Great write-up! I work at another femtech company and will share this with some of my colleagues.
Three thoughts/comments: 1. Clue is very focused on period dates (being a period tracker) and doesn’t have very accurate ovulation data. Therefore, cyclical changes in metrics that are affected by ovulation rather than by period will look dimmer due to differences between the length of peoples’ Luteal phase. E.g. the BBT signal would probably be much sharper if the x axis was “days relative to ovulation” rather than “days relative to period”, since the temperature change is being caused by hormonal changes at ovulation rather than at the period.
2. On sleep tracking, many of my colleagues track their sleep with the Oura ring and have noticed that their sleep is reported in the Oura app as being significantly worse during their Luteal phase (the phase between Ovulation and Period). Turns out this was not due to actual worse sleep, but due to Oura measuring their temperature and picking up on the BBT rise after ovulation. Oura assumed that higher temperatures = worse sleep (in general probably true, but only useful for people without menstrual cycles). Just something to watch out for — surprisingly many products still aren’t designed to work properly for 50% of their potential users.
3. Echoing what remizidae says: although these patterns show up with enough data, only a few of them are usable on individuals. I’ve looked at trying to apply predictive models for several of these metrics on individuals and it’s very rare that you get anything that feels more accurate than pure noise. The only exceptions are the really physical ones such as heart rate and temperature.
Great write-up! I work at another femtech company and will share this with some of my colleagues.
Three thoughts/comments:
1. Clue is very focused on period dates (being a period tracker) and doesn’t have very accurate ovulation data. Therefore, cyclical changes in metrics that are affected by ovulation rather than by period will look dimmer due to differences between the length of peoples’ Luteal phase. E.g. the BBT signal would probably be much sharper if the x axis was “days relative to ovulation” rather than “days relative to period”, since the temperature change is being caused by hormonal changes at ovulation rather than at the period.
2. On sleep tracking, many of my colleagues track their sleep with the Oura ring and have noticed that their sleep is reported in the Oura app as being significantly worse during their Luteal phase (the phase between Ovulation and Period). Turns out this was not due to actual worse sleep, but due to Oura measuring their temperature and picking up on the BBT rise after ovulation. Oura assumed that higher temperatures = worse sleep (in general probably true, but only useful for people without menstrual cycles). Just something to watch out for — surprisingly many products still aren’t designed to work properly for 50% of their potential users.
3. Echoing what remizidae says: although these patterns show up with enough data, only a few of them are usable on individuals. I’ve looked at trying to apply predictive models for several of these metrics on individuals and it’s very rare that you get anything that feels more accurate than pure noise. The only exceptions are the really physical ones such as heart rate and temperature.