🌙 CycleTracker
A privacy-first menstrual cycle tracking application — built to empower, not extract.
CycleTracker is an open-source menstrual health companion focused on accuracy, inclusivity, privacy, and clinical usefulness. Unlike traditional period apps that monetize personal health data, CycleTracker stores everything locally on your device, never leaves your browser, and is fully transparent and community-driven.
This project is in Alpha, but fully functional for everyday cycle logging and pattern discovery.
✨ What CycleTracker Does 🔐 Privacy-First Design
No accounts, no tracking, no analytics
All data is saved locally using the browser’s LocalStorage API
No servers — your cycle data never touches the internet
🩸 Daily Logging
Track everything you need to understand your cycle:
Bleeding (flow intensity)
Pain and cramps
Ovulation signs (cervical fluid, LH results, BBT)
PMS symptoms
Mood + energy
Sexual activity
Sleep, stress, and lifestyle factors
Logging is done through an intuitive Log Modal with tabbed sections, allowing fast and structured daily entries.
📅 Cycle Predictions
Based on your historical data, CycleTracker estimates:
Next expected period
Fertile window
Ovulation likelihood
PMS windows
Cycle length variation
Luteal phase health
These predictions automatically improve as you add more logs.
🩻 Support for Medical Conditions
CycleTracker includes adaptive settings for:
PCOS
Endometriosis
Adenomyosis
PMDD
Irregular cycles
Hormonal treatment adjustments
These settings modify predictions, symptom interpretations, and cycle analysis to better match real-world physiology.
🧾 Provider Report (Printable)
You can generate a clinical-style PDF report containing:
Overview of recent cycles
Symptom patterns
Trigger correlations
Cycle irregularities
Fertility-related data
Notes for your doctor
The report is designed to be taken to gynecologists, fertility specialists, or primary care providers.
📊 Long-Term Insights
CycleTracker includes a timeline and calendar-style UI that helps identify:
Irregular patterns
Symptom clusters
Pain trends
Lifestyle correlations (e.g., stress → cycle shifts)
Cycle length variability