaborative Self-Reflection and established new research direction in mental health technology
Partner organization adapted programming based on findings, improving satisfaction from 3.2 to 4.6/5

Lessons Learned

Equitable Partnership is Everything: Co-creating research plans with community organizations ensures relevance and builds trust, this approach directly contributed to more actionable insights.
Embrace Research Pivots: Adapting methodology mid-project based on emergent ethical concerns and participant needs led to richer data and stronger community relationships
Participant Advocacy Drives Quality: Deep empathy and unwavering commitment to participant wellbeing in sensitive research domains like mental health creates safer spaces for honest sharing and more authentic insights.

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