Background and Context

Nonprofessional family caregivers dedicate their time, attention, and emotion toward improving the wellbeing of their loved ones with chronic conditions. Oftentimes, caregivers forget their own needs in the process.

Our objective was to leverage COCO's existing mobile app and AI-powered chatbot to facilitate better self-care among these caregivers.

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Foundational Research

After initial conversations with our stakeholders, we launched our research phase with the following questions: 

• What typical events and challenges characterize a caregiver's daily experience?
• What methods do caregivers currently employ to discuss their own well-being?
• How do individuals perceive and interact with AI-based chatbots?

Our research included:

• a competitive analysis of 18 related mental health and chatbot apps
• a 7-day diary study involving 5 participants
• 5 comprehensive user interviews
• a scrutiny of 62 peer-reviewed academic papers.

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Research Outcomes

We triangulated our research findings to conclude:

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We also constructed personas and journey maps to understand our users better.

Our personas, user journey maps and other research artefacts helped us inform the design question

"How might we help caregivers value self-wellness through empathetic interventions with minimal cognitive burden?"

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DESIGN CONSIDERATIONS

• Enhance caregivers’ wellness practices on top of their busy schedules
• Integrate with the COCO Bot, providing an encouraging, supportive, and empathetic conversation flow
• Recognize caregivers with affirmations, memories, and guided gratitude journaling
• Provide personalization and customization, because people have different interpretations of wellness

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Rapid Iterative Testing and Evaluation

After initial design phases, we aimed to validate if our solutions met user needs. Through evaluative research, we assessed the appeal of new features and studied how users engaged with them individually and in combination.

For the evaluative stage of our project, we went through: 

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Final Designs

COCO's chatbot utilizes dialectical behavior therapy techniques in its user interactions. We enhanced it with a photo-journaling prompt feature and the ability to reference past photo journals when relevant to the therapeutic dialogue.

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Guided by the chatbot, users receive tailored onboarding for establishing self-care goals, taking into account their interests and time constraints. The system enables caregivers to monitor their 'Recharge Time' and easily modify reminders.

Outcome

Upon completing the project, COCO's design and engineering units integrated two key features, "Memory Journal" and "Recharge Time," into the initial release of the Minimum Viable Product (MVP).

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Learnings and Reflections

Navigating the complexities of caregiving presented an intricate challenge, offering multiple avenues for design intervention. This project elucidated the importance of a multi-disciplinary approach, integrating aspects of psychology, user experience design, and data-driven decision-making to provide targeted solutions for family caregivers.

1. Criticality of Deep User Research: The 7-day diary study, in conjunction with user interviews and competitive analysis, offered valuable insights into the nuanced needs of caregivers. These deep dives informed the design parameters, underscoring the need for customization and personalization within the caregiving context.
2. Design as a Continuum: As a Product Designer, one of my pivotal learnings was the recognition that design is an ongoing, iterative process. The evaluative research after the initial design stages was not a checkpoint but a dialogue that informed subsequent design cycles.
3. Stakeholder Engagement: Collaboration with cross-functional teams, including engineering and business stakeholders, was integral to the project's success. This coordination ensured that the features were not only user-centered but also aligned with COCO's business objectives and technical constraints.
4. Importance of Metrics in Design: The introduction of a 'Weekly Insights' feature was a testament to the indispensability of actionable metrics in healthcare applications. Quantifiable data helped caregivers to adjust their self-care strategies effectively.
5. Time Management: With a span of six months, prioritizing features for implementation within this timeframe while ensuring quality required effective time management skills, affirming the adage that 'design is as much about what to leave out as it is about what to put in.'

This project was a comprehensive learning experience, reinforcing the importance of iterative research and stakeholder collaboration in crafting user-centric solutions. It offered me a platform to apply theoretical knowledge in a real-world context, enriching my overall professional acumen.

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