 We find that women located at different ends of the spectrum of menstrual patterns , based on the consistency of their cycle length statistics, exhibit statistically significant differences in  cycle characteristics and symptom tracking .
<meaning-changed>  We find that women located at different ends of the spectrum of menstrual patterns , based on the consistency of their cycle length statistics, exhibit statistically significant differences in  cycle characteristics and symptom tracking .
 We find that women located at different ends of the spectrum of menstrual patterns , based on the consistency of their cycle length statistics, exhibit statistically significant differences in cycle characteristics and symptom tracking patterns. We also find that cycle and period length statistics are stationary over the app usage timeline across the variability spectrum. The symptoms that we identify as showing statistically significant association with timing data can be useful to clinicians and users for predicting cycle variability from symptoms or as potential health indicators for conditions like endometriosis.