Samsung, Natural Cycles partner to add temperature-based period tracking to Galaxy Watch5

Samsung is partnering with Natural Cycles to add temperature-based period tracking to its Galaxy Watch5 series of smartwatches.

According to the electronics giant, the new cycle tracking capabilities will launch in the Samsung Health app in the second quarter in 32 markets in Europe, North America and Asia, including the U.S. The company said adding Natural Cycles’ technology will “give users more detailed insight into their menstrual cycle.”

Natural Cycles, a contraceptive app that received FDA De Novo clearance in 2018, uses body temperature and menstrual cycle information to determine the user’s fertile window and help prevent pregnancy. Over the summer, the company announced a partnership with Oura, maker of a health tracking ring, to use the wearable to monitor body temperature instead of manually recording it each morning. 

Samsung said its cycle tracking feature isn’t meant to be used as contraception, but it noted this is the first time the Natural Cycles algorithm has been adapted for a smartwatch. 

“Consumers can now easily track their menstrual cycle right from their wrist, combining Natural Cycles’ innovative fertility technology with Samsung’s superior temperature sensor to provide a more holistic understanding of their health and wellbeing,” Hon Pak, vice president and head of the digital health team, MX business at Samsung, said in a statement. “This is another demonstration of Samsung’s open collaboration philosophy with other industry leaders to create better health experiences.”


Samsung first added period tracking to its health app in 2020, after competitors like Apple, Garmin and Fitbit. The company has previously partnered with other digital health and wellness players, like Calm and Sleep Cycle, to integrate their services into its offerings.

Apple has also been adding to its reproductive health tracking. The Apple Watch Series 8 includes two temperature sensors that the tech giant said can be used to retrospectively estimate when ovulation occurred while improving period predictions.

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