'Natural Cycles' is a Swedish smartphone app that is changing the way women track their fertility. By using first-hand data and an advanced algorithm, the app shows women exactly which days they are most fertile.
Natural Cycles is a user-friendly smartphone app and thermometer that helps women track their ovulation and fertility. To use the system, a woman begins by taking her temperature first thing each morning and entering the corresponding data in to the app. Over time, the app uses the data collected to show the woman when she is the most fertile. The app is backed by clinical studies that show it can be extremely accurate, even for women with irregular periods or those with conditions such as Endometriosis and Polycystic Ovary Syndrome.
By combining real data about a woman's basal body temperature with an ovulation-tracking algorithm, Natural Cycles gives women a simple and accurate way to predict pregnancy.
'Natural Cycles' Uses Data to Help Women Track Their Fertility
1. Algorithm-based Fertility Tracking - Applying advanced algorithms to fertility tracking data provides women with a more accurate and convenient method of predicting pregnancy.
2. User-friendly Fertility Apps - Developing smartphone apps that are easy to use and integrate with wearable devices allows women to track their fertility with greater convenience and accuracy.
3. Personalized Fertility Assistance - Leveraging real data and clinical studies to provide personalized fertility tracking solutions enables women with irregular periods or conditions to enhance their reproductive health.
1. Healthtech - The HealthTech industry can leverage algorithm-based fertility tracking apps to provide innovative solutions that empower women to manage and optimize their reproductive health.
2. Wearable Technology - Integrating fertility tracking apps with wearable devices opens up opportunities for the wearable technology industry to enhance women's fertility monitoring experience.
3. Data Analysis - The data analysis industry can contribute to the advancement of fertility tracking by developing algorithms that process and interpret fertility data to provide accurate predictions.