🌙 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