Even before the Quantified Self movement<\/a> became a thing, healthcare providers, health sites and iPhone apps encouraged women to track signals like their basal body temperature, cervical fluid, emotions and ovulation test results to figure out when they might ovulate.<\/p>\n But while most apps and traditional pen and paper methods typically rely on historical cycles to pinpoint a woman\u2019s fertile window, Ovuline says it uses machine learning to more precisely predict ovulation.<\/p>\n \u201cNow, it\u2019s all based on what happened in the past. The problem is that a lot of people have irregular cycles,\u201d said CEO and co-founder Paris Wallace. \u201cWe\u2019ve created the first pro-active ovulation calculator. \u2026 We\u2019re understanding your cycle based on information you couldn\u2019t otherwise glean yourself.\u201d<\/p>\n The startup, which first debuted its app in September, said it\u2019s been used by about 55,000 women. Now that its algorithms have learned from more than 2.5 million data points (instead of the 10,000 data points it started with), Ovuline is on Thursday taking its product of beta and launching with a more robust service.<\/p>\n Like plenty of other fertility-tracking apps on the market, Ovuline starts by helping women track their health indicators. But it analyzes an individual user\u2019s data within the greater universe of its entire database and clinical guidelines to identify meaningful correlations and advise her when she\u2019s approaching ovulation. According to the company, its service can help women get pregnant three times faster than the national average (which is four to six months).<\/p>\n Its newest version integrates with wearable fitness trackers like Fitbit devices (see disclosure), provides push notifications with personalized advice and lets women easily view an entire timeline of their data. If a user frequently reports feeling \u201cstressed,\u201d the app might send a note alerting her to the negative fertility consequences of excess levels of stress, or if she records lower than normal hours of sleep, she might receive messages on how low sleep levels can result in fertility-impeding hormones.<\/p>\n Enthusiastic Quantified Selfers \u2014 who carefully log and analyze their health data to uncover helpful insights \u2014 tend to be men. But using machine learning to make sense of women\u2019s personal health data points the way to a future of data-driven medicine and shows the meaningful application of health-tracking activities that some currently see as mere naval-gazing.<\/p>\n Ovuline offers a free app that predicts ovulation, but premium versions (which cost up to $49.99) give women access to fertility experts and personal advice, the option to share data with partners and doctors and other features. Later this year, the company plans to roll out another application for pregnant women that similarly helps them track symptoms and lets them see how common or rare their experiences are relative to other users.<\/p>\n Disclosure: Fitbit is backed by True Ventures, a venture capital firm that is an investor in the parent company of this blog, Giga Omni Media. Om Malik, founder of Giga Omni Media, is also a venture partner at True.<\/em><\/p>\n <\/a>Image by Valentyn Volkov<\/a> via Shutterstock.<\/em><\/p>\n Related research and analysis from GigaOM Pro:<\/strong>![\"\"](\"http:\/\/stats.wordpress.com\/b.gif?host=gigaom.com&blog=14960843&%23038;post=643644&%23038;subd=gigaom2&%23038;ref=&%23038;feed=1\")
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