The Quiet Cycle
A privacy-first symptom tracking app for people living with endometriosis.
The Quiet Cycle is a privacy-first symptom and cycle-tracking app designed for people with endometriosis and chronic pelvic pain. The app provides a calm, supportive space for logging symptoms, identifying patterns, and connecting anonymously with users who share similar symptom profiles without storing any reproductive data in the cloud.
3 week project | Role: Lead UX Designer
The Context
Endometriosis affects over 10% of menstruating people worldwide, yet diagnosis is often delayed by years due to stigma, inconsistent care, and the difficulty of articulating symptoms over time. While symptom tracking is critical for diagnosis and treatment, most digital health tools collect, store, and centralize reproductive data, creating fear and distrust, particularly following the overturning of Roe v. Wade.
Core Question
Problem Framing
People living with endometriosis need tools to track symptoms, identify patterns, and seek support, but existing platforms require them to trade privacy, safety, or emotional comfort for functionality. This places the burden of risk management on users rather than on the system itself.
How might a symptom-tracking system support pattern recognition and peer support while keeping all reproductive health data private, local, and fully user-controlled?
User Motivations and Research Findings
Privacy fear is a primary barrier to symptom tracking, not a secondary concern
Trust is shaped more by system architecture than by branding or messaging
Users avoid tools that feel clinical, judgmental, or extractive
Pattern recognition is emotionally taxing without careful framing and pacing
User Archetypes
System Commitments
Quiet Cycle treats privacy and emotional safety as system architecture rather than features. Instead of optimizing for data aggregation or predictive intelligence, the system is intentionally designed to keep all reproductive health data local, limit interpretation, and preserve user control at every stage of use.
Why This Architecture Matters
These decisions deliberately trade analytical sophistication for trust, safety, and long-term engagement. In the context of reproductive health, the ability to withhold, control, or exit data sharing is as important as insight generation itself.
Quiet Cycle was an opportunity to explore how AI can accelerate prototyping without replacing the UX design process. Every research insight, user need, and product decision was developed independently through user interviews, competitive analysis, literature reviews, and conversations within online endometriosis communities. AI was intentionally introduced only after the research phase had been completed.
Once the information architecture and feature set were established, I used ChatGPT to synthesize my research into a structured product specification. This document became the foundation for a series of prompts used in Claude to generate an interactive prototype. Rather than designing the experience for me, AI served as a development partner, rapidly translating validated UX decisions into a functional proof of concept.
This process demonstrated how AI can support designers by accelerating implementation while keeping research, strategy, and user centered thinking firmly in human hands.
While the AI generated prototype established the application's core functionality and interaction patterns, it did not represent the final user experience. I recreated each screen in Figma, refining the layout, visual hierarchy, and interface components to align with Quiet Cycle's visual brand language. This process allowed me to move beyond a functional proof of concept and develop an interface that reflected the trust, privacy, and clarity central to the patient experience.
The following pages highlight the final interface designs, demonstrating how research driven decisions, AI assisted prototyping, and thoughtful visual design came together to create a cohesive user experience. Each screen was designed to simplify symptom tracking, encourage long term engagement, and provide patients with an environment that feels secure, approachable, and easy to navigate.
AI Assisted Prototyping
App Pages
Design Strategy
Outcome
Quiet Cycle demonstrates how privacy-forward system design can support symptom awareness and emotional validation without requiring centralized data storage or predictive medical modeling. By prioritizing local processing and user control, the system reframes symptom tracking as a personal, low-risk practice rather than a data extraction exercise.
Key Tradeoffs
Reduced analytical depth in favor of trust and safety
Slower pattern emergence to avoid emotional overwhelm
Limited social persistence to protect identity and anonymity
These tradeoffs were intentional and aligned with the system’s ethical commitments.
This project shifted my understanding of digital health design from feature optimization toward responsibility-driven system boundaries. Designing Quiet Cycle required resisting conventional assumptions about scale, intelligence, and data aggregation in order to center user agency, consent, and emotional safety.
Reflection
