 In this paper, we show that self-reported data from menstrual trackers can reveal statistically significant relationships between per-person variability of cycle length and self-reported qualitative symptoms.
<meaning-changed>  In this paper, we show that self-reported data from menstrual trackers can reveal statistically significant relationships between per-person variability of cycle length and self-reported qualitative symptoms.
 By exploring a database of user-tracked observations from the Clue app by BioWink of over 378,000 users and 4.9 million natural cycles, we show that self-reported data from menstrual trackers can reveal statistically significant relationships between per-person variability of cycle length and self-reported qualitative symptoms.